file_path stringlengths 3 280 | file_language stringclasses 66 values | content stringlengths 1 1.04M | repo_name stringlengths 5 92 | repo_stars int64 0 154k | repo_description stringlengths 0 402 | repo_primary_language stringclasses 108 values | developer_username stringlengths 1 25 | developer_name stringlengths 0 30 | developer_company stringlengths 0 82 |
|---|---|---|---|---|---|---|---|---|---|
tooling/tailwind/eslint.config.js | JavaScript | // FIXME: This kinda stinks...
/// <reference types="../../tooling/eslint/types.d.ts" />
import baseConfig from "@acme/eslint-config/base";
export default [...baseConfig];
| ymc9/my-t3-turbo | 0 | TypeScript | ymc9 | Yiming Cao | zenstackhq | |
tooling/tailwind/native.ts | TypeScript | import type { Config } from "tailwindcss";
import base from "./base";
export default {
content: base.content,
presets: [base],
theme: {},
} satisfies Config;
| ymc9/my-t3-turbo | 0 | TypeScript | ymc9 | Yiming Cao | zenstackhq | |
tooling/tailwind/web.ts | TypeScript | import type { Config } from "tailwindcss";
import animate from "tailwindcss-animate";
import base from "./base";
export default {
content: base.content,
presets: [base],
theme: {
container: {
center: true,
padding: "2rem",
screens: {
"2xl": "1400px",
},
},
extend: {
borderRadius: {
lg: "var(--radius)",
md: "calc(var(--radius) - 2px)",
sm: "calc(var(--radius) - 4px)",
},
keyframes: {
"accordion-down": {
from: { height: "0" },
to: { height: "var(--radix-accordion-content-height)" },
},
"accordion-up": {
from: { height: "var(--radix-accordion-content-height)" },
to: { height: "0" },
},
},
animation: {
"accordion-down": "accordion-down 0.2s ease-out",
"accordion-up": "accordion-up 0.2s ease-out",
},
},
},
plugins: [animate],
} satisfies Config;
| ymc9/my-t3-turbo | 0 | TypeScript | ymc9 | Yiming Cao | zenstackhq | |
turbo/generators/config.ts | TypeScript | import { execSync } from "node:child_process";
import type { PlopTypes } from "@turbo/gen";
interface PackageJson {
name: string;
scripts: Record<string, string>;
dependencies: Record<string, string>;
devDependencies: Record<string, string>;
}
export default function generator(plop: PlopTypes.NodePlopAPI): void {
plop.setGenerator("init", {
description: "Generate a new package for the Acme Monorepo",
prompts: [
{
type: "input",
name: "name",
message:
"What is the name of the package? (You can skip the `@acme/` prefix)",
},
{
type: "input",
name: "deps",
message:
"Enter a space separated list of dependencies you would like to install",
},
],
actions: [
(answers) => {
if ("name" in answers && typeof answers.name === "string") {
if (answers.name.startsWith("@acme/")) {
answers.name = answers.name.replace("@acme/", "");
}
}
return "Config sanitized";
},
{
type: "add",
path: "packages/{{ name }}/eslint.config.js",
templateFile: "templates/eslint.config.js.hbs",
},
{
type: "add",
path: "packages/{{ name }}/package.json",
templateFile: "templates/package.json.hbs",
},
{
type: "add",
path: "packages/{{ name }}/tsconfig.json",
templateFile: "templates/tsconfig.json.hbs",
},
{
type: "add",
path: "packages/{{ name }}/src/index.ts",
template: "export const name = '{{ name }}';",
},
{
type: "modify",
path: "packages/{{ name }}/package.json",
async transform(content, answers) {
if ("deps" in answers && typeof answers.deps === "string") {
const pkg = JSON.parse(content) as PackageJson;
for (const dep of answers.deps.split(" ").filter(Boolean)) {
const version = await fetch(
`https://registry.npmjs.org/-/package/${dep}/dist-tags`,
)
.then((res) => res.json())
.then((json) => json.latest);
if (!pkg.dependencies) pkg.dependencies = {};
pkg.dependencies[dep] = `^${version}`;
}
return JSON.stringify(pkg, null, 2);
}
return content;
},
},
async (answers) => {
/**
* Install deps and format everything
*/
if ("name" in answers && typeof answers.name === "string") {
// execSync("pnpm dlx sherif@latest --fix", {
// stdio: "inherit",
// });
execSync("pnpm i", { stdio: "inherit" });
execSync(
`pnpm prettier --write packages/${answers.name}/** --list-different`,
);
return "Package scaffolded";
}
return "Package not scaffolded";
},
],
});
}
| ymc9/my-t3-turbo | 0 | TypeScript | ymc9 | Yiming Cao | zenstackhq | |
scripts/ceval/eval.py | Python | # This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import os
import argparse
import pandas as pd
import torch
import json
from llama_evaluator import Llama_Evaluator
import time
choices = ["A", "B", "C", "D"]
def main(args, evaluator,take):
assert os.path.exists("subject_mapping.json"), "subject_mapping.json not found!"
with open("subject_mapping.json") as f:
subject_mapping = json.load(f)
filenames = os.listdir("data/val")
subject_list = [val_file.replace("_val.csv","") for val_file in filenames]
accuracy, summary = {}, {}
run_date=time.strftime('%Y-%m-%d_%H-%M-%S',time.localtime(time.time()))
output_dir = args.output_dir
save_result_dir=os.path.join(output_dir,f"take{take}")
if not os.path.exists(save_result_dir):
os.makedirs(save_result_dir,exist_ok=True)
all_answers = {}
for index,subject_name in enumerate(subject_list):
print(f"{index/len(subject_list)} Inference starts at {run_date} on {args.model_path} with subject of {subject_name}!")
val_file_path=os.path.join('data/val',f'{subject_name}_val.csv')
dev_file_path=os.path.join('data/dev',f'{subject_name}_dev.csv')
test_file_path=os.path.join('data/test',f'{subject_name}_test.csv')
val_df=pd.read_csv(val_file_path) if args.do_test is False else pd.read_csv(test_file_path)
dev_df=pd.read_csv(dev_file_path) if args.few_shot else None
correct_ratio, answers = evaluator.eval_subject(subject_name, val_df, dev_df,
save_result_dir=save_result_dir if args.do_save_csv else None,
few_shot=args.few_shot,
cot=args.cot,
with_prompt=args.with_prompt,
constrained_decoding=args.constrained_decoding,
do_test=args.do_test)
print(f"Subject: {subject_name}")
print(f"Acc: {correct_ratio}")
accuracy[subject_name] = correct_ratio
summary[subject_name] = {"score":correct_ratio,
"num":len(val_df),
"correct":correct_ratio*len(val_df)/100}
all_answers[subject_name] = answers
json.dump(all_answers,open(save_result_dir+'/submission.json','w'),ensure_ascii=False,indent=4)
print("Accuracy:")
for k, v in accuracy.items():
print(k, ": ", v)
total_num = 0
total_correct = 0
summary['grouped'] = {
"STEM": {"correct": 0.0, "num": 0},
"Social Science": {"correct": 0.0, "num": 0},
"Humanities": {"correct": 0.0, "num": 0},
"Other": {"correct": 0.0, "num": 0}
}
for subj, info in subject_mapping.items():
group = info[2]
summary['grouped'][group]["num"] += summary[subj]['num']
summary['grouped'][group]["correct"] += summary[subj]['correct']
for group, info in summary['grouped'].items():
info['score'] = info["correct"] / info["num"]
total_num += info["num"]
total_correct += info["correct"]
summary['All'] = {"score": total_correct / total_num, "num": total_num, "correct": total_correct}
json.dump(summary,open(save_result_dir+'/summary.json','w'),ensure_ascii=False,indent=2)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str)
parser.add_argument("--cot",choices=["False","True"], default="False")
parser.add_argument("--few_shot", choices=["False","True"], default="True")
parser.add_argument("--ntrain", "-k", type=int, default=5)
parser.add_argument("--with_prompt", choices=["False","True"], default="False")
parser.add_argument("--constrained_decoding", choices=["False","True"], default="True")
parser.add_argument("--temperature",type=float,default=0.2)
parser.add_argument("--n_times", default=1,type=int)
parser.add_argument("--do_save_csv", choices=["False","True"], default="False")
parser.add_argument("--output_dir", type=str)
parser.add_argument("--do_test", choices=["False","True"], default="False")
args = parser.parse_args()
args.cot = args.cot == "True"
args.few_shot = args.few_shot == "True"
args.with_prompt = args.with_prompt == "True"
args.constrained_decoding = args.constrained_decoding == "True"
args.do_test = args.do_test == "True"
args.do_save_csv = args.do_save_csv == "True"
if args.constrained_decoding is True:
args.n_times=max(args.n_times,1)
print(args)
device = torch.device(0)
print(device)
evaluator=Llama_Evaluator(
choices=choices,
k=args.ntrain,
model_path=args.model_path,
device=device,
temperature = args.temperature
)
for i in range(args.n_times):
main(args,evaluator=evaluator,take=i)
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/ceval/evaluator.py | Python | # This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import string
class Evaluator:
def __init__(self, choices, model_name, k=-1):
self.choices = choices
self.model_name = model_name
self.k = k
self.puncs = list(string.punctuation)
def format_example(self, line, include_answer=True):
example = line['question']
for choice in self.choices:
example += f'\n{choice}. {line[f"{choice}"]}'
example += '\n答案：'
if include_answer:
example += f'{line["answer"]}\n\n'
return example
def generate_few_shot_prompt(self, subject, dev_df):
prompt = f"以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n"
k = self.k
if self.k == -1:
k = dev_df.shape[0]
for i in range(k):
prompt += self.format_example(dev_df.iloc[i, :])
return prompt
def eval_subject(self, subject_name, test_df, dev_df=None, few_shot=False, save_result_dir=None):
pass
def normalize_answer(self,s):
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude=set(self.puncs)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_punc(lower(s)))
def exact_match(self,pred, target):
return self.normalize_answer(pred)==self.normalize_answer(target)
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/ceval/llama_evaluator.py | Python | # This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import os
import re
from tqdm import tqdm
import random
import numpy as np
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
from evaluator import Evaluator
class Llama_Evaluator(Evaluator):
def __init__(self, choices, k, model_path, device, temperature=0.2):
super(Llama_Evaluator, self).__init__(choices, model_path, k)
load_type = torch.float16
self.model_path = model_path
self.device = device
self.tokenizer = LlamaTokenizer.from_pretrained(model_path)
self.model = LlamaForCausalLM.from_pretrained(
model_path,
load_in_8bit=False,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto')
self.generation_config = dict(
temperature=temperature,
top_k=40,
top_p=0.9,
do_sample=True,
num_beams=1,
repetition_penalty=1.1,
max_new_tokens=20
)
self.sA_id = self.tokenizer.encode("A", add_special_tokens=False)[0]
self.sB_id = self.tokenizer.encode("B", add_special_tokens=False)[0]
self.sC_id = self.tokenizer.encode("C", add_special_tokens=False)[0]
self.sD_id = self.tokenizer.encode("D", add_special_tokens=False)[0]
self.A_id = self.tokenizer.encode("：A")[-1]
self.B_id = self.tokenizer.encode("：B")[-1]
self.C_id = self.tokenizer.encode("：C")[-1]
self.D_id = self.tokenizer.encode("：D")[-1]
def eval_subject(self, subject_name,
test_df,
dev_df=None,
few_shot=False,
cot=False,
save_result_dir=None,
with_prompt=False,
constrained_decoding=False,
do_test=False):
all_answers = {}
if constrained_decoding is True:
self.generation_config['output_scores'] = True
self.generation_config['return_dict_in_generate'] = True
self.generation_config['max_new_tokens'] = 1
self.generation_config['top_p'] = 1.0
self.generation_config['top_k'] = 0
correct_num = 0
if save_result_dir:
result = []
score = []
if few_shot:
history = self.generate_few_shot_prompt(subject_name, dev_df, cot=cot)
else:
history = ''
answers = ['NA'] * len(test_df) if do_test is True else list(test_df['answer'])
for row_index, row in tqdm(test_df.iterrows(), total=len(test_df)):
question = self.format_example(row, include_answer=False, cot=cot,with_prompt=with_prompt)
instruction = history + question
if with_prompt:
prompt_template = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response: ")
instruction = prompt_template.format_map({'instruction': instruction,'subject':subject_name})
inputs = self.tokenizer(instruction, return_tensors="pt")
generation_output = self.model.generate(
input_ids = inputs["input_ids"].to(self.device),
attention_mask = inputs['attention_mask'].to(self.device),
eos_token_id=self.tokenizer.eos_token_id,
pad_token_id=self.tokenizer.pad_token_id,
**self.generation_config
)
batch_size, length = inputs.input_ids.shape
if constrained_decoding is True:
logits = generation_output.scores[0][0]
logits = logits.float().cpu().detach()
choices1_logits = logits[[self.sA_id,self.sB_id,self.sC_id,self.sD_id]]
choices2_logits = logits[[self.A_id,self.B_id,self.C_id,self.D_id]]
choicesAll_logits = (choices1_logits + choices2_logits).numpy()
assert not (np.any(np.isinf(choicesAll_logits)) or np.any(np.isnan(choicesAll_logits)))
ans = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(choicesAll_logits)]
response = self.tokenizer.decode([logits.argmax(-1).item()])
else:
response = self.tokenizer.decode(generation_output[0, length:], skip_special_tokens=True)
ans, direct_extract = self.extract_answer(row, response)
if ans == answers[row_index]:
correct_num += 1
correct = 1
else:
correct = 0
print(f"\n=======begin {str(row_index)}=======")
print("question: ", question)
print("response: ", response)
print("ans: ", ans)
print("ground truth: ", answers[row_index], "\n")
if save_result_dir:
result.append(response)
score.append(correct)
print(f"=======end {str(row_index)}=======")
all_answers[str(row_index)] = ans
correct_ratio = 100*correct_num/len(answers)
if save_result_dir:
test_df['model_output'] = result
test_df['correctness'] = score
test_df.to_csv(os.path.join(save_result_dir, f'{subject_name}_test.csv'))
return correct_ratio, all_answers
def format_example(self, line, include_answer=True, cot=False, with_prompt=False):
example = line['question']
for choice in self.choices:
example += f'\n{choice}. {line[f"{choice}"]}'
if include_answer:
if cot:
example += "\n答案：让我们一步一步思考，\n" + \
line["explanation"] + f"\n所以答案是{line['answer']}。\n\n"
else:
example += '\n答案：' + line["answer"] + '\n\n'
else:
if with_prompt is False:
if cot:
example += "\n答案：让我们一步一步思考，\n1."
else:
example += '\n答案：'
else:
if cot:
example += "\n答案是什么？让我们一步一步思考，\n1."
else:
example += '\n答案是什么？ '
return example
def generate_few_shot_prompt(self, subject, dev_df, cot=False):
prompt = f"以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n"
k = self.k
if self.k == -1:
k = dev_df.shape[0]
for i in range(k):
prompt += self.format_example(
dev_df.iloc[i, :],
include_answer=True,
cot=cot
)
return prompt
def extract_answer(self, line, gen_ans):
m = re.findall(r'所以答案是(.+?)。', gen_ans, re.M)
if len(m) > 0 and m[-1] in self.choices:
return m[-1], True
answer_patterns = [
r'([ABCD])是正确的',
r'选项([ABCD])正确',
r'答案为([ABCD])',
r'答案是([ABCD])',
r'答案([ABCD])',
r'选择([ABCD])',
r'答案：([ABCD])',
r'选择答案([ABCD])'
]
# RE extraction
for answer_pattern in answer_patterns:
m = re.search(answer_pattern, gen_ans, re.M)
if m:
answer = m.group(1)
return answer, False
# only containing one choice-character
m = re.findall(r'[ABCD]', gen_ans, re.M)
if len(m) >= 1:
answer = m[0]
return answer, False
# only containing one choice-context
choices_dict = {}
pattern = ""
for c in self.choices:
choices_dict[str(line[f'{c}'])] = c
pattern += re.escape(str(line[f'{c}']))+"|"
pattern = pattern[:-1]
m = re.findall(pattern, gen_ans, re.M)
print("w/ escape:",repr(pattern),gen_ans,(len(m)>=1))
if len(m) >= 1:
answer = choices_dict[m[0]]
return answer, False
return random.choice('ABCD'), False
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/crawl_prompt.py | Python | import openai
import sys
import random
openai.api_key = "" # you must provide your OpenAI API key before crawling
if not openai.api_key:
raise ValueError("OpenAI API key not provided. Please set the 'openai.api_key' variable.")
def return_random_prompt():
system_prompt = "你需要尽可能给出多样化的任务指令和对应的回答。我们将用于人工评估ChatGPT模型对指令的完成情况。要求:\n"
# generate random topics
topic_list = ["科技", "娱乐", "体育", "金融", "时政", "教育", "医疗", "旅游", "美食", "汽车", "房产", "文化", "历史", "地理", "自然", "人文", "社会", "法律", "军事", "政治", "经济", "文学", "艺术", "宗教", "哲学", "语言", "数学", "物理", "化学", "生物", "地球科学", "天文学", "计算机科学", "工程", "建筑", "设计", "音乐", "舞蹈", "电影", "电视", "动漫", "游戏", "健康", "美容", "时尚", "家居", "家电", "家具", "家装", "母婴", "育儿", "职场", "工作", "生活", "养生", "心理", "情感", "人际", "社交", "交友", "恋爱", "婚姻", "家庭", "亲子", "宠物", "动物", "植物", "食品", "饮料", "餐饮", "酒店", "购物", "消费", "理财", "税务", "法规", "法院", "司法", "刑事", "民事", "行政", "战争"]
system_prompt += "1. 主题多样化，涵盖各个领域，例如：" + "、".join(random.sample(topic_list, 10)) + "等。\n"
# generate random tasks
task_list = ["开放式生成", "分类", "问答", "编辑", "摘要", "写作", "翻译", "写代码", "分析", "代码解析", "常识推理", "写信", "抽取", "推荐"]
system_prompt += "2. 表述多样化，结合真实问题；指令类型多样化，例如：" + "、".join(random.sample(task_list, 10)) + "等。\n"
# other requirements
system_prompt += "3. 如果遇到无法处理的指令（只靠文本无法回答），给出无法处理的回复。\n"
system_prompt += "4. 除非特别要求，请使用中文，指令可以是命令句、疑问句、或其他合适的类型。\n"
system_prompt += "5. 为指令生成一个适当且涉及真实情况的<input>，不应该只包含简单的占位符。<input>应提供实质性的内容，具有挑战性。字数不超过" + str(random.randint(80, 120)) + "字。\n"
system_prompt += "6. <output>应该是对指令的适当且真实的回应，不能只回复答应或拒绝请求。如果需要额外信息才能回复时，请努力预测用户意图并尝试回复。<output>的内容应少于" + str(random.randint(128, 512)) + "字。\n\n"
system_prompt += "请给出满足条件的20条JSON格式数据：\n"
return system_prompt
if __name__ == "__main__":
if len(sys.argv) != 2:
print("Usage: python crawl_prompt.py <output_file>")
exit(1)
output_file = open(sys.argv[1], 'w')
MAX_EPOCHS = 1 # number of data to generate (each prompt contains 20 JSON-formatted data)
for k in range(MAX_EPOCHS):
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo", # here we use `gpt-3.5-turbo` model, while Stanford-Alpaca uses `text-davinci-003`
messages=[
{"role": "user", "content": return_random_prompt()},
]
)
output_file.write(response["choices"][0]["message"]["content"] + '\n')
output_file.close()
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/inference/gradio_demo.py | Python | import torch
from transformers import (
LlamaForCausalLM,
LlamaTokenizer,
StoppingCriteria,
)
import gradio as gr
import argparse
import os
from queue import Queue
from threading import Thread
import traceback
import gc
# Parse command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument(
'--base_model',
default=None,
type=str,
required=True,
help='Base model path')
parser.add_argument('--lora_model', default=None, type=str,
help="If None, perform inference on the base model")
parser.add_argument(
'--tokenizer_path',
default=None,
type=str,
help='If None, lora model path or base model path will be used')
parser.add_argument(
'--gpus',
default="0",
type=str,
help='If None, cuda:0 will be used. Inference using multi-cards: --gpus=0,1,... ')
parser.add_argument('--share', default=True, help='Share gradio domain name')
parser.add_argument('--port', default=19324, type=int, help='Port of gradio demo')
parser.add_argument(
'--max_memory',
default=256,
type=int,
help='Maximum input prompt length, if exceeded model will receive prompt[-max_memory:]')
parser.add_argument(
'--load_in_8bit',
action='store_true',
help='Use 8 bit quantified model')
parser.add_argument(
'--only_cpu',
action='store_true',
help='Only use CPU for inference')
parser.add_argument(
'--alpha',
type=str,
default="1.0",
help="The scaling factor of NTK method, can be a float or 'auto'. ")
args = parser.parse_args()
if args.only_cpu is True:
args.gpus = ""
from patches import apply_attention_patch, apply_ntk_scaling_patch
apply_attention_patch(use_memory_efficient_attention=True)
apply_ntk_scaling_patch(args.alpha)
# Set CUDA devices if available
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
# Peft library can only import after setting CUDA devices
from peft import PeftModel
# Set up the required components: model and tokenizer
def setup():
global tokenizer, model, device, share, port, max_memory
max_memory = args.max_memory
port = args.port
share = args.share
load_in_8bit = args.load_in_8bit
load_type = torch.float16
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
if args.tokenizer_path is None:
args.tokenizer_path = args.lora_model
if args.lora_model is None:
args.tokenizer_path = args.base_model
tokenizer = LlamaTokenizer.from_pretrained(args.tokenizer_path)
base_model = LlamaForCausalLM.from_pretrained(
args.base_model,
load_in_8bit=load_in_8bit,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
)
model_vocab_size = base_model.get_input_embeddings().weight.size(0)
tokenzier_vocab_size = len(tokenizer)
print(f"Vocab of the base model: {model_vocab_size}")
print(f"Vocab of the tokenizer: {tokenzier_vocab_size}")
if model_vocab_size != tokenzier_vocab_size:
assert tokenzier_vocab_size > model_vocab_size
print("Resize model embeddings to fit tokenizer")
base_model.resize_token_embeddings(tokenzier_vocab_size)
if args.lora_model is not None:
print("loading peft model")
model = PeftModel.from_pretrained(
base_model,
args.lora_model,
torch_dtype=load_type,
device_map='auto',
)
else:
model = base_model
if device == torch.device('cpu'):
model.float()
model.eval()
# Reset the user input
def reset_user_input():
return gr.update(value='')
# Reset the state
def reset_state():
return []
# Generate the prompt for the input of LM model
def generate_prompt(instruction):
return f"""
Below is an instruction that describes a task. Write a response that appropriately completes the request.
{instruction}
"""
# User interaction function for chat
def user(user_message, history):
return gr.update(value="", interactive=False), history + \
[[user_message, None]]
class Stream(StoppingCriteria):
def __init__(self, callback_func=None):
self.callback_func = callback_func
def __call__(self, input_ids, scores) -> bool:
if self.callback_func is not None:
self.callback_func(input_ids[0])
return False
class Iteratorize:
"""
Transforms a function that takes a callback
into a lazy iterator (generator).
Adapted from: https://stackoverflow.com/a/9969000
"""
def __init__(self, func, kwargs=None, callback=None):
self.mfunc = func
self.c_callback = callback
self.q = Queue()
self.sentinel = object()
self.kwargs = kwargs or {}
self.stop_now = False
def _callback(val):
if self.stop_now:
raise ValueError
self.q.put(val)
def gentask():
try:
ret = self.mfunc(callback=_callback, **self.kwargs)
except ValueError:
pass
except Exception:
traceback.print_exc()
clear_torch_cache()
self.q.put(self.sentinel)
if self.c_callback:
self.c_callback(ret)
self.thread = Thread(target=gentask)
self.thread.start()
def __iter__(self):
return self
def __next__(self):
obj = self.q.get(True, None)
if obj is self.sentinel:
raise StopIteration
else:
return obj
def __del__(self):
clear_torch_cache()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
self.stop_now = True
clear_torch_cache()
def clear_torch_cache():
gc.collect()
if torch.cuda.device_count() > 0:
torch.cuda.empty_cache()
# Perform prediction based on the user input and history
@torch.no_grad()
def predict(
history,
max_new_tokens=128,
top_p=0.75,
temperature=0.1,
top_k=40,
do_sample=True,
repetition_penalty=1.0
):
history[-1][1] = ""
if len(history) != 0:
input = "".join(["### Instruction:\n" +
i[0] +
"\n\n" +
"### Response: " +
i[1] +
("\n\n" if i[1] != "" else "") for i in history])
if len(input) > max_memory:
input = input[-max_memory:]
prompt = generate_prompt(input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generate_params = {
'input_ids': input_ids,
'max_new_tokens': max_new_tokens,
'top_p': top_p,
'temperature': temperature,
'top_k': top_k,
'do_sample': do_sample,
'repetition_penalty': repetition_penalty,
}
def generate_with_callback(callback=None, **kwargs):
if 'stopping_criteria' in kwargs:
kwargs['stopping_criteria'].append(Stream(callback_func=callback))
else:
kwargs['stopping_criteria'] = [Stream(callback_func=callback)]
clear_torch_cache()
with torch.no_grad():
model.generate(**kwargs)
def generate_with_streaming(**kwargs):
return Iteratorize(generate_with_callback, kwargs, callback=None)
with generate_with_streaming(**generate_params) as generator:
for output in generator:
next_token_ids = output[len(input_ids[0]):]
if next_token_ids[0] == tokenizer.eos_token_id:
break
new_tokens = tokenizer.decode(
next_token_ids, skip_special_tokens=True)
if isinstance(tokenizer, LlamaTokenizer) and len(next_token_ids) > 0:
if tokenizer.convert_ids_to_tokens(int(next_token_ids[0])).startswith('▁'):
new_tokens = ' ' + new_tokens
history[-1][1] = new_tokens
yield history
if len(next_token_ids) >= max_new_tokens:
break
# Call the setup function to initialize the components
setup()
# Create the Gradio interface
with gr.Blocks() as demo:
github_banner_path = 'https://raw.githubusercontent.com/ymcui/Chinese-LLaMA-Alpaca/main/pics/banner.png'
gr.HTML(f'<p align="center"><a href="https://github.com/ymcui/Chinese-LLaMA-Alpaca"><img src={github_banner_path} width="700"/></a></p>')
gr.Markdown("> 为了促进大模型在中文NLP社区的开放研究，本项目开源了中文LLaMA模型和指令精调的Alpaca大模型。这些模型在原版LLaMA的基础上扩充了中文词表并使用了中文数据进行二次预训练，进一步提升了中文基础语义理解能力。同时，中文Alpaca模型进一步使用了中文指令数据进行精调，显著提升了模型对指令的理解和执行能力。")
chatbot = gr.Chatbot()
with gr.Row():
with gr.Column(scale=4):
with gr.Column(scale=12):
user_input = gr.Textbox(
show_label=False,
placeholder="Shift + Enter发送消息...",
lines=10).style(
container=False)
with gr.Column(min_width=32, scale=1):
submitBtn = gr.Button("Submit", variant="primary")
with gr.Column(scale=1):
emptyBtn = gr.Button("Clear History")
max_new_token = gr.Slider(
0,
4096,
value=512,
step=1.0,
label="Maximum New Token Length",
interactive=True)
top_p = gr.Slider(0, 1, value=0.9, step=0.01,
label="Top P", interactive=True)
temperature = gr.Slider(
0,
1,
value=0.5,
step=0.01,
label="Temperature",
interactive=True)
top_k = gr.Slider(1, 40, value=40, step=1,
label="Top K", interactive=True)
do_sample = gr.Checkbox(
value=True,
label="Do Sample",
info="use random sample strategy",
interactive=True)
repetition_penalty = gr.Slider(
1.0,
3.0,
value=1.1,
step=0.1,
label="Repetition Penalty",
interactive=True)
params = [user_input, chatbot]
predict_params = [
chatbot,
max_new_token,
top_p,
temperature,
top_k,
do_sample,
repetition_penalty]
submitBtn.click(
user,
params,
params,
queue=False).then(
predict,
predict_params,
chatbot).then(
lambda: gr.update(
interactive=True),
None,
[user_input],
queue=False)
user_input.submit(
user,
params,
params,
queue=False).then(
predict,
predict_params,
chatbot).then(
lambda: gr.update(
interactive=True),
None,
[user_input],
queue=False)
submitBtn.click(reset_user_input, [], [user_input])
emptyBtn.click(reset_state, outputs=[chatbot], show_progress=True)
# Launch the Gradio interface
demo.queue().launch(
share=share,
inbrowser=True,
server_name='0.0.0.0',
server_port=port)
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/inference/inference_hf.py | Python | import argparse
import json, os
parser = argparse.ArgumentParser()
parser.add_argument('--base_model', default=None, type=str, required=True)
parser.add_argument('--lora_model', default=None, type=str,help="If None, perform inference on the base model")
parser.add_argument('--tokenizer_path',default=None,type=str)
parser.add_argument('--data_file',default=None, type=str,help="A file that contains instructions (one instruction per line)")
parser.add_argument('--with_prompt',action='store_true',help="wrap the input with the prompt automatically")
parser.add_argument('--interactive',action='store_true',help="run in the instruction mode (single-turn)")
parser.add_argument('--predictions_file', default='./predictions.json', type=str)
parser.add_argument('--gpus', default="0", type=str)
parser.add_argument('--only_cpu',action='store_true',help='only use CPU for inference')
parser.add_argument('--alpha',type=str,default="1.0", help="The scaling factor of NTK method, can be a float or 'auto'. ")
parser.add_argument('--load_in_8bit',action='store_true', help="Load the LLM in the 8bit mode")
args = parser.parse_args()
if args.only_cpu is True:
args.gpus = ""
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
import torch
from transformers import LlamaForCausalLM, LlamaTokenizer
from peft import PeftModel
from patches import apply_attention_patch, apply_ntk_scaling_patch
apply_attention_patch(use_memory_efficient_attention=True)
apply_ntk_scaling_patch(args.alpha)
generation_config = dict(
temperature=0.2,
top_k=40,
top_p=0.9,
do_sample=True,
num_beams=1,
repetition_penalty=1.1,
max_new_tokens=400
)
# The prompt template below is taken from llama.cpp
# and is slightly different from the one used in training.
# But we find it gives better results
prompt_input = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n\n{instruction}\n\n### Response:\n\n"
)
sample_data = ["为什么要减少污染，保护环境？"]
def generate_prompt(instruction, input=None):
if input:
instruction = instruction + '\n' + input
return prompt_input.format_map({'instruction': instruction})
if __name__ == '__main__':
load_type = torch.float16
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
if args.tokenizer_path is None:
args.tokenizer_path = args.lora_model
if args.lora_model is None:
args.tokenizer_path = args.base_model
tokenizer = LlamaTokenizer.from_pretrained(args.tokenizer_path)
base_model = LlamaForCausalLM.from_pretrained(
args.base_model,
load_in_8bit=args.load_in_8bit,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
)
model_vocab_size = base_model.get_input_embeddings().weight.size(0)
tokenzier_vocab_size = len(tokenizer)
print(f"Vocab of the base model: {model_vocab_size}")
print(f"Vocab of the tokenizer: {tokenzier_vocab_size}")
if model_vocab_size!=tokenzier_vocab_size:
assert tokenzier_vocab_size > model_vocab_size
print("Resize model embeddings to fit tokenizer")
base_model.resize_token_embeddings(tokenzier_vocab_size)
if args.lora_model is not None:
print("loading peft model")
model = PeftModel.from_pretrained(base_model, args.lora_model,torch_dtype=load_type,device_map='auto',)
else:
model = base_model
if device==torch.device('cpu'):
model.float()
# test data
if args.data_file is None:
examples = sample_data
else:
with open(args.data_file,'r') as f:
examples = [l.strip() for l in f.readlines()]
print("first 10 examples:")
for example in examples[:10]:
print(example)
model.eval()
with torch.no_grad():
if args.interactive:
print("Start inference with instruction mode.")
print('='*85)
print("+ 该模式下仅支持单轮问答，无多轮对话能力。\n"
"+ 如要进行多轮对话，请使用llama.cpp或llamachat工具。")
print('-'*85)
print("+ This mode only supports single-turn QA.\n"
"+ If you want to experience multi-turn dialogue, please use llama.cpp or llamachat.")
print('='*85)
while True:
raw_input_text = input("Input:")
if len(raw_input_text.strip())==0:
break
if args.with_prompt:
input_text = generate_prompt(instruction=raw_input_text)
else:
input_text = raw_input_text
inputs = tokenizer(input_text,return_tensors="pt") #add_special_tokens=False ?
generation_output = model.generate(
input_ids = inputs["input_ids"].to(device),
attention_mask = inputs['attention_mask'].to(device),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
**generation_config
)
s = generation_output[0]
output = tokenizer.decode(s,skip_special_tokens=True)
if args.with_prompt:
response = output.split("### Response:")[1].strip()
else:
response = output
print("Response: ",response)
print("\n")
else:
print("Start inference.")
results = []
for index, example in enumerate(examples):
if args.with_prompt is True:
input_text = generate_prompt(instruction=example)
else:
input_text = example
inputs = tokenizer(input_text,return_tensors="pt") #add_special_tokens=False ?
generation_output = model.generate(
input_ids = inputs["input_ids"].to(device),
attention_mask = inputs['attention_mask'].to(device),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
**generation_config
)
s = generation_output[0]
output = tokenizer.decode(s,skip_special_tokens=True)
if args.with_prompt:
response = output.split("### Response:")[1].strip()
else:
response = output
print(f"======={index}=======")
print(f"Input: {example}\n")
print(f"Output: {response}\n")
results.append({"Input":input_text,"Output":response})
dirname = os.path.dirname(args.predictions_file)
os.makedirs(dirname,exist_ok=True)
with open(args.predictions_file,'w') as f:
json.dump(results,f,ensure_ascii=False,indent=2)
with open(dirname+'/generation_config.json','w') as f:
json.dump(generation_config,f,ensure_ascii=False,indent=2)
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/inference/patches.py | Python | import torch
from torch import nn
from typing import Optional, Tuple, Union
import transformers
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, rotate_half
import math
try:
from xformers import ops as xops
except ImportError:
xops = None
print(
"Xformers is not installed correctly. If you want to use memory_efficient_attention use the following command to install Xformers\npip install xformers."
)
STORE_KV_BEFORE_ROPE = False
USE_MEM_EFF_ATTENTION = False
ALPHA = 1.0
def apply_rotary_pos_emb_single(q, cos, sin, position_ids):
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
q_embed = (q * cos) + (rotate_half(q) * sin)
return q_embed
def xformers_forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
if STORE_KV_BEFORE_ROPE is False:
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
# [bsz, nh, t, hd]
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
else:
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states = apply_rotary_pos_emb_single(query_states, cos, sin, position_ids)
position_ids = torch.arange(kv_seq_len, dtype=torch.long, device=cos.device)
position_ids = position_ids.unsqueeze(0).view(-1, kv_seq_len)
key_states = apply_rotary_pos_emb_single(key_states, cos, sin, position_ids)
if xops is not None and USE_MEM_EFF_ATTENTION:
attn_weights = None
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
attn_bias = None if (query_states.size(1)==1 and key_states.size(1)>1) else xops.LowerTriangularMask()
attn_output = xops.memory_efficient_attention(
query_states, key_states, value_states, attn_bias=attn_bias, p=0)
else:
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
attn_weights = torch.max(
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min, device=attn_weights.device)
)
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
old_init = transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__
def adaptive_ntk_init(self, dim, max_position_embeddings=2048, base=10000, device=None):
self.dim = dim
self.alpha = ALPHA
if isinstance(ALPHA,(float,int)):
base = base * ALPHA ** (dim / (dim-2))
self.base = base
elif ALPHA=='auto':
self.base = base
else:
raise ValueError(ALPHA)
old_init(self, dim, max_position_embeddings, base, device)
ntk_inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
self.register_buffer("ntk_inv_freq", ntk_inv_freq, persistent=False)
def adaptive_ntk_forward(self, x, seq_len=None):
if seq_len > self.max_seq_len_cached:
if isinstance(self.alpha,(float,int)):
self.max_seq_len_cached = seq_len
t = torch.arange(seq_len, device=x.device, dtype=self.ntk_inv_freq.dtype)
freqs = torch.einsum("i,j->ij", t, self.ntk_inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
)
elif self.alpha=='auto':
t = torch.arange(seq_len, device=x.device, dtype=self.ntk_inv_freq.dtype)
dim = self.dim
alpha = (seq_len / 1024 - 1) * 1.1
base = self.base * alpha ** (dim / (dim-2))
ntk_inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(x.device) / dim ))
freqs = torch.einsum("i,j->ij", t, ntk_inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
cos_cached = emb.cos()[None, None, :, :]
sin_cached = emb.sin()[None, None, :, :]
return (
cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype)
)
else:
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype)
)
def apply_attention_patch(
use_memory_efficient_attention=False,
store_kv_before_rope=False
):
global USE_MEM_EFF_ATTENTION, STORE_KV_BEFORE_ROPE
if use_memory_efficient_attention is True and xops is not None:
USE_MEM_EFF_ATTENTION = use_memory_efficient_attention
print("USE_MEM_EFF_ATTENTION: ",USE_MEM_EFF_ATTENTION)
STORE_KV_BEFORE_ROPE = store_kv_before_rope
print("STORE_KV_BEFORE_ROPE:", STORE_KV_BEFORE_ROPE)
transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward
def apply_ntk_scaling_patch(alpha: Union[float,str]):
global ALPHA
ALPHA = alpha
try:
ALPHA = float(ALPHA)
except ValueError:
if ALPHA!="auto":
raise ValueError(f"Alpha can only be a float or 'auto', but given {ALPHA}")
print(f"Apply NTK scaling with ALPHA={ALPHA}")
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__ = adaptive_ntk_init
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.forward = adaptive_ntk_forward | ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/langchain/langchain_qa.py | Python | import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument('--file_path',required=True,type=str)
parser.add_argument('--embedding_path',required=True,type=str)
parser.add_argument('--model_path',required=True,type=str)
parser.add_argument('--gpus', default="0", type=str)
parser.add_argument('--chain_type', default="refine", type=str)
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
# os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION']='python'
file_path = args.file_path
embedding_path = args.embedding_path
model_path = args.model_path
import torch
from langchain import HuggingFacePipeline
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader
from langchain.prompts import PromptTemplate
from langchain.chains import RetrievalQA
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
prompt_template = ("Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{context}\n{question}\n\n### Response: ")
refine_prompt_template = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n"
"这是原始问题: {question}\n"
"已有的回答: {existing_answer}\n"
"现在还有一些文字，（如果有需要）你可以根据它们完善现有的回答。"
"\n\n"
"{context_str}\n"
"\\nn"
"请根据新的文段，进一步完善你的回答。\n\n"
"### Response: "
)
initial_qa_template = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n"
"以下为背景知识：\n"
"{context_str}"
"\n"
"请根据以上背景知识, 回答这个问题：{question}。\n\n"
"### Response: "
)
if __name__ == '__main__':
load_type = torch.float16
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
loader = TextLoader(file_path)
documents = loader.load()
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=600, chunk_overlap=100)
texts = text_splitter.split_documents(documents)
print("Loading the embedding model...")
embeddings = HuggingFaceEmbeddings(model_name=embedding_path)
docsearch = FAISS.from_documents(texts, embeddings)
print("loading LLM...")
model = HuggingFacePipeline.from_model_id(model_id=model_path,
task="text-generation",
model_kwargs={
"torch_dtype" : load_type,
"low_cpu_mem_usage" : True,
"temperature": 0.2,
"max_length": 1000,
"device_map": "auto",
"repetition_penalty":1.1}
)
if args.chain_type == "stuff":
PROMPT = PromptTemplate(
template=prompt_template, input_variables=["context", "question"]
)
chain_type_kwargs = {"prompt": PROMPT}
qa = RetrievalQA.from_chain_type(
llm=model,
chain_type="stuff",
retriever=docsearch.as_retriever(search_kwargs={"k": 1}),
chain_type_kwargs=chain_type_kwargs)
elif args.chain_type == "refine":
refine_prompt = PromptTemplate(
input_variables=["question", "existing_answer", "context_str"],
template=refine_prompt_template,
)
initial_qa_prompt = PromptTemplate(
input_variables=["context_str", "question"],
template=initial_qa_template,
)
chain_type_kwargs = {"question_prompt": initial_qa_prompt, "refine_prompt": refine_prompt}
qa = RetrievalQA.from_chain_type(
llm=model, chain_type="refine",
retriever=docsearch.as_retriever(search_kwargs={"k": 1}),
chain_type_kwargs=chain_type_kwargs)
while True:
query = input("请输入问题：")
if len(query.strip())==0:
break
print(qa.run(query))
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/langchain/langchain_sum.py | Python | import argparse
import os
parser = argparse.ArgumentParser()
parser.add_argument('--file_path',required=True,type=str)
parser.add_argument('--model_path',required=True,type=str)
parser.add_argument('--gpus', default="0", type=str)
parser.add_argument('--chain_type', default="refine", type=str)
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
# os.environ['PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION']='python'
file_path = args.file_path
model_path = args.model_path
import torch
from langchain import HuggingFacePipeline
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.prompts import PromptTemplate
from langchain.chains.summarize import load_summarize_chain
prompt_template = ("Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n请为以下文字写一段摘要:\n{text}\n\n### Response: ")
refine_template = (
"Below is an instruction that describes a task."
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n"
"已有一段摘要：{existing_answer}\n"
"现在还有一些文字，（如果有需要）你可以根据它们完善现有的摘要。"
"\n"
"{text}\n"
"\n"
"如果这段文字没有用，返回原来的摘要即可。请你生成一个最终的摘要。"
"\n\n### Response: "
)
if __name__ == '__main__':
load_type = torch.float16
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
text_splitter = RecursiveCharacterTextSplitter(chunk_size=600, chunk_overlap=100, length_function=len)
with open(file_path) as f:
text = f.read()
docs = text_splitter.create_documents([text])
print("loading LLM...")
model = HuggingFacePipeline.from_model_id(model_id=model_path,
task="text-generation",
model_kwargs={
"torch_dtype" : load_type,
"low_cpu_mem_usage" : True,
"temperature": 0.2,
"max_length": 1000,
"device_map": "auto",
"repetition_penalty":1.1}
)
PROMPT = PromptTemplate(template=prompt_template, input_variables=["text"])
REFINE_PROMPT = PromptTemplate(
template=refine_template,input_variables=["existing_answer", "text"],
)
if args.chain_type == "stuff":
chain = load_summarize_chain(model, chain_type="stuff", prompt=PROMPT)
elif args.chain_type == "refine":
chain = load_summarize_chain(model, chain_type="refine", question_prompt=PROMPT, refine_prompt=REFINE_PROMPT)
print(chain.run(docs))
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/merge_llama_with_chinese_lora.py | Python | """
Usage:
python merge_llama_with_chinese_lora.py \
--base_model path/to/llama/model \
--lora_model path/to/first/lora/model [path/to/second/lora/model] \
--output_type [pth|huggingface] \
--output_dir path/to/output/dir
"""
import argparse
import json
import os
import gc
import torch
import peft
from peft import PeftModel
from transformers import LlamaForCausalLM, LlamaTokenizer
from huggingface_hub import hf_hub_download
parser = argparse.ArgumentParser()
parser.add_argument('--base_model', default=None, required=True,
type=str, help="Please specify a base_model")
parser.add_argument('--lora_model', default=None, required=True,
type=str, help="Please specify LoRA models to be merged (ordered); use commas to separate multiple LoRA models.")
parser.add_argument('--offload_dir', default=None, type=str,
help="(Optional) Please specify a temp folder for offloading (useful for low-RAM machines). Default None (disable offload).")
parser.add_argument('--output_type', default='pth',choices=['pth','huggingface'], type=str,
help="save the merged model in pth or huggingface format.")
parser.add_argument('--output_dir', default='./', type=str)
emb_to_model_size = {
4096 : '7B',
5120 : '13B',
6656 : '33B',
8192 : '65B',
}
num_shards_of_models = {'7B': 1, '13B': 2, '33B': 4, '65B': 8}
params_of_models = {
'7B':
{
"dim": 4096,
"multiple_of": 256,
"n_heads": 32,
"n_layers": 32,
"norm_eps": 1e-06,
"vocab_size": -1,
},
'13B':
{
"dim": 5120,
"multiple_of": 256,
"n_heads": 40,
"n_layers": 40,
"norm_eps": 1e-06,
"vocab_size": -1,
},
'33B':
{
"dim": 6656,
"multiple_of": 256,
"n_heads": 52,
"n_layers": 60,
"norm_eps": 1e-06,
"vocab_size": -1,
},
'65B':
{
"dim": 8192,
"multiple_of": 256,
"n_heads": 64,
"n_layers": 80,
"norm_eps": 1e-05,
"vocab_size": -1,
},
}
def transpose(weight, fan_in_fan_out):
return weight.T if fan_in_fan_out else weight
# Borrowed and modified from https://github.com/tloen/alpaca-lora
def translate_state_dict_key(k):
k = k.replace("base_model.model.", "")
if k == "model.embed_tokens.weight":
return "tok_embeddings.weight"
elif k == "model.norm.weight":
return "norm.weight"
elif k == "lm_head.weight":
return "output.weight"
elif k.startswith("model.layers."):
layer = k.split(".")[2]
if k.endswith(".self_attn.q_proj.weight"):
return f"layers.{layer}.attention.wq.weight"
elif k.endswith(".self_attn.k_proj.weight"):
return f"layers.{layer}.attention.wk.weight"
elif k.endswith(".self_attn.v_proj.weight"):
return f"layers.{layer}.attention.wv.weight"
elif k.endswith(".self_attn.o_proj.weight"):
return f"layers.{layer}.attention.wo.weight"
elif k.endswith(".mlp.gate_proj.weight"):
return f"layers.{layer}.feed_forward.w1.weight"
elif k.endswith(".mlp.down_proj.weight"):
return f"layers.{layer}.feed_forward.w2.weight"
elif k.endswith(".mlp.up_proj.weight"):
return f"layers.{layer}.feed_forward.w3.weight"
elif k.endswith(".input_layernorm.weight"):
return f"layers.{layer}.attention_norm.weight"
elif k.endswith(".post_attention_layernorm.weight"):
return f"layers.{layer}.ffn_norm.weight"
elif k.endswith("rotary_emb.inv_freq") or "lora" in k:
return None
else:
print(layer, k)
raise NotImplementedError
else:
print(k)
raise NotImplementedError
def unpermute(w):
return (
w.view(n_heads, 2, dim // n_heads // 2, dim).transpose(1, 2).reshape(dim, dim)
)
def save_shards(model_sd, num_shards: int):
# Add the no_grad context manager
with torch.no_grad():
if num_shards == 1:
new_state_dict = {}
for k, v in model_sd.items():
new_k = translate_state_dict_key(k)
if new_k is not None:
if "wq" in new_k or "wk" in new_k:
new_state_dict[new_k] = unpermute(v)
else:
new_state_dict[new_k] = v
os.makedirs(output_dir, exist_ok=True)
print(f"Saving shard 1 of {num_shards} into {output_dir}/consolidated.00.pth")
torch.save(new_state_dict, output_dir + "/consolidated.00.pth")
with open(output_dir + "/params.json", "w") as f:
json.dump(params, f)
else:
new_state_dicts = [dict() for _ in range(num_shards)]
for k in list(model_sd.keys()):
v = model_sd[k]
new_k = translate_state_dict_key(k)
if new_k is not None:
if new_k=='tok_embeddings.weight':
print(f"Processing {new_k}")
assert v.size(1)%num_shards==0
splits = v.split(v.size(1)//num_shards,dim=1)
elif new_k=='output.weight':
print(f"Processing {new_k}")
if v.size(0)%num_shards==0:
splits = v.split(v.size(0)//num_shards,dim=0)
else:
size_list = [v.size(0)//num_shards] * num_shards
size_list[-1] += v.size(0)%num_shards
splits = v.split(size_list, dim=0) # 13B: size_list == [24976,24977]
elif new_k=='norm.weight':
print(f"Processing {new_k}")
splits = [v] * num_shards
elif 'ffn_norm.weight' in new_k:
print(f"Processing {new_k}")
splits = [v] * num_shards
elif 'attention_norm.weight' in new_k:
print(f"Processing {new_k}")
splits = [v] * num_shards
elif 'w1.weight' in new_k:
print(f"Processing {new_k}")
splits = v.split(v.size(0)//num_shards,dim=0)
elif 'w2.weight' in new_k:
print(f"Processing {new_k}")
splits = v.split(v.size(1)//num_shards,dim=1)
elif 'w3.weight' in new_k:
print(f"Processing {new_k}")
splits = v.split(v.size(0)//num_shards,dim=0)
elif 'wo.weight' in new_k:
print(f"Processing {new_k}")
splits = v.split(v.size(1)//num_shards,dim=1)
elif 'wv.weight' in new_k:
print(f"Processing {new_k}")
splits = v.split(v.size(0)//num_shards,dim=0)
elif "wq.weight" in new_k or "wk.weight" in new_k:
print(f"Processing {new_k}")
v = unpermute(v)
splits = v.split(v.size(0)//num_shards,dim=0)
else:
print(f"Unexpected key {new_k}")
raise ValueError
for sd,split in zip(new_state_dicts,splits):
sd[new_k] = split.clone()
del split
del splits
del model_sd[k],v
gc.collect() # Effectively enforce garbage collection
os.makedirs(output_dir, exist_ok=True)
for i,new_state_dict in enumerate(new_state_dicts):
print(f"Saving shard {i+1} of {num_shards} into {output_dir}/consolidated.0{i}.pth")
torch.save(new_state_dict, output_dir + f"/consolidated.0{i}.pth")
with open(output_dir + "/params.json", "w") as f:
print(f"Saving params.json into {output_dir}/params.json")
json.dump(params, f)
if __name__=='__main__':
args = parser.parse_args()
base_model_path = args.base_model
lora_model_paths = [s.strip() for s in args.lora_model.split(',') if len(s.strip())!=0]
output_dir = args.output_dir
output_type = args.output_type
offload_dir = args.offload_dir
print(f"Base model: {base_model_path}")
print(f"LoRA model(s) {lora_model_paths}:")
if offload_dir is not None:
# Load with offloading, which is useful for low-RAM machines.
# Note that if you have enough RAM, please use original method instead, as it is faster.
base_model = LlamaForCausalLM.from_pretrained(
base_model_path,
load_in_8bit=False,
torch_dtype=torch.float16,
offload_folder=offload_dir,
offload_state_dict=True,
low_cpu_mem_usage=True,
device_map={"": "cpu"},
)
else:
# Original method without offloading
base_model = LlamaForCausalLM.from_pretrained(
base_model_path,
load_in_8bit=False,
torch_dtype=torch.float16,
device_map={"": "cpu"},
)
## infer the model size from the checkpoint
embedding_size = base_model.get_input_embeddings().weight.size(1)
model_size = emb_to_model_size[embedding_size]
print(f"Peft version: {peft.__version__}")
print(f"Loading LoRA for {model_size} model")
lora_model = None
lora_model_sd = None
for lora_index, lora_model_path in enumerate(lora_model_paths):
print(f"Loading LoRA {lora_model_path}...")
tokenizer = LlamaTokenizer.from_pretrained(lora_model_path)
print(f"base_model vocab size: {base_model.get_input_embeddings().weight.size(0)}")
print(f"tokenizer vocab size: {len(tokenizer)}")
model_vocab_size = base_model.get_input_embeddings().weight.size(0)
assert len(tokenizer) >= model_vocab_size, \
(f"The vocab size of the tokenizer {len(tokenizer)} is smaller than the vocab size of the base model {model_vocab_size}\n"
"This is not the intended use. Please check your model and tokenizer.")
if model_vocab_size != len(tokenizer):
base_model.resize_token_embeddings(len(tokenizer))
print(f"Extended vocabulary size to {len(tokenizer)}")
first_weight = base_model.model.layers[0].self_attn.q_proj.weight
first_weight_old = first_weight.clone()
print(f"Loading LoRA weights")
if hasattr(peft.LoraModel,'merge_and_unload'):
try:
lora_model = PeftModel.from_pretrained(
base_model,
lora_model_path,
device_map={"": "cpu"},
torch_dtype=torch.float16,
)
except RuntimeError as e:
if '[49953, 4096]' in str(e):
print("The vocab size of the tokenizer does not match the vocab size of the LoRA weight. \n"
"Did you misuse the LLaMA tokenizer with the Alpaca-LoRA weight?\n"
"Make sure that you use LLaMA tokenizer with the LLaMA-LoRA weight and Alpaca tokenizer with the Alpaca-LoRA weight!")
raise e
assert torch.allclose(first_weight_old, first_weight)
print(f"Merging with merge_and_unload...")
base_model = lora_model.merge_and_unload()
else:
base_model_sd = base_model.state_dict()
try:
lora_model_sd = torch.load(os.path.join(lora_model_path,'adapter_model.bin'),map_location='cpu')
except FileNotFoundError:
print("Cannot find lora model on the disk. Downloading lora model from hub...")
filename = hf_hub_download(repo_id=lora_model_path,filename='adapter_model.bin')
lora_model_sd = torch.load(filename,map_location='cpu')
if 'base_model.model.model.embed_tokens.weight' in lora_model_sd:
assert lora_model_sd['base_model.model.model.embed_tokens.weight'].shape[0]==len(tokenizer), \
("The vocab size of the tokenizer does not match the vocab size of the LoRA weight. \n"
"Did you misuse the LLaMA tokenizer with the Alpaca-LoRA weight?\n"
"Make sure that you use LLaMA tokenizer with the LLaMA-LoRA weight and Alpaca tokenizer with the Alpaca-LoRA weight!")
lora_config = peft.LoraConfig.from_pretrained(lora_model_path)
lora_scaling = lora_config.lora_alpha / lora_config.r
fan_in_fan_out = lora_config.fan_in_fan_out
lora_keys = [k for k in lora_model_sd if 'lora_A' in k]
non_lora_keys = [k for k in lora_model_sd if not 'lora_' in k]
for k in non_lora_keys:
print(f"merging {k}")
original_k = k.replace('base_model.model.','')
base_model_sd[original_k].copy_(lora_model_sd[k])
for k in lora_keys:
print(f"merging {k}")
original_key = k.replace('.lora_A','').replace('base_model.model.','')
assert original_key in base_model_sd
lora_a_key = k
lora_b_key = k.replace('lora_A','lora_B')
base_model_sd[original_key] += (
transpose(lora_model_sd[lora_b_key].float() @ lora_model_sd[lora_a_key].float(),fan_in_fan_out) * lora_scaling
)
assert base_model_sd[original_key].dtype == torch.float16
# did we do anything?
assert not torch.allclose(first_weight_old, first_weight)
tokenizer.save_pretrained(output_dir)
if output_type=='huggingface':
print("Saving to Hugging Face format...")
LlamaForCausalLM.save_pretrained(base_model, output_dir) #, state_dict=deloreanized_sd)
else: # output_type=='pth
print("Saving to pth format...")
base_model_sd = base_model.state_dict()
del lora_model, base_model, lora_model_sd
params = params_of_models[model_size]
num_shards = num_shards_of_models[model_size]
n_layers = params["n_layers"]
n_heads = params["n_heads"]
dim = params["dim"]
dims_per_head = dim // n_heads
base = 10000.0
inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
save_shards(model_sd=base_model_sd, num_shards=num_shards)
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/merge_llama_with_chinese_lora_low_mem.py | Python | """
Usage:
python merge_llama_with_chinese_lora_low_mem.py \
--base_model path/to/llama/model \
--lora_model path/to/first/lora[,path/to/second/lora] \
--output_type [pth|huggingface] \
--output_dir path/to/output/dir
"""
import argparse
import json
import os
import gc
import torch
import peft
from transformers import LlamaTokenizer
from transformers.modeling_utils import dtype_byte_size
from huggingface_hub import snapshot_download
import re
parser = argparse.ArgumentParser()
parser.add_argument('--base_model', default=None, required=True,
type=str, help="Please specify a base model")
parser.add_argument('--lora_model', default=None, required=True,
type=str, help="Please specify LoRA models to be merged (ordered); use commas to separate multiple LoRA models")
parser.add_argument('--output_type', default='pth',choices=['pth','huggingface'],
type=str, help="Save the merged model in pth or huggingface format")
parser.add_argument('--output_dir', default='./merged_model',
type=str, help="The output folder to save the merged model")
parser.add_argument('--verbose', default=False, action='store_true',
help="Show detailed messages")
emb_to_model_size = {
4096 : '7B',
5120 : '13B',
6656 : '33B',
8192 : '65B',
}
num_shards_of_models = {'7B': 1, '13B': 2, '33B': 4, '65B': 8}
params_of_models = {
'7B':
{
"dim": 4096,
"multiple_of": 256,
"n_heads": 32,
"n_layers": 32,
"norm_eps": 1e-06,
"vocab_size": -1,
},
'13B':
{
"dim": 5120,
"multiple_of": 256,
"n_heads": 40,
"n_layers": 40,
"norm_eps": 1e-06,
"vocab_size": -1,
},
'33B':
{
"dim": 6656,
"multiple_of": 256,
"n_heads": 52,
"n_layers": 60,
"norm_eps": 1e-06,
"vocab_size": -1,
},
'65B':
{
"dim": 8192,
"multiple_of": 256,
"n_heads": 64,
"n_layers": 80,
"norm_eps": 1e-05,
"vocab_size": -1,
},
}
def transpose(weight, fan_in_fan_out):
return weight.T if fan_in_fan_out else weight
# Borrowed and modified from https://github.com/tloen/alpaca-lora
def translate_state_dict_key(k):
k = k.replace("base_model.model.", "")
if k == "model.embed_tokens.weight":
return "tok_embeddings.weight"
elif k == "model.norm.weight":
return "norm.weight"
elif k == "lm_head.weight":
return "output.weight"
elif k.startswith("model.layers."):
layer = k.split(".")[2]
if k.endswith(".self_attn.q_proj.weight"):
return f"layers.{layer}.attention.wq.weight"
elif k.endswith(".self_attn.k_proj.weight"):
return f"layers.{layer}.attention.wk.weight"
elif k.endswith(".self_attn.v_proj.weight"):
return f"layers.{layer}.attention.wv.weight"
elif k.endswith(".self_attn.o_proj.weight"):
return f"layers.{layer}.attention.wo.weight"
elif k.endswith(".mlp.gate_proj.weight"):
return f"layers.{layer}.feed_forward.w1.weight"
elif k.endswith(".mlp.down_proj.weight"):
return f"layers.{layer}.feed_forward.w2.weight"
elif k.endswith(".mlp.up_proj.weight"):
return f"layers.{layer}.feed_forward.w3.weight"
elif k.endswith(".input_layernorm.weight"):
return f"layers.{layer}.attention_norm.weight"
elif k.endswith(".post_attention_layernorm.weight"):
return f"layers.{layer}.ffn_norm.weight"
elif k.endswith("rotary_emb.inv_freq") or "lora" in k:
return None
else:
print(layer, k)
raise NotImplementedError
else:
print(k)
raise NotImplementedError
def unpermute(w):
return (
w.view(n_heads, 2, dim // n_heads // 2, dim).transpose(1, 2).reshape(dim, dim)
)
def save_shards(model_sd, num_shards: int, prefix="", verbose=False):
"""
Convert and save the HF format weights to PTH format weights
"""
with torch.no_grad():
if num_shards == 1:
new_state_dict = {}
for k, v in model_sd.items():
new_k = translate_state_dict_key(k)
if new_k is not None:
if "wq" in new_k or "wk" in new_k:
new_state_dict[new_k] = unpermute(v)
else:
new_state_dict[new_k] = v
os.makedirs(output_dir, exist_ok=True)
print(f"Saving shard 1 of {num_shards} into {output_dir}/{prefix}consolidated.00.pth")
torch.save(new_state_dict, output_dir + f"/{prefix}consolidated.00.pth")
else:
new_state_dicts = [dict() for _ in range(num_shards)]
for k in list(model_sd.keys()):
v = model_sd[k]
new_k = translate_state_dict_key(k)
if new_k is not None:
if new_k=='tok_embeddings.weight':
assert v.size(1)%num_shards==0
splits = v.split(v.size(1)//num_shards,dim=1)
elif new_k=='output.weight':
if v.size(0)%num_shards==0:
splits = v.split(v.size(0)//num_shards,dim=0)
else:
size_list = [v.size(0)//num_shards] * num_shards
size_list[-1] += v.size(0)%num_shards
splits = v.split(size_list, dim=0) # 13B: size_list == [24976,24977]
elif new_k=='norm.weight':
splits = [v] * num_shards
elif 'ffn_norm.weight' in new_k:
splits = [v] * num_shards
elif 'attention_norm.weight' in new_k:
splits = [v] * num_shards
elif 'w1.weight' in new_k:
splits = v.split(v.size(0)//num_shards,dim=0)
elif 'w2.weight' in new_k:
splits = v.split(v.size(1)//num_shards,dim=1)
elif 'w3.weight' in new_k:
splits = v.split(v.size(0)//num_shards,dim=0)
elif 'wo.weight' in new_k:
splits = v.split(v.size(1)//num_shards,dim=1)
elif 'wv.weight' in new_k:
splits = v.split(v.size(0)//num_shards,dim=0)
elif "wq.weight" in new_k or "wk.weight" in new_k:
v = unpermute(v)
splits = v.split(v.size(0)//num_shards,dim=0)
else:
print(f"Unexpected key {new_k}")
raise ValueError
if verbose:
print(f"Processing {new_k}")
for sd,split in zip(new_state_dicts,splits):
sd[new_k] = split.clone()
del split
del splits
del model_sd[k],v
gc.collect() # Effectively enforce garbage collection
os.makedirs(output_dir, exist_ok=True)
for i,new_state_dict in enumerate(new_state_dicts):
print(f"Saving shard {i+1} of {num_shards} into {output_dir}/{prefix}consolidated.0{i}.pth")
torch.save(new_state_dict, output_dir + f"/{prefix}consolidated.0{i}.pth")
def merge_shards(output_dir, num_shards: int):
ckpt_filenames = sorted([f for f in os.listdir(output_dir) if re.match('L(\d+)-consolidated.(\d+).pth',f)])
for i in range(num_shards):
shards_filenames = sorted([f for f in ckpt_filenames if re.match(f'L(\d+)-consolidated.0{i}.pth',f)])
print(f"Loading {shards_filenames} ...")
shards_dicts = [torch.load(os.path.join(output_dir,fn)) for fn in shards_filenames]
shards_merged = {}
for d in shards_dicts:
shards_merged |= d
print(f"Saving the merged shard to " + os.path.join(output_dir, f"consolidated.0{i}.pth"))
torch.save(shards_merged, os.path.join(output_dir, f"consolidated.0{i}.pth"))
print("Cleaning up...")
del shards_merged
for d in shards_dicts:
del d
del shards_dicts
gc.collect() # Effectively enforce garbage collection
for fn in shards_filenames:
os.remove(os.path.join(output_dir,fn))
if __name__=='__main__':
args = parser.parse_args()
base_model_path = args.base_model
lora_model_paths = [s.strip() for s in args.lora_model.split(',') if len(s.strip())!=0]
output_dir = args.output_dir
output_type = args.output_type
os.makedirs(output_dir, exist_ok=True)
print(f"Base model: {base_model_path}")
print(f"LoRA model(s) {lora_model_paths}:")
tokenizers_and_loras = []
for lora_model_path in lora_model_paths:
print(f"Loading {lora_model_path}")
if not os.path.exists(lora_model_path):
print("Cannot find lora model on the disk. Downloading lora model from hub...")
lora_model_path = snapshot_download(repo_id=lora_model_path)
tokenizer = LlamaTokenizer.from_pretrained(lora_model_path)
lora_config = peft.LoraConfig.from_pretrained(lora_model_path)
lora_state_dict = torch.load(os.path.join(lora_model_path,'adapter_model.bin'),map_location='cpu')
if 'base_model.model.model.embed_tokens.weight' in lora_state_dict:
lora_vocab_size = lora_state_dict['base_model.model.model.embed_tokens.weight'].shape[0]
assert lora_vocab_size==len(tokenizer), \
(f"The vocab size of the tokenizer {len(tokenizer)} does not match the vocab size of the LoRA weight {lora_vocab_size}.\n"
"Make sure that you use LLaMA tokenizer with the LLaMA-LoRA weight and Alpaca tokenizer with the Alpaca-LoRA weight!")
tokenizers_and_loras.append(
{
"tokenizer" :tokenizer,
"state_dict" :lora_state_dict,
"config": lora_config,
"scaling": lora_config.lora_alpha / lora_config.r,
"fan_in_fan_out" : lora_config.fan_in_fan_out,
})
if len(tokenizers_and_loras)==2:
t1_vocab_size = len(tokenizers_and_loras[0]["tokenizer"])
t2_vocab_size = len(tokenizers_and_loras[1]["tokenizer"])
assert t1_vocab_size<=t2_vocab_size, \
(f"The vocab size of the first tokenizer is {t1_vocab_size}\n"
f"The vocab size of the second tokenizer is {t2_vocab_size}, found to be smaller than {t1_vocab_size}\n"
"This is not the intended use. Please check your model and tokenizer.")
if not os.path.exists(base_model_path):
print("Cannot find lora model on the disk. Downloading lora model from hub...")
base_model_path = snapshot_download(repo_id=base_model_path)
ckpt_filenames = sorted([f for f in os.listdir(base_model_path) if re.match('pytorch_model-(\d+)-of-(\d+).bin',f)])
embedding_size = None
model_size = None
total_size = 0
for index, filename in enumerate(ckpt_filenames):
print(f"Loading ckpt {filename}")
state_dict = torch.load(os.path.join(base_model_path,filename), map_location='cpu')
if index == 0:
embedding_size = state_dict['model.embed_tokens.weight'].shape[1]
model_size = emb_to_model_size[embedding_size]
if output_type=='pth':
params = params_of_models[model_size]
num_shards = num_shards_of_models[model_size]
n_layers = params["n_layers"]
n_heads = params["n_heads"]
dim = params["dim"]
dims_per_head = dim // n_heads
base = 10000.0
inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
print("Merging...")
for k in state_dict:
for tl_idx, t_and_l in enumerate(tokenizers_and_loras):
saved_key = 'base_model.model.'+k
lora_key_A = saved_key.replace('.weight','.lora_A.weight')
if saved_key in t_and_l['state_dict']:
if args.verbose:
print(f"copying {saved_key} from {tl_idx}-th LoRA weight to {k}")
state_dict[k] = t_and_l['state_dict'][saved_key].half().clone() # do we need half()?
if lora_key_A in t_and_l['state_dict']:
lora_key_B = lora_key_A.replace('lora_A.weight','lora_B.weight')
if args.verbose:
print(f"merging {lora_key_A} and lora_B.weight form {tl_idx}-th LoRA weight to {k}")
state_dict[k] += (
transpose(
t_and_l['state_dict'][lora_key_B].float()
@ t_and_l['state_dict'][lora_key_A].float(), t_and_l['fan_in_fan_out']) * t_and_l['scaling']
)
weight_size = state_dict[k].numel() * dtype_byte_size(state_dict[k].dtype)
total_size += weight_size
if output_type=='huggingface':
print(f"Saving ckpt {filename} to {output_dir} in HF format...")
torch.save(state_dict,os.path.join(output_dir, filename))
elif output_type=='pth':
print(f"Converting to pth format...")
save_shards(model_sd=state_dict, num_shards=num_shards,prefix=f"L{index+1}-", verbose=args.verbose)
del state_dict
gc.collect() # Effectively enforce garbage collection
print(f"Saving tokenizer")
tokenizers_and_loras[-1]['tokenizer'].save_pretrained(output_dir)
if output_type == 'pth':
with open(output_dir + "/params.json", "w") as f:
print(f"Saving params.json into {output_dir}/params.json")
json.dump(params, f)
merge_shards(output_dir, num_shards=num_shards)
if output_type=='huggingface':
configs = ('config.json', 'generation_config.json', 'pytorch_model.bin.index.json')
for config in configs:
if os.path.exists(os.path.join(base_model_path, config)):
print(f"Saving {config}")
with open(os.path.join(base_model_path, config),'r') as f:
obj = json.load(f)
if config=='config.json':
obj['vocab_size'] = len(tokenizers_and_loras[-1]['tokenizer'])
if config=='pytorch_model.bin.index.json':
obj['metadata']['total_size'] = total_size
with open(os.path.join(output_dir, config), 'w') as f:
json.dump(obj, f, indent=2)
print("Done.")
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/merge_tokenizer/merge_tokenizers.py | Python | import os
os.environ["PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION"]="python"
from transformers import LlamaTokenizer
from sentencepiece import sentencepiece_model_pb2 as sp_pb2_model
import sentencepiece as spm
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--llama_tokenizer_dir', default=None, type=str, required=True)
parser.add_argument('--chinese_sp_model_file', default='./chinese_sp.model', type=str)
args = parser.parse_args()
llama_tokenizer_dir = args.llama_tokenizer_dir
chinese_sp_model_file = args.chinese_sp_model_file
# load
llama_tokenizer = LlamaTokenizer.from_pretrained(llama_tokenizer_dir)
chinese_sp_model = spm.SentencePieceProcessor()
chinese_sp_model.Load(chinese_sp_model_file)
llama_spm = sp_pb2_model.ModelProto()
llama_spm.ParseFromString(llama_tokenizer.sp_model.serialized_model_proto())
chinese_spm = sp_pb2_model.ModelProto()
chinese_spm.ParseFromString(chinese_sp_model.serialized_model_proto())
# print number of tokens
print(len(llama_tokenizer),len(chinese_sp_model))
print(llama_tokenizer.all_special_tokens)
print(llama_tokenizer.all_special_ids)
print(llama_tokenizer.special_tokens_map)
## Add Chinese tokens to LLaMA tokenizer
llama_spm_tokens_set=set(p.piece for p in llama_spm.pieces)
print(len(llama_spm_tokens_set))
print(f"Before:{len(llama_spm_tokens_set)}")
for p in chinese_spm.pieces:
piece = p.piece
if piece not in llama_spm_tokens_set:
new_p = sp_pb2_model.ModelProto().SentencePiece()
new_p.piece = piece
new_p.score = 0
llama_spm.pieces.append(new_p)
print(f"New model pieces: {len(llama_spm.pieces)}")
## Save
output_sp_dir = 'merged_tokenizer_sp'
output_hf_dir = 'merged_tokenizer_hf' # the path to save Chinese-LLaMA tokenizer
os.makedirs(output_sp_dir,exist_ok=True)
with open(output_sp_dir+'/chinese_llama.model', 'wb') as f:
f.write(llama_spm.SerializeToString())
tokenizer = LlamaTokenizer(vocab_file=output_sp_dir+'/chinese_llama.model')
tokenizer.save_pretrained(output_hf_dir)
print(f"Chinese-LLaMA tokenizer has been saved to {output_hf_dir}")
# Test
llama_tokenizer = LlamaTokenizer.from_pretrained(llama_tokenizer_dir)
chinese_llama_tokenizer = LlamaTokenizer.from_pretrained(output_hf_dir)
print(tokenizer.all_special_tokens)
print(tokenizer.all_special_ids)
print(tokenizer.special_tokens_map)
text='''白日依山尽，黄河入海流。欲穷千里目，更上一层楼。
The primary use of LLaMA is research on large language models, including'''
print("Test text:\n",text)
print(f"Tokenized by LLaMA tokenizer:{llama_tokenizer.tokenize(text)}")
print(f"Tokenized by Chinese-LLaMA tokenizer:{chinese_llama_tokenizer.tokenize(text)}") | ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/openai_server_demo/openai_api_protocol.py | Python | from typing import Optional, List, Dict, Any, Union
import time
import shortuuid
from pydantic import BaseModel, Field
class ChatCompletionRequest(BaseModel):
model: str = "chinese-llama-alpaca"
messages: Union[str, List[Dict[str, str]]]
temperature: Optional[float] = 0.7
top_p: Optional[float] = 1.0
top_k: Optional[int] = 40
n: Optional[int] = 1
max_tokens: Optional[int] = 128
num_beams: Optional[int] = 1
stop: Optional[Union[str, List[str]]] = None
stream: Optional[bool] = False
repetition_penalty: Optional[float] = 1.0
user: Optional[str] = None
do_sample: Optional[bool] = True
class ChatMessage(BaseModel):
role: str
content: str
class ChatCompletionResponseChoice(BaseModel):
index: int
message: ChatMessage
class ChatCompletionResponse(BaseModel):
id: str = Field(default_factory=lambda: f"chatcmpl-{shortuuid.random()}")
object: str = "chat.completion"
created: int = Field(default_factory=lambda: int(time.time()))
model: str = "chinese-llama-alpaca"
choices: List[ChatCompletionResponseChoice]
class EmbeddingsRequest(BaseModel):
input: Union[str, List[Any]]
user: Optional[str] = None
class EmbeddingsResponse(BaseModel):
object: str = "list"
data: List[Dict[str, Any]]
model: str = "chinese-llama-alpaca"
class CompletionRequest(BaseModel):
prompt: Union[str, List[Any]]
temperature: Optional[float] = 0.1
n: Optional[int] = 1
max_tokens: Optional[int] = 128
stop: Optional[Union[str, List[str]]] = None
stream: Optional[bool] = False
top_p: Optional[float] = 0.75
top_k: Optional[int] = 40
num_beams: Optional[int] = 1
logprobs: Optional[int] = None
echo: Optional[bool] = False
repetition_penalty: Optional[float] = 1.0
user: Optional[str] = None
do_sample: Optional[bool] = True
class CompletionResponseChoice(BaseModel):
index: int
text: str
class CompletionResponse(BaseModel):
id: Optional[str] = Field(default_factory=lambda: f"cmpl-{shortuuid.random()}")
object: Optional[str] = "text_completion"
created: Optional[int] = Field(default_factory=lambda: int(time.time()))
model: Optional[str] = 'chinese-llama-alpaca'
choices: List[CompletionResponseChoice]
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/openai_server_demo/openai_api_server.py | Python | import argparse
import os
from fastapi import FastAPI
import uvicorn
parser = argparse.ArgumentParser()
parser.add_argument('--base_model', default=None, type=str, required=True)
parser.add_argument('--lora_model', default=None, type=str,help="If None, perform inference on the base model")
parser.add_argument('--tokenizer_path',default=None,type=str)
parser.add_argument('--gpus', default="0", type=str)
parser.add_argument('--load_in_8bit',action='store_true', help='use 8 bit model')
parser.add_argument('--only_cpu',action='store_true',help='only use CPU for inference')
parser.add_argument('--alpha',type=str,default="1.0", help="The scaling factor of NTK method, can be a float or 'auto'. ")
args = parser.parse_args()
load_in_8bit = args.load_in_8bit
if args.only_cpu is True:
args.gpus = ""
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
import torch
import torch.nn.functional as F
from transformers import LlamaForCausalLM, LlamaTokenizer, GenerationConfig
from peft import PeftModel
from patches import apply_attention_patch, apply_ntk_scaling_patch
apply_attention_patch(use_memory_efficient_attention=True)
apply_ntk_scaling_patch(args.alpha)
from openai_api_protocol import (
ChatCompletionRequest,
ChatCompletionResponse,
ChatMessage,
ChatCompletionResponseChoice,
CompletionRequest,
CompletionResponse,
CompletionResponseChoice,
EmbeddingsRequest,
EmbeddingsResponse,
)
load_type = torch.float16
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
if args.tokenizer_path is None:
args.tokenizer_path = args.lora_model
if args.lora_model is None:
args.tokenizer_path = args.base_model
tokenizer = LlamaTokenizer.from_pretrained(args.tokenizer_path)
base_model = LlamaForCausalLM.from_pretrained(
args.base_model,
load_in_8bit=load_in_8bit,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto' if not args.only_cpu else None,
)
model_vocab_size = base_model.get_input_embeddings().weight.size(0)
tokenzier_vocab_size = len(tokenizer)
print(f"Vocab of the base model: {model_vocab_size}")
print(f"Vocab of the tokenizer: {tokenzier_vocab_size}")
if model_vocab_size!=tokenzier_vocab_size:
assert tokenzier_vocab_size > model_vocab_size
print("Resize model embeddings to fit tokenizer")
base_model.resize_token_embeddings(tokenzier_vocab_size)
if args.lora_model is not None:
print("loading peft model")
model = PeftModel.from_pretrained(base_model, args.lora_model,torch_dtype=load_type,device_map='auto',)
else:
model = base_model
if device==torch.device('cpu'):
model.float()
model.eval()
def generate_completion_prompt(instruction: str):
"""Generate prompt for completion"""
return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{instruction}
### Response: """
def generate_chat_prompt(messages: list):
"""Generate prompt for chat completion"""
system_msg = '''Below is an instruction that describes a task. Write a response that appropriately completes the request.'''
for msg in messages:
if msg.role == 'system':
system_msg = msg.content
prompt = f"{system_msg}\n\n"
for msg in messages:
if msg.role == 'system':
continue
if msg.role == 'assistant':
prompt += f"### Response: {msg.content}\n\n"
if msg.role == 'user':
prompt += f"### Instruction:\n{msg.content}\n\n"
prompt += "### Response: "
return prompt
def predict(
input,
max_new_tokens=128,
top_p=0.75,
temperature=0.1,
top_k=40,
num_beams=4,
repetition_penalty=1.0,
do_sample=True,
**kwargs,
):
"""
Main inference method
type(input) == str -> /v1/completions
type(input) == list -> /v1/chat/completions
"""
if isinstance(input, str):
prompt = generate_completion_prompt(input)
else:
prompt = generate_chat_prompt(input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
do_sample=do_sample,
**kwargs,
)
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=False,
max_new_tokens=max_new_tokens,
repetition_penalty=float(repetition_penalty),
)
s = generation_output.sequences[0]
output = tokenizer.decode(s, skip_special_tokens=True)
output = output.split("### Response:")[-1].strip()
return output
def get_embedding(input):
"""Get embedding main function"""
with torch.no_grad():
if tokenizer.pad_token == None:
tokenizer.add_special_tokens({'pad_token': '[PAD]'})
encoding = tokenizer(
input, padding=True, return_tensors="pt"
)
input_ids = encoding["input_ids"].to(device)
attention_mask = encoding["attention_mask"].to(device)
model_output = model(
input_ids, attention_mask, output_hidden_states=True
)
data = model_output.hidden_states[-1]
mask = attention_mask.unsqueeze(-1).expand(data.size()).float()
masked_embeddings = data * mask
sum_embeddings = torch.sum(masked_embeddings, dim=1)
seq_length = torch.sum(mask, dim=1)
embedding = sum_embeddings / seq_length
normalized_embeddings = F.normalize(embedding, p=2, dim=1)
ret = normalized_embeddings.squeeze(0).tolist()
return ret
app = FastAPI()
@app.post("/v1/chat/completions")
async def create_chat_completion(request: ChatCompletionRequest):
"""Creates a completion for the chat message"""
msgs = request.messages
if isinstance(msgs, str):
msgs = [ChatMessage(role='user',content=msgs)]
else:
msgs = [ChatMessage(role=x['role'],content=x['message']) for x in msgs]
output = predict(
input=msgs,
max_new_tokens=request.max_tokens,
top_p=request.top_p,
top_k=request.top_k,
temperature=request.temperature,
num_beams=request.num_beams,
repetition_penalty=request.repetition_penalty,
do_sample=request.do_sample,
)
choices = [ChatCompletionResponseChoice(index = i, message = msg) for i, msg in enumerate(msgs)]
choices += [ChatCompletionResponseChoice(index = len(choices), message = ChatMessage(role='assistant',content=output))]
return ChatCompletionResponse(choices = choices)
@app.post("/v1/completions")
async def create_completion(request: CompletionRequest):
"""Creates a completion"""
output = predict(
input=request.prompt,
max_new_tokens=request.max_tokens,
top_p=request.top_p,
top_k=request.top_k,
temperature=request.temperature,
num_beams=request.num_beams,
repetition_penalty=request.repetition_penalty,
do_sample=request.do_sample,
)
choices = [CompletionResponseChoice(index = 0, text = output)]
return CompletionResponse(choices = choices)
@app.post("/v1/embeddings")
async def create_embeddings(request: EmbeddingsRequest):
"""Creates text embedding"""
embedding = get_embedding(request.input)
data = [{
"object": "embedding",
"embedding": embedding,
"index": 0
}]
return EmbeddingsResponse(data=data)
if __name__ == "__main__":
log_config = uvicorn.config.LOGGING_CONFIG
log_config["formatters"]["access"]["fmt"] = "%(asctime)s - %(levelname)s - %(message)s"
log_config["formatters"]["default"]["fmt"] = "%(asctime)s - %(levelname)s - %(message)s"
uvicorn.run(app, host='0.0.0.0', port=19327, workers=1, log_config=log_config)
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/openai_server_demo/patches.py | Python | import torch
from torch import nn
from typing import Optional, Tuple, Union
import transformers
from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, rotate_half
import math
try:
from xformers import ops as xops
except ImportError:
xops = None
print(
"Xformers is not installed correctly. If you want to use memory_efficient_attention use the following command to install Xformers\npip install xformers."
)
STORE_KV_BEFORE_ROPE = False
USE_MEM_EFF_ATTENTION = False
ALPHA = 1.0
def apply_rotary_pos_emb_single(q, cos, sin, position_ids):
# The first two dimensions of cos and sin are always 1, so we can `squeeze` them.
cos = cos.squeeze(1).squeeze(0) # [seq_len, dim]
sin = sin.squeeze(1).squeeze(0) # [seq_len, dim]
cos = cos[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
sin = sin[position_ids].unsqueeze(1) # [bs, 1, seq_len, dim]
q_embed = (q * cos) + (rotate_half(q) * sin)
return q_embed
def xformers_forward(
self,
hidden_states: torch.Tensor,
attention_mask: Optional[torch.Tensor] = None,
position_ids: Optional[torch.LongTensor] = None,
past_key_value: Optional[Tuple[torch.Tensor]] = None,
output_attentions: bool = False,
use_cache: bool = False,
) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
bsz, q_len, _ = hidden_states.size()
query_states = self.q_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
key_states = self.k_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
value_states = self.v_proj(hidden_states).view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2)
kv_seq_len = key_states.shape[-2]
if past_key_value is not None:
kv_seq_len += past_key_value[0].shape[-2]
if STORE_KV_BEFORE_ROPE is False:
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
# [bsz, nh, t, hd]
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
else:
if past_key_value is not None:
# reuse k, v, self_attention
key_states = torch.cat([past_key_value[0], key_states], dim=2)
value_states = torch.cat([past_key_value[1], value_states], dim=2)
past_key_value = (key_states, value_states) if use_cache else None
cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
query_states = apply_rotary_pos_emb_single(query_states, cos, sin, position_ids)
position_ids = torch.arange(kv_seq_len, dtype=torch.long, device=cos.device)
position_ids = position_ids.unsqueeze(0).view(-1, kv_seq_len)
key_states = apply_rotary_pos_emb_single(key_states, cos, sin, position_ids)
if xops is not None and USE_MEM_EFF_ATTENTION:
attn_weights = None
query_states = query_states.transpose(1, 2)
key_states = key_states.transpose(1, 2)
value_states = value_states.transpose(1, 2)
attn_bias = None if (query_states.size(1)==1 and key_states.size(1)>1) else xops.LowerTriangularMask()
attn_output = xops.memory_efficient_attention(
query_states, key_states, value_states, attn_bias=attn_bias, p=0)
else:
attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim)
if attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
raise ValueError(
f"Attention weights should be of size {(bsz * self.num_heads, q_len, kv_seq_len)}, but is"
f" {attn_weights.size()}"
)
if attention_mask is not None:
if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
raise ValueError(
f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
)
attn_weights = attn_weights + attention_mask
attn_weights = torch.max(
attn_weights, torch.tensor(torch.finfo(attn_weights.dtype).min, device=attn_weights.device)
)
# upcast attention to fp32
attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype)
attn_output = torch.matmul(attn_weights, value_states)
if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim):
raise ValueError(
f"`attn_output` should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is"
f" {attn_output.size()}"
)
attn_output = attn_output.transpose(1, 2)
attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
attn_output = self.o_proj(attn_output)
if not output_attentions:
attn_weights = None
return attn_output, attn_weights, past_key_value
old_init = transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__
def adaptive_ntk_init(self, dim, max_position_embeddings=2048, base=10000, device=None):
self.dim = dim
self.alpha = ALPHA
if isinstance(ALPHA,(float,int)):
base = base * ALPHA ** (dim / (dim-2))
self.base = base
elif ALPHA=='auto':
self.base = base
else:
raise ValueError(ALPHA)
old_init(self, dim, max_position_embeddings, base, device)
ntk_inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
self.register_buffer("ntk_inv_freq", ntk_inv_freq, persistent=False)
def adaptive_ntk_forward(self, x, seq_len=None):
if seq_len > self.max_seq_len_cached:
if isinstance(self.alpha,(float,int)):
self.max_seq_len_cached = seq_len
t = torch.arange(seq_len, device=x.device, dtype=self.ntk_inv_freq.dtype)
freqs = torch.einsum("i,j->ij", t, self.ntk_inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
)
elif self.alpha=='auto':
t = torch.arange(seq_len, device=x.device, dtype=self.ntk_inv_freq.dtype)
dim = self.dim
alpha = (seq_len / 1024 - 1) * 1.1
base = self.base * alpha ** (dim / (dim-2))
ntk_inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(x.device) / dim ))
freqs = torch.einsum("i,j->ij", t, ntk_inv_freq)
emb = torch.cat((freqs, freqs), dim=-1).to(x.device)
cos_cached = emb.cos()[None, None, :, :]
sin_cached = emb.sin()[None, None, :, :]
return (
cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype)
)
else:
return (
self.cos_cached[:, :, :seq_len, ...].to(dtype=x.dtype),
self.sin_cached[:, :, :seq_len, ...].to(dtype=x.dtype)
)
def apply_attention_patch(
use_memory_efficient_attention=False,
store_kv_before_rope=False
):
global USE_MEM_EFF_ATTENTION, STORE_KV_BEFORE_ROPE
if use_memory_efficient_attention is True and xops is not None:
USE_MEM_EFF_ATTENTION = use_memory_efficient_attention
print("USE_MEM_EFF_ATTENTION: ",USE_MEM_EFF_ATTENTION)
STORE_KV_BEFORE_ROPE = store_kv_before_rope
print("STORE_KV_BEFORE_ROPE:", STORE_KV_BEFORE_ROPE)
transformers.models.llama.modeling_llama.LlamaAttention.forward = xformers_forward
def apply_ntk_scaling_patch(alpha: Union[float,str]):
global ALPHA
ALPHA = alpha
try:
ALPHA = float(ALPHA)
except ValueError:
if ALPHA!="auto":
raise ValueError(f"Alpha can only be a float or 'auto', but given {ALPHA}")
print(f"Apply NTK scaling with ALPHA={ALPHA}")
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.__init__ = adaptive_ntk_init
transformers.models.llama.modeling_llama.LlamaRotaryEmbedding.forward = adaptive_ntk_forward | ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/build_dataset.py | Python | import logging
import os
from dataclasses import dataclass
from typing import Dict, Sequence, Union, List
import datasets
import torch
from datasets import load_dataset, concatenate_datasets
import transformers
IGNORE_INDEX = -100
logger = logging.getLogger('__name__')
PROMPT_TEMPLATE = (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response: "
)
def build_instruction_dataset(data_path: Union[List[str],str],
tokenizer: transformers.PreTrainedTokenizer,
max_seq_length: int, data_cache_dir = None,
preprocessing_num_workers = None,
):
def tokenization(examples):
sources = []
targets = []
prompt = PROMPT_TEMPLATE
for instruction, input, output in zip(examples['instruction'],examples['input'],examples['output']):
if input is not None and input !="":
instruction = instruction+'\n'+input
source = prompt.format_map({'instruction':instruction})
target = f"{output}{tokenizer.eos_token}"
sources.append(source)
targets.append(target)
tokenized_sources = tokenizer(sources,return_attention_mask=False)
tokenized_targets = tokenizer(targets,return_attention_mask=False,add_special_tokens=False)
all_input_ids = []
all_labels = []
for s,t in zip(tokenized_sources['input_ids'],tokenized_targets['input_ids']):
input_ids = torch.LongTensor(s + t)[:max_seq_length]
labels = torch.LongTensor([IGNORE_INDEX] * len(s) + t)[:max_seq_length]
assert len(input_ids) == len(labels)
all_input_ids.append(input_ids)
all_labels.append(labels)
results = {'input_ids':all_input_ids, 'labels': all_labels}
return results
logging.warning("building dataset...")
all_datasets = []
if not isinstance(data_path,(list,tuple)):
data_path = [data_path]
for file in data_path:
if data_cache_dir is None:
data_cache_dir = str(os.path.dirname(file))
cache_path = os.path.join(data_cache_dir,os.path.basename(file).split('.')[0])
os.makedirs(cache_path, exist_ok=True)
try:
processed_dataset = datasets.load_from_disk(cache_path)
logger.info(f'training datasets-{file} has been loaded from disk')
except Exception:
raw_dataset = load_dataset("json", data_files=file, cache_dir=cache_path)
tokenization_func = tokenization
tokenized_dataset = raw_dataset.map(
tokenization_func,
batched=True,
num_proc=preprocessing_num_workers,
remove_columns=["instruction","input","output"],
keep_in_memory=False,
desc="preprocessing on dataset",
)
processed_dataset = tokenized_dataset
processed_dataset.save_to_disk(cache_path)
processed_dataset.set_format('torch')
all_datasets.append(processed_dataset['train'])
all_datasets = concatenate_datasets(all_datasets)
return all_datasets
@dataclass
class DataCollatorForSupervisedDataset(object):
"""Collate examples for supervised fine-tuning."""
tokenizer: transformers.PreTrainedTokenizer
def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]:
input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels"))
input_ids = torch.nn.utils.rnn.pad_sequence(
input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id
)
labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=-100)
return dict(
input_ids=input_ids,
labels=labels,
attention_mask=input_ids.ne(self.tokenizer.pad_token_id),
)
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/run_clm_pt_with_peft.py | Python | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=text-generation
"""
# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
import logging
import numpy as np
import math
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, List, Dict, Any, Mapping
from pathlib import Path
import datasets
import torch
from datasets import load_dataset, concatenate_datasets
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_CAUSAL_LM_MAPPING,
AutoConfig,
AutoModelForCausalLM,
LlamaForCausalLM,
LlamaTokenizer,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
is_torch_tpu_available,
set_seed,
)
from transformers.testing_utils import CaptureLogger
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import send_example_telemetry
from transformers.utils.versions import require_version
from sklearn.metrics import accuracy_score
from peft import LoraConfig, TaskType, get_peft_model, PeftModel, get_peft_model_state_dict
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
class SavePeftModelCallback(transformers.TrainerCallback):
def save_model(self, args, state, kwargs):
if state.best_model_checkpoint is not None:
checkpoint_folder = os.path.join(state.best_model_checkpoint, "pt_lora_model")
else:
checkpoint_folder = os.path.join(args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}")
peft_model_path = os.path.join(checkpoint_folder, "pt_lora_model")
kwargs["model"].save_pretrained(peft_model_path)
kwargs["tokenizer"].save_pretrained(peft_model_path)
def on_save(self, args, state, control, **kwargs):
self.save_model(args, state, kwargs)
return control
def on_train_end(self, args, state, control, **kwargs):
peft_model_path = os.path.join(args.output_dir, "pt_lora_model")
kwargs["model"].save_pretrained(peft_model_path)
kwargs["tokenizer"].save_pretrained(peft_model_path)
def accuracy(predictions, references, normalize=True, sample_weight=None):
return {
"accuracy": float(
accuracy_score(references, predictions, normalize=normalize, sample_weight=sample_weight)
)
}
def compute_metrics(eval_preds):
preds, labels = eval_preds
# preds have the same shape as the labels, after the argmax(-1) has been calculated
# by preprocess_logits_for_metrics but we need to shift the labels
labels = labels[:, 1:].reshape(-1)
preds = preds[:, :-1].reshape(-1)
return accuracy(predictions=preds, references=labels)
def preprocess_logits_for_metrics(logits, labels):
if isinstance(logits, tuple):
# Depending on the model and config, logits may contain extra tensors,
# like past_key_values, but logits always come first
logits = logits[0]
return logits.argmax(dim=-1)
def fault_tolerance_data_collator(features: List) -> Dict[str, Any]:
if not isinstance(features[0], Mapping):
features = [vars(f) for f in features]
first = features[0]
batch = {}
# Special handling for labels.
# Ensure that tensor is created with the correct type
# (it should be automatically the case, but let's make sure of it.)
if "label" in first and first["label"] is not None:
label = first["label"].item() if isinstance(first["label"], torch.Tensor) else first["label"]
dtype = torch.long if isinstance(label, int) else torch.float
batch["labels"] = torch.tensor([f["label"] for f in features], dtype=dtype)
elif "label_ids" in first and first["label_ids"] is not None:
if isinstance(first["label_ids"], torch.Tensor):
batch["labels"] = torch.stack([f["label_ids"] for f in features])
else:
dtype = torch.long if isinstance(first["label_ids"][0], int) else torch.float
batch["labels"] = torch.tensor([f["label_ids"] for f in features], dtype=dtype)
# Handling of all other possible keys.
# Again, we will use the first element to figure out which key/values are not None for this model.
try:
for k, v in first.items():
if k not in ("label", "label_ids") and v is not None and not isinstance(v, str):
if isinstance(v, torch.Tensor):
batch[k] = torch.stack([f[k] for f in features])
elif isinstance(v, np.ndarray):
batch[k] = torch.tensor(np.stack([f[k] for f in features]))
else:
batch[k] = torch.tensor([f[k] for f in features])
except ValueError: # quick fix by simply take the first example
for k, v in first.items():
if k not in ("label", "label_ids") and v is not None and not isinstance(v, str):
if isinstance(v, torch.Tensor):
batch[k] = torch.stack([features[0][k]] * len(features))
elif isinstance(v, np.ndarray):
batch[k] = torch.tensor(np.stack([features[0][k]] * len(features)))
else:
batch[k] = torch.tensor([features[0][k]] * len(features))
return batch
MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
)
},
)
tokenizer_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The tokenizer for weights initialization.Don't set if you want to train a model from scratch."
)
},
)
model_type: Optional[str] = field(
default=None,
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
)
config_overrides: Optional[str] = field(
default=None,
metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
},
)
torch_dtype: Optional[str] = field(
default=None,
metadata={
"help": (
"Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
"dtype will be automatically derived from the model's weights."
),
"choices": ["auto", "bfloat16", "float16", "float32"],
},
)
def __post_init__(self):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path"
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_dir: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"})
block_size: Optional[int] = field(
default=None,
metadata={
"help": (
"Optional input sequence length after tokenization. "
"The training dataset will be truncated in block of this size for training. "
"Default to the model max input length for single sentence inputs (take into account special tokens)."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
validation_split_percentage: Optional[float] = field(
default=0.05,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
keep_linebreaks: bool = field(
default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."}
)
data_cache_dir: Optional[str] = field(default="./", metadata={"help": "The datasets processed stored"})
def __post_init__(self):
if self.streaming:
require_version("datasets>=2.0.0", "The streaming feature requires `datasets>=2.0.0`")
@dataclass
class MyTrainingArguments(TrainingArguments):
trainable : Optional[str] = field(default="q_proj,v_proj")
lora_rank : Optional[int] = field(default=8)
lora_dropout : Optional[float] = field(default=0.1)
lora_alpha : Optional[float] = field(default=32.)
modules_to_save : Optional[str] = field(default=None)
debug_mode : Optional[bool] = field(default=False)
peft_path : Optional[str] = field(default=None)
logger = logging.getLogger(__name__)
def main():
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, MyTrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_clm", model_args, data_args)
# Setup logging
logging.basicConfig(format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO, # if training_args.local_rank in [-1, 0] else logging.WARN,
handlers=[logging.StreamHandler(sys.stdout)],)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# transformers.tokenization_utils.logging.set_verbosity_warning()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
config_kwargs = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}")
config.update_from_string(model_args.config_overrides)
logger.info(f"New config: {config}")
tokenizer_kwargs = {
"cache_dir": model_args.cache_dir,
"use_fast": model_args.use_fast_tokenizer,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
elif model_args.tokenizer_name_or_path:
tokenizer = LlamaTokenizer.from_pretrained(model_args.tokenizer_name_or_path, **tokenizer_kwargs)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
# Preprocessing the datasets.
# First we tokenize all the texts.
# since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
def tokenize_function(examples):
with CaptureLogger(tok_logger) as cl:
output = tokenizer(examples["text"])
# clm input could be much much longer than block_size
if "Token indices sequence length is longer than the" in cl.out:
tok_logger.warning(
"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits"
" before being passed to the model."
)
return output
if data_args.block_size is None:
block_size = tokenizer.model_max_length
if block_size > 1024:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`."
)
block_size = 1024
else:
if data_args.block_size > tokenizer.model_max_length:
logger.warning(
f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
)
block_size = min(data_args.block_size, tokenizer.model_max_length)
# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
if total_length >= block_size:
total_length = (total_length // block_size) * block_size
# Split by chunks of max_len.
result = {
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
result["labels"] = result["input_ids"].copy()
return result
with training_args.main_process_first(desc="dataset map tokenization and grouping"):
lm_datasets = []
path = Path(data_args.dataset_dir)
files = [file.name for file in path.glob("*.txt")]
if training_args.debug_mode is True:
files = [files[0]]
for idx, file in enumerate(files):
data_file = os.path.join(path, file)
filename = ''.join(file.split(".")[:-1])
cache_path = os.path.join(data_args.data_cache_dir, filename)
os.makedirs(cache_path, exist_ok=True)
try:
processed_dataset = datasets.load_from_disk(cache_path, keep_in_memory=False)
logger.info(f'training datasets-{filename} has been loaded from disk')
except Exception:
cache_dir = os.path.join(data_args.data_cache_dir, filename+"_text")
os.makedirs(cache_dir, exist_ok=True)
raw_dataset = load_dataset("text", data_files=data_file, cache_dir=cache_dir, keep_in_memory=False)
logger.info(f"{file} has been loaded")
tokenized_dataset = raw_dataset.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns="text",
load_from_cache_file=True,
keep_in_memory=False,
cache_file_names = {k: os.path.join(cache_dir, 'tokenized.arrow') for k in raw_dataset},
desc="Running tokenizer on dataset",
)
grouped_datasets = tokenized_dataset.map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=True,
keep_in_memory=False,
cache_file_names = {k: os.path.join(cache_dir, 'grouped.arrow') for k in tokenized_dataset},
desc=f"Grouping texts in chunks of {block_size}",
)
processed_dataset = grouped_datasets
processed_dataset.save_to_disk(cache_path)
if idx == 0:
lm_datasets = processed_dataset['train']
else:
assert lm_datasets.features.type == processed_dataset["train"].features.type
lm_datasets = concatenate_datasets([lm_datasets, processed_dataset["train"]])
lm_datasets = lm_datasets.train_test_split(test_size = data_args.validation_split_percentage)
if training_args.do_train:
train_dataset = lm_datasets['train']
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
logger.info(f"Num train_samples {len(train_dataset)}")
logger.info("training example:")
logger.info(tokenizer.decode(train_dataset[0]['input_ids']))
if training_args.do_eval:
eval_dataset = lm_datasets["test"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
logger.info(f"Num eval_samples {len(eval_dataset)}")
logger.info("training example:")
logger.info(tokenizer.decode(eval_dataset[0]['input_ids']))
if model_args.model_name_or_path:
torch_dtype = (
model_args.torch_dtype
if model_args.torch_dtype in ["auto", None]
else getattr(torch, model_args.torch_dtype)
)
model = LlamaForCausalLM.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
torch_dtype=torch_dtype,
low_cpu_mem_usage=True
)
else:
model = AutoModelForCausalLM.from_config(config)
n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params")
model_vocab_size = model.get_output_embeddings().weight.size(0)
if not (
(model_vocab_size==32000 and len(tokenizer)==49953) or \
(model_vocab_size==32000 and len(tokenizer)==32000) or \
(model_vocab_size==49953 and len(tokenizer)==49953) or \
(model_vocab_size==49954 and len(tokenizer)==49954)
):
raise ValueError(
f"The combination of base model (size: {model_vocab_size}) and tokenizer (size: {len(tokenizer)}) is not a valid configuration. Please check our project wiki for further information. \n"
"Valid configurations (base model / tokenizer):\n"
"- Continue pre-training original LLaMA: 32000 / 32000 \n"
"- Pre-training Chinese LLaMA based on original LLaMA: 32000 / 49953 \n"
"- Continue pre-training Chinese LLaMA: 49953 / 49953 \n"
"- Continue pre-training Chinese Alpaca: 49954 / 49954 \n")
model.resize_token_embeddings(len(tokenizer))
if training_args.peft_path is not None:
logger.info("Peft from pre-trained model")
model = PeftModel.from_pretrained(model, training_args.peft_path)
else:
logger.info("Init new peft model")
target_modules = training_args.trainable.split(',')
modules_to_save = training_args.modules_to_save
if modules_to_save is not None:
modules_to_save = modules_to_save.split(',')
lora_rank = training_args.lora_rank
lora_dropout = training_args.lora_dropout
lora_alpha = training_args.lora_alpha
logger.info(f"target_modules: {target_modules}")
logger.info(f"lora_rank: {lora_rank}")
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
target_modules=target_modules,
inference_mode=False,
r=lora_rank, lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
modules_to_save=modules_to_save)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
old_state_dict = model.state_dict
model.state_dict = (
lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())
).__get__(model, type(model))
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
tokenizer=tokenizer,
data_collator=fault_tolerance_data_collator,
compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
preprocess_logits_for_metrics=preprocess_logits_for_metrics
if training_args.do_eval and not is_torch_tpu_available()
else None,
)
trainer.add_callback(SavePeftModelCallback)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
try:
perplexity = math.exp(metrics["eval_loss"])
except OverflowError:
perplexity = float("inf")
metrics["perplexity"] = perplexity
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
if __name__ == "__main__":
main()
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/run_clm_sft_with_peft.py | Python | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=text-generation
"""
# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from pathlib import Path
import datasets
import torch
from build_dataset import build_instruction_dataset, DataCollatorForSupervisedDataset
import transformers
from transformers import (
CONFIG_MAPPING,
AutoConfig,
AutoModelForCausalLM,
LlamaForCausalLM,
LlamaTokenizer,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import send_example_telemetry
from transformers.utils.versions import require_version
from peft import LoraConfig, TaskType, get_peft_model, PeftModel, get_peft_model_state_dict
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR
IGNORE_INDEX = -100
DEFAULT_PAD_TOKEN = "[PAD]"
DEFAULT_EOS_TOKEN = "</s>"
DEFAULT_BOS_TOKEN = "<s>"
DEFAULT_UNK_TOKEN = "<unk>"
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
class SavePeftModelCallback(transformers.TrainerCallback):
def save_model(self, args, state, kwargs):
if state.best_model_checkpoint is not None:
checkpoint_folder = os.path.join(state.best_model_checkpoint, "sft_lora_model")
else:
checkpoint_folder = os.path.join(args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}")
peft_model_path = os.path.join(checkpoint_folder, "sft_lora_model")
kwargs["model"].save_pretrained(peft_model_path)
kwargs["tokenizer"].save_pretrained(peft_model_path)
def on_save(self, args, state, control, **kwargs):
self.save_model(args, state, kwargs)
return control
def on_train_end(self, args, state, control, **kwargs):
peft_model_path = os.path.join(args.output_dir, "sft_lora_model")
kwargs["model"].save_pretrained(peft_model_path)
kwargs["tokenizer"].save_pretrained(peft_model_path)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
)
},
)
tokenizer_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The tokenizer for weights initialization.Don't set if you want to train a model from scratch."
)
},
)
config_overrides: Optional[str] = field(
default=None,
metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=True,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
},
)
torch_dtype: Optional[str] = field(
default=None,
metadata={
"help": (
"Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
"dtype will be automatically derived from the model's weights."
),
"choices": ["auto", "bfloat16", "float16", "float32"],
},
)
def __post_init__(self):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path"
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_dir: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
validation_split_percentage: Optional[float] = field(
default=0.05,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
keep_linebreaks: bool = field(
default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."}
)
data_cache_dir: Optional[str] = field(default=None, metadata={"help": "The datasets processed stored"})
max_seq_length: Optional[int] = field(default=512)
@dataclass
class MyTrainingArguments(TrainingArguments):
trainable : Optional[str] = field(default="q_proj,v_proj")
lora_rank : Optional[int] = field(default=8)
lora_dropout : Optional[float] = field(default=0.1)
lora_alpha : Optional[float] = field(default=32.)
modules_to_save : Optional[str] = field(default=None)
peft_path : Optional[str] = field(default=None)
force_resize_embeddings: bool = field(default=False)
logger = logging.getLogger(__name__)
def main():
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, MyTrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
send_example_telemetry("run_clm", model_args, data_args)
# Setup logging
logging.basicConfig(format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO, # if training_args.local_rank in [-1, 0] else logging.WARN,
handlers=[logging.StreamHandler(sys.stdout)],)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# transformers.tokenization_utils.logging.set_verbosity_warning()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
config_kwargs = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}")
config.update_from_string(model_args.config_overrides)
logger.info(f"New config: {config}")
tokenizer_kwargs = {
"cache_dir": model_args.cache_dir,
"use_fast": model_args.use_fast_tokenizer,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
elif model_args.tokenizer_name_or_path:
tokenizer = LlamaTokenizer.from_pretrained(model_args.tokenizer_name_or_path, **tokenizer_kwargs)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if (len(tokenizer))!=49954:
raise ValueError(f"The vocab size of the tokenizer must be 49954, but found {len(tokenizer)}.\n"
"Please use Chinese Alpaca tokenizer!")
if tokenizer.pad_token is None:
print(f"Adding pad token {DEFAULT_PAD_TOKEN}")
tokenizer.add_special_tokens(dict(pad_token=DEFAULT_PAD_TOKEN))
data_collator = DataCollatorForSupervisedDataset(tokenizer=tokenizer)
eval_dataset=None
train_dataset = None
if training_args.do_train:
with training_args.main_process_first(desc="loading and tokenization"):
path = Path(data_args.dataset_dir)
files = [os.path.join(path,file.name) for file in path.glob("*.json")]
logger.info(f"Training files: {' '.join(files)}")
train_dataset = build_instruction_dataset(
data_path=files,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
data_cache_dir = None,
preprocessing_num_workers = data_args.preprocessing_num_workers)
logger.info(f"Num train_samples {len(train_dataset)}")
logger.info("training example:")
logger.info(tokenizer.decode(train_dataset[0]['input_ids']))
if training_args.do_eval:
with training_args.main_process_first(desc="loading and tokenization"):
files = [data_args.validation_file]
logger.info(f"Evaluation files: {' '.join(files)}")
eval_dataset = build_instruction_dataset(
data_path=files,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
data_cache_dir = None,
preprocessing_num_workers = data_args.preprocessing_num_workers)
logger.info(f"Num eval_samples {len(eval_dataset)}")
logger.info("eval example:")
logger.info(tokenizer.decode(eval_dataset[0]['input_ids']))
if model_args.model_name_or_path:
torch_dtype = (
model_args.torch_dtype
if model_args.torch_dtype in ["auto", None]
else getattr(torch, model_args.torch_dtype)
)
model = LlamaForCausalLM.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
torch_dtype=torch_dtype,
low_cpu_mem_usage=True
)
else:
model = AutoModelForCausalLM.from_config(config)
n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params")
logger.info(f"len(tokenizer):{len(tokenizer)}")
embedding_size = model.get_input_embeddings().weight.shape[0]
if len(tokenizer) != embedding_size:
logger.info("resize the embedding size by the size of the tokenizer")
model.resize_token_embeddings(len(tokenizer))
if training_args.peft_path is not None:
logger.info("Peft from pre-trained model")
model = PeftModel.from_pretrained(model, training_args.peft_path)
else:
logger.info("Init new peft model")
target_modules = training_args.trainable.split(',')
modules_to_save = training_args.modules_to_save
if modules_to_save is not None:
modules_to_save = modules_to_save.split(',')
lora_rank = training_args.lora_rank
lora_dropout = training_args.lora_dropout
lora_alpha = training_args.lora_alpha
logger.info(f"target_modules: {target_modules}")
logger.info(f"lora_rank: {lora_rank}")
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
target_modules=target_modules,
inference_mode=False,
r=lora_rank, lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
modules_to_save=modules_to_save)
model = get_peft_model(model, peft_config)
#model.base_model.tie_weights()
model.print_trainable_parameters()
logger.info(f"model.modules_to_save: {model.modules_to_save}")
old_state_dict = model.state_dict
model.state_dict = (
lambda self, *_, **__: get_peft_model_state_dict(self, old_state_dict())
).__get__(model, type(model))
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=tokenizer,
data_collator=data_collator,
)
trainer.add_callback(SavePeftModelCallback)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
metrics = train_result.metrics
metrics["train_samples"] = len(train_dataset)
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
metrics["eval_samples"] =len(eval_dataset)
try:
perplexity = math.exp(metrics["eval_loss"])
except OverflowError:
perplexity = float("inf")
metrics["perplexity"] = perplexity
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
if __name__ == "__main__":
main()
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/run_pt.sh | Shell | lr=2e-4
lora_rank=8
lora_alpha=32
lora_trainable="q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj"
modules_to_save="embed_tokens,lm_head"
lora_dropout=0.05
pretrained_model=path/to/hf/llama/dir
chinese_tokenizer_path=path/to/chinese/llama/tokenizer/dir
dataset_dir=path/to/pt/data/dir
data_cache=temp_data_cache_dir
per_device_train_batch_size=1
per_device_eval_batch_size=1
gradient_accumulation_steps=8
output_dir=output_dir
deepspeed_config_file=ds_zero2_no_offload.json
torchrun --nnodes 1 --nproc_per_node 1 run_clm_pt_with_peft.py \
--deepspeed ${deepspeed_config_file} \
--model_name_or_path ${pretrained_model} \
--tokenizer_name_or_path ${chinese_tokenizer_path} \
--dataset_dir ${dataset_dir} \
--data_cache_dir ${data_cache} \
--validation_split_percentage 0.001 \
--per_device_train_batch_size ${per_device_train_batch_size} \
--per_device_eval_batch_size ${per_device_eval_batch_size} \
--do_train \
--seed $RANDOM \
--fp16 \
--num_train_epochs 1 \
--lr_scheduler_type cosine \
--learning_rate ${lr} \
--warmup_ratio 0.05 \
--weight_decay 0.01 \
--logging_strategy steps \
--logging_steps 10 \
--save_strategy steps \
--save_total_limit 3 \
--save_steps 200 \
--gradient_accumulation_steps ${gradient_accumulation_steps} \
--preprocessing_num_workers 8 \
--block_size 512 \
--output_dir ${output_dir} \
--overwrite_output_dir \
--ddp_timeout 30000 \
--logging_first_step True \
--lora_rank ${lora_rank} \
--lora_alpha ${lora_alpha} \
--trainable ${lora_trainable} \
--modules_to_save ${modules_to_save} \
--lora_dropout ${lora_dropout} \
--torch_dtype float16 \
--gradient_checkpointing \
--ddp_find_unused_parameters False
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/run_sft.sh | Shell | lr=1e-4
lora_rank=8
lora_alpha=32
lora_trainable="q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj"
modules_to_save="embed_tokens,lm_head"
lora_dropout=0.05
pretrained_model=path/to/hf/llama/or/merged/llama/dir/or/model_id
chinese_tokenizer_path=path/to/chinese/llama/tokenizer/dir
dataset_dir=path/to/sft/data/dir
per_device_train_batch_size=1
per_device_eval_batch_size=1
gradient_accumulation_steps=8
output_dir=output_dir
peft_model=path/to/peft/model/dir
validation_file=validation_file_name
deepspeed_config_file=ds_zero2_no_offload.json
torchrun --nnodes 1 --nproc_per_node 1 run_clm_sft_with_peft.py \
--deepspeed ${deepspeed_config_file} \
--model_name_or_path ${pretrained_model} \
--tokenizer_name_or_path ${chinese_tokenizer_path} \
--dataset_dir ${dataset_dir} \
--validation_split_percentage 0.001 \
--per_device_train_batch_size ${per_device_train_batch_size} \
--per_device_eval_batch_size ${per_device_eval_batch_size} \
--do_train \
--do_eval \
--seed $RANDOM \
--fp16 \
--num_train_epochs 1 \
--lr_scheduler_type cosine \
--learning_rate ${lr} \
--warmup_ratio 0.03 \
--weight_decay 0 \
--logging_strategy steps \
--logging_steps 10 \
--save_strategy steps \
--save_total_limit 3 \
--evaluation_strategy steps \
--eval_steps 100 \
--save_steps 200 \
--gradient_accumulation_steps ${gradient_accumulation_steps} \
--preprocessing_num_workers 8 \
--max_seq_length 512 \
--output_dir ${output_dir} \
--overwrite_output_dir \
--ddp_timeout 30000 \
--logging_first_step True \
--lora_rank ${lora_rank} \
--lora_alpha ${lora_alpha} \
--trainable ${lora_trainable} \
--modules_to_save ${modules_to_save} \
--lora_dropout ${lora_dropout} \
--torch_dtype float16 \
--validation_file ${validation_file} \
--peft_path ${peft_model} \
--gradient_checkpointing \
--ddp_find_unused_parameters False
| ymcui/Chinese-LLaMA-Alpaca | 18,964 | 中文LLaMA&Alpaca大语言模型+本地CPU/GPU训练部署 (Chinese LLaMA & Alpaca LLMs) | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/ceval/eval.py | Python | # This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import os
import argparse
import pandas as pd
import torch
import json
from llama_evaluator import Llama_Evaluator
import time
choices = ["A", "B", "C", "D"]
def main(args, evaluator, take):
assert os.path.exists("subject_mapping.json"), "subject_mapping.json not found!"
with open("subject_mapping.json") as f:
subject_mapping = json.load(f)
filenames = os.listdir("data/val")
subject_list = [val_file.replace("_val.csv","") for val_file in filenames]
accuracy, summary = {}, {}
run_date=time.strftime('%Y-%m-%d_%H-%M-%S',time.localtime(time.time()))
output_dir = args.output_dir
save_result_dir=os.path.join(output_dir,f"take{take}")
if not os.path.exists(save_result_dir):
os.makedirs(save_result_dir,exist_ok=True)
all_answers = {}
for index,subject_name in enumerate(subject_list):
print(f"{index/len(subject_list)} Inference starts at {run_date} on {args.model_path} with subject of {subject_name}!")
val_file_path=os.path.join('data/val',f'{subject_name}_val.csv')
dev_file_path=os.path.join('data/dev',f'{subject_name}_dev.csv')
test_file_path=os.path.join('data/test',f'{subject_name}_test.csv')
val_df=pd.read_csv(val_file_path) if args.do_test is False else pd.read_csv(test_file_path)
dev_df=pd.read_csv(dev_file_path) if args.few_shot else None
correct_ratio, answers = evaluator.eval_subject(subject_name, val_df, dev_df,
save_result_dir=save_result_dir if args.do_save_csv else None,
few_shot=args.few_shot,
with_prompt=args.with_prompt,
do_test=args.do_test)
print(f"Subject: {subject_name}")
print(f"Acc: {correct_ratio}")
accuracy[subject_name] = correct_ratio
summary[subject_name] = {"score":correct_ratio,
"num":len(val_df),
"correct":correct_ratio*len(val_df)/100}
all_answers[subject_name] = answers
json.dump(all_answers,open(save_result_dir+'/submission.json','w'),ensure_ascii=False,indent=4)
print("Accuracy:")
for k, v in accuracy.items():
print(k, ": ", v)
total_num = 0
total_correct = 0
summary['grouped'] = {
"STEM": {"correct": 0.0, "num": 0},
"Social Science": {"correct": 0.0, "num": 0},
"Humanities": {"correct": 0.0, "num": 0},
"Other": {"correct": 0.0, "num": 0}
}
for subj, info in subject_mapping.items():
group = info[2]
summary['grouped'][group]["num"] += summary[subj]['num']
summary['grouped'][group]["correct"] += summary[subj]['correct']
for group, info in summary['grouped'].items():
info['score'] = info["correct"] / info["num"]
total_num += info["num"]
total_correct += info["correct"]
summary['All'] = {"score": total_correct / total_num, "num": total_num, "correct": total_correct}
json.dump(summary,open(save_result_dir+'/summary.json','w'),ensure_ascii=False,indent=2)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str)
parser.add_argument("--few_shot", choices=["False","True"], default="True")
parser.add_argument("--ntrain", "-k", type=int, default=5)
parser.add_argument("--with_prompt", choices=["False","True"], default="False")
parser.add_argument("--use_flash_attention_2", action="store_true")
parser.add_argument("--n_times", default=1,type=int)
parser.add_argument("--do_save_csv", choices=["False","True"], default="False")
parser.add_argument("--output_dir", type=str)
parser.add_argument("--do_test", choices=["False","True"], default="False")
parser.add_argument("--verbose", action="store_true", help="Print detailed information of each example.")
args = parser.parse_args()
args.few_shot = args.few_shot == "True"
args.with_prompt = args.with_prompt == "True"
args.do_test = args.do_test == "True"
args.do_save_csv = args.do_save_csv == "True"
args.n_times=max(args.n_times,1)
print(args)
# Move the model to the MPS device if available
if torch.backends.mps.is_available():
device = torch.device("mps")
else:
device = torch.device(0)
print(f"Using device: {device}")
evaluator=Llama_Evaluator(
choices=choices,
k=args.ntrain,
model_path=args.model_path,
device=device,
use_flash_attention_2=args.use_flash_attention_2,
verbose=args.verbose
)
for i in range(args.n_times):
main(args,evaluator=evaluator,take=i)
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/ceval/llama_evaluator.py | Python | # This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import os
import re
from tqdm import tqdm
import random
import numpy as np
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import GenerationConfig
DEFAULT_SYSTEM_PROMPT = """You are a helpful assistant. 你是一个乐于助人的助手。"""
system_format='<|start_header_id|>system<|end_header_id|>\n\n{content}<|eot_id|>'
user_format='<|start_header_id|>user<|end_header_id|>\n\n{content}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'
assistant_format='{content}<|eot_id|>'
TASK2DESC = {
"high_school_physics": "高中物理",
"fire_engineer": "注册消防工程师",
"computer_network": "计算机网络",
"advanced_mathematics": "高等数学",
"logic": "逻辑学",
"middle_school_physics": "初中物理",
"clinical_medicine": "临床医学",
"probability_and_statistics": "概率统计",
"ideological_and_moral_cultivation": "思想道德修养与法律基础",
"operating_system": "操作系统",
"middle_school_mathematics": "初中数学",
"chinese_language_and_literature": "中国语言文学",
"electrical_engineer": "注册电气工程师",
"business_administration": "工商管理",
"high_school_geography": "高中地理",
"modern_chinese_history": "近代史纲要",
"legal_professional": "法律职业资格",
"middle_school_geography": "初中地理",
"middle_school_chemistry": "初中化学",
"high_school_biology": "高中生物",
"high_school_chemistry": "高中化学",
"physician": "医师资格",
"high_school_chinese": "高中语文",
"tax_accountant": "税务师",
"high_school_history": "高中历史",
"mao_zedong_thought": "毛泽东思想和中国特色社会主义理论概论",
"high_school_mathematics": "高中数学",
"professional_tour_guide": "导游资格",
"veterinary_medicine": "兽医学",
"environmental_impact_assessment_engineer": "环境影响评价工程师",
"basic_medicine": "基础医学",
"education_science": "教育学",
"urban_and_rural_planner": "注册城乡规划师",
"middle_school_biology": "初中生物",
"plant_protection": "植物保护",
"middle_school_history": "初中历史",
"high_school_politics": "高中政治",
"metrology_engineer": "注册计量师",
"art_studies": "艺术学",
"college_economics": "大学经济学",
"college_chemistry": "大学化学",
"law": "法学",
"sports_science": "体育学",
"civil_servant": "公务员",
"college_programming": "大学编程",
"middle_school_politics": "初中政治",
"teacher_qualification": "教师资格",
"computer_architecture": "计算机组成",
"college_physics": "大学物理",
"discrete_mathematics": "离散数学",
"marxism": "马克思主义基本原理",
"accountant": "注册会计师",
}
class Llama_Evaluator():
def __init__(self, choices, k, model_path, device, use_flash_attention_2=False, verbose=False):
load_type = torch.float16
self.choices = choices
self.k = k
self.device = device
self.verbose = verbose
self.use_flash_attention_2 = use_flash_attention_2
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
self.model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
attn_implementation="flash_attention_2" if self.use_flash_attention_2 else "sdpa"
)
self.generation_config = GenerationConfig(
temperature=0.2,
top_k=0,
top_p=1.0,
do_sample=True,
num_beams=1,
repetition_penalty=1.1,
max_new_tokens=1,
output_scores=True,
return_dict_in_generate=True
)
self.sA_id = self.tokenizer.encode("A", add_special_tokens=False)[0]
self.sB_id = self.tokenizer.encode("B", add_special_tokens=False)[0]
self.sC_id = self.tokenizer.encode("C", add_special_tokens=False)[0]
self.sD_id = self.tokenizer.encode("D", add_special_tokens=False)[0]
self.A_id = self.tokenizer.encode("：A")[-1]
self.B_id = self.tokenizer.encode("：B")[-1]
self.C_id = self.tokenizer.encode("：C")[-1]
self.D_id = self.tokenizer.encode("：D")[-1]
def eval_subject(self, subject_name,
test_df,
dev_df=None,
few_shot=False,
save_result_dir=None,
with_prompt=False,
do_test=False):
all_answers = {}
correct_num = 0
if save_result_dir:
result = []
score = []
history = f"以下是中国关于{TASK2DESC[subject_name]}考试的单项选择题，请选出其中的正确答案。\n\n"
if few_shot:
if with_prompt:
history = self.generate_alpaca3_few_shot_prompt(history, dev_df, subject=TASK2DESC[subject_name])
else:
history = self.generate_llama3_few_shot_prompt(history, dev_df)
answers = ['NA'] * len(test_df) if do_test is True else list(test_df['answer'])
for row_index, row in tqdm(test_df.iterrows(), total=len(test_df)):
question = self.format_example(row, few_shot=False)
instruction = history + question
if with_prompt:
if few_shot:
instruction = history + user_format.format_map({'content': question})
else:
instruction = system_format.format(content=DEFAULT_SYSTEM_PROMPT) + user_format.format_map({'content': instruction})
inputs = self.tokenizer(instruction, return_tensors="pt")
terminators = [
self.tokenizer.eos_token_id,
self.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
generation_output = self.model.generate(
input_ids = inputs["input_ids"].to(self.device),
attention_mask = inputs['attention_mask'].to(self.device),
eos_token_id=terminators,
pad_token_id=self.tokenizer.eos_token_id,
generation_config = self.generation_config
)
logits = generation_output.scores[0][0]
logits = logits.float().cpu().detach()
choices1_logits = logits[[self.sA_id,self.sB_id,self.sC_id,self.sD_id]]
choices2_logits = logits[[self.A_id,self.B_id,self.C_id,self.D_id]]
choicesAll_logits = (choices1_logits + choices2_logits).numpy()
assert not (np.any(np.isinf(choicesAll_logits)) or np.any(np.isnan(choicesAll_logits)))
ans = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(choicesAll_logits)]
response = self.tokenizer.decode([logits.argmax(-1).item()])
if ans == answers[row_index]:
correct_num += 1
correct = 1
else:
correct = 0
if self.verbose is True:
print(f"\n======={str(row_index)}=======")
print(f"question: {question}\n")
print(f"instruction: {instruction}\n")
print(f"response: {response}\n")
print(f"extracted answer: {ans}")
print(f"ground truth: {answers[row_index]} \n")
if save_result_dir:
result.append(response)
score.append(correct)
all_answers[str(row_index)] = ans
correct_ratio = 100*correct_num/len(answers)
if save_result_dir:
test_df['model_output'] = result
test_df['correctness'] = score
test_df.to_csv(os.path.join(save_result_dir, f'{subject_name}_test.csv'))
return correct_ratio, all_answers
def format_example(self, line, few_shot=False):
example = line['question']
for choice in self.choices:
example += f'\n{choice}. {line[f"{choice}"]}'
if few_shot:
example += '\n答案：' + line["answer"] + '\n\n'
else:
example += '\n答案：'
return example
def generate_llama3_few_shot_prompt(self, history, dev_df):
prompt = history
k = self.k
if self.k == -1:
k = dev_df.shape[0]
for i in range(k):
prompt += self.format_example(dev_df.iloc[i, :], few_shot=True)
return prompt
def generate_alpaca3_few_shot_prompt(self, history, dev_df, subject=None):
prompt = history
prompt_template = (
"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_prompt}<|eot_id|>"
"<|start_header_id|>user<|end_header_id|>\n\n{instruction}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
"好的，我会结合{subject}相关知识回答<|eot_id|>"
)
prompt = prompt_template.format_map({'instruction':prompt, 'system_prompt':DEFAULT_SYSTEM_PROMPT, 'subject':subject})
k = self.k
if self.k == -1:
k = dev_df.shape[0]
for i in range(k):
line = dev_df.iloc[i, :]
q=line['question']
for choice in self.choices:
q += f'\n{choice}. {line[f"{choice}"]}'
a = line['answer']
q += "\n答案："
prompt += user_format.format(content=q) + assistant_format.format(content=a)
return prompt
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/cmmlu/categories.py | Python | # This code is modified from CMMLU Project: https://github.com/haonan-li/CMMLU
name_en2zh = {
"agronomy": "农学",
"anatomy": "解剖学",
"ancient_chinese": "古汉语",
"arts": "艺术学",
"astronomy": "天文学",
"business_ethics": "商业伦理",
"chinese_civil_service_exam": "中国公务员考试",
"chinese_driving_rule": "中国驾驶规则",
"chinese_food_culture": "中国饮食文化",
"chinese_foreign_policy": "中国外交政策",
"chinese_history":"中国历史",
"chinese_literature": "中国文学",
"chinese_teacher_qualification": "中国教师资格",
"clinical_knowledge": "临床知识",
"college_actuarial_science":"大学精算学",
"college_education":"大学教育学",
"college_engineering_hydrology": "大学工程水文学",
"college_law": "大学法律",
"college_mathematics": "大学数学",
"college_medical_statistics":"大学医学统计",
"college_medicine": "大学医学",
"computer_science": "计算机科学",
"computer_security": "计算机安全",
"conceptual_physics": "概念物理学",
"construction_project_management": "建设工程管理",
"economics": "经济学",
"education": "教育学",
"electrical_engineering": "电气工程",
"elementary_chinese":"小学语文",
"elementary_commonsense":"小学常识",
"elementary_information_and_technology": "小学信息技术",
"elementary_mathematics": "初等数学",
"ethnology": "民族学",
"food_science": "食品科学",
"genetics": "遗传学",
"global_facts": "全球事实",
"high_school_biology": "高中生物",
"high_school_chemistry": "高中化学",
"high_school_geography": "高中地理",
"high_school_mathematics": "高中数学",
"high_school_physics": "高中物理学",
"high_school_politics": "高中政治",
"human_sexuality": "人类性行为",
"international_law": "国际法学",
"journalism": "新闻学",
"jurisprudence": "法理学",
"legal_and_moral_basis": "法律与道德基础",
"logical": "逻辑学",
"machine_learning": "机器学习",
"management": "管理学",
"marketing": "市场营销",
"marxist_theory": "马克思主义理论",
"modern_chinese": "现代汉语",
"nutrition": "营养学",
"philosophy": "哲学",
"professional_accounting": "专业会计",
"professional_law": "专业法学",
"professional_medicine": "专业医学",
"professional_psychology": "专业心理学",
"public_relations": "公共关系",
"security_study":"安全研究",
"sociology": "社会学",
"sports_science": "体育学",
"traditional_chinese_medicine": "中医中药",
"virology": "病毒学",
"world_history":"世界历史",
"world_religions": "世界宗教",
}
subcategories = {
"agronomy": ['other'],
"anatomy": ['biology'],
"ancient_chinese": ['linguistics','china specific'],
"arts": ['arts'],
"astronomy": ['physics'],
"business_ethics": ['business'],
"chinese_civil_service_exam": ['politics','china specific'],
"chinese_driving_rule": ['other','china specific'],
"chinese_food_culture": ['culture','china specific'],
"chinese_foreign_policy": ['politics','china specific'],
"chinese_history":['history','china specific'],
"chinese_literature": ['literature','china specific'],
"chinese_teacher_qualification": ['education','china specific'],
"college_actuarial_science":['math'],
"college_education":['education'],
"college_engineering_hydrology": ['engineering'],
"college_law": ['law'],
"college_mathematics": ['math'],
"college_medical_statistics":['statistics'],
"clinical_knowledge": ['other'],
"college_medicine": ['other'],
"computer_science": ['computer science'],
"computer_security": ['other'],
"conceptual_physics": ['physics'],
"construction_project_management": ['other','china specific'],
"economics": ['economics'],
"education": ['education'],
"elementary_chinese":['linguistics','china specific'],
"elementary_commonsense":['other','china specific'],
"elementary_information_and_technology": ['other'],
"electrical_engineering": ['engineering'],
"elementary_mathematics": ['math'],
"ethnology": ['culture','china specific'],
"food_science": ['other'],
"genetics": ['biology'],
"global_facts": ['global'],
"high_school_biology": ['biology'],
"high_school_chemistry": ['chemistry'],
"high_school_geography": ['geography'],
"high_school_mathematics": ['math'],
"high_school_physics": ['physics'],
"high_school_politics": ['politics','china specific'],
"human_sexuality": ['other'],
"international_law": ['law'],
"journalism": ['sociology'],
"jurisprudence": ['law'],
"legal_and_moral_basis": ['other'],
"logical": ['philosophy'],
"machine_learning": ['computer science'],
"management": ['business'],
"marketing": ['business'],
"marxist_theory": ['philosophy'],
"modern_chinese": ['linguistics','china specific'],
"nutrition": ['other'],
"philosophy": ['philosophy'],
"professional_accounting": ['business'],
"professional_law": ['law'],
"professional_medicine": ['other'],
"professional_psychology": ['psychology'],
"public_relations": ['politics'],
"security_study": ['politics'],
"sociology": ['culture'],
"sports_science": ['other'],
"traditional_chinese_medicine": ['other','china specific'],
"virology": ['biology'],
"world_history":['history'],
"world_religions": ['global'],
}
categories = {
"STEM": ["physics", "chemistry", "biology", "computer science", "math", "engineering", "statistics"],
"Humanities": ["history", "philosophy", "law", "arts", "literature", "global"],
"Social Science": ['linguistics',"business", "politics", "culture", "economics", "geography", "psychology", "education", "sociology"],
"Other":["other"],
"China specific": ["china specific"],
} | ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/cmmlu/eval.py | Python | # This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import os
import argparse
import pandas as pd
import torch
import json
from llama_evaluator import Llama_Evaluator
from glob import glob
import time
from collections import defaultdict
from categories import name_en2zh, subcategories, categories
choices = ["A", "B", "C", "D"]
category2subject = defaultdict(list)
for k,v in categories.items():
for subject, subcat in subcategories.items():
for c in subcat:
if c in v:
category2subject[k].append(subject)
category2subject_list = defaultdict(list)
for key,value in category2subject.items():
for val in value:
category2subject_list[val]=[val,name_en2zh[val],key]
category2subject= category2subject_list
choices = ["A", "B", "C", "D"]
def main(args, evaluator,take):
subject_mapping = category2subject #json.load(f)
filenames = [s.split('/')[-1] for s in glob(args.input_dir+"/test/*csv")]
subject_list = [val_file.replace(".csv","") for val_file in filenames]
accuracy, summary = {}, {}
run_date=time.strftime('%Y-%m-%d_%H-%M-%S',time.localtime(time.time()))
output_dir = args.output_dir
save_result_dir=os.path.join(output_dir,f"take{take}")
if not os.path.exists(save_result_dir):
os.makedirs(save_result_dir,exist_ok=True)
all_answers = {}
for index,subject_name in enumerate(subject_list):
print(f"{index/len(subject_list)} Inference starts at {run_date} on {args.model_path} with subject of {subject_name}!")
val_file_path=os.path.join(args.input_dir+'/test',f'{subject_name}.csv')
dev_file_path=os.path.join(args.input_dir+'/dev',f'{subject_name}.csv')
val_df=pd.read_csv(val_file_path)
dev_df=pd.read_csv(dev_file_path) if args.few_shot else None
correct_ratio, answers = evaluator.eval_subject(name_en2zh[subject_name], val_df, dev_df,
save_result_dir=save_result_dir if args.do_save_csv else None,
few_shot=args.few_shot,
with_prompt=args.with_prompt,
do_test=False)
print(f"Subject: {subject_name}")
print(f"Acc: {correct_ratio}")
accuracy[subject_name] = correct_ratio
summary[subject_name] = {"score":correct_ratio,
"num":len(val_df),
"correct":correct_ratio*len(val_df)/100}
all_answers[subject_name] = answers
json.dump(all_answers,open(save_result_dir+'/submission.json','w'),ensure_ascii=False,indent=4)
print("\n\nModel:",args.model_path)
print("Accuracy:")
for k, v in accuracy.items():
print(k, ": ", v)
total_num = 0
total_correct = 0
summary['grouped'] = {
"China specific": {"correct": 0.0, "num": 0},
"STEM": {"correct": 0.0, "num": 0},
"Social Science": {"correct": 0.0, "num": 0},
"Humanities": {"correct": 0.0, "num": 0},
"Other": {"correct": 0.0, "num": 0}
}
for subj, info in subject_mapping.items():
group = info[2]
summary['grouped'][group]["num"] += summary[subj]['num']
summary['grouped'][group]["correct"] += summary[subj]['correct']
for group, info in summary['grouped'].items():
info['score'] = info["correct"] / info["num"]
total_num += info["num"]
total_correct += info["correct"]
summary['All'] = {"score": total_correct / total_num, "num": total_num, "correct": total_correct}
json.dump(summary,open(save_result_dir+'/summary.json','w'),ensure_ascii=False,indent=2)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--ntrain", "-k", type=int, default=5)
parser.add_argument("--model_path", type=str)
parser.add_argument("--few_shot", choices=["False","True"], default="True")
parser.add_argument("--with_prompt", choices=["False","True"], default="False")
parser.add_argument("--use_flash_attention_2", action="store_true")
parser.add_argument("--n_times", default=1,type=int)
parser.add_argument("--do_save_csv", choices=["False","True"], default="False")
parser.add_argument("--output_dir", type=str)
parser.add_argument("--input_dir", type=str)
parser.add_argument("--verbose", action="store_true", help="Print detailed information of each example.")
args = parser.parse_args()
args.few_shot = args.few_shot == "True"
args.with_prompt = args.with_prompt == "True"
args.do_save_csv = args.do_save_csv == "True"
args.n_times=max(args.n_times,1)
print(args)
# Move the model to the MPS device if available
if torch.backends.mps.is_available():
device = torch.device("mps")
else:
device = torch.device(0)
print(f"Using device: {device}")
evaluator=Llama_Evaluator(
choices=choices,
k=args.ntrain,
model_path=args.model_path,
device=device,
use_flash_attention_2=args.use_flash_attention_2,
verbose=args.verbose
)
for i in range(args.n_times):
main(args,evaluator=evaluator,take=i) | ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/cmmlu/llama_evaluator.py | Python | # This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import os
import re
from tqdm import tqdm
import random
import numpy as np
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import GenerationConfig
DEFAULT_SYSTEM_PROMPT = """You are a helpful assistant. 你是一个乐于助人的助手。"""
system_format='<|start_header_id|>system<|end_header_id|>\n\n{content}<|eot_id|>'
user_format='<|start_header_id|>user<|end_header_id|>\n\n{content}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'
assistant_format='{content}<|eot_id|>'
class Llama_Evaluator():
def __init__(self, choices, k, model_path, device, use_flash_attention_2=False, verbose=False):
load_type = torch.float16
self.choices = choices
self.k = k
self.device = device
self.verbose = verbose
self.use_flash_attention_2 = use_flash_attention_2
self.tokenizer = AutoTokenizer.from_pretrained(model_path)
self.model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
attn_implementation="flash_attention_2" if self.use_flash_attention_2 else "sdpa"
)
self.generation_config = GenerationConfig(
temperature=0.2,
top_k=0,
top_p=1.0,
do_sample=True,
num_beams=1,
repetition_penalty=1.1,
max_new_tokens=1,
output_scores=True,
return_dict_in_generate=True
)
self.sA_id = self.tokenizer.encode("A", add_special_tokens=False)[0]
self.sB_id = self.tokenizer.encode("B", add_special_tokens=False)[0]
self.sC_id = self.tokenizer.encode("C", add_special_tokens=False)[0]
self.sD_id = self.tokenizer.encode("D", add_special_tokens=False)[0]
self.A_id = self.tokenizer.encode("：A")[-1]
self.B_id = self.tokenizer.encode("：B")[-1]
self.C_id = self.tokenizer.encode("：C")[-1]
self.D_id = self.tokenizer.encode("：D")[-1]
def eval_subject(self, subject_name,
test_df,
dev_df=None,
few_shot=False,
save_result_dir=None,
with_prompt=False,
do_test=False):
all_answers = {}
correct_num = 0
if save_result_dir:
result = []
score = []
history = f"以下是中国关于{subject_name}考试的单项选择题，请选出其中的正确答案。\n\n"
if few_shot:
if with_prompt:
history = self.generate_alpaca3_few_shot_prompt(history, dev_df, subject=subject_name)
else:
history = self.generate_llama3_few_shot_prompt(history, dev_df)
answers = ['NA'] * len(test_df) if do_test is True else list(test_df['Answer'])
for row_index, row in tqdm(test_df.iterrows(), total=len(test_df)):
question = self.format_example(row, few_shot=False)
instruction = history + question
if with_prompt:
if few_shot:
instruction = history + user_format.format_map({'content': question})
else:
instruction = system_format.format(content=DEFAULT_SYSTEM_PROMPT) + user_format.format_map({'content': instruction})
inputs = self.tokenizer(instruction, return_tensors="pt")
terminators = [
self.tokenizer.eos_token_id,
self.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
generation_output = self.model.generate(
input_ids = inputs["input_ids"].to(self.device),
attention_mask = inputs['attention_mask'].to(self.device),
eos_token_id=terminators,
pad_token_id=self.tokenizer.eos_token_id,
generation_config = self.generation_config
)
logits = generation_output.scores[0][0]
logits = logits.float().cpu().detach()
choices1_logits = logits[[self.sA_id,self.sB_id,self.sC_id,self.sD_id]]
choices2_logits = logits[[self.A_id,self.B_id,self.C_id,self.D_id]]
choicesAll_logits = (choices1_logits + choices2_logits).numpy()
assert not (np.any(np.isinf(choicesAll_logits)) or np.any(np.isnan(choicesAll_logits)))
ans = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(choicesAll_logits)]
response = self.tokenizer.decode([logits.argmax(-1).item()])
if ans == answers[row_index]:
correct_num += 1
correct = 1
else:
correct = 0
if self.verbose is True:
print(f"\n======={str(row_index)}=======")
print(f"question: {question}\n")
print(f"instruction: {instruction}\n")
print(f"response: {response}\n")
print(f"extracted answer: {ans}")
print(f"ground truth: {answers[row_index]} \n")
if save_result_dir:
result.append(response)
score.append(correct)
all_answers[str(row_index)] = ans
correct_ratio = 100*correct_num/len(answers)
if save_result_dir:
test_df['model_output'] = result
test_df['correctness'] = score
test_df.to_csv(os.path.join(save_result_dir, f'{subject_name}_test.csv'))
return correct_ratio, all_answers
def format_example(self, line, few_shot=False):
example = line['Question']
for choice in self.choices:
example += f'\n{choice}. {line[f"{choice}"]}'
if few_shot:
example += '\n答案：' + line["Answer"] + '\n\n'
else:
example += '\n答案：'
return example
def generate_llama3_few_shot_prompt(self, history, dev_df):
prompt = history
k = self.k
if self.k == -1:
k = dev_df.shape[0]
for i in range(k):
prompt += self.format_example(dev_df.iloc[i, :], few_shot=True)
return prompt
def generate_alpaca3_few_shot_prompt(self, history, dev_df, subject=None):
prompt = history
prompt_template = (
"<|start_header_id|>system<|end_header_id|>\n\n{system_prompt}<|eot_id|>"
"<|start_header_id|>user<|end_header_id|>\n\n{instruction}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
"好的，我会结合{subject}相关知识回答<|eot_id|>"
)
prompt = prompt_template.format_map({'instruction':prompt, 'system_prompt':DEFAULT_SYSTEM_PROMPT, 'subject':subject})
k = self.k
if self.k == -1:
k = dev_df.shape[0]
for i in range(k):
line = dev_df.iloc[i, :]
q=line['Question']
for choice in self.choices:
q += f'\n{choice}. {line[f"{choice}"]}'
a = line['Answer']
q += "\n答案："
prompt += user_format.format(content=q) + assistant_format.format(content=a)
return prompt
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/inference/inference_hf.py | Python | import argparse
import json, os
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers import GenerationConfig
from transformers import BitsAndBytesConfig
DEFAULT_SYSTEM_PROMPT = """You are a helpful assistant. 你是一个乐于助人的助手。"""
system_format='<|start_header_id|>system<|end_header_id|>\n\n{content}<|eot_id|>'
user_format='<|start_header_id|>user<|end_header_id|>\n\n{content}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'
assistant_format='{content}<|eot_id|>'
parser = argparse.ArgumentParser()
parser.add_argument('--base_model', default=None, type=str, required=True)
parser.add_argument('--tokenizer_path', default=None, type=str)
parser.add_argument('--data_file', default=None, type=str, help="A file that contains instructions (one instruction per line)")
parser.add_argument('--with_prompt', action='store_true', help="wrap the input with the prompt automatically")
parser.add_argument('--interactive', action='store_true', help="run in the instruction mode (single-turn)")
parser.add_argument('--predictions_file', default='./predictions.json', type=str)
parser.add_argument('--gpus', default="0", type=str)
parser.add_argument('--only_cpu', action='store_true', help='only use CPU for inference')
parser.add_argument('--load_in_8bit', action='store_true', help="Load the LLM in the 8bit mode")
parser.add_argument('--load_in_4bit', action='store_true', help="Load the LLM in the 4bit mode")
parser.add_argument("--use_vllm", action='store_true', help="Use vLLM as back-end LLM service.")
parser.add_argument('--use_flash_attention_2', action='store_true', help="Use flash attention to replace the Llama attention")
args = parser.parse_args()
if args.use_vllm:
if args.load_in_8bit or args.load_in_4bit:
raise ValueError("vLLM currently does not support quantization, please use fp16 (default) or unuse --use_vllm.")
if args.only_cpu:
raise ValueError("vLLM requires GPUs with compute capability not less than 7.0. If you want to run only on CPU, please unuse --use_vllm.")
if args.load_in_8bit and args.load_in_4bit:
raise ValueError("Only one quantization method can be chosen for inference. Please check your arguments")
if args.only_cpu is True:
args.gpus = ""
if args.load_in_8bit or args.load_in_4bit:
raise ValueError("Quantization is unavailable on CPU.")
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
if args.use_vllm:
from vllm import LLM, SamplingParams
if args.use_vllm:
generation_config = dict(
temperature=0.2,
top_k=40,
top_p=0.9,
max_tokens=400,
presence_penalty=1.0,
)
else:
generation_config = GenerationConfig(
temperature=0.2,
top_k=40,
top_p=0.9,
do_sample=True,
num_beams=1,
repetition_penalty=1.1,
max_new_tokens=400
)
sample_data = ["为什么要减少污染，保护环境？"]
def generate_prompt(instruction):
return system_format.format(content=DEFAULT_SYSTEM_PROMPT) + user_format.format(content=instruction)
if __name__ == '__main__':
load_type = torch.float16
# Move the model to the MPS device if available
if torch.backends.mps.is_available():
device = torch.device("mps")
else:
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
print(f"Using device: {device}")
if args.tokenizer_path is None:
args.tokenizer_path = args.base_model
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
if args.use_vllm:
model = LLM(model=args.base_model,
tokenizer=args.tokenizer_path,
tensor_parallel_size=len(args.gpus.split(',')),
dtype=load_type
)
generation_config["stop_token_ids"] = terminators
generation_config["stop"] = ["<|eot_id|>", "<|end_of_text|>"]
else:
if args.load_in_4bit or args.load_in_8bit:
quantization_config = BitsAndBytesConfig(
load_in_4bit=args.load_in_4bit,
load_in_8bit=args.load_in_8bit,
bnb_4bit_compute_dtype=load_type,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4"
)
model = AutoModelForCausalLM.from_pretrained(
args.base_model,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
quantization_config=quantization_config if (args.load_in_4bit or args.load_in_8bit) else None,
attn_implementation="flash_attention_2" if args.use_flash_attention_2 else "sdpa"
)
if device==torch.device('cpu'):
model.float()
model.eval()
# test data
if args.data_file is None:
examples = sample_data
else:
with open(args.data_file,'r') as f:
examples = [line.strip() for line in f.readlines()]
print("first 10 examples:")
for example in examples[:10]:
print(example)
with torch.no_grad():
if args.interactive:
print("Start inference with instruction mode.")
print('='*85)
print("+ 该模式下仅支持单轮问答，无多轮对话能力。\n"
"+ 如要进行多轮对话，请使用llama.cpp")
print('-'*85)
print("+ This mode only supports single-turn QA.\n"
"+ If you want to experience multi-turn dialogue, please use llama.cpp")
print('='*85)
while True:
raw_input_text = input("Input:")
if len(raw_input_text.strip())==0:
break
if args.with_prompt:
input_text = generate_prompt(instruction=raw_input_text)
else:
input_text = raw_input_text
if args.use_vllm:
output = model.generate([input_text], SamplingParams(**generation_config), use_tqdm=False)
response = output[0].outputs[0].text
else:
inputs = tokenizer(input_text,return_tensors="pt") #add_special_tokens=False ?
generation_output = model.generate(
input_ids = inputs["input_ids"].to(device),
attention_mask = inputs['attention_mask'].to(device),
eos_token_id=terminators,
pad_token_id=tokenizer.eos_token_id,
generation_config = generation_config
)
s = generation_output[0]
output = tokenizer.decode(s, skip_special_tokens=True)
if args.with_prompt:
response = output.split("assistant\n\n")[-1].strip()
else:
response = output
print("Response: ",response)
print("\n")
else:
print("Start inference.")
results = []
if args.use_vllm:
if args.with_prompt is True:
inputs = [generate_prompt(example) for example in examples]
else:
inputs = examples
outputs = model.generate(inputs, SamplingParams(**generation_config))
for index, (example, output) in enumerate(zip(examples, outputs)):
response = output.outputs[0].text
print(f"======={index}=======")
print(f"Input: {example}\n")
print(f"Output: {response}\n")
results.append({"Input":example,"Output":response})
else:
for index, example in enumerate(examples):
if args.with_prompt:
input_text = generate_prompt(instruction=example)
else:
input_text = example
inputs = tokenizer(input_text,return_tensors="pt") #add_special_tokens=False ?
generation_output = model.generate(
input_ids = inputs["input_ids"].to(device),
attention_mask = inputs['attention_mask'].to(device),
eos_token_id=terminators,
pad_token_id=tokenizer.eos_token_id,
generation_config = generation_config
)
s = generation_output[0]
output = tokenizer.decode(s,skip_special_tokens=True)
if args.with_prompt:
response = output.split("assistant\n\n")[1].strip()
else:
response = output
print(f"======={index}=======")
print(f"Input: {example}\n")
print(f"Output: {response}\n")
results.append({"Input":input_text,"Output":response})
dirname = os.path.dirname(args.predictions_file)
os.makedirs(dirname,exist_ok=True)
with open(args.predictions_file,'w') as f:
json.dump(results,f,ensure_ascii=False,indent=2)
if args.use_vllm:
with open(dirname+'/generation_config.json','w') as f:
json.dump(generation_config,f,ensure_ascii=False,indent=2)
else:
generation_config.save_pretrained('./')
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/llama_cpp/chat.sh | Shell | #!/bin/bash
# script to chat with Llama-3-Chinese-Instruct model
# usage: ./chat.sh llama-3-chinese-instruct-gguf-model-path your-first-instruction
# WARNING: the hyperparameters are not optimal, please tune them yourself
FIRST_INSTRUCTION=$2
SYSTEM_PROMPT="You are a helpful assistant. 你是一个乐于助人的助手。"
./main -m $1 --color -i \
-c 0 -t 6 --temp 0.2 --repeat_penalty 1.1 -ngl 999 \
-r '<|eot_id|>' \
--in-prefix '<|start_header_id|>user<|end_header_id|>\n\n' \
--in-suffix '<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n' \
-p "<|start_header_id|>system<|end_header_id|>\n\n$SYSTEM_PROMPT<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n$FIRST_INSTRUCTION<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/longbench/eval.py | Python | # The script is from https://github.com/THUDM/LongBench
import os
import json
import argparse
import numpy as np
from metrics import (
qa_f1_score,
rouge_zh_score,
qa_f1_zh_score,
rouge_score,
classification_score,
retrieval_score,
retrieval_zh_score,
count_score,
code_sim_score,
)
dataset2metric = {
"narrativeqa": qa_f1_score,
"qasper": qa_f1_score,
"multifieldqa_en": qa_f1_score,
"multifieldqa_zh": qa_f1_zh_score,
"hotpotqa": qa_f1_score,
"2wikimqa": qa_f1_score,
"musique": qa_f1_score,
"dureader": rouge_zh_score,
"gov_report": rouge_score,
"qmsum": rouge_score,
"multi_news": rouge_score,
"vcsum": rouge_zh_score,
"trec": classification_score,
"triviaqa": qa_f1_score,
"samsum": rouge_score,
"lsht": classification_score,
"passage_retrieval_en": retrieval_score,
"passage_count": count_score,
"passage_retrieval_zh": retrieval_zh_score,
"lcc": code_sim_score,
"repobench-p": code_sim_score,
}
def parse_args(args=None):
parser = argparse.ArgumentParser()
parser.add_argument('--output_dir')
parser.add_argument('--e', action='store_true', help="Evaluate on LongBench-E")
return parser.parse_args(args)
def scorer_e(dataset, predictions, answers, lengths, all_classes):
scores = {"0-4k": [], "4-8k": [], "8k+": []}
for (prediction, ground_truths, length) in zip(predictions, answers, lengths):
score = 0.
if dataset in ["trec", "triviaqa", "samsum", "lsht"]:
prediction = prediction.lstrip('\n').split('\n')[0]
for ground_truth in ground_truths:
score = max(score, dataset2metric[dataset](prediction, ground_truth, all_classes=all_classes))
if length < 4000:
scores["0-4k"].append(score)
elif length < 8000:
scores["4-8k"].append(score)
else:
scores["8k+"].append(score)
for key in scores.keys():
scores[key] = round(100 * np.mean(scores[key]), 2)
return scores
def scorer(dataset, predictions, answers, all_classes):
total_score = 0.
for (prediction, ground_truths) in zip(predictions, answers):
score = 0.
if dataset in ["trec", "triviaqa", "samsum", "lsht"]:
prediction = prediction.lstrip('\n').split('\n')[0]
for ground_truth in ground_truths:
score = max(score, dataset2metric[dataset](prediction, ground_truth, all_classes=all_classes))
total_score += score
return round(100 * total_score / len(predictions), 2)
if __name__ == '__main__':
args = parse_args()
scores = dict()
if args.e:
path = f"{args.output_dir}/pred_e/"
else:
path = f"{args.output_dir}/pred/"
all_files = os.listdir(path)
print("Evaluating on:", all_files)
for filename in all_files:
if not filename.endswith("jsonl"):
continue
predictions, answers, lengths = [], [], []
dataset = filename.split('.')[0]
with open(f"{path}{filename}", "r", encoding="utf-8") as f:
print(filename)
for line in f:
data = json.loads(line)
predictions.append(data["pred"])
answers.append(data["answers"])
all_classes = data["all_classes"]
if "length" in data:
lengths.append(data["length"])
if args.e:
score = scorer_e(dataset, predictions, answers, lengths, all_classes)
else:
score = scorer(dataset, predictions, answers, all_classes)
scores[dataset] = score
if args.e:
out_path = f"{args.output_dir}/pred_e/result.json"
else:
out_path = f"{args.output_dir}/pred/result.json"
with open(out_path, "w") as f:
json.dump(scores, f, ensure_ascii=False, indent=4)
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/longbench/metrics.py | Python | # The script is from https://github.com/THUDM/LongBench
import re
import string
import jieba
from fuzzywuzzy import fuzz
import difflib
from collections import Counter
from rouge import Rouge
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
return re.sub(r"\b(a|an|the)\b", " ", text)
def white_space_fix(text):
return " ".join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def normalize_zh_answer(s):
"""Lower text and remove punctuation, extra whitespace."""
def white_space_fix(text):
return "".join(text.split())
def remove_punc(text):
cn_punctuation = "！？｡。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏."
all_punctuation = set(string.punctuation + cn_punctuation)
return "".join(ch for ch in text if ch not in all_punctuation)
def lower(text):
return text.lower()
return white_space_fix(remove_punc(lower(s)))
def count_score(prediction, ground_truth, **kwargs):
numbers = re.findall(r"\d+", prediction)
right_num = 0
for number in numbers:
if str(number) == str(ground_truth):
right_num += 1
final_score = 0.0 if len(numbers) == 0 else right_num / len(numbers)
return float(final_score)
def retrieval_score(prediction, ground_truth, **kwargs):
pattern = r'Paragraph (\d+)'
matches = re.findall(pattern, ground_truth)
ground_truth_id = matches[0]
numbers = re.findall(r"\d+", prediction)
right_num = 0
for number in numbers:
if str(number) == str(ground_truth_id):
right_num += 1
final_score = 0.0 if len(numbers) == 0 else right_num / len(numbers)
return float(final_score)
def retrieval_zh_score(prediction, ground_truth, **kwargs):
pattern = r'段落(\d+)'
matches = re.findall(pattern, ground_truth)
ground_truth_id = matches[0]
numbers = re.findall(r"\d+", prediction)
right_num = 0
for number in numbers:
if str(number) == str(ground_truth_id):
right_num += 1
final_score = 0.0 if len(numbers) == 0 else right_num / len(numbers)
return float(final_score)
def code_sim_score(prediction, ground_truth, **kwargs):
all_lines = prediction.lstrip('\n').split('\n')
prediction = ""
for line in all_lines:
if ('`' not in line) and ('#' not in line) and ('//' not in line):
prediction = line
break
return (fuzz.ratio(prediction, ground_truth) / 100)
def classification_score(prediction, ground_truth, **kwargs):
em_match_list = []
all_classes = kwargs["all_classes"]
for class_name in all_classes:
if class_name in prediction:
em_match_list.append(class_name)
for match_term in em_match_list:
if match_term in ground_truth and match_term != ground_truth:
em_match_list.remove(match_term)
if em_match_list != 0:
if ground_truth in em_match_list:
score = (1.0 / len(em_match_list))
else:
score = 0.0
else:
best_match = None
highest_similarity = 0
for string in all_classes:
similarity = difflib.SequenceMatcher(None, string, prediction).ratio()
if similarity > highest_similarity:
highest_similarity = similarity
best_match = string
score = float(best_match == ground_truth)
return score
def rouge_score(prediction, ground_truth, **kwargs):
rouge = Rouge()
try:
scores = rouge.get_scores([prediction], [ground_truth], avg=True)
except Exception:
return 0.0
return scores["rouge-l"]["f"]
def rouge_zh_score(prediction, ground_truth, **kwargs):
prediction = " ".join(list(jieba.cut(prediction, cut_all=False)))
ground_truth = " ".join(list(jieba.cut(ground_truth, cut_all=False)))
score = rouge_score(prediction, ground_truth)
return score
def f1_score(prediction, ground_truth, **kwargs):
common = Counter(prediction) & Counter(ground_truth)
num_same = sum(common.values())
if num_same == 0:
return 0
precision = 1.0 * num_same / len(prediction)
recall = 1.0 * num_same / len(ground_truth)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def qa_f1_score(prediction, ground_truth, **kwargs):
normalized_prediction = normalize_answer(prediction)
normalized_ground_truth = normalize_answer(ground_truth)
prediction_tokens = normalized_prediction.split()
ground_truth_tokens = normalized_ground_truth.split()
return f1_score(prediction_tokens, ground_truth_tokens)
def qa_f1_zh_score(prediction, ground_truth, **kwargs):
prediction_tokens = list(jieba.cut(prediction, cut_all=False))
ground_truth_tokens = list(jieba.cut(ground_truth, cut_all=False))
prediction_tokens = [normalize_zh_answer(token) for token in prediction_tokens]
ground_truth_tokens = [normalize_zh_answer(token) for token in ground_truth_tokens]
prediction_tokens = [token for token in prediction_tokens if len(token) > 0]
ground_truth_tokens = [token for token in ground_truth_tokens if len(token) > 0]
return f1_score(prediction_tokens, ground_truth_tokens)
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/longbench/pred.py | Python | # The script is modified from https://github.com/THUDM/LongBench/blob/main/pred.py
from datasets import load_dataset
import torch
import random
import numpy as np
import json
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import BitsAndBytesConfig
from tqdm import tqdm
import os
import argparse
dir_path = os.path.dirname(os.path.realpath(__file__))
DEFAULT_SYSTEM_PROMPT = """You are a helpful assistant. 你是一个乐于助人的助手。"""
system_format='<|start_header_id|>system<|end_header_id|>\n\n{content}<|eot_id|>'
user_format='<|start_header_id|>user<|end_header_id|>\n\n{content}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'
assistant_format='{content}<|eot_id|>'
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', type=str)
parser.add_argument('--predict_on',type=str, default='zh')
parser.add_argument('--output_dir',type=str, default='pred')
parser.add_argument('--gpus',type=str, default=None)
parser.add_argument('--max_length',type=int, default=4096-512)
parser.add_argument('--with_inst', choices=['true','false','auto'], default = 'false',
help="Whether use the system prompt and template of Chinese-Alpaca-2 when constructing the instructions.")
parser.add_argument('--e', action='store_true', help="Evaluate on LongBench-E")
parser.add_argument('--use_flash_attention_2', action='store_true', help="Use flash attention to replace the LLaMA attention")
args = parser.parse_args()
model_path = args.model_path
predict_on = args.predict_on
output_dir = args.output_dir
gpus=args.gpus
max_length = args.max_length
DO_SAMPLE =True
TEMPERATURE = 0.2
REPETITION_PENALTY = 1.1
TOP_P = 0.95
TOP_K = 40
if gpus is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = gpus
def fill_llama3_prompt_template(instruction, with_inst=True, system_prompt=DEFAULT_SYSTEM_PROMPT):
if with_inst is False:
return instruction
else:
return system_format.format(content=system_prompt) + user_format.format(content=instruction)
def get_pred(model, tokenizer, data, max_length, max_gen, prompt_format, dataset, device):
preds = []
for json_obj in tqdm(data):
prompt = prompt_format.format(**json_obj)
# truncate to fit max_length (we suggest truncate in the middle, since the left and right side may contain crucial instructions)
tokenized_prompt = tokenizer(prompt, truncation=False, return_tensors="pt").input_ids[0]
if len(tokenized_prompt) > max_length:
half = int(max_length/2)
prompt = tokenizer.decode(tokenized_prompt[:half], skip_special_tokens=True)+tokenizer.decode(tokenized_prompt[-half:], skip_special_tokens=True)
if args.with_inst == 'auto':
if dataset not in ["trec", "triviaqa", "samsum", "lsht", "lcc", "repobench-p"]: # chat models are better off without build prompts on these tasks
prompt = fill_llama3_prompt_template(instruction=prompt)
elif args.with_inst == 'true':
prompt = fill_llama3_prompt_template(instruction=prompt, with_inst=True)
else:
prompt = fill_llama3_prompt_template(instruction=prompt, with_inst=False)
input_data = tokenizer(prompt, truncation=False, return_tensors="pt").to(device)
context_length = input_data.input_ids.shape[-1]
if dataset == "samsum": # prevent illegal output on samsum (model endlessly repeat "\nDialogue"), might be a prompting issue
output = model.generate(
**input_data,
max_new_tokens=max_gen,
num_beams=1,
do_sample=DO_SAMPLE,
repetition_penalty = REPETITION_PENALTY,
top_p = TOP_P,
top_k = TOP_K,
temperature=TEMPERATURE,
min_length=context_length+1,
eos_token_id=[tokenizer.eos_token_id, tokenizer.encode("\n", add_special_tokens=False)[-1]],
)[0]
else:
output = model.generate(
**input_data,
max_new_tokens=max_gen,
num_beams=1,
do_sample=DO_SAMPLE,
repetition_penalty=REPETITION_PENALTY,
top_p=TOP_P,
top_k=TOP_K,
temperature=TEMPERATURE
)[0]
pred = tokenizer.decode(output[context_length:], skip_special_tokens=True)
# print(pred)
preds.append({"pred": pred, "answers": json_obj["answers"], "all_classes": json_obj["all_classes"], "length": json_obj["length"]})
return preds
def seed_everything(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed_all(seed)
if __name__ == '__main__':
seed_everything(42)
load_type = torch.float16
# Move the model to the MPS device if available
if torch.backends.mps.is_available():
device = torch.device("mps")
else:
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
print(f"Using device: {device}")
if args.e:
en_datasets = [ "hotpotqa","2wikimqa",
"qasper", "multifieldqa_en", "gov_report",
"trec", "samsum", "triviaqa",
"passage_count", "passage_retrieval_en", "multi_news"]
zh_datasets = []
code_datasets = [ "lcc", "repobench-p" ]
if not os.path.exists(f"{output_dir}/pred_e"):
os.makedirs(f"{output_dir}/pred_e")
else:
en_datasets = [ "hotpotqa","2wikimqa", "musique", "narrativeqa",
"qasper", "multifieldqa_en", "gov_report",
"qmsum", "trec", "samsum", "triviaqa",
"passage_count", "passage_retrieval_en", "multi_news"]
zh_datasets = [ "dureader", "multifieldqa_zh",
"vcsum","lsht", "passage_retrieval_zh"]
code_datasets = [ "lcc", "repobench-p" ]
if not os.path.exists(f"{output_dir}/pred"):
os.makedirs(f"{output_dir}/pred")
datasets = []
for data_type in predict_on.split(','):
if data_type == 'zh':
datasets += zh_datasets
elif data_type == 'en':
datasets += en_datasets
elif data_type == 'code':
datasets += code_datasets
print(datasets)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
use_flash_attention_2=args.use_flash_attention_2,
trust_remote_code=True
)
model = model.eval()
model_vocab_size = model.get_input_embeddings().weight.size(0)
print(f"Vocab of the base model: {model_vocab_size}")
tokenizer_vocab_size = len(tokenizer)
print(f"Vocab of the tokenizer: {tokenizer_vocab_size}")
# we design specific prompt format and max generation length for each task, feel free to modify them to optimize model output
dataset2prompt = json.load(open(dir_path + "/config/dataset2prompt.json", "r"))
dataset2maxlen = json.load(open(dir_path + "/config/dataset2maxlen.json", "r"))
# predict on each dataset
for dataset in datasets:
print(f"Loading dataset {dataset}")
if args.e:
data = load_dataset('THUDM/LongBench', dataset+'_e', split='test')
output_path = f"{output_dir}/pred_e/{dataset}.jsonl"
else:
data = load_dataset('THUDM/LongBench', dataset, split='test')
output_path = f"{output_dir}/pred/{dataset}.jsonl"
prompt_format = dataset2prompt[dataset]
max_gen = dataset2maxlen[dataset]
preds = get_pred(model, tokenizer, data, max_length, max_gen, prompt_format, dataset, device)
with open(output_path, "w", encoding="utf-8") as f:
for pred in preds:
json.dump(pred, f, ensure_ascii=False)
f.write('\n') | ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/merge_llama3_with_chinese_lora_low_mem.py | Python | """
Usage:
python merge_llama3_with_chinese_lora_low_mem.py \
--base_model path/to/llama-3-hf-model \
--lora_model path/to/llama-3-chinese-lora \
--output_type [huggingface|pth|] \
--output_dir path/to/output-dir
"""
import argparse
import json
import os
import gc
import torch
import peft
from transformers import AutoTokenizer
from transformers.modeling_utils import dtype_byte_size
from huggingface_hub import snapshot_download
import re
import shutil
import safetensors
from safetensors.torch import load_file as safe_load_file
parser = argparse.ArgumentParser(description='Script to merge Llama-3-hf with Llama-3-Chinese or Llama-3-Chinese-Instruct LoRA weights')
parser.add_argument('--base_model', default=None, required=True,
type=str, help="Base model path (basically Llama-3-hf)")
parser.add_argument('--lora_model', default=None, required=True,
type=str, help="LoRA model path (Llama-3-Chinese-LoRA, Llama-3-Chinese-Instruct-LoRA)")
parser.add_argument('--output_type', default='huggingface',choices=['huggingface', 'pth'],
type=str, help="Output model type can be 'huggingface' (default) or 'pth' format")
parser.add_argument('--output_dir', default='./merged_model',
type=str, help="Output path for the merged model")
parser.add_argument('--verbose', default=False, action='store_true',
help="Show detailed debugging messages")
WEIGHTS_NAME = "adapter_model.bin"
SAFETENSORS_WEIGHTS_NAME = "adapter_model.safetensors"
layers_to_model_size = {
32 : '8B',
80 : '70B',
}
num_shards_of_models = {'8B': 1, '70B': 8}
params_of_models = {
'8B':
{
"dim": 4096,
"n_heads": 32,
"n_layers": 32,
"norm_eps": 1e-05,
"vocab_size": -1,
},
'70B':
{
"dim": 8192,
"n_heads": 64,
"n_layers": 80,
"norm_eps": 1e-05,
"vocab_size": -1,
},
}
def transpose(weight, fan_in_fan_out):
return weight.T if fan_in_fan_out else weight
def jsonload(filename):
with open(filename, "r") as file:
d = json.load(file)
return d
# Borrowed and modified from https://github.com/tloen/alpaca-lora
def translate_state_dict_key(k):
k = k.replace("base_model.model.", "")
if k == "model.embed_tokens.weight":
return "tok_embeddings.weight"
elif k == "model.norm.weight":
return "norm.weight"
elif k == "lm_head.weight":
return "output.weight"
elif k.startswith("model.layers."):
layer = k.split(".")[2]
if k.endswith(".self_attn.q_proj.weight"):
return f"layers.{layer}.attention.wq.weight"
elif k.endswith(".self_attn.k_proj.weight"):
return f"layers.{layer}.attention.wk.weight"
elif k.endswith(".self_attn.v_proj.weight"):
return f"layers.{layer}.attention.wv.weight"
elif k.endswith(".self_attn.o_proj.weight"):
return f"layers.{layer}.attention.wo.weight"
elif k.endswith(".mlp.gate_proj.weight"):
return f"layers.{layer}.feed_forward.w1.weight"
elif k.endswith(".mlp.down_proj.weight"):
return f"layers.{layer}.feed_forward.w2.weight"
elif k.endswith(".mlp.up_proj.weight"):
return f"layers.{layer}.feed_forward.w3.weight"
elif k.endswith(".input_layernorm.weight"):
return f"layers.{layer}.attention_norm.weight"
elif k.endswith(".post_attention_layernorm.weight"):
return f"layers.{layer}.ffn_norm.weight"
elif k.endswith("rotary_emb.inv_freq") or "lora" in k:
return None
else:
print(layer, k)
raise NotImplementedError
else:
print(k)
raise NotImplementedError
def unpermute(w):
return (
w.view(n_heads, 2, dim // n_heads // 2, dim).transpose(1, 2).reshape(dim, dim)
)
def save_shards(model_sd, num_shards: int, prefix="", verbose=False):
"""
Convert and save the HF format weights to PTH format weights
"""
with torch.no_grad():
if num_shards == 1:
new_state_dict = {}
for k, v in model_sd.items():
new_k = translate_state_dict_key(k)
if new_k is not None:
if "wq" in new_k or "wk" in new_k:
new_state_dict[new_k] = unpermute(v)
else:
new_state_dict[new_k] = v
os.makedirs(output_dir, exist_ok=True)
print(f"Saving shard 1 of {num_shards} into {output_dir}/{prefix}consolidated.00.pth")
torch.save(new_state_dict, output_dir + f"/{prefix}consolidated.00.pth")
else:
new_state_dicts = [dict() for _ in range(num_shards)]
for k in list(model_sd.keys()):
v = model_sd[k]
new_k = translate_state_dict_key(k)
if new_k is not None:
if new_k=='tok_embeddings.weight':
assert v.size(1)%num_shards==0
splits = v.split(v.size(1)//num_shards,dim=1)
elif new_k=='output.weight':
if v.size(0)%num_shards==0:
splits = v.split(v.size(0)//num_shards,dim=0)
else:
size_list = [v.size(0)//num_shards] * num_shards
size_list[-1] += v.size(0)%num_shards
splits = v.split(size_list, dim=0)
elif new_k=='norm.weight':
splits = [v] * num_shards
elif 'ffn_norm.weight' in new_k:
splits = [v] * num_shards
elif 'attention_norm.weight' in new_k:
splits = [v] * num_shards
elif 'w1.weight' in new_k:
splits = v.split(v.size(0)//num_shards,dim=0)
elif 'w2.weight' in new_k:
splits = v.split(v.size(1)//num_shards,dim=1)
elif 'w3.weight' in new_k:
splits = v.split(v.size(0)//num_shards,dim=0)
elif 'wo.weight' in new_k:
splits = v.split(v.size(1)//num_shards,dim=1)
elif 'wv.weight' in new_k:
splits = v.split(v.size(0)//num_shards,dim=0)
elif "wq.weight" in new_k or "wk.weight" in new_k:
v = unpermute(v)
splits = v.split(v.size(0)//num_shards,dim=0)
else:
print(f"Unexpected key {new_k}")
raise ValueError
if verbose:
print(f"Processing {new_k}")
for sd,split in zip(new_state_dicts,splits):
sd[new_k] = split.clone()
del split
del splits
del model_sd[k],v
gc.collect() # Effectively enforce garbage collection
os.makedirs(output_dir, exist_ok=True)
for i,new_state_dict in enumerate(new_state_dicts):
print(f"Saving shard {i+1} of {num_shards} into {output_dir}/{prefix}consolidated.0{i}.pth")
torch.save(new_state_dict, output_dir + f"/{prefix}consolidated.0{i}.pth")
def merge_shards(output_dir, num_shards: int):
ckpt_filenames = sorted([f for f in os.listdir(output_dir) if re.match('L(\d+)-consolidated.(\d+).pth',f)])
for i in range(num_shards):
shards_filenames = sorted([f for f in ckpt_filenames if re.match(f'L(\d+)-consolidated.0{i}.pth',f)])
print(f"Loading {shards_filenames} ...")
shards_dicts = [torch.load(os.path.join(output_dir,fn)) for fn in shards_filenames]
shards_merged = {}
for d in shards_dicts:
shards_merged |= d
print(f"Saving the merged shard to " + os.path.join(output_dir, f"consolidated.0{i}.pth"))
torch.save(shards_merged, os.path.join(output_dir, f"consolidated.0{i}.pth"))
print("Cleaning up...")
del shards_merged
for d in shards_dicts:
del d
del shards_dicts
gc.collect() # Effectively enforce garbage collection
for fn in shards_filenames:
os.remove(os.path.join(output_dir,fn))
if __name__=='__main__':
args = parser.parse_args()
base_model_path = args.base_model
lora_model_path = args.lora_model
output_dir = args.output_dir
output_type = args.output_type
os.makedirs(output_dir, exist_ok=True)
print(f"="*80)
print(f"Base model: {base_model_path}")
print(f"LoRA model: {lora_model_path}")
tokenizers_and_loras = []
print(f"Loading {lora_model_path}")
if not os.path.exists(lora_model_path):
print("Cannot find lora model on the disk. Downloading lora model from hub...")
lora_model_path = snapshot_download(repo_id=lora_model_path)
tokenizer = AutoTokenizer.from_pretrained(lora_model_path)
lora_config = peft.LoraConfig.from_pretrained(lora_model_path)
if os.path.exists(os.path.join(lora_model_path, SAFETENSORS_WEIGHTS_NAME)):
lora_filename = os.path.join(lora_model_path, SAFETENSORS_WEIGHTS_NAME)
use_safetensors = True
elif os.path.exists(os.path.join(lora_model_path, WEIGHTS_NAME)):
lora_filename = os.path.join(lora_model_path, WEIGHTS_NAME)
use_safetensors = False
else:
raise ValueError(
f"Please check that the file {WEIGHTS_NAME} or {SAFETENSORS_WEIGHTS_NAME} is present at {lora_model_path}."
)
if use_safetensors:
lora_state_dict = safe_load_file(lora_filename, device="cpu")
else:
lora_state_dict = torch.load(lora_filename, map_location='cpu')
# lora_state_dict = torch.load(os.path.join(lora_model_path,'adapter_model.bin'), map_location='cpu')
if 'base_model.model.model.embed_tokens.weight' in lora_state_dict:
lora_vocab_size = lora_state_dict['base_model.model.model.embed_tokens.weight'].shape[0]
assert lora_vocab_size == len(tokenizer), \
(f"The vocab size of the tokenizer {len(tokenizer)} does not match the vocab size of the LoRA weight {lora_vocab_size}!\n")
tokenizers_and_loras.append(
{
"tokenizer" :tokenizer,
"state_dict" :lora_state_dict,
"config": lora_config,
"scaling": lora_config.lora_alpha / lora_config.r,
"fan_in_fan_out" : lora_config.fan_in_fan_out,
})
if not os.path.exists(base_model_path):
print("Cannot find lora model on the disk. Downloading lora model from hub...")
base_model_path = snapshot_download(repo_id=base_model_path)
if os.path.exists(os.path.join(base_model_path, "pytorch_model.bin")):
ckpt_filenames = ["pytorch_model.bin"]
elif os.path.exists(os.path.join(base_model_path, "model.safetensors.index.json")):
ckpt_filenames = sorted([f for f in os.listdir(base_model_path) if re.match('model-(\d+)-of-(\d+).safetensors',f)])
elif os.path.exists(os.path.join(base_model_path, "pytorch_model.index.json")):
ckpt_filenames = sorted([f for f in os.listdir(base_model_path) if re.match('pytorch_model-(\d+)-of-(\d+).bin',f)])
if len(ckpt_filenames) == 0:
raise FileNotFoundError(f"Cannot find base model checkpoints in ${base_model_path}. Please make sure the checkpoints are saved in the HF format.")
layers = jsonload(os.path.join(base_model_path, "config.json"))["num_hidden_layers"]
model_size = None
total_size = 0
for index, filename in enumerate(ckpt_filenames):
print(f"Loading ckpt {filename}")
if re.match('(.*).safetensors', filename):
state_dict = safe_load_file(os.path.join(base_model_path,filename), device="cpu")
else:
state_dict = torch.load(os.path.join(base_model_path,filename), map_location='cpu')
# state_dict = torch.load(os.path.join(base_model_path,filename), map_location='cpu')
if index == 0:
model_size = layers_to_model_size[layers]
if output_type == 'pth':
params = params_of_models[model_size]
num_shards = num_shards_of_models[model_size]
n_layers = params["n_layers"]
n_heads = params["n_heads"]
dim = params["dim"]
dims_per_head = dim // n_heads
base = 500000.0 # llama-3
inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
print("Merging...")
for k in state_dict:
for tl_idx, t_and_l in enumerate(tokenizers_and_loras):
saved_key = 'base_model.model.'+k
lora_key_A = saved_key.replace('.weight','.lora_A.weight')
if saved_key in t_and_l['state_dict']:
if args.verbose:
print(f"copying {saved_key} from {tl_idx}-th LoRA weight to {k}")
state_dict[k] = t_and_l['state_dict'][saved_key].half().clone() # do we need half()?
if lora_key_A in t_and_l['state_dict']:
lora_key_B = lora_key_A.replace('lora_A.weight','lora_B.weight')
if args.verbose:
print(f"merging {lora_key_A} and lora_B.weight form {tl_idx}-th LoRA weight to {k}")
state_dict[k] += (
transpose(
t_and_l['state_dict'][lora_key_B].float()
@ t_and_l['state_dict'][lora_key_A].float(), t_and_l['fan_in_fan_out']) * t_and_l['scaling']
)
weight_size = state_dict[k].numel() * dtype_byte_size(state_dict[k].dtype)
total_size += weight_size
if output_type == 'huggingface':
print(f"Saving ckpt {filename} to {output_dir} in HF format...")
if use_safetensors:
safetensors.torch.save_file(
state_dict, os.path.join(output_dir, filename), metadata={"format": "pt"}
)
else:
torch.save(state_dict, os.path.join(output_dir, filename))
elif output_type == 'pth':
print(f"Converting to pth format...")
save_shards(model_sd=state_dict, num_shards=num_shards,prefix=f"L{index+1}-", verbose=args.verbose)
del state_dict
gc.collect() # Effectively enforce garbage collection
print(f"Saving tokenizer")
tokenizers_and_loras[-1]['tokenizer'].save_pretrained(output_dir)
if output_type == 'pth':
with open(output_dir + "/params.json", "w") as f:
print(f"Saving params.json into {output_dir}/params.json")
json.dump(params, f)
merge_shards(output_dir, num_shards=num_shards)
if output_type=='huggingface':
configs = ('config.json', 'generation_config.json', 'pytorch_model.bin.index.json', "model.safetensors.index.json")
if model_size == "1.3B":
configs = ('config.json', 'generation_config.json')
for config in configs:
if os.path.exists(os.path.join(lora_model_path, config)):
print(f"Saving {config} from {lora_model_path}")
with open(os.path.join(lora_model_path, config),'r') as f:
obj = json.load(f)
else:
if os.path.exists(os.path.join(base_model_path, config)):
print(f"Saving {config} from {base_model_path}")
with open(os.path.join(base_model_path, config),'r') as f:
obj = json.load(f)
if config == 'config.json':
obj['vocab_size'] = len(tokenizers_and_loras[-1]['tokenizer'])
if config == 'pytorch_model.bin.index.json' or config == "model.safetensors.index.json":
obj['metadata']['total_size'] = total_size
if os.path.exists(os.path.join(base_model_path, config)):
with open(os.path.join(output_dir, config), 'w') as f:
json.dump(obj, f, indent=2)
# for f in os.listdir(lora_model_path):
# if re.match('(.*).py', f):
# shutil.copy2(os.path.join(lora_model_path, f), output_dir)
print("Done.")
print(f"Check output dir: {output_dir}") | ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/mmlu/categories.py | Python | subcategories = {
"abstract_algebra": ["math"],
"anatomy": ["health"],
"astronomy": ["physics"],
"business_ethics": ["business"],
"clinical_knowledge": ["health"],
"college_biology": ["biology"],
"college_chemistry": ["chemistry"],
"college_computer_science": ["computer science"],
"college_mathematics": ["math"],
"college_medicine": ["health"],
"college_physics": ["physics"],
"computer_security": ["computer science"],
"conceptual_physics": ["physics"],
"econometrics": ["economics"],
"electrical_engineering": ["engineering"],
"elementary_mathematics": ["math"],
"formal_logic": ["philosophy"],
"global_facts": ["other"],
"high_school_biology": ["biology"],
"high_school_chemistry": ["chemistry"],
"high_school_computer_science": ["computer science"],
"high_school_european_history": ["history"],
"high_school_geography": ["geography"],
"high_school_government_and_politics": ["politics"],
"high_school_macroeconomics": ["economics"],
"high_school_mathematics": ["math"],
"high_school_microeconomics": ["economics"],
"high_school_physics": ["physics"],
"high_school_psychology": ["psychology"],
"high_school_statistics": ["math"],
"high_school_us_history": ["history"],
"high_school_world_history": ["history"],
"human_aging": ["health"],
"human_sexuality": ["culture"],
"international_law": ["law"],
"jurisprudence": ["law"],
"logical_fallacies": ["philosophy"],
"machine_learning": ["computer science"],
"management": ["business"],
"marketing": ["business"],
"medical_genetics": ["health"],
"miscellaneous": ["other"],
"moral_disputes": ["philosophy"],
"moral_scenarios": ["philosophy"],
"nutrition": ["health"],
"philosophy": ["philosophy"],
"prehistory": ["history"],
"professional_accounting": ["other"],
"professional_law": ["law"],
"professional_medicine": ["health"],
"professional_psychology": ["psychology"],
"public_relations": ["politics"],
"security_studies": ["politics"],
"sociology": ["culture"],
"us_foreign_policy": ["politics"],
"virology": ["health"],
"world_religions": ["philosophy"],
}
categories = {
"STEM": ["physics", "chemistry", "biology", "computer science", "math", "engineering"],
"humanities": ["history", "philosophy", "law"],
"social sciences": ["politics", "culture", "economics", "geography", "psychology"],
"other (business, health, misc.)": ["other", "business", "health"],
} | ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/mmlu/eval.py | Python | # modified from https://github.com/baichuan-inc/Baichuan-7B/blob/main/evaluation/evaluate_mmlu.py
import argparse
import os
import torch
import numpy as np
import pandas as pd
from categories import subcategories, categories
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
choices = ["A", "B", "C", "D"]
def format_subject(subject):
line = subject.split("_")
s = ""
for entry in line:
s += " " + entry
return s
def format_example(df, idx, include_answer=True):
prompt = df.iloc[idx, 0]
k = df.shape[1] - 2
for j in range(k):
prompt += "\n{}. {}".format(choices[j], df.iloc[idx, j + 1])
prompt += "\nAnswer:"
if include_answer:
prompt += " {}\n\n".format(df.iloc[idx, k + 1])
return prompt
def gen_prompt(train_df, subject, k=-1):
prompt = "The following are multiple choice questions (with answers) about {}.\n\n".format(
format_subject(subject)
)
if k == -1:
k = train_df.shape[0]
for i in range(k):
prompt += format_example(train_df, i)
return prompt
@torch.no_grad()
def mmlu_eval(args, subject, model, tokenizer, dev_df, test_df, device):
cors = []
all_probs = []
for i in range(test_df.shape[0]):
# get prompt and make sure it fits
k = args.ntrain
prompt_end = format_example(test_df, i, include_answer=False)
train_prompt = gen_prompt(dev_df, subject, k)
prompt = train_prompt + prompt_end
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
label = test_df.iloc[i, test_df.shape[1] - 1]
logits = model(
input_ids=input_ids,
).logits[:,-1].flatten()
probs = (
torch.nn.functional.softmax(
torch.tensor(
[
logits[tokenizer("A").input_ids[-1]],
logits[tokenizer("B").input_ids[-1]],
logits[tokenizer("C").input_ids[-1]],
logits[tokenizer("D").input_ids[-1]],
]
).to(device),
dim=0,
)
.detach()
.cpu()
.to(torch.float32)
.numpy()
)
pred = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(probs)]
cor = pred == label
cors.append(cor)
all_probs.append(probs)
acc = np.mean(cors)
cors = np.array(cors)
all_probs = np.array(all_probs)
print("Average accuracy {:.3f} - {}".format(acc, subject))
return cors, acc, all_probs
def main(args):
# Move the model to the MPS device if available
if torch.backends.mps.is_available():
device = torch.device("mps")
else:
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
print(f"Using device: {device}")
tokenizer = AutoTokenizer.from_pretrained(args.model_path)
model = AutoModelForCausalLM.from_pretrained(
args.model_path,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
device_map='auto',
attn_implementation="flash_attention_2" if args.use_flash_attention_2 else "sdpa"
).to(device).eval()
subjects = sorted(
[
f.split("_test.csv")[0]
for f in os.listdir(os.path.join(args.data_dir, "test"))
if "_test.csv" in f
]
)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
if not os.path.exists(os.path.join(args.save_dir, "results")):
os.makedirs(os.path.join(args.save_dir, "results"))
all_cors = []
subcat_cors = {
subcat: [] for subcat_lists in subcategories.values() for subcat in subcat_lists
}
cat_cors = {cat: [] for cat in categories}
for subject in subjects:
dev_df = pd.read_csv(
os.path.join(args.data_dir, "dev", subject + "_dev.csv"), header=None
)[: args.ntrain]
if args.do_test:
test_df = pd.read_csv(
os.path.join(args.data_dir, "test", subject + "_test.csv"), header=None
)
else:
test_df = pd.read_csv(
os.path.join(args.data_dir, "val", subject + "_val.csv"), header=None
)
cors, _, probs = mmlu_eval(args, subject, model, tokenizer, dev_df, test_df, device)
subcats = subcategories[subject]
for subcat in subcats:
subcat_cors[subcat].append(cors)
for key in categories.keys():
if subcat in categories[key]:
cat_cors[key].append(cors)
all_cors.append(cors)
test_df["correct"] = cors
for j in range(probs.shape[1]):
choice = choices[j]
test_df["choice{}_probs".format(choice)] = probs[:, j]
test_df.to_csv(
os.path.join(
args.save_dir, "results", f"{subject}.csv"
),
index=None,
)
for subcat in subcat_cors:
subcat_acc = np.mean(np.concatenate(subcat_cors[subcat]))
print("Average accuracy {:.3f} - {}".format(subcat_acc, subcat))
for cat in cat_cors:
cat_acc = np.mean(np.concatenate(cat_cors[cat]))
print("Average accuracy {:.3f} - {}".format(cat_acc, cat))
weighted_acc = np.mean(np.concatenate(all_cors))
print("Average accuracy: {:.3f}".format(weighted_acc))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--ntrain", "-k", type=int, default=5)
parser.add_argument("--data_dir", "-d", type=str, default="data")
parser.add_argument("--save_dir", "-s", type=str, default="results")
parser.add_argument(
"--model_path",
"-m",
type=str,
)
parser.add_argument(
"--do_test",
action="store_true"
)
parser.add_argument(
"--use_flash_attention_2",
action="store_true"
)
args = parser.parse_args()
main(args) | ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/oai_api_demo/openai_api_protocol.py | Python | from typing import Optional, List, Dict, Any, Union, Literal
import time
import shortuuid
from pydantic import BaseModel, Field
class ChatCompletionRequest(BaseModel):
model: str = "llama-3-chinese"
messages: Union[str, List[Dict[str, str]]]
temperature: Optional[float] = 0.2
top_p: Optional[float] = 0.9
top_k: Optional[int] = 40
n: Optional[int] = 1
max_tokens: Optional[int] = 512
num_beams: Optional[int] = 1
stop: Optional[Union[str, List[str]]] = None
stream: Optional[bool] = False
repetition_penalty: Optional[float] = 1.1
user: Optional[str] = None
do_sample: Optional[bool] = True
class ChatMessage(BaseModel):
role: str
content: str
class DeltaMessage(BaseModel):
role: Optional[Literal["user", "assistant", "system"]] = None
content: Optional[str] = None
class ChatCompletionResponseChoice(BaseModel):
index: int
message: ChatMessage
class ChatCompletionResponseStreamChoice(BaseModel):
index: int
delta: DeltaMessage
finish_reason: Optional[Literal["stop", "length"]]
class ChatCompletionResponse(BaseModel):
id: str = Field(default_factory=lambda: f"chatcmpl-{shortuuid.random()}")
object: str = "chat.completion"
created: int = Field(default_factory=lambda: int(time.time()))
model: str = "llama-3-chinese"
choices: List[
Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]
]
class EmbeddingsRequest(BaseModel):
input: Union[str, List[Any]]
user: Optional[str] = None
class EmbeddingsResponse(BaseModel):
object: str = "list"
data: List[Dict[str, Any]]
model: str = "llama-3-chinese"
class CompletionRequest(BaseModel):
prompt: Union[str, List[Any]]
temperature: Optional[float] = 0.2
n: Optional[int] = 1
max_tokens: Optional[int] = 512
stop: Optional[Union[str, List[str]]] = None
stream: Optional[bool] = False
top_p: Optional[float] = 0.9
top_k: Optional[int] = 40
num_beams: Optional[int] = 1
logprobs: Optional[int] = None
echo: Optional[bool] = False
repetition_penalty: Optional[float] = 1.1
user: Optional[str] = None
do_sample: Optional[bool] = True
class CompletionResponseChoice(BaseModel):
index: int
text: str
class CompletionResponse(BaseModel):
id: Optional[str] = Field(default_factory=lambda: f"cmpl-{shortuuid.random()}")
object: Optional[str] = "text_completion"
created: Optional[int] = Field(default_factory=lambda: int(time.time()))
model: Optional[str] = "llama-3-chinese"
choices: List[CompletionResponseChoice]
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/oai_api_demo/openai_api_server.py | Python | import argparse
import os
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
from threading import Thread
from sse_starlette.sse import EventSourceResponse
parser = argparse.ArgumentParser()
parser.add_argument('--base_model', default=None, type=str, required=True)
parser.add_argument('--lora_model', default=None, type=str,help="If None, perform inference on the base model")
parser.add_argument('--tokenizer_path',default=None,type=str)
parser.add_argument('--gpus', default="0", type=str)
parser.add_argument('--load_in_8bit',action='store_true', help='Load the model in 8bit mode')
parser.add_argument('--load_in_4bit',action='store_true', help='Load the model in 4bit mode')
parser.add_argument('--only_cpu',action='store_true',help='Only use CPU for inference')
parser.add_argument('--use_flash_attention_2', action='store_true', help="Use flash-attention2 to accelerate inference")
args = parser.parse_args()
if args.only_cpu is True:
args.gpus = ""
if args.load_in_8bit or args.load_in_4bit:
raise ValueError("Quantization is unavailable on CPU.")
if args.load_in_8bit and args.load_in_4bit:
raise ValueError("Only one quantization method can be chosen for inference. Please check your arguments")
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
import torch
import torch.nn.functional as F
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
GenerationConfig,
TextIteratorStreamer,
BitsAndBytesConfig
)
from peft import PeftModel
import sys
parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(parent_dir)
from openai_api_protocol import (
ChatCompletionRequest,
ChatCompletionResponse,
ChatMessage,
ChatCompletionResponseChoice,
CompletionRequest,
CompletionResponse,
CompletionResponseChoice,
EmbeddingsRequest,
EmbeddingsResponse,
ChatCompletionResponseStreamChoice,
DeltaMessage,
)
load_type = torch.float16
# Move the model to the MPS device if available
if torch.backends.mps.is_available():
device = torch.device("mps")
else:
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
print(f"Using device: {device}")
if args.tokenizer_path is None:
args.tokenizer_path = args.lora_model
if args.lora_model is None:
args.tokenizer_path = args.base_model
tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path, legacy=True)
if args.load_in_4bit or args.load_in_8bit:
quantization_config = BitsAndBytesConfig(
load_in_4bit=args.load_in_4bit,
load_in_8bit=args.load_in_8bit,
bnb_4bit_compute_dtype=load_type,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4"
)
base_model = AutoModelForCausalLM.from_pretrained(
args.base_model,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto' if not args.only_cpu else None,
#load_in_4bit=args.load_in_4bit,
#load_in_8bit=args.load_in_8bit,
quantization_config=quantization_config if (args.load_in_4bit or args.load_in_8bit) else None,
attn_implementation="flash_attention_2" if args.use_flash_attention_2 else "sdpa",
trust_remote_code=True
)
model_vocab_size = base_model.get_input_embeddings().weight.size(0)
tokenizer_vocab_size = len(tokenizer)
print(f"Vocab of the base model: {model_vocab_size}")
print(f"Vocab of the tokenizer: {tokenizer_vocab_size}")
if model_vocab_size != tokenizer_vocab_size:
print("Resize model embeddings to fit tokenizer")
base_model.resize_token_embeddings(tokenizer_vocab_size)
if args.lora_model is not None:
print("loading peft model")
model = PeftModel.from_pretrained(
base_model,
args.lora_model,
torch_dtype=load_type,
device_map="auto",
)
else:
model = base_model
if device == torch.device("cpu"):
model.float()
model.eval()
DEFAULT_SYSTEM_PROMPT = "You are a helpful assistant. 你是一个乐于助人的助手。"
TEMPLATE_WITH_SYSTEM_PROMPT = (
"""<|start_header_id|>system<|end_header_id|>\n\n{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>\n\n{message}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"""
)
TEMPLATE_WITHOUT_SYSTEM_PROMPT = """<|start_header_id|>user<|end_header_id|>\n\n{message}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"""
def generate_prompt(
message, response="", with_system_prompt=False, system_prompt=None
):
if with_system_prompt is True:
if system_prompt is None:
system_prompt = DEFAULT_SYSTEM_PROMPT
prompt = TEMPLATE_WITH_SYSTEM_PROMPT.format_map(
{"message": message, "system_prompt": system_prompt}
)
else:
prompt = TEMPLATE_WITHOUT_SYSTEM_PROMPT.format_map({"message": message})
if len(response) > 0:
prompt += " " + response
return prompt
def generate_completion_prompt(message: str):
"""Generate prompt for completion"""
return generate_prompt(message, response="", with_system_prompt=False)
def generate_chat_prompt(messages: list):
"""Generate prompt for chat completion"""
system_msg = None
for msg in messages:
if msg.role == "system":
system_msg = msg.content
prompt = ""
is_first_user_content = True
for msg in messages:
if msg.role == "system":
continue
if msg.role == "user":
if is_first_user_content is True:
prompt += generate_prompt(
msg.content, with_system_prompt=True, system_prompt=system_msg
)
is_first_user_content = False
else:
prompt += generate_prompt(msg.content, with_system_prompt=False)
if msg.role == "assistant":
prompt += f"{msg.content}" + "<|eot_id|>"
return prompt
def predict(
input,
max_new_tokens=1024,
top_p=0.9,
temperature=0.2,
top_k=40,
num_beams=1,
repetition_penalty=1.1,
do_sample=True,
**kwargs,
):
"""
Main inference method
type(input) == str -> /v1/completions
type(input) == list -> /v1/chat/completions
"""
if isinstance(input, str):
prompt = generate_completion_prompt(input)
else:
prompt = generate_chat_prompt(input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
attention_mask = inputs['attention_mask'].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
do_sample=do_sample,
**kwargs,
)
generation_config.return_dict_in_generate = True
generation_config.output_scores = False
generation_config.max_new_tokens = max_new_tokens
generation_config.repetition_penalty = float(repetition_penalty)
# c.f. llama-3-instruct generation_config
llama3_eos_ids = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<|eot_id|>")]
# For the reason why pad_token_id = eos_token_id, see:
# https://github.com/meta-llama/llama-recipes/blob/f7aa02af9f2c427ebb70853191b72636130b9df5/src/llama_recipes/finetuning.py#L141
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
eos_token_id=llama3_eos_ids,
pad_token_id=tokenizer.eos_token_id,
generation_config=generation_config,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s, skip_special_tokens=True)
#output = output.split("<|eot_id|>")[-1].strip()
output = output.split("assistant\n\n")[-1].strip()
return output
def stream_predict(
input,
max_new_tokens=1024,
top_p=0.9,
temperature=0.2,
top_k=40,
num_beams=4,
repetition_penalty=1.1,
do_sample=True,
model_id="llama-3-chinese",
**kwargs,
):
choice_data = ChatCompletionResponseStreamChoice(
index=0, delta=DeltaMessage(role="assistant"), finish_reason=None
)
chunk = ChatCompletionResponse(
model=model_id,
choices=[choice_data],
object="chat.completion.chunk",
)
yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
if isinstance(input, str):
prompt = generate_completion_prompt(input)
else:
prompt = generate_chat_prompt(input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
do_sample=do_sample,
**kwargs,
)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
generation_kwargs = dict(
streamer=streamer,
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=False,
max_new_tokens=max_new_tokens,
repetition_penalty=float(repetition_penalty),
)
Thread(target=model.generate, kwargs=generation_kwargs).start()
for new_text in streamer:
choice_data = ChatCompletionResponseStreamChoice(
index=0, delta=DeltaMessage(content=new_text), finish_reason=None
)
chunk = ChatCompletionResponse(
model=model_id, choices=[choice_data], object="chat.completion.chunk"
)
yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
choice_data = ChatCompletionResponseStreamChoice(
index=0, delta=DeltaMessage(), finish_reason="stop"
)
chunk = ChatCompletionResponse(
model=model_id, choices=[choice_data], object="chat.completion.chunk"
)
yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
yield "[DONE]"
def get_embedding(input):
"""Get embedding main function"""
with torch.no_grad():
encoding = tokenizer(input, padding=True, return_tensors="pt")
input_ids = encoding["input_ids"].to(device)
attention_mask = encoding["attention_mask"].to(device)
model_output = model(input_ids, attention_mask, output_hidden_states=True)
data = model_output.hidden_states[-1]
mask = attention_mask.unsqueeze(-1).expand(data.size()).float()
masked_embeddings = data * mask
sum_embeddings = torch.sum(masked_embeddings, dim=1)
seq_length = torch.sum(mask, dim=1)
embedding = sum_embeddings / seq_length
normalized_embeddings = F.normalize(embedding, p=2, dim=1)
ret = normalized_embeddings.squeeze(0).tolist()
return ret
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.post("/v1/chat/completions")
async def create_chat_completion(request: ChatCompletionRequest):
"""Creates a completion for the chat message"""
msgs = request.messages
if isinstance(msgs, str):
msgs = [ChatMessage(role="user", content=msgs)]
else:
msgs = [ChatMessage(role=x["role"], content=x["content"]) for x in msgs]
if request.stream:
generate = stream_predict(
input=msgs,
max_new_tokens=request.max_tokens,
top_p=request.top_p,
top_k=request.top_k,
temperature=request.temperature,
num_beams=request.num_beams,
repetition_penalty=request.repetition_penalty,
do_sample=request.do_sample,
)
return EventSourceResponse(generate, media_type="text/event-stream")
output = predict(
input=msgs,
max_new_tokens=request.max_tokens,
top_p=request.top_p,
top_k=request.top_k,
temperature=request.temperature,
num_beams=request.num_beams,
repetition_penalty=request.repetition_penalty,
do_sample=request.do_sample,
)
choices = [
ChatCompletionResponseChoice(index=i, message=msg) for i, msg in enumerate(msgs)
]
choices += [
ChatCompletionResponseChoice(
index=len(choices), message=ChatMessage(role="assistant", content=output)
)
]
return ChatCompletionResponse(choices=choices)
@app.post("/v1/completions")
async def create_completion(request: CompletionRequest):
"""Creates a completion"""
output = predict(
input=request.prompt,
max_new_tokens=request.max_tokens,
top_p=request.top_p,
top_k=request.top_k,
temperature=request.temperature,
num_beams=request.num_beams,
repetition_penalty=request.repetition_penalty,
do_sample=request.do_sample,
)
choices = [CompletionResponseChoice(index=0, text=output)]
return CompletionResponse(choices=choices)
@app.post("/v1/embeddings")
async def create_embeddings(request: EmbeddingsRequest):
"""Creates text embedding"""
embedding = get_embedding(request.input)
data = [{"object": "embedding", "embedding": embedding, "index": 0}]
return EmbeddingsResponse(data=data)
if __name__ == "__main__":
log_config = uvicorn.config.LOGGING_CONFIG
log_config["formatters"]["access"][
"fmt"
] = "%(asctime)s - %(levelname)s - %(message)s"
log_config["formatters"]["default"][
"fmt"
] = "%(asctime)s - %(levelname)s - %(message)s"
uvicorn.run(app, host="0.0.0.0", port=19327, workers=1, log_config=log_config)
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/build_dataset.py | Python | import logging
import os
from typing import Union, List
import datasets
import torch
from datasets import load_dataset, concatenate_datasets
import transformers
IGNORE_INDEX = -100
logger = logging.getLogger('__name__')
DEFAULT_SYSTEM_PROMPT = """You are a helpful assistant. 你是一个乐于助人的助手。"""
system_format='<|start_header_id|>system<|end_header_id|>\n\n{content}<|eot_id|>'
user_format='<|start_header_id|>user<|end_header_id|>\n\n{content}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n'
assistant_format='{content}<|eot_id|>'
def build_instruction_dataset(data_path: Union[List[str],str],
tokenizer: transformers.PreTrainedTokenizer,
max_seq_length: int, data_cache_dir = None,
preprocessing_num_workers = None,
):
def tokenization(examples):
sources = []
targets = []
for instruction, input_text, output in zip(examples['instruction'],examples['input'],examples['output']):
if input_text is not None and input_text !="":
instruction = instruction+'\n'+input_text
source = system_format.format(content=DEFAULT_SYSTEM_PROMPT) + user_format.format(content=instruction)
target = assistant_format.format(content=output)
sources.append(source)
targets.append(target)
tokenized_sources = tokenizer(sources, return_attention_mask=False, add_special_tokens=False)
tokenized_targets = tokenizer(targets, return_attention_mask=False, add_special_tokens=False)
all_input_ids = []
all_labels = []
for s,t in zip(tokenized_sources['input_ids'],tokenized_targets['input_ids']):
input_ids = torch.LongTensor(s + t)[:max_seq_length]
labels = torch.LongTensor([IGNORE_INDEX] * len(s) + t)[:max_seq_length]
all_input_ids.append(input_ids)
all_labels.append(labels)
results = {'input_ids':all_input_ids, 'labels': all_labels}
return results
logging.warning("building dataset...")
all_datasets = []
if not isinstance(data_path,(list,tuple)):
data_path = [data_path]
for file in data_path:
if data_cache_dir is None:
data_cache_dir = str(os.path.dirname(file))
cache_path = os.path.join(data_cache_dir,os.path.basename(file).split('.')[0]+f"_{max_seq_length}")
os.makedirs(cache_path, exist_ok=True)
try:
processed_dataset = datasets.load_from_disk(cache_path)
logger.info(f'training datasets-{file} has been loaded from disk')
except Exception:
raw_dataset = load_dataset("json", data_files=file, cache_dir=cache_path)
tokenization_func = tokenization
tokenized_dataset = raw_dataset.map(
tokenization_func,
batched=True,
num_proc=preprocessing_num_workers,
remove_columns=["instruction","input","output"],
keep_in_memory=False,
desc="preprocessing on dataset",
)
processed_dataset = tokenized_dataset
processed_dataset.save_to_disk(cache_path)
processed_dataset.set_format('torch')
all_datasets.append(processed_dataset['train'])
all_datasets = concatenate_datasets(all_datasets)
return all_datasets
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/run_clm_pt_with_peft.py | Python | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=text-generation
"""
# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
import logging
import numpy as np
import math
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, List, Dict, Any, Mapping
from pathlib import Path
import datasets
import torch
from datasets import load_dataset, concatenate_datasets
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_CAUSAL_LM_MAPPING,
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
is_torch_xla_available,
set_seed,
BitsAndBytesConfig
)
from transformers.testing_utils import CaptureLogger
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
from sklearn.metrics import accuracy_score
from peft import LoraConfig, TaskType, get_peft_model, PeftModel, prepare_model_for_kbit_training
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.40.0")
def accuracy(predictions, references, normalize=True, sample_weight=None):
return {
"accuracy": float(
accuracy_score(references, predictions, normalize=normalize, sample_weight=sample_weight)
)
}
def compute_metrics(eval_preds):
preds, labels = eval_preds
# preds have the same shape as the labels, after the argmax(-1) has been calculated
# by preprocess_logits_for_metrics but we need to shift the labels
labels = labels[:, 1:].reshape(-1)
preds = preds[:, :-1].reshape(-1)
return accuracy(predictions=preds, references=labels)
def preprocess_logits_for_metrics(logits, labels):
if isinstance(logits, tuple):
# Depending on the model and config, logits may contain extra tensors,
# like past_key_values, but logits always come first
logits = logits[0]
return logits.argmax(dim=-1)
def fault_tolerance_data_collator(features: List) -> Dict[str, Any]:
if not isinstance(features[0], Mapping):
features = [vars(f) for f in features]
first = features[0]
batch = {}
# Special handling for labels.
# Ensure that tensor is created with the correct type
# (it should be automatically the case, but let's make sure of it.)
if "label" in first and first["label"] is not None:
label = first["label"].item() if isinstance(first["label"], torch.Tensor) else first["label"]
dtype = torch.long if isinstance(label, int) else torch.float
batch["labels"] = torch.tensor([f["label"] for f in features], dtype=dtype)
elif "label_ids" in first and first["label_ids"] is not None:
if isinstance(first["label_ids"], torch.Tensor):
batch["labels"] = torch.stack([f["label_ids"] for f in features])
else:
dtype = torch.long if isinstance(first["label_ids"][0], int) else torch.float
batch["labels"] = torch.tensor([f["label_ids"] for f in features], dtype=dtype)
# Handling of all other possible keys.
# Again, we will use the first element to figure out which key/values are not None for this model.
try:
for k, v in first.items():
if k not in ("label", "label_ids") and v is not None and not isinstance(v, str):
if isinstance(v, torch.Tensor):
batch[k] = torch.stack([f[k] for f in features])
elif isinstance(v, np.ndarray):
batch[k] = torch.tensor(np.stack([f[k] for f in features]))
else:
batch[k] = torch.tensor([f[k] for f in features])
except ValueError: # quick fix by simply take the first example
for k, v in first.items():
if k not in ("label", "label_ids") and v is not None and not isinstance(v, str):
if isinstance(v, torch.Tensor):
batch[k] = torch.stack([features[0][k]] * len(features))
elif isinstance(v, np.ndarray):
batch[k] = torch.tensor(np.stack([features[0][k]] * len(features)))
else:
batch[k] = torch.tensor([features[0][k]] * len(features))
return batch
MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
)
},
)
tokenizer_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The tokenizer for weights initialization.Don't set if you want to train a model from scratch."
)
},
)
model_type: Optional[str] = field(
default=None,
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
)
config_overrides: Optional[str] = field(
default=None,
metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=False,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
},
)
torch_dtype: Optional[str] = field(
default=None,
metadata={
"help": (
"Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
"dtype will be automatically derived from the model's weights."
),
"choices": ["auto", "bfloat16", "float16", "float32"],
},
)
low_cpu_mem_usage: bool = field(
default=False,
metadata={
"help": (
"It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. "
"set True will benefit LLM loading time and RAM consumption."
)
},
)
def __post_init__(self):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path"
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_dir: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"})
block_size: Optional[int] = field(
default=None,
metadata={
"help": (
"Optional input sequence length after tokenization. "
"The training dataset will be truncated in block of this size for training. "
"Default to the model max input length for single sentence inputs (take into account special tokens)."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
validation_split_percentage: Optional[float] = field(
default=0.05,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
keep_linebreaks: bool = field(
default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."}
)
data_cache_dir: Optional[str] = field(default="./", metadata={"help": "The datasets processed stored"})
def __post_init__(self):
if self.streaming:
require_version("datasets>=2.0.0", "The streaming feature requires `datasets>=2.0.0`")
@dataclass
class MyTrainingArguments(TrainingArguments):
trainable : Optional[str] = field(default="q_proj,v_proj")
lora_rank : Optional[int] = field(default=8)
lora_dropout : Optional[float] = field(default=0.1)
lora_alpha : Optional[float] = field(default=32.)
modules_to_save : Optional[str] = field(default=None)
debug_mode : Optional[bool] = field(default=False)
peft_path : Optional[str] = field(default=None)
use_flash_attention_2 : Optional[bool] = field(default=False)
double_quant: Optional[bool] = field(default=True)
quant_type: Optional[str] = field(default="nf4")
load_in_kbits: Optional[int] = field(default=16)
full_finetuning : Optional[bool] = field(default=False)
logger = logging.getLogger(__name__)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, MyTrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_clm", model_args, data_args)
# Setup logging
logging.basicConfig(format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO, # if training_args.local_rank in [-1, 0] else logging.WARN,
handlers=[logging.StreamHandler(sys.stdout)],)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
logger.info(f"Training/evaluation parameters {training_args}")
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
config_kwargs = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}")
config.update_from_string(model_args.config_overrides)
logger.info(f"New config: {config}")
tokenizer_kwargs = {
"cache_dir": model_args.cache_dir,
"use_fast": model_args.use_fast_tokenizer,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
elif model_args.tokenizer_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name_or_path, **tokenizer_kwargs)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
# Preprocessing the datasets.
# First we tokenize all the texts.
# since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
def tokenize_function(examples):
with CaptureLogger(tok_logger) as cl:
output = tokenizer(examples["text"])
# clm input could be much much longer than block_size
if "Token indices sequence length is longer than the" in cl.out:
tok_logger.warning(
"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits"
" before being passed to the model."
)
return output
if data_args.block_size is None:
block_size = tokenizer.model_max_length
if block_size > 1024:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`."
)
block_size = 1024
else:
if data_args.block_size > tokenizer.model_max_length:
logger.warning(
f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
)
block_size = min(data_args.block_size, tokenizer.model_max_length)
# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
if total_length >= block_size:
total_length = (total_length // block_size) * block_size
# Split by chunks of max_len.
result = {
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
result["labels"] = result["input_ids"].copy()
return result
with training_args.main_process_first(desc="dataset map tokenization and grouping"):
lm_datasets = []
path = Path(data_args.dataset_dir)
files = [file.name for file in path.glob("*.txt")]
if training_args.debug_mode is True:
files = [files[0]]
for idx, file in enumerate(files):
data_file = os.path.join(path, file)
filename = ''.join(file.split(".")[:-1])
cache_path = os.path.join(data_args.data_cache_dir, filename+f"_{block_size}")
os.makedirs(cache_path, exist_ok=True)
try:
processed_dataset = datasets.load_from_disk(cache_path, keep_in_memory=False)
logger.info(f'training datasets-{filename} has been loaded from disk')
except Exception:
cache_dir = os.path.join(data_args.data_cache_dir, filename+f"_text_{block_size}")
os.makedirs(cache_dir, exist_ok=True)
raw_dataset = load_dataset("text", data_files=data_file, cache_dir=cache_dir, keep_in_memory=False)
logger.info(f"{file} has been loaded")
tokenized_dataset = raw_dataset.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns="text",
load_from_cache_file=True,
keep_in_memory=False,
cache_file_names = {k: os.path.join(cache_dir, 'tokenized.arrow') for k in raw_dataset},
desc="Running tokenizer on dataset",
)
grouped_datasets = tokenized_dataset.map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=True,
keep_in_memory=False,
cache_file_names = {k: os.path.join(cache_dir, 'grouped.arrow') for k in tokenized_dataset},
desc=f"Grouping texts in chunks of {block_size}",
)
processed_dataset = grouped_datasets
processed_dataset.save_to_disk(cache_path)
if idx == 0:
lm_datasets = processed_dataset['train']
else:
assert lm_datasets.features.type == processed_dataset["train"].features.type
lm_datasets = concatenate_datasets([lm_datasets, processed_dataset["train"]])
lm_datasets = lm_datasets.train_test_split(test_size = data_args.validation_split_percentage)
if training_args.do_train:
train_dataset = lm_datasets['train']
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
logger.info(f"Num train_samples {len(train_dataset)}")
logger.info("Training example:")
logger.info(tokenizer.decode(train_dataset[0]['input_ids']))
if training_args.do_eval:
eval_dataset = lm_datasets["test"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
logger.info(f"Num eval_samples {len(eval_dataset)}")
logger.info("Evaluation example:")
logger.info(tokenizer.decode(eval_dataset[0]['input_ids']))
compute_dtype = (torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
if training_args.load_in_kbits in [4, 8]:
if training_args.modules_to_save is not None:
load_in_8bit_skip_modules = training_args.modules_to_save.split(',')
else:
load_in_8bit_skip_modules = None
quantization_config = BitsAndBytesConfig(
load_in_4bit=training_args.load_in_kbits == 4,
load_in_8bit=training_args.load_in_kbits == 8,
llm_int8_threshold=6.0,
load_in_8bit_skip_modules=load_in_8bit_skip_modules,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=training_args.double_quant,
bnb_4bit_quant_type=training_args.quant_type # {'fp4', 'nf4'}
)
else:
quantization_config = None
if quantization_config is not None:
logger.info(f"quantization_config:{quantization_config.to_dict()}")
if model_args.model_name_or_path:
torch_dtype = (
model_args.torch_dtype
if model_args.torch_dtype in ["auto", None]
else getattr(torch, model_args.torch_dtype)
)
device_map = {"":int(os.environ.get("LOCAL_RANK") or 0)}
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
torch_dtype=torch_dtype,
low_cpu_mem_usage=model_args.low_cpu_mem_usage,
device_map=device_map,
quantization_config=quantization_config,
attn_implementation="flash_attention_2" if training_args.use_flash_attention_2 else "sdpa"
)
else:
model = AutoModelForCausalLM.from_config(config)
n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params")
if training_args.load_in_kbits in [4, 8]:
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=training_args.gradient_checkpointing)
model.config.use_cache = False
model_vocab_size = model.get_output_embeddings().weight.size(0)
tokenizer_vocab_size = len(tokenizer)
logger.info(f"Model vocab size: {model_vocab_size}")
logger.info(f"Tokenizer vocab size: {tokenizer_vocab_size}")
if model_vocab_size != tokenizer_vocab_size:
logger.info(f"Resize model vocab size to {tokenizer_vocab_size}")
model.resize_token_embeddings(len(tokenizer))
if not training_args.full_finetuning:
if training_args.peft_path is not None:
logger.info("Peft from pre-trained model")
model = PeftModel.from_pretrained(model, training_args.peft_path, device_map=device_map, is_trainable=True)
else:
logger.info("Init new peft model")
target_modules = training_args.trainable.split(',')
modules_to_save = training_args.modules_to_save
if modules_to_save is not None:
modules_to_save = modules_to_save.split(',')
lora_rank = training_args.lora_rank
lora_dropout = training_args.lora_dropout
lora_alpha = training_args.lora_alpha
logger.info(f"target_modules: {target_modules}")
logger.info(f"lora_rank: {lora_rank}")
logger.info(f"modules_to_save: {modules_to_save}")
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
target_modules=target_modules,
inference_mode=False,
r=lora_rank, lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
modules_to_save=modules_to_save)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
tokenizer=tokenizer,
data_collator=fault_tolerance_data_collator,
compute_metrics=compute_metrics if training_args.do_eval and not is_torch_xla_available() else None,
preprocess_logits_for_metrics=preprocess_logits_for_metrics
if training_args.do_eval and not is_torch_xla_available()
else None,
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model() # Saves the tokenizer too for easy upload
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
try:
perplexity = math.exp(metrics["eval_loss"])
except OverflowError:
perplexity = float("inf")
metrics["perplexity"] = perplexity
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
if __name__ == "__main__":
main()
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/run_clm_sft_with_peft.py | Python | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=text-generation
"""
# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from pathlib import Path
import datasets
import torch
from build_dataset import build_instruction_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
AutoConfig,
BitsAndBytesConfig,
AutoModelForCausalLM,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
DataCollatorForSeq2Seq
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
from peft import LoraConfig, TaskType, get_peft_model, PeftModel, prepare_model_for_kbit_training
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.40.0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
)
},
)
tokenizer_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The tokenizer for weights initialization.Don't set if you want to train a model from scratch."
)
},
)
config_overrides: Optional[str] = field(
default=None,
metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=False,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
},
)
torch_dtype: Optional[str] = field(
default=None,
metadata={
"help": (
"Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
"dtype will be automatically derived from the model's weights."
),
"choices": ["auto", "bfloat16", "float16", "float32"],
},
)
low_cpu_mem_usage: bool = field(
default=False,
metadata={
"help": (
"It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. "
"set True will benefit LLM loading time and RAM consumption."
)
},
)
def __post_init__(self):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path"
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_dir: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
validation_split_percentage: Optional[float] = field(
default=0.05,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
keep_linebreaks: bool = field(
default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."}
)
data_cache_dir: Optional[str] = field(default=None, metadata={"help": "The datasets processed stored"})
max_seq_length: Optional[int] = field(default=1024)
@dataclass
class MyTrainingArguments(TrainingArguments):
trainable : Optional[str] = field(default="q_proj,v_proj")
lora_rank : Optional[int] = field(default=8)
lora_dropout : Optional[float] = field(default=0.1)
lora_alpha : Optional[float] = field(default=32.)
modules_to_save : Optional[str] = field(default=None)
peft_path : Optional[str] = field(default=None)
use_flash_attention_2 : Optional[bool] = field(default=False)
double_quant: Optional[bool] = field(default=True)
quant_type: Optional[str] = field(default="nf4")
load_in_kbits: Optional[int] = field(default=16)
full_finetuning : Optional[bool] = field(default=False)
logger = logging.getLogger(__name__)
def main():
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, MyTrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
send_example_telemetry("run_clm", model_args, data_args)
# Setup logging
logging.basicConfig(format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO, # if training_args.local_rank in [-1, 0] else logging.WARN,
handlers=[logging.StreamHandler(sys.stdout)],)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16 or training_args.bf16}"
)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
config_kwargs = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}")
config.update_from_string(model_args.config_overrides)
logger.info(f"New config: {config}")
tokenizer_kwargs = {
"cache_dir": model_args.cache_dir,
"use_fast": model_args.use_fast_tokenizer,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
elif model_args.tokenizer_name_or_path:
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name_or_path, **tokenizer_kwargs)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer)
eval_dataset=None
train_dataset = None
if training_args.do_train:
with training_args.main_process_first(desc="loading and tokenization"):
path = Path(data_args.dataset_dir)
files = [os.path.join(path,file.name) for file in path.glob("*.json")]
logger.info(f"Training files: {' '.join(files)}")
train_dataset = build_instruction_dataset(
data_path=files,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
data_cache_dir=None,
preprocessing_num_workers = data_args.preprocessing_num_workers)
logger.info(f"Num train_samples {len(train_dataset)}")
logger.info("Training example:")
logger.info(tokenizer.decode(train_dataset[0]['input_ids']))
if training_args.do_eval:
with training_args.main_process_first(desc="loading and tokenization"):
files = [data_args.validation_file]
logger.info(f"Evaluation files: {' '.join(files)}")
eval_dataset = build_instruction_dataset(
data_path=files,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
data_cache_dir = None,
preprocessing_num_workers = data_args.preprocessing_num_workers)
logger.info(f"Num eval_samples {len(eval_dataset)}")
logger.info("Evaluation example:")
logger.info(tokenizer.decode(eval_dataset[0]['input_ids']))
torch_dtype = (
model_args.torch_dtype
if model_args.torch_dtype in ["auto", None]
else getattr(torch, model_args.torch_dtype)
)
compute_dtype = (torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
if training_args.load_in_kbits in [4, 8]:
if training_args.modules_to_save is not None:
load_in_8bit_skip_modules = training_args.modules_to_save.split(',')
else:
load_in_8bit_skip_modules = None
quantization_config = BitsAndBytesConfig(
load_in_4bit=training_args.load_in_kbits == 4,
load_in_8bit=training_args.load_in_kbits == 8,
llm_int8_threshold=6.0,
load_in_8bit_skip_modules=load_in_8bit_skip_modules,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=training_args.double_quant,
bnb_4bit_quant_type=training_args.quant_type # {'fp4', 'nf4'}
)
else:
quantization_config = None
if quantization_config is not None:
logger.info(f"quantization_config:{quantization_config.to_dict()}")
device_map = {"":int(os.environ.get("LOCAL_RANK") or 0)}
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path,
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
torch_dtype=torch_dtype,
low_cpu_mem_usage=model_args.low_cpu_mem_usage,
device_map=device_map,
quantization_config=quantization_config,
attn_implementation="flash_attention_2" if training_args.use_flash_attention_2 else "sdpa"
)
if training_args.load_in_kbits in [4, 8]:
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=training_args.gradient_checkpointing)
model.config.use_cache = False
model_vocab_size = model.get_input_embeddings().weight.shape[0]
logger.info(f"Model vocab size: {model_vocab_size}")
logger.info(f"len(tokenizer):{len(tokenizer)}")
if model_vocab_size != len(tokenizer):
logger.info(f"Resize model vocab size to {len(tokenizer)}")
model.resize_token_embeddings(len(tokenizer))
if not training_args.full_finetuning:
if training_args.peft_path is not None:
logger.info("Peft from pre-trained model")
model = PeftModel.from_pretrained(model, training_args.peft_path, device_map=device_map, is_trainable=True)
else:
logger.info("Init new peft model")
target_modules = training_args.trainable.split(',')
modules_to_save = training_args.modules_to_save
if modules_to_save is not None:
modules_to_save = modules_to_save.split(',')
lora_rank = training_args.lora_rank
lora_dropout = training_args.lora_dropout
lora_alpha = training_args.lora_alpha
logger.info(f"target_modules: {target_modules}")
logger.info(f"lora_rank: {lora_rank}")
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
target_modules=target_modules,
inference_mode=False,
r=lora_rank, lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
modules_to_save=modules_to_save)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=tokenizer,
data_collator=data_collator,
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model() # Saves the tokenizer too for easy upload
metrics = train_result.metrics
metrics["train_samples"] = len(train_dataset)
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
metrics["eval_samples"] =len(eval_dataset)
try:
perplexity = math.exp(metrics["eval_loss"])
except OverflowError:
perplexity = float("inf")
metrics["perplexity"] = perplexity
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
if __name__ == "__main__":
main()
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/run_pt.sh | Shell | #!/bin/bash
## 运行脚本前请仔细阅读wiki(https://github.com/ymcui/Chinese-LLaMA-Alpaca-3/wiki/pt_scripts_zh)
## Read the wiki(https://github.com/ymcui/Chinese-LLaMA-Alpaca-3/wiki/pt_scripts_en) carefully before running the script
lr=1e-4
lora_rank=64
lora_alpha=128
lora_trainable="q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj"
modules_to_save="embed_tokens,lm_head"
lora_dropout=0.05
pretrained_model=path/to/hf/meta-llama-3-8b/dir
tokenizer_name_or_path=${pretrained_model}
dataset_dir=path/to/pt/data/dir
data_cache=temp_data_cache_dir
per_device_train_batch_size=1
gradient_accumulation_steps=8
block_size=1024
output_dir=output_dir
torchrun --nnodes 1 --nproc_per_node 1 run_clm_pt_with_peft.py \
--model_name_or_path ${pretrained_model} \
--tokenizer_name_or_path ${tokenizer_name_or_path} \
--dataset_dir ${dataset_dir} \
--data_cache_dir ${data_cache} \
--validation_split_percentage 0.001 \
--per_device_train_batch_size ${per_device_train_batch_size} \
--do_train \
--low_cpu_mem_usage \
--seed $RANDOM \
--bf16 \
--num_train_epochs 1 \
--lr_scheduler_type cosine \
--learning_rate ${lr} \
--warmup_ratio 0.05 \
--weight_decay 0.01 \
--logging_strategy steps \
--logging_steps 10 \
--save_strategy steps \
--save_total_limit 3 \
--save_steps 200 \
--gradient_accumulation_steps ${gradient_accumulation_steps} \
--preprocessing_num_workers 8 \
--block_size ${block_size} \
--output_dir ${output_dir} \
--overwrite_output_dir \
--ddp_timeout 30000 \
--logging_first_step True \
--lora_rank ${lora_rank} \
--lora_alpha ${lora_alpha} \
--trainable ${lora_trainable} \
--lora_dropout ${lora_dropout} \
--modules_to_save ${modules_to_save} \
--torch_dtype bfloat16 \
--load_in_kbits 16 \
--ddp_find_unused_parameters False
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/run_sft.sh | Shell | #!/bin/bash
## 运行脚本前请仔细阅读wiki(https://github.com/ymcui/Chinese-LLaMA-Alpaca-3/wiki/sft_scripts_zh)
## Read the wiki(https://github.com/ymcui/Chinese-LLaMA-Alpaca-3/wiki/sft_scripts_en) carefully before running the script
lr=1e-4
lora_rank=64
lora_alpha=128
lora_trainable="q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj"
modules_to_save="embed_tokens,lm_head"
lora_dropout=0.05
pretrained_model=path/to/hf/meta-llama-3-8b/or/llama-3-chinese-8b/dir/or/model_id
tokenizer_name_or_path=${pretrained_model}
dataset_dir=path/to/sft/data/dir
per_device_train_batch_size=1
per_device_eval_batch_size=1
gradient_accumulation_steps=8
max_seq_length=512
output_dir=output_dir
validation_file=validation_file_name
torchrun --nnodes 1 --nproc_per_node 1 run_clm_sft_with_peft.py \
--model_name_or_path ${pretrained_model} \
--tokenizer_name_or_path ${tokenizer_name_or_path} \
--dataset_dir ${dataset_dir} \
--per_device_train_batch_size ${per_device_train_batch_size} \
--per_device_eval_batch_size ${per_device_eval_batch_size} \
--do_train \
--low_cpu_mem_usage \
--do_eval \
--seed $RANDOM \
--bf16 \
--num_train_epochs 3 \
--lr_scheduler_type cosine \
--learning_rate ${lr} \
--warmup_ratio 0.03 \
--logging_strategy steps \
--logging_steps 10 \
--save_strategy steps \
--save_total_limit 3 \
--evaluation_strategy steps \
--eval_steps 100 \
--save_steps 200 \
--gradient_accumulation_steps ${gradient_accumulation_steps} \
--preprocessing_num_workers 8 \
--max_seq_length ${max_seq_length} \
--output_dir ${output_dir} \
--overwrite_output_dir \
--ddp_timeout 30000 \
--logging_first_step True \
--lora_rank ${lora_rank} \
--lora_alpha ${lora_alpha} \
--trainable ${lora_trainable} \
--lora_dropout ${lora_dropout} \
--modules_to_save ${modules_to_save} \
--torch_dtype bfloat16 \
--validation_file ${validation_file} \
--load_in_kbits 16 \
--ddp_find_unused_parameters False
| ymcui/Chinese-LLaMA-Alpaca-3 | 1,964 | 中文羊驼大模型三期项目 (Chinese Llama-3 LLMs) developed from Meta Llama 3 | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/ceval/eval.py | Python | # This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import os
import argparse
import pandas as pd
import torch
import json
from mixtral_evaluator import Mixtral_Evaluator
import time
choices = ["A", "B", "C", "D"]
def main(args, evaluator,take):
assert os.path.exists("subject_mapping.json"), "subject_mapping.json not found!"
with open("subject_mapping.json") as f:
subject_mapping = json.load(f)
filenames = os.listdir("data/val")
subject_list = [val_file.replace("_val.csv","") for val_file in filenames]
accuracy, summary = {}, {}
run_date=time.strftime('%Y-%m-%d_%H-%M-%S',time.localtime(time.time()))
output_dir = args.output_dir
save_result_dir=os.path.join(output_dir,f"take{take}")
if not os.path.exists(save_result_dir):
os.makedirs(save_result_dir,exist_ok=True)
all_answers = {}
for index,subject_name in enumerate(subject_list):
print(f"{index/len(subject_list)} Inference starts at {run_date} on {args.model_path} with subject of {subject_name}!")
val_file_path=os.path.join('data/val',f'{subject_name}_val.csv')
dev_file_path=os.path.join('data/dev',f'{subject_name}_dev.csv')
test_file_path=os.path.join('data/test',f'{subject_name}_test.csv')
val_df=pd.read_csv(val_file_path) if args.do_test is False else pd.read_csv(test_file_path)
dev_df=pd.read_csv(dev_file_path) if args.few_shot else None
correct_ratio, answers = evaluator.eval_subject(subject_name, val_df, dev_df,
save_result_dir=save_result_dir if args.do_save_csv else None,
few_shot=args.few_shot,
cot=args.cot,
with_prompt=args.with_prompt,
constrained_decoding=args.constrained_decoding,
do_test=args.do_test)
print(f"Subject: {subject_name}")
print(f"Acc: {correct_ratio}")
accuracy[subject_name] = correct_ratio
summary[subject_name] = {"score":correct_ratio,
"num":len(val_df),
"correct":correct_ratio*len(val_df)/100}
all_answers[subject_name] = answers
json.dump(all_answers,open(save_result_dir+'/submission.json','w'),ensure_ascii=False,indent=4)
print("Accuracy:")
for k, v in accuracy.items():
print(k, ": ", v)
total_num = 0
total_correct = 0
summary['grouped'] = {
"STEM": {"correct": 0.0, "num": 0},
"Social Science": {"correct": 0.0, "num": 0},
"Humanities": {"correct": 0.0, "num": 0},
"Other": {"correct": 0.0, "num": 0}
}
for subj, info in subject_mapping.items():
group = info[2]
summary['grouped'][group]["num"] += summary[subj]['num']
summary['grouped'][group]["correct"] += summary[subj]['correct']
for group, info in summary['grouped'].items():
info['score'] = info["correct"] / info["num"]
total_num += info["num"]
total_correct += info["correct"]
summary['All'] = {"score": total_correct / total_num, "num": total_num, "correct": total_correct}
json.dump(summary,open(save_result_dir+'/summary.json','w'),ensure_ascii=False,indent=2)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model_path", type=str)
parser.add_argument("--cot",choices=["False","True"], default="False")
parser.add_argument("--few_shot", choices=["False","True"], default="True")
parser.add_argument("--ntrain", "-k", type=int, default=5)
parser.add_argument("--with_prompt", choices=["False","True"], default="False")
parser.add_argument("--constrained_decoding", choices=["False","True"], default="True")
parser.add_argument("--temperature",type=float,default=0.2)
parser.add_argument("--n_times", default=1,type=int)
parser.add_argument("--do_save_csv", choices=["False","True"], default="False")
parser.add_argument("--output_dir", type=str)
parser.add_argument("--do_test", choices=["False","True"], default="False")
parser.add_argument("--verbose", action="store_true", help="Print detailed information of each example.")
parser.add_argument("--load_in_4bit", action="store_true", help="The model was loaded by 4-bit quantization")
parser.add_argument("--use_flash_attention_2", action="store_true", help="Use flash_attention2 to replace the mixtral attention")
args = parser.parse_args()
args.cot = args.cot == "True"
args.few_shot = args.few_shot == "True"
args.with_prompt = args.with_prompt == "True"
args.constrained_decoding = args.constrained_decoding == "True"
args.do_test = args.do_test == "True"
args.do_save_csv = args.do_save_csv == "True"
if args.constrained_decoding is True:
args.n_times=max(args.n_times,1)
print(args)
device = torch.device(0)
print(device)
evaluator=Mixtral_Evaluator(
choices=choices,
k=args.ntrain,
model_path=args.model_path,
device=device,
temperature=args.temperature,
load_in_4bit=args.load_in_4bit,
use_flash_attention_2=args.use_flash_attention_2,
verbose=args.verbose
)
for i in range(args.n_times):
main(args,evaluator=evaluator,take=i)
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/ceval/evaluator.py | Python | # This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import string
class Evaluator:
def __init__(self, choices, model_name, k=-1):
self.choices = choices
self.model_name = model_name
self.k = k
self.puncs = list(string.punctuation)
def format_example(self, line, include_answer=True):
example = line['question']
for choice in self.choices:
example += f'\n{choice}. {line[f"{choice}"]}'
example += '\n答案：'
if include_answer:
example += f'{line["answer"]}\n\n'
return example
def generate_few_shot_prompt(self, subject, dev_df):
prompt = f"以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n"
k = self.k
if self.k == -1:
k = dev_df.shape[0]
for i in range(k):
prompt += self.format_example(dev_df.iloc[i, :])
return prompt
def eval_subject(self, subject_name, test_df, dev_df=None, few_shot=False, save_result_dir=None):
pass
def normalize_answer(self,s):
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude=set(self.puncs)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_punc(lower(s)))
def exact_match(self,pred, target):
return self.normalize_answer(pred)==self.normalize_answer(target)
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/ceval/mixtral_evaluator.py | Python | # This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import os
import re
from tqdm import tqdm
import random
import numpy as np
import torch
from transformers import AutoModelForCausalLM, LlamaTokenizer, BitsAndBytesConfig
from transformers import GenerationConfig
from evaluator import Evaluator
class Mixtral_Evaluator(Evaluator):
def __init__(self, choices, k, model_path, device, temperature=0.2, load_in_4bit=False, use_flash_attention_2=False, verbose=False):
super(Mixtral_Evaluator, self).__init__(choices, model_path, k)
load_type = torch.float16
self.model_path = model_path
self.device = device
self.verbose = verbose
self.load_in_4bit = load_in_4bit
self.use_flash_attention_2 = use_flash_attention_2
self.tokenizer = LlamaTokenizer.from_pretrained(model_path, legacy=True)
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
load_in_8bit=False,
bnb_4bit_compute_dtype=load_type,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4"
)
self.model = AutoModelForCausalLM.from_pretrained(
model_path,
quantization_config=quantization_config if self.load_in_4bit else None,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
attn_implementation="flash_attention_2" if self.use_flash_attention_2 else "sdpa"
)
self.generation_config = GenerationConfig(
temperature=temperature,
top_k=40,
top_p=0.9,
do_sample=True,
num_beams=1,
repetition_penalty=1.1,
max_new_tokens=20
)
self.sA_id = self.tokenizer.encode("A", add_special_tokens=False)[0]
self.sB_id = self.tokenizer.encode("B", add_special_tokens=False)[0]
self.sC_id = self.tokenizer.encode("C", add_special_tokens=False)[0]
self.sD_id = self.tokenizer.encode("D", add_special_tokens=False)[0]
self.A_id = self.tokenizer.encode("：A")[-1]
self.B_id = self.tokenizer.encode("：B")[-1]
self.C_id = self.tokenizer.encode("：C")[-1]
self.D_id = self.tokenizer.encode("：D")[-1]
def eval_subject(self, subject_name,
test_df,
dev_df=None,
few_shot=False,
cot=False,
save_result_dir=None,
with_prompt=False,
constrained_decoding=False,
do_test=False):
all_answers = {}
if constrained_decoding is True:
self.generation_config.output_scores = True
self.generation_config.return_dict_in_generate = True
self.generation_config.max_new_tokens = 1
self.generation_config.top_p = 1.0
self.generation_config.top_k = 0
correct_num = 0
if save_result_dir:
result = []
score = []
if few_shot:
if with_prompt:
history = self.generate_mixtral_inst_few_shot_prompt(subject_name, dev_df, cot=cot)
else:
history = self.generate_mixtral_few_shot_prompt(subject_name, dev_df, cot=cot)
else:
history = ''
answers = ['NA'] * len(test_df) if do_test is True else list(test_df['answer'])
for row_index, row in tqdm(test_df.iterrows(), total=len(test_df)):
question = self.format_example(row, include_answer=False, cot=cot,with_prompt=with_prompt)
instruction = question
if with_prompt:
prompt_template = (
"[INST] {instruction} [/INST]"
)
instruction = prompt_template.format_map({'instruction': instruction})
instruction = history + instruction
inputs = self.tokenizer(instruction, return_tensors="pt")
generation_output = self.model.generate(
input_ids = inputs["input_ids"].to(self.device),
attention_mask = inputs['attention_mask'].to(self.device),
eos_token_id=self.tokenizer.eos_token_id,
pad_token_id=self.tokenizer.eos_token_id,
generation_config = self.generation_config
)
_, length = inputs.input_ids.shape
if constrained_decoding is True:
logits = generation_output.scores[0][0]
logits = logits.float().cpu().detach()
choices1_logits = logits[[self.sA_id,self.sB_id,self.sC_id,self.sD_id]]
choices2_logits = logits[[self.A_id,self.B_id,self.C_id,self.D_id]]
choicesAll_logits = (choices1_logits + choices2_logits).numpy()
assert not (np.any(np.isinf(choicesAll_logits)) or np.any(np.isnan(choicesAll_logits)))
ans = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(choicesAll_logits)]
response = self.tokenizer.decode([logits.argmax(-1).item()])
else:
response = self.tokenizer.decode(generation_output[0, length:], skip_special_tokens=True)
ans, _ = self.extract_answer(row, response)
if ans == answers[row_index]:
correct_num += 1
correct = 1
else:
correct = 0
if self.verbose is True:
print(f"\n======={str(row_index)}=======")
print(f"question: {question}\n")
print(f"response: {response}\n")
print(f"extracted answer: {ans}")
print(f"ground truth: {answers[row_index]} \n")
if save_result_dir:
result.append(response)
score.append(correct)
all_answers[str(row_index)] = ans
correct_ratio = 100*correct_num/len(answers)
if save_result_dir:
test_df['model_output'] = result
test_df['correctness'] = score
test_df.to_csv(os.path.join(save_result_dir, f'{subject_name}_test.csv'))
return correct_ratio, all_answers
def format_example(self, line, include_answer=True, cot=False, with_prompt=False):
example = line['question']
for choice in self.choices:
example += f'\n{choice}. {line[f"{choice}"]}'
if include_answer:
if cot:
example += "\n答案：让我们一步一步思考，\n" + \
line["explanation"] + f"\n所以答案是{line['answer']}。\n\n"
else:
example += '\n答案：' + line["answer"] + '\n\n'
else:
if with_prompt is False:
if cot:
example += "\n答案：让我们一步一步思考，\n1."
else:
example += '\n答案：'
else:
if cot:
example += "\n答案是什么？让我们一步一步思考，\n1."
else:
example += '\n答案：'
return example
def generate_mixtral_few_shot_prompt(self, subject, dev_df, cot=False):
prompt = f"以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n"
k = self.k
if self.k == -1:
k = dev_df.shape[0]
for i in range(k):
prompt += self.format_example(
dev_df.iloc[i, :],
include_answer=True,
cot=cot
)
return prompt
def generate_mixtral_inst_few_shot_prompt(self, subject, dev_df, cot=False):
prompt = f"以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n"
prompt_template = (
"[INST] {instruction} [/INST]好的，我会结合{subject}相关知识回答"
)
prompt = prompt_template.format_map({'instruction':prompt, 'subject':subject})
k = self.k
if self.k == -1:
k = dev_df.shape[0]
for i in range(k):
line = dev_df.iloc[i, :]
q=line['question']
for choice in self.choices:
q += f'\n{choice}. {line[f"{choice}"]}'
a = line['answer']
prompt += "[INST] "+q+"\n答案： [/INST]"+a+"\n"
return prompt
def extract_answer(self, line, gen_ans):
m = re.findall(r'所以答案是(.+?)。', gen_ans, re.M)
if len(m) > 0 and m[-1] in self.choices:
return m[-1], True
answer_patterns = [
r'([ABCD])是正确的',
r'选项([ABCD])正确',
r'答案为([ABCD])',
r'答案是([ABCD])',
r'答案([ABCD])',
r'选择([ABCD])',
r'答案：([ABCD])',
r'选择答案([ABCD])'
]
# RE extraction
for answer_pattern in answer_patterns:
m = re.search(answer_pattern, gen_ans, re.M)
if m:
answer = m.group(1)
return answer, False
# only containing one choice-character
m = re.findall(r'[ABCD]', gen_ans, re.M)
if len(m) >= 1:
answer = m[0]
return answer, False
# only containing one choice-context
choices_dict = {}
pattern = ""
for c in self.choices:
choices_dict[str(line[f'{c}'])] = c
pattern += re.escape(str(line[f'{c}']))+"|"
pattern = pattern[:-1]
m = re.findall(pattern, gen_ans, re.M)
print("w/ escape:",repr(pattern),gen_ans,(len(m)>=1))
if len(m) >= 1:
answer = choices_dict[m[0]]
return answer, False
return random.sample('ABCD', 1)[0], False
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/cmmlu/categories.py | Python | # This code is modified from CMMLU Project: https://github.com/haonan-li/CMMLU
name_en2zh = {
"agronomy": "农学",
"anatomy": "解剖学",
"ancient_chinese": "古汉语",
"arts": "艺术学",
"astronomy": "天文学",
"business_ethics": "商业伦理",
"chinese_civil_service_exam": "中国公务员考试",
"chinese_driving_rule": "中国驾驶规则",
"chinese_food_culture": "中国饮食文化",
"chinese_foreign_policy": "中国外交政策",
"chinese_history":"中国历史",
"chinese_literature": "中国文学",
"chinese_teacher_qualification": "中国教师资格",
"clinical_knowledge": "临床知识",
"college_actuarial_science":"大学精算学",
"college_education":"大学教育学",
"college_engineering_hydrology": "大学工程水文学",
"college_law": "大学法律",
"college_mathematics": "大学数学",
"college_medical_statistics":"大学医学统计",
"college_medicine": "大学医学",
"computer_science": "计算机科学",
"computer_security": "计算机安全",
"conceptual_physics": "概念物理学",
"construction_project_management": "建设工程管理",
"economics": "经济学",
"education": "教育学",
"electrical_engineering": "电气工程",
"elementary_chinese":"小学语文",
"elementary_commonsense":"小学常识",
"elementary_information_and_technology": "小学信息技术",
"elementary_mathematics": "初等数学",
"ethnology": "民族学",
"food_science": "食品科学",
"genetics": "遗传学",
"global_facts": "全球事实",
"high_school_biology": "高中生物",
"high_school_chemistry": "高中化学",
"high_school_geography": "高中地理",
"high_school_mathematics": "高中数学",
"high_school_physics": "高中物理学",
"high_school_politics": "高中政治",
"human_sexuality": "人类性行为",
"international_law": "国际法学",
"journalism": "新闻学",
"jurisprudence": "法理学",
"legal_and_moral_basis": "法律与道德基础",
"logical": "逻辑学",
"machine_learning": "机器学习",
"management": "管理学",
"marketing": "市场营销",
"marxist_theory": "马克思主义理论",
"modern_chinese": "现代汉语",
"nutrition": "营养学",
"philosophy": "哲学",
"professional_accounting": "专业会计",
"professional_law": "专业法学",
"professional_medicine": "专业医学",
"professional_psychology": "专业心理学",
"public_relations": "公共关系",
"security_study":"安全研究",
"sociology": "社会学",
"sports_science": "体育学",
"traditional_chinese_medicine": "中医中药",
"virology": "病毒学",
"world_history":"世界历史",
"world_religions": "世界宗教",
}
subcategories = {
"agronomy": ['other'],
"anatomy": ['biology'],
"ancient_chinese": ['linguistics','china specific'],
"arts": ['arts'],
"astronomy": ['physics'],
"business_ethics": ['business'],
"chinese_civil_service_exam": ['politics','china specific'],
"chinese_driving_rule": ['other','china specific'],
"chinese_food_culture": ['culture','china specific'],
"chinese_foreign_policy": ['politics','china specific'],
"chinese_history":['history','china specific'],
"chinese_literature": ['literature','china specific'],
"chinese_teacher_qualification": ['education','china specific'],
"college_actuarial_science":['math'],
"college_education":['education'],
"college_engineering_hydrology": ['engineering'],
"college_law": ['law'],
"college_mathematics": ['math'],
"college_medical_statistics":['statistics'],
"clinical_knowledge": ['other'],
"college_medicine": ['other'],
"computer_science": ['computer science'],
"computer_security": ['other'],
"conceptual_physics": ['physics'],
"construction_project_management": ['other','china specific'],
"economics": ['economics'],
"education": ['education'],
"elementary_chinese":['linguistics','china specific'],
"elementary_commonsense":['other','china specific'],
"elementary_information_and_technology": ['other'],
"electrical_engineering": ['engineering'],
"elementary_mathematics": ['math'],
"ethnology": ['culture','china specific'],
"food_science": ['other'],
"genetics": ['biology'],
"global_facts": ['global'],
"high_school_biology": ['biology'],
"high_school_chemistry": ['chemistry'],
"high_school_geography": ['geography'],
"high_school_mathematics": ['math'],
"high_school_physics": ['physics'],
"high_school_politics": ['politics','china specific'],
"human_sexuality": ['other'],
"international_law": ['law'],
"journalism": ['sociology'],
"jurisprudence": ['law'],
"legal_and_moral_basis": ['other'],
"logical": ['philosophy'],
"machine_learning": ['computer science'],
"management": ['business'],
"marketing": ['business'],
"marxist_theory": ['philosophy'],
"modern_chinese": ['linguistics','china specific'],
"nutrition": ['other'],
"philosophy": ['philosophy'],
"professional_accounting": ['business'],
"professional_law": ['law'],
"professional_medicine": ['other'],
"professional_psychology": ['psychology'],
"public_relations": ['politics'],
"security_study": ['politics'],
"sociology": ['culture'],
"sports_science": ['other'],
"traditional_chinese_medicine": ['other','china specific'],
"virology": ['biology'],
"world_history":['history'],
"world_religions": ['global'],
}
categories = {
"STEM": ["physics", "chemistry", "biology", "computer science", "math", "engineering", "statistics"],
"Humanities": ["history", "philosophy", "law", "arts", "literature", "global"],
"Social Science": ['linguistics',"business", "politics", "culture", "economics", "geography", "psychology", "education", "sociology"],
"Other":["other"],
"China specific": ["china specific"],
}
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/cmmlu/eval.py | Python | # This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import os
import argparse
import pandas as pd
import torch
import json
from mxitral_evaluator import Mixtral_Evaluator
from glob import glob
import time
from collections import defaultdict
from categories import name_en2zh, subcategories, categories
choices = ["A", "B", "C", "D"]
category2subject = defaultdict(list)
for k,v in categories.items():
for subject, subcat in subcategories.items():
for c in subcat:
if c in v:
category2subject[k].append(subject)
category2subject_list = defaultdict(list)
for key,value in category2subject.items():
for val in value:
category2subject_list[val]=[val,name_en2zh[val],key]
category2subject=category2subject_list
choices = ["A", "B", "C", "D"]
def main(args, evaluator,take):
subject_mapping = category2subject #json.load(f)
filenames = [s.split('/')[-1] for s in glob(args.input_dir+"/test/*csv")]
subject_list = [val_file.replace(".csv","") for val_file in filenames]
accuracy, summary = {}, {}
run_date=time.strftime('%Y-%m-%d_%H-%M-%S',time.localtime(time.time()))
output_dir = args.output_dir
save_result_dir=os.path.join(output_dir,f"take{take}")
if not os.path.exists(save_result_dir):
os.makedirs(save_result_dir,exist_ok=True)
all_answers = {}
for index,subject_name in enumerate(subject_list):
print(f"{index/len(subject_list)} Inference starts at {run_date} on {args.model_path} with subject of {subject_name}!")
val_file_path=os.path.join(args.input_dir+'/test',f'{subject_name}.csv')
dev_file_path=os.path.join(args.input_dir+'/dev',f'{subject_name}.csv')
val_df=pd.read_csv(val_file_path)
dev_df=pd.read_csv(dev_file_path) if args.few_shot else None
correct_ratio, answers = evaluator.eval_subject(subject_name, val_df, dev_df,
save_result_dir=save_result_dir if args.do_save_csv else None,
few_shot=args.few_shot,
cot=args.cot,
with_prompt=args.with_prompt,
constrained_decoding=args.constrained_decoding,
do_test=False)
print(f"Subject: {subject_name}")
print(f"Acc: {correct_ratio}")
accuracy[subject_name] = correct_ratio
summary[subject_name] = {"score":correct_ratio,
"num":len(val_df),
"correct":correct_ratio*len(val_df)/100}
all_answers[subject_name] = answers
json.dump(all_answers,open(save_result_dir+'/submission.json','w'),ensure_ascii=False,indent=4)
print("\n\nModel:",args.model_path)
print("Accuracy:")
for k, v in accuracy.items():
print(k, ": ", v)
total_num = 0
total_correct = 0
summary['grouped'] = {
"China specific": {"correct": 0.0, "num": 0},
"STEM": {"correct": 0.0, "num": 0},
"Social Science": {"correct": 0.0, "num": 0},
"Humanities": {"correct": 0.0, "num": 0},
"Other": {"correct": 0.0, "num": 0}
}
for subj, info in subject_mapping.items():
group = info[2]
summary['grouped'][group]["num"] += summary[subj]['num']
summary['grouped'][group]["correct"] += summary[subj]['correct']
for group, info in summary['grouped'].items():
info['score'] = info["correct"] / info["num"]
total_num += info["num"]
total_correct += info["correct"]
summary['All'] = {"score": total_correct / total_num, "num": total_num, "correct": total_correct}
json.dump(summary,open(save_result_dir+'/summary.json','w'),ensure_ascii=False,indent=2)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--ntrain", "-k", type=int, default=5)
parser.add_argument("--model_path", type=str)
parser.add_argument("--cot",choices=["False","True"], default="False")
parser.add_argument("--few_shot", choices=["False","True"], default="True")
parser.add_argument("--with_prompt", choices=["False","True"], default="False")
parser.add_argument("--constrained_decoding", choices=["False","True"], default="False")
parser.add_argument("--temperature",type=float,default=0.2)
parser.add_argument("--n_times", default=1,type=int)
parser.add_argument("--do_save_csv", choices=["False","True"], default="False")
parser.add_argument("--output_dir", type=str)
parser.add_argument("--input_dir", type=str)
parser.add_argument("--verbose", action="store_true", help="Print detailed information of each example.")
parser.add_argument("--load_in_4bit", action="store_true", help="The model was loaded by 4-bit quantization")
parser.add_argument("--use_flash_attention_2", action="store_true", help="Use flash_attention2 to replace the mixtral attention")
args = parser.parse_args()
args.cot = args.cot == "True"
args.few_shot = args.few_shot == "True"
args.with_prompt = args.with_prompt == "True"
args.do_save_csv = args.do_save_csv == "True"
args.constrained_decoding = args.constrained_decoding == "True"
if args.constrained_decoding is True:
args.n_times=max(args.n_times,1)
print(args)
device = torch.device(0)
print(device)
evaluator=Mixtral_Evaluator(
choices=choices,
k=args.ntrain,
model_path=args.model_path,
device=device,
temperature=args.temperature,
load_in_4bit=args.load_in_4bit,
use_flash_attention_2=args.use_flash_attention_2,
verbose=args.verbose
)
for i in range(args.n_times):
main(args,evaluator=evaluator,take=i)
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/cmmlu/evaluator.py | Python | # This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import string
class Evaluator:
def __init__(self, choices, model_path, k=-1):
self.choices = choices
self.model_path = model_path
self.k = k
self.puncs = list(string.punctuation)
def format_example(self, line, include_answer=True):
example = line['question']
# print(example)
for choice in self.choices:
example += f'\n{choice}. {line[f"{choice}"]}'
example += '\n答案：'
if include_answer:
example += f'{line["answer"]}\n\n'
return example
def generate_few_shot_prompt(self, subject, dev_df):
prompt = f"以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n"
k = self.k
if self.k == -1:
k = dev_df.shape[0]
for i in range(k):
prompt += self.format_example(dev_df.iloc[i, :])
return prompt
def eval_subject(self, subject_name, test_df, dev_df=None, few_shot=False, save_result_dir=None):
pass
def normalize_answer(self,s):
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude=set(self.puncs)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_punc(lower(s)))
def exact_match(self,pred, target):
return self.normalize_answer(pred)==self.normalize_answer(target)
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/cmmlu/mixtral_evaluator.py | Python | # This code is modified from C-Eval Project: https://github.com/SJTU-LIT/ceval
import os
import re
from tqdm import tqdm
import random
import numpy as np
import torch
from transformers import AutoModelForCausalLM, LlamaTokenizer, BitsAndBytesConfig
from transformers import GenerationConfig
from evaluator import Evaluator
class Mixtral_Evaluator(Evaluator):
def __init__(self, choices, k, model_path, device, temperature=0.2, load_in_4bit=False, use_flash_attention_2=False, verbose=False):
super(Mixtral_Evaluator, self).__init__(choices, model_path, k)
load_type = torch.float16
self.model_path = model_path
self.device = device
self.verbose = verbose
self.load_in_4bit = load_in_4bit
self.use_flash_attention_2 = use_flash_attention_2
self.tokenizer = LlamaTokenizer.from_pretrained(model_path, legacy=True)
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
load_in_8bit=False,
bnb_4bit_compute_dtype=load_type,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4"
)
self.model = AutoModelForCausalLM.from_pretrained(
model_path,
quantization_config=quantization_config if self.load_in_4bit else None,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
attn_implementation="flash_attention_2" if self.use_flash_attention_2 else "sdpa"
)
self.generation_config = GenerationConfig(
temperature=temperature,
top_k=40,
top_p=0.9,
do_sample=True,
num_beams=1,
repetition_penalty=1.1,
max_new_tokens=20
)
self.sA_id = self.tokenizer.encode("A", add_special_tokens=False)[0]
self.sB_id = self.tokenizer.encode("B", add_special_tokens=False)[0]
self.sC_id = self.tokenizer.encode("C", add_special_tokens=False)[0]
self.sD_id = self.tokenizer.encode("D", add_special_tokens=False)[0]
self.A_id = self.tokenizer.encode("：A")[-1]
self.B_id = self.tokenizer.encode("：B")[-1]
self.C_id = self.tokenizer.encode("：C")[-1]
self.D_id = self.tokenizer.encode("：D")[-1]
def eval_subject(self, subject_name,
test_df,
dev_df=None,
few_shot=False,
cot=False,
save_result_dir=None,
with_prompt=False,
constrained_decoding=False,
do_test=False):
all_answers = {}
if constrained_decoding is True:
self.generation_config.output_scores = True
self.generation_config.return_dict_in_generate = True
self.generation_config.max_new_tokens = 1
self.generation_config.top_p = 1.0
self.generation_config.top_k = 0
correct_num = 0
if save_result_dir:
result = []
score = []
if few_shot:
if with_prompt:
history = self.generate_few_shot_prompt(subject_name, dev_df, cot=cot)
else:
history = self.generate_few_shot_noprompt(subject_name, dev_df, cot=cot)
else:
history = ''
answers = ['NA'] * len(test_df) if do_test is True else list(test_df['Answer'])
for row_index, row in tqdm(test_df.iterrows(), total=len(test_df)):
question = self.format_example(row, include_answer=False, cot=cot,with_prompt=with_prompt)
instruction = question
if with_prompt:
prompt_template = (
"[INST] {instruction} [/INST]"
)
instruction = prompt_template.format_map({'instruction': instruction})
instruction=history+instruction
inputs = self.tokenizer(instruction, return_tensors="pt")
generation_output = self.model.generate(
input_ids = inputs["input_ids"].to(self.device),
attention_mask = inputs['attention_mask'].to(self.device),
eos_token_id=self.tokenizer.eos_token_id,
pad_token_id=self.tokenizer.eos_token_id,
generation_config = self.generation_config
)
_, length = inputs.input_ids.shape
if constrained_decoding is True:
logits = generation_output.scores[0][0]
logits = logits.float().cpu().detach()
choices1_logits = logits[[self.sA_id,self.sB_id,self.sC_id,self.sD_id]]
choices2_logits = logits[[self.A_id,self.B_id,self.C_id,self.D_id]]
choicesAll_logits = (choices1_logits + choices2_logits).numpy()
assert not (np.any(np.isinf(choicesAll_logits)) or np.any(np.isnan(choicesAll_logits)))
ans = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(choicesAll_logits)]
response = self.tokenizer.decode([logits.argmax(-1).item()])
else:
response = self.tokenizer.decode(generation_output[0, length:], skip_special_tokens=True)
ans, _ = self.extract_answer(row, response)
if ans == answers[row_index]:
correct_num += 1
correct = 1
else:
correct = 0
if self.verbose is True:
print(f"\n======={str(row_index)}=======")
print(f"question: {question}\n")
print(f"response: {response}\n")
print(f"extracted answer: {ans}")
print(f"ground truth: {answers[row_index]} \n")
if save_result_dir:
result.append(response)
score.append(correct)
all_answers[str(row_index)] = ans
correct_ratio = 100*correct_num/len(answers)
if save_result_dir:
test_df['model_output'] = result
test_df['correctness'] = score
test_df.to_csv(os.path.join(save_result_dir, f'{subject_name}_test.csv'))
return correct_ratio, all_answers
def format_example(self, line, include_answer=True, cot=False, with_prompt=False):
example = line['Question']
suffix = ""
for choice in self.choices:
example += f'\n{choice}. {line[f"{choice}"]}'
if include_answer:
if cot:
example += "\n答案：让我们一步一步思考，\n" + \
line["explanation"] + f"\n所以答案是{line['Answer']}。\n\n"
else:
example += '\n答案：' + suffix + line["Answer"] + '\n\n'
else:
if with_prompt is False:
if cot:
example += "\n答案：让我们一步一步思考，\n1."
else:
example += '\n答案：' + suffix
else:
if cot:
example += "\n答案是什么？让我们一步一步思考，\n1."
else:
example += '\n答案：'
return example
def generate_few_shot_noprompt(self, subject, dev_df, cot=False):
prompt = f"以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n"
k = self.k
if self.k == -1:
k = dev_df.shape[0]
for i in range(k):
prompt += self.format_example(
dev_df.iloc[i, :],
include_answer=True,
cot=cot
)
return prompt
def generate_few_shot_prompt(self, subject, dev_df, cot=False):
prompt = f"以下是中国关于{subject}考试的单项选择题，请选出其中的正确答案。\n\n"
prompt_template = (
"[INST] {instruction} [/INST]好的，我会结合{subject}相关知识回答"
)
prompt = prompt_template.format_map({'instruction':prompt, "subject":subject})
k = self.k
if self.k == -1:
k = dev_df.shape[0]
for i in range(k):
line=dev_df.iloc[i, :]
q=line['Question']
for choice in self.choices:
q += f'\n{choice}. {line[f"{choice}"]}'
a=line['Answer']
prompt+="[INST] "+q+"\n答案： [/INST]"+a+"\n"
return prompt
def extract_answer(self, line, gen_ans):
m = re.findall(r'所以答案是(.+?)。', gen_ans, re.M)
if len(m) > 0 and m[-1] in self.choices:
return m[-1], True
answer_patterns = [
r'([ABCD])是正确的',
r'选项([ABCD])正确',
r'答案为([ABCD])',
r'答案是([ABCD])',
r'答案([ABCD])',
r'选择([ABCD])',
r'答案：([ABCD])',
r'选择答案([ABCD])'
]
# RE extraction
for answer_pattern in answer_patterns:
m = re.search(answer_pattern, gen_ans, re.M)
if m:
answer = m.group(1)
return answer, False
# only containing one choice-character
m = re.findall(r'[ABCD]', gen_ans, re.M)
if len(m) >= 1:
answer = m[0]
return answer, False
choices_dict = {}
pattern = ""
for c in self.choices:
choices_dict[str(line[f'{c}'])] = c
pattern += re.escape(str(line[f'{c}']))+"|"
pattern = pattern[:-1]
m = re.findall(pattern, gen_ans, re.M)
print("w/ escape:",repr(pattern),gen_ans,(len(m)>=1))
if len(m) >= 1:
answer = choices_dict[m[0]]
return answer, False
return random.sample('ABCD', 1)[0], False
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/inference/inference_hf.py | Python | import argparse
import json, os
TEMPLATE = (
"[INST] {instruction} [/INST]"
)
parser = argparse.ArgumentParser()
parser.add_argument('--base_model', default=None, type=str, required=True)
parser.add_argument('--tokenizer_path', default=None, type=str)
parser.add_argument('--data_file', default=None, type=str, help="A file that contains instructions (one instruction per line)")
parser.add_argument('--with_prompt', action='store_true', help="wrap the input with the prompt automatically")
parser.add_argument('--interactive', action='store_true', help="run in the instruction mode (single-turn)")
parser.add_argument('--predictions_file', default='./predictions.json', type=str)
parser.add_argument('--gpus', default="0", type=str)
parser.add_argument('--only_cpu', action='store_true', help='only use CPU for inference')
parser.add_argument('--load_in_8bit', action='store_true', help="Load the LLM in the 8bit mode")
parser.add_argument('--load_in_4bit', action='store_true', help="Load the LLM in the 4bit mode")
parser.add_argument("--use_vllm", action='store_true', help="Use vLLM as back-end LLM service.")
parser.add_argument('--use_flash_attention_2', action='store_true', help="Use flash attention to replace the Mixtral attention")
args = parser.parse_args()
if args.use_vllm:
if args.load_in_8bit or args.load_in_4bit:
raise ValueError("vLLM currently does not support quantization, please use fp16 (default) or unuse --use_vllm.")
if args.only_cpu:
raise ValueError("vLLM requires GPUs with compute capability not less than 7.0. If you want to run only on CPU, please unuse --use_vllm.")
if args.load_in_8bit and args.load_in_4bit:
raise ValueError("Only one quantization method can be chosen for inference. Please check your arguments")
if args.only_cpu is True:
args.gpus = ""
if args.load_in_8bit or args.load_in_4bit:
raise ValueError("Quantization is unavailable on CPU.")
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
import torch
from transformers import AutoModelForCausalLM, LlamaTokenizer
from transformers import GenerationConfig
from transformers import BitsAndBytesConfig
if args.use_vllm:
from vllm import LLM, SamplingParams
import sys
parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(parent_dir)
if args.use_vllm:
generation_config = dict(
temperature=0.2,
top_k=40,
top_p=0.9,
max_tokens=400,
presence_penalty=1.0,
)
else:
generation_config = GenerationConfig(
temperature=0.2,
top_k=40,
top_p=0.9,
do_sample=True,
num_beams=1,
repetition_penalty=1.1,
max_new_tokens=400
)
sample_data = ["为什么要减少污染，保护环境？"]
def generate_prompt(instruction):
return TEMPLATE.format_map({'instruction': instruction})
if __name__ == '__main__':
load_type = torch.float16
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
if args.tokenizer_path is None:
args.tokenizer_path = args.base_model
if args.use_vllm:
model = LLM(model=args.base_model,
tokenizer=args.tokenizer_path,
tokenizer_mode='slow',
tensor_parallel_size=len(args.gpus.split(','))
)
tokenizer = LlamaTokenizer.from_pretrained(args.tokenizer_path, legacy=True)
else:
tokenizer = LlamaTokenizer.from_pretrained(args.tokenizer_path, legacy=True)
if args.load_in_4bit or args.load_in_8bit:
quantization_config = BitsAndBytesConfig(
load_in_4bit=args.load_in_4bit,
load_in_8bit=args.load_in_8bit,
bnb_4bit_compute_dtype=load_type,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4"
)
model = AutoModelForCausalLM.from_pretrained(
args.base_model,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
load_in_4bit=args.load_in_4bit,
load_in_8bit=args.load_in_8bit,
quantization_config=quantization_config if (args.load_in_4bit or args.load_in_8bit) else None,
attn_implementation="flash_attention_2" if args.use_flash_attention_2 else "sdpa"
)
if device==torch.device('cpu'):
model.float()
model.eval()
# test data
if args.data_file is None:
examples = sample_data
else:
with open(args.data_file,'r') as f:
examples = [line.strip() for line in f.readlines()]
print("first 10 examples:")
for example in examples[:10]:
print(example)
with torch.no_grad():
if args.interactive:
print("Start inference with instruction mode.")
print('='*85)
print("+ 该模式下仅支持单轮问答，无多轮对话能力。\n"
"+ 如要进行多轮对话，请使用llama.cpp")
print('-'*85)
print("+ This mode only supports single-turn QA.\n"
"+ If you want to experience multi-turn dialogue, please use llama.cpp")
print('='*85)
while True:
raw_input_text = input("Input:")
if len(raw_input_text.strip())==0:
break
if args.with_prompt:
input_text = generate_prompt(instruction=raw_input_text)
else:
input_text = raw_input_text
if args.use_vllm:
output = model.generate([input_text], SamplingParams(**generation_config), use_tqdm=False)
response = output[0].outputs[0].text
else:
inputs = tokenizer(input_text,return_tensors="pt") #add_special_tokens=False ?
generation_output = model.generate(
input_ids = inputs["input_ids"].to(device),
attention_mask = inputs['attention_mask'].to(device),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
generation_config = generation_config
)
s = generation_output[0]
output = tokenizer.decode(s,skip_special_tokens=True)
if args.with_prompt:
response = output.split("[/INST]")[-1].strip()
else:
response = output
print("Response: ",response)
print("\n")
else:
print("Start inference.")
results = []
if args.use_vllm:
if args.with_prompt is True:
inputs = [generate_prompt(example) for example in examples]
else:
inputs = examples
outputs = model.generate(inputs, SamplingParams(**generation_config))
for index, (example, output) in enumerate(zip(examples, outputs)):
response = output.outputs[0].text
print(f"======={index}=======")
print(f"Input: {example}\n")
print(f"Output: {response}\n")
results.append({"Input":example,"Output":response})
else:
for index, example in enumerate(examples):
if args.with_prompt:
input_text = generate_prompt(instruction=example)
else:
input_text = example
inputs = tokenizer(input_text,return_tensors="pt") #add_special_tokens=False ?
generation_output = model.generate(
input_ids = inputs["input_ids"].to(device),
attention_mask = inputs['attention_mask'].to(device),
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
generation_config = generation_config
)
s = generation_output[0]
output = tokenizer.decode(s,skip_special_tokens=True)
if args.with_prompt:
response = output.split("[/INST]")[1].strip()
else:
response = output
print(f"======={index}=======")
print(f"Input: {example}\n")
print(f"Output: {response}\n")
results.append({"Input":input_text,"Output":response})
dirname = os.path.dirname(args.predictions_file)
os.makedirs(dirname,exist_ok=True)
with open(args.predictions_file,'w') as f:
json.dump(results,f,ensure_ascii=False,indent=2)
if args.use_vllm:
with open(dirname+'/generation_config.json','w') as f:
json.dump(generation_config,f,ensure_ascii=False,indent=2)
else:
generation_config.save_pretrained('./')
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/llamacpp/chat.sh | Shell | #!/bin/bash
# script to chat with Chinese-Mixtral-Instruct model
# usage: ./chat.sh chinese-mixtral-instruct-gguf-model-path
# WARNING: the hyperparameters are not optimal, please tune them yourself
./main -m $1 --color --interactive-first \
-c 4096 -t 6 --temp 0.2 --repeat_penalty 1.1 -ngl 999 \
--in-prefix ' [INST] ' --in-suffix ' [/INST]' | ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/longbench/eval.py | Python | # The script is from https://github.com/THUDM/LongBench
import os
import json
import argparse
import numpy as np
from metrics import (
qa_f1_score,
rouge_zh_score,
qa_f1_zh_score,
rouge_score,
classification_score,
retrieval_score,
retrieval_zh_score,
count_score,
code_sim_score,
)
dataset2metric = {
"narrativeqa": qa_f1_score,
"qasper": qa_f1_score,
"multifieldqa_en": qa_f1_score,
"multifieldqa_zh": qa_f1_zh_score,
"hotpotqa": qa_f1_score,
"2wikimqa": qa_f1_score,
"musique": qa_f1_score,
"dureader": rouge_zh_score,
"gov_report": rouge_score,
"qmsum": rouge_score,
"multi_news": rouge_score,
"vcsum": rouge_zh_score,
"trec": classification_score,
"triviaqa": qa_f1_score,
"samsum": rouge_score,
"lsht": classification_score,
"passage_retrieval_en": retrieval_score,
"passage_count": count_score,
"passage_retrieval_zh": retrieval_zh_score,
"lcc": code_sim_score,
"repobench-p": code_sim_score,
}
def parse_args(args=None):
parser = argparse.ArgumentParser()
parser.add_argument('--output_dir')
parser.add_argument('--e', action='store_true', help="Evaluate on LongBench-E")
return parser.parse_args(args)
def scorer_e(dataset, predictions, answers, lengths, all_classes):
scores = {"0-4k": [], "4-8k": [], "8k+": []}
for (prediction, ground_truths, length) in zip(predictions, answers, lengths):
score = 0.
if dataset in ["trec", "triviaqa", "samsum", "lsht"]:
prediction = prediction.lstrip('\n').split('\n')[0]
for ground_truth in ground_truths:
score = max(score, dataset2metric[dataset](prediction, ground_truth, all_classes=all_classes))
if length < 4000:
scores["0-4k"].append(score)
elif length < 8000:
scores["4-8k"].append(score)
else:
scores["8k+"].append(score)
for key in scores.keys():
scores[key] = round(100 * np.mean(scores[key]), 2)
return scores
def scorer(dataset, predictions, answers, all_classes):
total_score = 0.
for (prediction, ground_truths) in zip(predictions, answers):
score = 0.
if dataset in ["trec", "triviaqa", "samsum", "lsht"]:
prediction = prediction.lstrip('\n').split('\n')[0]
for ground_truth in ground_truths:
score = max(score, dataset2metric[dataset](prediction, ground_truth, all_classes=all_classes))
total_score += score
return round(100 * total_score / len(predictions), 2)
if __name__ == '__main__':
args = parse_args()
scores = dict()
if args.e:
path = f"{args.output_dir}/pred_e/"
else:
path = f"{args.output_dir}/pred/"
all_files = os.listdir(path)
print("Evaluating on:", all_files)
for filename in all_files:
if not filename.endswith("jsonl"):
continue
predictions, answers, lengths = [], [], []
dataset = filename.split('.')[0]
with open(f"{path}{filename}", "r", encoding="utf-8") as f:
print(filename)
for line in f:
data = json.loads(line)
predictions.append(data["pred"])
answers.append(data["answers"])
all_classes = data["all_classes"]
if "length" in data:
lengths.append(data["length"])
if args.e:
score = scorer_e(dataset, predictions, answers, lengths, all_classes)
else:
score = scorer(dataset, predictions, answers, all_classes)
scores[dataset] = score
if args.e:
out_path = f"{args.output_dir}/pred_e/result.json"
else:
out_path = f"{args.output_dir}/pred/result.json"
with open(out_path, "w") as f:
json.dump(scores, f, ensure_ascii=False, indent=4)
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/longbench/metrics.py | Python | # The script is from https://github.com/THUDM/LongBench
import re
import string
import jieba
from fuzzywuzzy import fuzz
import difflib
from collections import Counter
from rouge import Rouge
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
return re.sub(r"\b(a|an|the)\b", " ", text)
def white_space_fix(text):
return " ".join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return "".join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def normalize_zh_answer(s):
"""Lower text and remove punctuation, extra whitespace."""
def white_space_fix(text):
return "".join(text.split())
def remove_punc(text):
cn_punctuation = "！？｡。＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏."
all_punctuation = set(string.punctuation + cn_punctuation)
return "".join(ch for ch in text if ch not in all_punctuation)
def lower(text):
return text.lower()
return white_space_fix(remove_punc(lower(s)))
def count_score(prediction, ground_truth, **kwargs):
numbers = re.findall(r"\d+", prediction)
right_num = 0
for number in numbers:
if str(number) == str(ground_truth):
right_num += 1
final_score = 0.0 if len(numbers) == 0 else right_num / len(numbers)
return float(final_score)
def retrieval_score(prediction, ground_truth, **kwargs):
pattern = r'Paragraph (\d+)'
matches = re.findall(pattern, ground_truth)
ground_truth_id = matches[0]
numbers = re.findall(r"\d+", prediction)
right_num = 0
for number in numbers:
if str(number) == str(ground_truth_id):
right_num += 1
final_score = 0.0 if len(numbers) == 0 else right_num / len(numbers)
return float(final_score)
def retrieval_zh_score(prediction, ground_truth, **kwargs):
pattern = r'段落(\d+)'
matches = re.findall(pattern, ground_truth)
ground_truth_id = matches[0]
numbers = re.findall(r"\d+", prediction)
right_num = 0
for number in numbers:
if str(number) == str(ground_truth_id):
right_num += 1
final_score = 0.0 if len(numbers) == 0 else right_num / len(numbers)
return float(final_score)
def code_sim_score(prediction, ground_truth, **kwargs):
all_lines = prediction.lstrip('\n').split('\n')
prediction = ""
for line in all_lines:
if ('`' not in line) and ('#' not in line) and ('//' not in line):
prediction = line
break
return (fuzz.ratio(prediction, ground_truth) / 100)
def classification_score(prediction, ground_truth, **kwargs):
em_match_list = []
all_classes = kwargs["all_classes"]
for class_name in all_classes:
if class_name in prediction:
em_match_list.append(class_name)
for match_term in em_match_list:
if match_term in ground_truth and match_term != ground_truth:
em_match_list.remove(match_term)
if em_match_list != 0:
if ground_truth in em_match_list:
score = (1.0 / len(em_match_list))
else:
score = 0.0
else:
best_match = None
highest_similarity = 0
for string in all_classes:
similarity = difflib.SequenceMatcher(None, string, prediction).ratio()
if similarity > highest_similarity:
highest_similarity = similarity
best_match = string
score = float(best_match == ground_truth)
return score
def rouge_score(prediction, ground_truth, **kwargs):
rouge = Rouge()
try:
scores = rouge.get_scores([prediction], [ground_truth], avg=True)
except Exception:
return 0.0
return scores["rouge-l"]["f"]
def rouge_zh_score(prediction, ground_truth, **kwargs):
prediction = " ".join(list(jieba.cut(prediction, cut_all=False)))
ground_truth = " ".join(list(jieba.cut(ground_truth, cut_all=False)))
score = rouge_score(prediction, ground_truth)
return score
def f1_score(prediction, ground_truth, **kwargs):
common = Counter(prediction) & Counter(ground_truth)
num_same = sum(common.values())
if num_same == 0:
return 0
precision = 1.0 * num_same / len(prediction)
recall = 1.0 * num_same / len(ground_truth)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def qa_f1_score(prediction, ground_truth, **kwargs):
normalized_prediction = normalize_answer(prediction)
normalized_ground_truth = normalize_answer(ground_truth)
prediction_tokens = normalized_prediction.split()
ground_truth_tokens = normalized_ground_truth.split()
return f1_score(prediction_tokens, ground_truth_tokens)
def qa_f1_zh_score(prediction, ground_truth, **kwargs):
prediction_tokens = list(jieba.cut(prediction, cut_all=False))
ground_truth_tokens = list(jieba.cut(ground_truth, cut_all=False))
prediction_tokens = [normalize_zh_answer(token) for token in prediction_tokens]
ground_truth_tokens = [normalize_zh_answer(token) for token in ground_truth_tokens]
prediction_tokens = [token for token in prediction_tokens if len(token) > 0]
ground_truth_tokens = [token for token in ground_truth_tokens if len(token) > 0]
return f1_score(prediction_tokens, ground_truth_tokens)
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/longbench/pred_mixtral.py | Python | # The script is modified from https://github.com/THUDM/LongBench/blob/main/pred.py
from datasets import load_dataset
import torch
import random
import numpy as np
import json
from transformers import LlamaTokenizer, AutoModelForCausalLM
from transformers import BitsAndBytesConfig
from tqdm import tqdm
import os
import argparse
import sys
parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(parent_dir)
dir_path = os.path.dirname(os.path.realpath(__file__))
parser = argparse.ArgumentParser()
parser.add_argument('--model_path', type=str)
parser.add_argument('--load_in_4bit',action='store_true')
parser.add_argument('--load_in_8bit',action='store_true')
parser.add_argument('--predict_on',type=str, default='zh')
parser.add_argument('--output_dir',type=str, default='pred')
parser.add_argument('--gpus',type=str, default=None)
parser.add_argument('--max_length',type=int, default=4096-512)
parser.add_argument('--e', action='store_true', help="Evaluate on LongBench-E")
parser.add_argument('--use_flash_attention_2', action='store_true', help="Use flash attention to replace the mixtral attention")
args = parser.parse_args()
model_path = args.model_path
load_in_4bit = args.load_in_4bit
load_in_8bit = args.load_in_8bit
predict_on = args.predict_on
output_dir = args.output_dir
gpus=args.gpus
max_length = args.max_length
DO_SAMPLE =True
TEMPERATURE = 0.2
REPETITION_PENALTY = 1.1
TOP_P = 0.95
TOP_K = 40
if gpus is not None:
os.environ["CUDA_VISIBLE_DEVICES"] = gpus
def get_pred(model, tokenizer, data, max_length, max_gen, prompt_format, dataset, device):
preds = []
for json_obj in tqdm(data):
prompt = prompt_format.format(**json_obj)
# truncate to fit max_length (we suggest truncate in the middle, since the left and right side may contain crucial instructions)
tokenized_prompt = tokenizer(prompt, truncation=False, return_tensors="pt").input_ids[0]
if len(tokenized_prompt) > max_length:
half = int(max_length/2)
prompt = tokenizer.decode(tokenized_prompt[:half], skip_special_tokens=True)+tokenizer.decode(tokenized_prompt[-half:], skip_special_tokens=True)
input_data = tokenizer(prompt, truncation=False, return_tensors="pt").to(device)
context_length = input_data.input_ids.shape[-1]
if dataset == "samsum": # prevent illegal output on samsum (model endlessly repeat "\nDialogue"), might be a prompting issue
output = model.generate(
**input_data,
max_new_tokens=max_gen,
num_beams=1,
do_sample=DO_SAMPLE,
repetition_penalty = REPETITION_PENALTY,
top_p = TOP_P,
top_k = TOP_K,
temperature=TEMPERATURE,
min_length=context_length+1,
eos_token_id=[tokenizer.eos_token_id, tokenizer.encode("\n", add_special_tokens=False)[-1]],
pad_token_id=tokenizer.eos_token_id
)[0]
else:
output = model.generate(
**input_data,
max_new_tokens=max_gen,
num_beams=1,
do_sample=DO_SAMPLE,
repetition_penalty = REPETITION_PENALTY,
top_p = TOP_P,
top_k = TOP_K,
temperature=TEMPERATURE,
pad_token_id=tokenizer.eos_token_id
)[0]
pred = tokenizer.decode(output[context_length:], skip_special_tokens=True)
#print(pred)
preds.append({"pred": pred, "answers": json_obj["answers"], "all_classes": json_obj["all_classes"], "length": json_obj["length"]})
return preds
def seed_everything(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
torch.cuda.manual_seed_all(seed)
if __name__ == '__main__':
seed_everything(42)
load_type = torch.float16
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device('cpu')
if args.e:
en_datasets = [ "hotpotqa","2wikimqa",
"qasper", "multifieldqa_en", "gov_report",
"trec", "samsum", "triviaqa",
"passage_count", "passage_retrieval_en", "multi_news"]
zh_datasets = []
code_datasets = [ "lcc", "repobench-p" ]
if not os.path.exists(f"{output_dir}/pred_e"):
os.makedirs(f"{output_dir}/pred_e")
else:
en_datasets = [ "hotpotqa","2wikimqa", "musique", "narrativeqa",
"qasper", "multifieldqa_en", "gov_report",
"qmsum", "trec", "samsum", "triviaqa",
"passage_count", "passage_retrieval_en", "multi_news"]
zh_datasets = [ "dureader", "multifieldqa_zh",
"vcsum","lsht", "passage_retrieval_zh"]
code_datasets = [ "lcc", "repobench-p" ]
if not os.path.exists(f"{output_dir}/pred"):
os.makedirs(f"{output_dir}/pred")
datasets = []
for data_type in predict_on.split(','):
if data_type == 'zh':
datasets += zh_datasets
elif data_type == 'en':
datasets += en_datasets
elif data_type == 'code':
datasets += code_datasets
print(datasets)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
tokenizer = LlamaTokenizer.from_pretrained(model_path, legacy=True)
model = None
if args.load_in_4bit or args.load_in_8bit:
quantization_config = BitsAndBytesConfig(
load_in_4bit=args.load_in_4bit,
load_in_8bit=args.load_in_8bit,
bnb_4bit_compute_dtype=load_type,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4"
)
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto',
quantization_config=quantization_config if (args.load_in_4bit or args.load_in_8bit) else None,
attn_implementation="flash_attention_2" if args.use_flash_attention_2 else "sdpa"
)
model = model.eval()
model_vocab_size = model.get_input_embeddings().weight.size(0)
print(f"Vocab of the base model: {model_vocab_size}")
tokenizer_vocab_size = len(tokenizer)
print(f"Vocab of the tokenizer: {tokenizer_vocab_size}")
# we design specific prompt format and max generation length for each task, feel free to modify them to optimize model output
dataset2prompt = json.load(open(dir_path + "/config/dataset2prompt.json", "r"))
dataset2maxlen = json.load(open(dir_path + "/config/dataset2maxlen.json", "r"))
# predict on each dataset
for dataset in datasets:
print(f"Loading dataset {dataset}")
if args.e:
data = load_dataset('THUDM/LongBench', dataset+'_e', split='test')
output_path = f"{output_dir}/pred_e/{dataset}.jsonl"
else:
data = load_dataset('THUDM/LongBench', dataset, split='test')
output_path = f"{output_dir}/pred/{dataset}.jsonl"
prompt_format = dataset2prompt[dataset]
max_gen = dataset2maxlen[dataset]
preds = get_pred(model, tokenizer, data, max_length, max_gen, prompt_format, dataset, device)
with open(output_path, "w", encoding="utf-8") as f:
for pred in preds:
json.dump(pred, f, ensure_ascii=False)
f.write('\n')
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/merge_mixtral_with_chinese_lora_low_mem.py | Python | """
Usage:
python merge_mixtral_with_chinese_lora_low_mem.py \
--base_model path/to/Mixtral-8x7B-v0.1 \
--lora_model path/to/chinese-Mixtral-8x7B-v0.1-lora \
--output_dir path/to/output-dir
"""
import argparse
import json
import os
import gc
import torch
import peft
from transformers import LlamaTokenizer
from transformers.modeling_utils import dtype_byte_size
from huggingface_hub import snapshot_download
import re
import safetensors
from safetensors.torch import load_file as safe_load_file
parser = argparse.ArgumentParser(description='Script to merge Mixtral-8x7B-v0.1 with Chinese-Mixtral-LoRA weights')
parser.add_argument('--base_model', default=None, required=True,
type=str, help="Base model path (basically Mixtral-8x7B-v0.1)")
parser.add_argument('--lora_model', default=None, required=True,
type=str, help="LoRA model path (Chinese-Mixtral-LoRA, Chinese-Mixtral-Instruct-LoRA)")
parser.add_argument('--output_dir', default='./merged_model',
type=str, help="Output path for the merged model")
parser.add_argument('--verbose', default=False, action='store_true',
help="Show detailed debugging messages")
WEIGHTS_NAME = "adapter_model.bin"
SAFETENSORS_WEIGHTS_NAME = "adapter_model.safetensors"
def transpose(weight, fan_in_fan_out):
return weight.T if fan_in_fan_out else weight
def jsonload(filename):
with open(filename, "r") as file:
d = json.load(file)
return d
if __name__=='__main__':
args = parser.parse_args()
base_model_path = args.base_model
lora_model_path = args.lora_model
output_dir = args.output_dir
os.makedirs(output_dir, exist_ok=True)
print(f"="*80)
print(f"Base model: {base_model_path}")
print(f"LoRA model: {lora_model_path}")
tokenizers_and_loras = []
print(f"Loading {lora_model_path}")
if not os.path.exists(lora_model_path):
print("Cannot find lora model on the disk. Downloading lora model from hub...")
lora_model_path = snapshot_download(repo_id=lora_model_path)
tokenizer = LlamaTokenizer.from_pretrained(lora_model_path, legacy=True)
lora_config = peft.LoraConfig.from_pretrained(lora_model_path)
if os.path.exists(os.path.join(lora_model_path, SAFETENSORS_WEIGHTS_NAME)):
lora_filename = os.path.join(lora_model_path, SAFETENSORS_WEIGHTS_NAME)
use_safetensors = True
elif os.path.exists(os.path.join(lora_model_path, WEIGHTS_NAME)):
lora_filename = os.path.join(lora_model_path, WEIGHTS_NAME)
use_safetensors = False
else:
raise ValueError(
f"Please check that the file {WEIGHTS_NAME} or {SAFETENSORS_WEIGHTS_NAME} is present at {lora_model_path}."
)
if use_safetensors:
lora_state_dict = safe_load_file(lora_filename, device="cpu")
else:
lora_state_dict = torch.load(lora_filename, map_location='cpu')
if 'base_model.model.model.embed_tokens.weight' in lora_state_dict:
lora_vocab_size = lora_state_dict['base_model.model.model.embed_tokens.weight'].shape[0]
assert lora_vocab_size == len(tokenizer), \
(f"The vocab size of the tokenizer {len(tokenizer)} does not match the vocab size of the LoRA weight {lora_vocab_size}!\n")
tokenizers_and_loras.append(
{
"tokenizer" :tokenizer,
"state_dict" :lora_state_dict,
"config": lora_config,
"scaling": lora_config.lora_alpha / lora_config.r,
"fan_in_fan_out" : lora_config.fan_in_fan_out,
})
if not os.path.exists(base_model_path):
print("Cannot find lora model on the disk. Downloading lora model from hub...")
base_model_path = snapshot_download(repo_id=base_model_path)
ckpt_filenames = sorted([f for f in os.listdir(base_model_path) if re.match(r'model-(\d+)-of-(\d+).safetensors',f)])
if len(ckpt_filenames) == 0:
raise FileNotFoundError(f"Cannot find base model checkpoints in ${base_model_path}. Please make sure the checkpoints are saved in the HF format.")
total_size = 0
for index, filename in enumerate(ckpt_filenames):
print(f"Loading ckpt {filename}")
if re.match('(.*).safetensors', filename):
state_dict = safe_load_file(os.path.join(base_model_path,filename), device="cpu")
else:
state_dict = torch.load(os.path.join(base_model_path,filename), map_location='cpu')
print("Merging...")
for k in state_dict:
for tl_idx, t_and_l in enumerate(tokenizers_and_loras):
saved_key = 'base_model.model.'+k
lora_key_a = saved_key.replace('.weight','.lora_A.weight')
if saved_key in t_and_l['state_dict']:
if args.verbose:
print(f"copying {saved_key} from {tl_idx}-th LoRA weight to {k}")
state_dict[k] = t_and_l['state_dict'][saved_key].half().clone() # do we need half()?
if lora_key_a in t_and_l['state_dict']:
lora_key_b = lora_key_a.replace('lora_A.weight','lora_B.weight')
if args.verbose:
print(f"merging {lora_key_a} and lora_B.weight form {tl_idx}-th LoRA weight to {k}")
state_dict[k] += (
transpose(
t_and_l['state_dict'][lora_key_b].float() @ t_and_l['state_dict'][lora_key_a].float(), t_and_l['fan_in_fan_out']) * t_and_l['scaling']
)
weight_size = state_dict[k].numel() * dtype_byte_size(state_dict[k].dtype)
total_size += weight_size
print(f"Saving ckpt {filename} to {output_dir} in HF format...")
if use_safetensors:
safetensors.torch.save_file(
state_dict, os.path.join(output_dir, filename), metadata={"format": "pt"}
)
else:
torch.save(state_dict, os.path.join(output_dir, filename))
del state_dict
gc.collect() # Effectively enforce garbage collection
print(f"Saving tokenizer")
tokenizers_and_loras[-1]['tokenizer'].save_pretrained(output_dir)
configs = ('config.json', 'generation_config.json', "model.safetensors.index.json")
for config in configs:
if os.path.exists(os.path.join(lora_model_path, config)):
print(f"Saving {config} from {lora_model_path}")
with open(os.path.join(lora_model_path, config),'r') as f:
obj = json.load(f)
else:
if os.path.exists(os.path.join(base_model_path, config)):
print(f"Saving {config} from {base_model_path}")
with open(os.path.join(base_model_path, config),'r') as f:
obj = json.load(f)
if config == 'config.json':
obj['vocab_size'] = len(tokenizers_and_loras[-1]['tokenizer'])
if config == "model.safetensors.index.json":
obj['metadata']['total_size'] = total_size
if os.path.exists(os.path.join(base_model_path, config)):
with open(os.path.join(output_dir, config), 'w') as f:
json.dump(obj, f, indent=2)
print("Done.")
print(f"Check output dir: {output_dir}") | ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/mmlu/categories.py | Python | subcategories = {
"abstract_algebra": ["math"],
"anatomy": ["health"],
"astronomy": ["physics"],
"business_ethics": ["business"],
"clinical_knowledge": ["health"],
"college_biology": ["biology"],
"college_chemistry": ["chemistry"],
"college_computer_science": ["computer science"],
"college_mathematics": ["math"],
"college_medicine": ["health"],
"college_physics": ["physics"],
"computer_security": ["computer science"],
"conceptual_physics": ["physics"],
"econometrics": ["economics"],
"electrical_engineering": ["engineering"],
"elementary_mathematics": ["math"],
"formal_logic": ["philosophy"],
"global_facts": ["other"],
"high_school_biology": ["biology"],
"high_school_chemistry": ["chemistry"],
"high_school_computer_science": ["computer science"],
"high_school_european_history": ["history"],
"high_school_geography": ["geography"],
"high_school_government_and_politics": ["politics"],
"high_school_macroeconomics": ["economics"],
"high_school_mathematics": ["math"],
"high_school_microeconomics": ["economics"],
"high_school_physics": ["physics"],
"high_school_psychology": ["psychology"],
"high_school_statistics": ["math"],
"high_school_us_history": ["history"],
"high_school_world_history": ["history"],
"human_aging": ["health"],
"human_sexuality": ["culture"],
"international_law": ["law"],
"jurisprudence": ["law"],
"logical_fallacies": ["philosophy"],
"machine_learning": ["computer science"],
"management": ["business"],
"marketing": ["business"],
"medical_genetics": ["health"],
"miscellaneous": ["other"],
"moral_disputes": ["philosophy"],
"moral_scenarios": ["philosophy"],
"nutrition": ["health"],
"philosophy": ["philosophy"],
"prehistory": ["history"],
"professional_accounting": ["other"],
"professional_law": ["law"],
"professional_medicine": ["health"],
"professional_psychology": ["psychology"],
"public_relations": ["politics"],
"security_studies": ["politics"],
"sociology": ["culture"],
"us_foreign_policy": ["politics"],
"virology": ["health"],
"world_religions": ["philosophy"],
}
categories = {
"STEM": ["physics", "chemistry", "biology", "computer science", "math", "engineering"],
"humanities": ["history", "philosophy", "law"],
"social sciences": ["politics", "culture", "economics", "geography", "psychology"],
"other (business, health, misc.)": ["other", "business", "health"],
} | ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/mmlu/eval.py | Python | # modified from https://github.com/baichuan-inc/Baichuan-7B/blob/main/evaluation/evaluate_mmlu.py
import argparse
import os
import torch
import numpy as np
import pandas as pd
from categories import subcategories, categories
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
choices = ["A", "B", "C", "D"]
def format_subject(subject):
line = subject.split("_")
s = ""
for entry in line:
s += " " + entry
return s
def format_example(df, idx, include_answer=True):
prompt = df.iloc[idx, 0]
k = df.shape[1] - 2
for j in range(k):
prompt += "\n{}. {}".format(choices[j], df.iloc[idx, j + 1])
prompt += "\nAnswer:"
if include_answer:
prompt += " {}\n\n".format(df.iloc[idx, k + 1])
return prompt
def gen_prompt(train_df, subject, k=-1):
prompt = "The following are multiple choice questions (with answers) about {}.\n\n".format(
format_subject(subject)
)
if k == -1:
k = train_df.shape[0]
for i in range(k):
prompt += format_example(train_df, i)
return prompt
@torch.no_grad()
def mmlu_eval(args, subject, model, tokenizer, dev_df, test_df):
cors = []
all_probs = []
for i in range(test_df.shape[0]):
# get prompt and make sure it fits
k = args.ntrain
prompt_end = format_example(test_df, i, include_answer=False)
train_prompt = gen_prompt(dev_df, subject, k)
prompt = train_prompt + prompt_end
input_ids = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
label = test_df.iloc[i, test_df.shape[1] - 1]
logits = model(
input_ids=input_ids,
).logits[:,-1].flatten()
probs = (
torch.nn.functional.softmax(
torch.tensor(
[
logits[tokenizer("A").input_ids[-1]],
logits[tokenizer("B").input_ids[-1]],
logits[tokenizer("C").input_ids[-1]],
logits[tokenizer("D").input_ids[-1]],
]
),
dim=0,
)
.detach()
.cpu()
.to(torch.float32)
.numpy()
)
pred = {0: "A", 1: "B", 2: "C", 3: "D"}[np.argmax(probs)]
cor = pred == label
cors.append(cor)
all_probs.append(probs)
acc = np.mean(cors)
cors = np.array(cors)
all_probs = np.array(all_probs)
print("Average accuracy {:.3f} - {}".format(acc, subject))
return cors, acc, all_probs
def main(args):
tokenizer = AutoTokenizer.from_pretrained(args.model_path, use_fast=False)
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
load_in_8bit=False,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4"
)
model = AutoModelForCausalLM.from_pretrained(
args.model_path,
quantization_config=quantization_config if args.load_in_4bit else None,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
device_map='auto',
attn_implementation="flash_attention_2" if args.use_flash_attention_2 else "sdpa"
).eval()
subjects = sorted(
[
f.split("_test.csv")[0]
for f in os.listdir(os.path.join(args.data_dir, "test"))
if "_test.csv" in f
]
)
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
if not os.path.exists(os.path.join(args.save_dir, "results")):
os.makedirs(os.path.join(args.save_dir, "results"))
all_cors = []
subcat_cors = {
subcat: [] for subcat_lists in subcategories.values() for subcat in subcat_lists
}
cat_cors = {cat: [] for cat in categories}
for subject in subjects:
dev_df = pd.read_csv(
os.path.join(args.data_dir, "dev", subject + "_dev.csv"), header=None
)[: args.ntrain]
if args.do_test:
test_df = pd.read_csv(
os.path.join(args.data_dir, "test", subject + "_test.csv"), header=None
)
else:
test_df = pd.read_csv(
os.path.join(args.data_dir, "val", subject + "_val.csv"), header=None
)
cors, _, probs = mmlu_eval(args, subject, model, tokenizer, dev_df, test_df)
subcats = subcategories[subject]
for subcat in subcats:
subcat_cors[subcat].append(cors)
for key in categories.keys():
if subcat in categories[key]:
cat_cors[key].append(cors)
all_cors.append(cors)
test_df["correct"] = cors
for j in range(probs.shape[1]):
choice = choices[j]
test_df["choice{}_probs".format(choice)] = probs[:, j]
test_df.to_csv(
os.path.join(
args.save_dir, "results", f"{subject}.csv"
),
index=None,
)
for subcat in subcat_cors:
subcat_acc = np.mean(np.concatenate(subcat_cors[subcat]))
print("Average accuracy {:.3f} - {}".format(subcat_acc, subcat))
for cat in cat_cors:
cat_acc = np.mean(np.concatenate(cat_cors[cat]))
print("Average accuracy {:.3f} - {}".format(cat_acc, cat))
weighted_acc = np.mean(np.concatenate(all_cors))
print("Average accuracy: {:.3f}".format(weighted_acc))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--ntrain", "-k", type=int, default=5)
parser.add_argument("--ngpu", "-g", type=int, default=8)
parser.add_argument("--data_dir", "-d", type=str, default="data")
parser.add_argument("--save_dir", "-s", type=str, default="results")
parser.add_argument(
"--model_path",
"-m",
type=str,
)
parser.add_argument(
"--do_test",
action="store_true"
)
parser.add_argument(
"--load_in_4bit",
action="store_true"
)
parser.add_argument(
"--use_flash_attention_2",
action="store_true"
)
args = parser.parse_args()
main(args) | ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/openai_server_demo/openai_api_protocol.py | Python | from typing import Optional, List, Dict, Any, Union, Literal
import time
import shortuuid
from pydantic import BaseModel, Field
class ChatCompletionRequest(BaseModel):
model: str = "chinese-mixtral"
messages: Union[str, List[Dict[str, str]]]
temperature: Optional[float] = 0.2
top_p: Optional[float] = 0.9
top_k: Optional[int] = 40
n: Optional[int] = 1
max_tokens: Optional[int] = 512
num_beams: Optional[int] = 1
stop: Optional[Union[str, List[str]]] = None
stream: Optional[bool] = False
repetition_penalty: Optional[float] = 1.1
user: Optional[str] = None
do_sample: Optional[bool] = True
class ChatMessage(BaseModel):
role: str
content: str
class DeltaMessage(BaseModel):
role: Optional[Literal["user", "assistant", "system"]] = None
content: Optional[str] = None
class ChatCompletionResponseChoice(BaseModel):
index: int
message: ChatMessage
class ChatCompletionResponseStreamChoice(BaseModel):
index: int
delta: DeltaMessage
finish_reason: Optional[Literal["stop", "length"]]
class ChatCompletionResponse(BaseModel):
id: str = Field(default_factory=lambda: f"chatcmpl-{shortuuid.random()}")
object: str = "chat.completion"
created: int = Field(default_factory=lambda: int(time.time()))
model: str = "chinese-mixtral"
choices: List[
Union[ChatCompletionResponseChoice, ChatCompletionResponseStreamChoice]
]
class EmbeddingsRequest(BaseModel):
input: Union[str, List[Any]]
user: Optional[str] = None
class EmbeddingsResponse(BaseModel):
object: str = "list"
data: List[Dict[str, Any]]
model: str = "chinese-mixtral"
class CompletionRequest(BaseModel):
prompt: Union[str, List[Any]]
temperature: Optional[float] = 0.2
n: Optional[int] = 1
max_tokens: Optional[int] = 512
stop: Optional[Union[str, List[str]]] = None
stream: Optional[bool] = False
top_p: Optional[float] = 0.9
top_k: Optional[int] = 40
num_beams: Optional[int] = 1
logprobs: Optional[int] = None
echo: Optional[bool] = False
repetition_penalty: Optional[float] = 1.1
user: Optional[str] = None
do_sample: Optional[bool] = True
class CompletionResponseChoice(BaseModel):
index: int
text: str
class CompletionResponse(BaseModel):
id: Optional[str] = Field(default_factory=lambda: f"cmpl-{shortuuid.random()}")
object: Optional[str] = "text_completion"
created: Optional[int] = Field(default_factory=lambda: int(time.time()))
model: Optional[str] = "chinese-mixtral"
choices: List[CompletionResponseChoice]
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/openai_server_demo/openai_api_server.py | Python | import argparse
import os
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
from threading import Thread
from sse_starlette.sse import EventSourceResponse
parser = argparse.ArgumentParser()
parser.add_argument('--base_model', default=None, type=str, required=True)
parser.add_argument('--lora_model', default=None, type=str,help="If None, perform inference on the base model")
parser.add_argument('--tokenizer_path',default=None,type=str)
parser.add_argument('--gpus', default="0", type=str)
parser.add_argument('--load_in_8bit',action='store_true', help='Load the model in 8bit mode')
parser.add_argument('--load_in_4bit',action='store_true', help='Load the model in 4bit mode')
parser.add_argument('--only_cpu',action='store_true',help='Only use CPU for inference')
parser.add_argument('--use_flash_attention_2', action='store_true', help="Use flash-attention2 to accelerate inference")
args = parser.parse_args()
if args.only_cpu is True:
args.gpus = ""
if args.load_in_8bit or args.load_in_4bit:
raise ValueError("Quantization is unavailable on CPU.")
if args.load_in_8bit and args.load_in_4bit:
raise ValueError("Only one quantization method can be chosen for inference. Please check your arguments")
os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
import torch
import torch.nn.functional as F
from transformers import (
AutoModelForCausalLM,
LlamaTokenizer,
GenerationConfig,
TextIteratorStreamer,
BitsAndBytesConfig
)
from peft import PeftModel
import sys
parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
sys.path.append(parent_dir)
from openai_api_protocol import (
ChatCompletionRequest,
ChatCompletionResponse,
ChatMessage,
ChatCompletionResponseChoice,
CompletionRequest,
CompletionResponse,
CompletionResponseChoice,
EmbeddingsRequest,
EmbeddingsResponse,
ChatCompletionResponseStreamChoice,
DeltaMessage,
)
load_type = torch.float16
if torch.cuda.is_available():
device = torch.device(0)
else:
device = torch.device("cpu")
if args.tokenizer_path is None:
args.tokenizer_path = args.lora_model
if args.lora_model is None:
args.tokenizer_path = args.base_model
tokenizer = LlamaTokenizer.from_pretrained(args.tokenizer_path, legacy=True)
if args.load_in_4bit or args.load_in_8bit:
quantization_config = BitsAndBytesConfig(
load_in_4bit=args.load_in_4bit,
load_in_8bit=args.load_in_8bit,
bnb_4bit_compute_dtype=load_type,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4"
)
base_model = AutoModelForCausalLM.from_pretrained(
args.base_model,
torch_dtype=load_type,
low_cpu_mem_usage=True,
device_map='auto' if not args.only_cpu else None,
#load_in_4bit=args.load_in_4bit,
#load_in_8bit=args.load_in_8bit,
quantization_config=quantization_config if (args.load_in_4bit or args.load_in_8bit) else None,
attn_implementation="flash_attention_2" if args.use_flash_attention_2 else "sdpa",
trust_remote_code=True
)
model_vocab_size = base_model.get_input_embeddings().weight.size(0)
tokenizer_vocab_size = len(tokenizer)
print(f"Vocab of the base model: {model_vocab_size}")
print(f"Vocab of the tokenizer: {tokenizer_vocab_size}")
if model_vocab_size != tokenizer_vocab_size:
print("Resize model embeddings to fit tokenizer")
base_model.resize_token_embeddings(tokenizer_vocab_size)
if args.lora_model is not None:
print("loading peft model")
model = PeftModel.from_pretrained(
base_model,
args.lora_model,
torch_dtype=load_type,
device_map="auto",
)
else:
model = base_model
if device == torch.device("cpu"):
model.float()
model.eval()
DEFAULT_SYSTEM_PROMPT = ""
# NOTE: this is an arbitrary template, as the original
# one does not contain the system prompt.
# You may need to adjust this template to fit your needs.
TEMPLATE_WITH_SYSTEM_PROMPT = (
"[INST] <sys> {system_prompt} </sys>\n" "{instruction} [/INST]"
)
TEMPLATE_WITHOUT_SYSTEM_PROMPT = "[INST] {instruction} [/INST]"
def generate_prompt(
instruction, response="", with_system_prompt=False, system_prompt=None
):
if with_system_prompt is True and system_prompt is not None:
prompt = TEMPLATE_WITH_SYSTEM_PROMPT.format_map(
{"instruction": instruction, "system_prompt": system_prompt}
)
else:
prompt = TEMPLATE_WITHOUT_SYSTEM_PROMPT.format_map({"instruction": instruction})
if len(response) > 0:
prompt += " " + response
return prompt
def generate_completion_prompt(instruction: str):
"""Generate prompt for completion"""
return generate_prompt(instruction, response="", with_system_prompt=False)
def generate_chat_prompt(messages: list):
"""Generate prompt for chat completion"""
system_msg = None
for msg in messages:
if msg.role == "system":
system_msg = msg.content
prompt = ""
is_first_user_content = True
for msg in messages:
if msg.role == "system":
continue
if msg.role == "user":
if is_first_user_content is True:
prompt += generate_prompt(
msg.content, with_system_prompt=False, system_prompt=system_msg
)
is_first_user_content = False
else:
prompt += "<s>" + generate_prompt(msg.content, with_system_prompt=False)
if msg.role == "assistant":
prompt += f" {msg.content}" + "</s>"
return prompt
def predict(
input,
max_new_tokens=512,
top_p=0.9,
temperature=0.2,
top_k=40,
num_beams=1,
repetition_penalty=1.1,
do_sample=True,
**kwargs,
):
"""
Main inference method
type(input) == str -> /v1/completions
type(input) == list -> /v1/chat/completions
"""
if isinstance(input, str):
prompt = generate_completion_prompt(input)
else:
prompt = generate_chat_prompt(input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
attention_mask = inputs['attention_mask'].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
do_sample=do_sample,
**kwargs,
)
generation_config.return_dict_in_generate = True
generation_config.output_scores = False
generation_config.max_new_tokens = max_new_tokens
generation_config.repetition_penalty = float(repetition_penalty)
# For the reason why pad_token_id = eos_token_id, see:
# https://github.com/meta-llama/llama-recipes/blob/f7aa02af9f2c427ebb70853191b72636130b9df5/src/llama_recipes/finetuning.py#L141
with torch.no_grad():
generation_output = model.generate(
input_ids=input_ids,
attention_mask=attention_mask,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.eos_token_id,
generation_config=generation_config,
)
s = generation_output.sequences[0]
output = tokenizer.decode(s, skip_special_tokens=True)
output = output.split("[/INST]")[-1].strip()
return output
def stream_predict(
input,
max_new_tokens=512,
top_p=0.9,
temperature=0.2,
top_k=40,
num_beams=4,
repetition_penalty=1.1,
do_sample=True,
model_id="chinese-mixtral",
**kwargs,
):
choice_data = ChatCompletionResponseStreamChoice(
index=0, delta=DeltaMessage(role="assistant"), finish_reason=None
)
chunk = ChatCompletionResponse(
model=model_id,
choices=[choice_data],
object="chat.completion.chunk",
)
yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
if isinstance(input, str):
prompt = generate_completion_prompt(input)
else:
prompt = generate_chat_prompt(input)
inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs["input_ids"].to(device)
generation_config = GenerationConfig(
temperature=temperature,
top_p=top_p,
top_k=top_k,
num_beams=num_beams,
do_sample=do_sample,
**kwargs,
)
streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
generation_kwargs = dict(
streamer=streamer,
input_ids=input_ids,
generation_config=generation_config,
return_dict_in_generate=True,
output_scores=False,
max_new_tokens=max_new_tokens,
repetition_penalty=float(repetition_penalty),
)
Thread(target=model.generate, kwargs=generation_kwargs).start()
for new_text in streamer:
choice_data = ChatCompletionResponseStreamChoice(
index=0, delta=DeltaMessage(content=new_text), finish_reason=None
)
chunk = ChatCompletionResponse(
model=model_id, choices=[choice_data], object="chat.completion.chunk"
)
yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
choice_data = ChatCompletionResponseStreamChoice(
index=0, delta=DeltaMessage(), finish_reason="stop"
)
chunk = ChatCompletionResponse(
model=model_id, choices=[choice_data], object="chat.completion.chunk"
)
yield "{}".format(chunk.json(exclude_unset=True, ensure_ascii=False))
yield "[DONE]"
def get_embedding(input):
"""Get embedding main function"""
with torch.no_grad():
encoding = tokenizer(input, padding=True, return_tensors="pt")
input_ids = encoding["input_ids"].to(device)
attention_mask = encoding["attention_mask"].to(device)
model_output = model(input_ids, attention_mask, output_hidden_states=True)
data = model_output.hidden_states[-1]
mask = attention_mask.unsqueeze(-1).expand(data.size()).float()
masked_embeddings = data * mask
sum_embeddings = torch.sum(masked_embeddings, dim=1)
seq_length = torch.sum(mask, dim=1)
embedding = sum_embeddings / seq_length
normalized_embeddings = F.normalize(embedding, p=2, dim=1)
ret = normalized_embeddings.squeeze(0).tolist()
return ret
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
@app.post("/v1/chat/completions")
async def create_chat_completion(request: ChatCompletionRequest):
"""Creates a completion for the chat message"""
msgs = request.messages
if isinstance(msgs, str):
msgs = [ChatMessage(role="user", content=msgs)]
else:
msgs = [ChatMessage(role=x["role"], content=x["content"]) for x in msgs]
if request.stream:
generate = stream_predict(
input=msgs,
max_new_tokens=request.max_tokens,
top_p=request.top_p,
top_k=request.top_k,
temperature=request.temperature,
num_beams=request.num_beams,
repetition_penalty=request.repetition_penalty,
do_sample=request.do_sample,
)
return EventSourceResponse(generate, media_type="text/event-stream")
output = predict(
input=msgs,
max_new_tokens=request.max_tokens,
top_p=request.top_p,
top_k=request.top_k,
temperature=request.temperature,
num_beams=request.num_beams,
repetition_penalty=request.repetition_penalty,
do_sample=request.do_sample,
)
choices = [
ChatCompletionResponseChoice(index=i, message=msg) for i, msg in enumerate(msgs)
]
choices += [
ChatCompletionResponseChoice(
index=len(choices), message=ChatMessage(role="assistant", content=output)
)
]
return ChatCompletionResponse(choices=choices)
@app.post("/v1/completions")
async def create_completion(request: CompletionRequest):
"""Creates a completion"""
output = predict(
input=request.prompt,
max_new_tokens=request.max_tokens,
top_p=request.top_p,
top_k=request.top_k,
temperature=request.temperature,
num_beams=request.num_beams,
repetition_penalty=request.repetition_penalty,
do_sample=request.do_sample,
)
choices = [CompletionResponseChoice(index=0, text=output)]
return CompletionResponse(choices=choices)
@app.post("/v1/embeddings")
async def create_embeddings(request: EmbeddingsRequest):
"""Creates text embedding"""
embedding = get_embedding(request.input)
data = [{"object": "embedding", "embedding": embedding, "index": 0}]
return EmbeddingsResponse(data=data)
if __name__ == "__main__":
log_config = uvicorn.config.LOGGING_CONFIG
log_config["formatters"]["access"][
"fmt"
] = "%(asctime)s - %(levelname)s - %(message)s"
log_config["formatters"]["default"][
"fmt"
] = "%(asctime)s - %(levelname)s - %(message)s"
uvicorn.run(app, host="0.0.0.0", port=19327, workers=1, log_config=log_config)
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/build_dataset.py | Python | import logging
import os
from typing import Union, List
import datasets
import torch
from datasets import load_dataset, concatenate_datasets
import transformers
IGNORE_INDEX = -100
logger = logging.getLogger('__name__')
PROMPT_TEMPLATE = (
"[INST] {instruction} [/INST]"
)
def build_instruction_dataset(data_path: Union[List[str],str],
tokenizer: transformers.PreTrainedTokenizer,
max_seq_length: int, data_cache_dir = None,
preprocessing_num_workers = None,
):
def tokenization(examples):
sources = []
targets = []
prompt = PROMPT_TEMPLATE
for instruction, input_text, output in zip(examples['instruction'],examples['input'],examples['output']):
if input_text is not None and input_text !="":
instruction = instruction+'\n' + input_text
source = prompt.format_map({'instruction':instruction})
target = f"{output}{tokenizer.eos_token}"
sources.append(source)
targets.append(target)
tokenized_sources = tokenizer(sources,return_attention_mask=False)
tokenized_targets = tokenizer(targets,return_attention_mask=False,add_special_tokens=False)
all_input_ids = []
all_labels = []
for s,t in zip(tokenized_sources['input_ids'],tokenized_targets['input_ids']):
if len(s) >= max_seq_length:
continue
input_ids = torch.LongTensor(s + t)[:max_seq_length]
labels = torch.LongTensor([IGNORE_INDEX] * len(s) + t)[:max_seq_length]
all_input_ids.append(input_ids)
all_labels.append(labels)
results = {'input_ids':all_input_ids, 'labels': all_labels}
return results
logging.warning("building dataset...")
all_datasets = []
if not isinstance(data_path,(list,tuple)):
data_path = [data_path]
for file in data_path:
if data_cache_dir is None:
data_cache_dir = str(os.path.dirname(file))
cache_path = os.path.join(data_cache_dir,os.path.basename(file).split('.')[0]+f"_{max_seq_length}")
os.makedirs(cache_path, exist_ok=True)
try:
processed_dataset = datasets.load_from_disk(cache_path)
logger.info(f'training datasets-{file} has been loaded from disk')
except Exception:
raw_dataset = load_dataset("json", data_files=file, cache_dir=cache_path)
tokenization_func = tokenization
tokenized_dataset = raw_dataset.map(
tokenization_func,
batched=True,
num_proc=preprocessing_num_workers,
remove_columns=["instruction","input","output"],
keep_in_memory=False,
desc="preprocessing on dataset",
)
processed_dataset = tokenized_dataset
processed_dataset.save_to_disk(cache_path)
processed_dataset.set_format('torch')
all_datasets.append(processed_dataset['train'])
all_datasets = concatenate_datasets(all_datasets)
return all_datasets
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/run_clm_pt_with_peft.py | Python | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=text-generation
"""
# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
import logging
import numpy as np
import math
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, List, Dict, Any, Mapping
from pathlib import Path
import datasets
import torch
from datasets import load_dataset, concatenate_datasets
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_CAUSAL_LM_MAPPING,
AutoConfig,
AutoModelForCausalLM,
MixtralForCausalLM,
LlamaTokenizer,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
BitsAndBytesConfig
)
from transformers.testing_utils import CaptureLogger
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import send_example_telemetry
from transformers.utils.versions import require_version
from peft import LoraConfig, TaskType, get_peft_model, PeftModel, prepare_model_for_kbit_training
def fault_tolerance_data_collator(features: List) -> Dict[str, Any]:
if not isinstance(features[0], Mapping):
features = [vars(f) for f in features]
first = features[0]
batch = {}
# Special handling for labels.
# Ensure that tensor is created with the correct type
# (it should be automatically the case, but let's make sure of it.)
if "label" in first and first["label"] is not None:
label = first["label"].item() if isinstance(first["label"], torch.Tensor) else first["label"]
dtype = torch.long if isinstance(label, int) else torch.float
batch["labels"] = torch.tensor([f["label"] for f in features], dtype=dtype)
elif "label_ids" in first and first["label_ids"] is not None:
if isinstance(first["label_ids"], torch.Tensor):
batch["labels"] = torch.stack([f["label_ids"] for f in features])
else:
dtype = torch.long if isinstance(first["label_ids"][0], int) else torch.float
batch["labels"] = torch.tensor([f["label_ids"] for f in features], dtype=dtype)
# Handling of all other possible keys.
# Again, we will use the first element to figure out which key/values are not None for this model.
try:
for k, v in first.items():
if k not in ("label", "label_ids") and v is not None and not isinstance(v, str):
if isinstance(v, torch.Tensor):
batch[k] = torch.stack([f[k] for f in features])
elif isinstance(v, np.ndarray):
batch[k] = torch.tensor(np.stack([f[k] for f in features]))
else:
batch[k] = torch.tensor([f[k] for f in features])
except ValueError: # quick fix by simply take the first example
for k, v in first.items():
if k not in ("label", "label_ids") and v is not None and not isinstance(v, str):
if isinstance(v, torch.Tensor):
batch[k] = torch.stack([features[0][k]] * len(features))
elif isinstance(v, np.ndarray):
batch[k] = torch.tensor(np.stack([features[0][k]] * len(features)))
else:
batch[k] = torch.tensor([features[0][k]] * len(features))
return batch
MODEL_CONFIG_CLASSES = list(MODEL_FOR_CAUSAL_LM_MAPPING.keys())
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
)
},
)
tokenizer_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The tokenizer for weights initialization.Don't set if you want to train a model from scratch."
)
},
)
model_type: Optional[str] = field(
default=None,
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
)
config_overrides: Optional[str] = field(
default=None,
metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=False,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
},
)
torch_dtype: Optional[str] = field(
default=None,
metadata={
"help": (
"Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
"dtype will be automatically derived from the model's weights."
),
"choices": ["auto", "bfloat16", "float16", "float32"],
},
)
def __post_init__(self):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path"
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_dir: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
dataset_config_name: Optional[str] = field(
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of training examples to this "
"value if set."
)
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
"help": (
"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
"value if set."
)
},
)
streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"})
block_size: Optional[int] = field(
default=None,
metadata={
"help": (
"Optional input sequence length after tokenization. "
"The training dataset will be truncated in block of this size for training. "
"Default to the model max input length for single sentence inputs (take into account special tokens)."
)
},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
validation_split_percentage: Optional[float] = field(
default=0.05,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
keep_linebreaks: bool = field(
default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."}
)
data_cache_dir: Optional[str] = field(default="./", metadata={"help": "The datasets processed stored"})
def __post_init__(self):
if self.streaming:
require_version("datasets>=2.0.0", "The streaming feature requires `datasets>=2.0.0`")
@dataclass
class MyTrainingArguments(TrainingArguments):
trainable : Optional[str] = field(default="q_proj,v_proj")
lora_rank : Optional[int] = field(default=8)
lora_dropout : Optional[float] = field(default=0.1)
lora_alpha : Optional[float] = field(default=32.)
modules_to_save : Optional[str] = field(default=None)
debug_mode : Optional[bool] = field(default=False)
peft_path : Optional[str] = field(default=None)
use_flash_attention_2 : Optional[bool] = field(default=False)
double_quant: Optional[bool] = field(default=True)
quant_type: Optional[str] = field(default="nf4")
load_in_kbits: Optional[int] = field(default=16)
output_router_logits: Optional[bool] = field(default=False)
logger = logging.getLogger(__name__)
def main():
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, MyTrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("run_clm", model_args, data_args)
# Setup logging
logging.basicConfig(format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO, # if training_args.local_rank in [-1, 0] else logging.WARN,
handlers=[logging.StreamHandler(sys.stdout)],)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# transformers.tokenization_utils.logging.set_verbosity_warning()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
config_kwargs = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
"output_router_logits": True if training_args.output_router_logits else False
}
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}")
config.update_from_string(model_args.config_overrides)
logger.info(f"New config: {config}")
tokenizer_kwargs = {
"cache_dir": model_args.cache_dir,
"use_fast": model_args.use_fast_tokenizer,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
elif model_args.tokenizer_name_or_path:
tokenizer = LlamaTokenizer.from_pretrained(model_args.tokenizer_name_or_path, **tokenizer_kwargs)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
tokenizer.add_eos_token = True
# Preprocessing the datasets.
# First we tokenize all the texts.
# since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
def tokenize_function(examples):
with CaptureLogger(tok_logger) as cl:
output = tokenizer(examples["text"])
# clm input could be much much longer than block_size
if "Token indices sequence length is longer than the" in cl.out:
tok_logger.warning(
"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits"
" before being passed to the model."
)
return output
if data_args.block_size is None:
block_size = tokenizer.model_max_length
if block_size > 1024:
logger.warning(
"The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"
" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"
" override this default with `--block_size xxx`."
)
block_size = 1024
else:
if data_args.block_size > tokenizer.model_max_length:
logger.warning(
f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model"
f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
)
block_size = min(data_args.block_size, tokenizer.model_max_length)
# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
def group_texts(examples):
# Concatenate all texts.
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
total_length = len(concatenated_examples[list(examples.keys())[0]])
# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
# customize this part to your needs.
if total_length >= block_size:
total_length = (total_length // block_size) * block_size
# Split by chunks of max_len.
result = {
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
for k, t in concatenated_examples.items()
}
result["labels"] = result["input_ids"].copy()
return result
with training_args.main_process_first(desc="dataset map tokenization and grouping"):
lm_datasets = []
path = Path(data_args.dataset_dir)
files = [file.name for file in path.glob("*.txt")]
if training_args.debug_mode is True:
files = [files[0]]
for idx, file in enumerate(files):
data_file = os.path.join(path, file)
filename = ''.join(file.split(".")[:-1])
cache_path = os.path.join(data_args.data_cache_dir, filename+f"_{block_size}")
os.makedirs(cache_path, exist_ok=True)
try:
processed_dataset = datasets.load_from_disk(cache_path, keep_in_memory=False)
logger.info(f'training datasets-{filename} has been loaded from disk')
except Exception:
cache_dir = os.path.join(data_args.data_cache_dir, filename+f"_text_{block_size}")
os.makedirs(cache_dir, exist_ok=True)
raw_dataset = load_dataset("text", data_files=data_file, cache_dir=cache_dir, keep_in_memory=False)
logger.info(f"{file} has been loaded")
tokenized_dataset = raw_dataset.map(
tokenize_function,
batched=True,
num_proc=data_args.preprocessing_num_workers,
remove_columns="text",
load_from_cache_file=True,
keep_in_memory=False,
cache_file_names = {k: os.path.join(cache_dir, 'tokenized.arrow') for k in raw_dataset},
desc="Running tokenizer on dataset",
)
grouped_datasets = tokenized_dataset.map(
group_texts,
batched=True,
num_proc=data_args.preprocessing_num_workers,
load_from_cache_file=True,
keep_in_memory=False,
cache_file_names = {k: os.path.join(cache_dir, 'grouped.arrow') for k in tokenized_dataset},
desc=f"Grouping texts in chunks of {block_size}",
)
processed_dataset = grouped_datasets
processed_dataset.save_to_disk(cache_path)
if idx == 0:
lm_datasets = processed_dataset['train']
else:
lm_datasets = concatenate_datasets([lm_datasets, processed_dataset["train"]])
lm_datasets = lm_datasets.train_test_split(test_size = data_args.validation_split_percentage)
if training_args.do_train:
train_dataset = lm_datasets['train']
if data_args.max_train_samples is not None:
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
train_dataset = train_dataset.select(range(max_train_samples))
logger.info(f"Num train_samples {len(train_dataset)}")
logger.info("Training example:")
logger.info(tokenizer.decode(train_dataset[0]['input_ids']))
if training_args.do_eval:
eval_dataset = lm_datasets["test"]
if data_args.max_eval_samples is not None:
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
eval_dataset = eval_dataset.select(range(max_eval_samples))
logger.info(f"Num eval_samples {len(eval_dataset)}")
logger.info("Evaluation example:")
logger.info(tokenizer.decode(eval_dataset[0]['input_ids']))
compute_dtype = (torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
if training_args.load_in_kbits in [4, 8]:
if training_args.modules_to_save is not None:
load_in_8bit_skip_modules = training_args.modules_to_save.split(',')
else:
load_in_8bit_skip_modules = None
quantization_config = BitsAndBytesConfig(
load_in_4bit=training_args.load_in_kbits == 4,
load_in_8bit=training_args.load_in_kbits == 8,
llm_int8_threshold=6.0,
load_in_8bit_skip_modules=load_in_8bit_skip_modules,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=training_args.double_quant,
bnb_4bit_quant_type=training_args.quant_type # {'fp4', 'nf4'}
)
else:
quantization_config = None
if quantization_config is not None:
logger.info(f"quantization_config:{quantization_config.to_dict()}")
if model_args.model_name_or_path:
torch_dtype = (
model_args.torch_dtype
if model_args.torch_dtype in ["auto", None]
else getattr(torch, model_args.torch_dtype)
)
device_map = {"":int(os.environ.get("LOCAL_RANK") or 0)}
model = MixtralForCausalLM.from_pretrained(
model_args.model_name_or_path,
from_tf=bool(".ckpt" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
torch_dtype=torch_dtype,
low_cpu_mem_usage=True,
device_map=device_map,
quantization_config=quantization_config,
attn_implementation="flash_attention_2" if training_args.use_flash_attention_2 else "sdpa"
)
else:
model = AutoModelForCausalLM.from_config(config)
n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params")
if training_args.load_in_kbits in [4, 8]:
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=training_args.gradient_checkpointing)
model.config.use_cache = False
model_vocab_size = model.get_output_embeddings().weight.size(0)
tokenizer_vocab_size = len(tokenizer)
logger.info(f"Model vocab size: {model_vocab_size}")
logger.info(f"Tokenizer vocab size: {tokenizer_vocab_size}")
if model_vocab_size != tokenizer_vocab_size:
logger.info(f"Resize model vocab size to {tokenizer_vocab_size}")
model.resize_token_embeddings(tokenizer_vocab_size)
if training_args.peft_path is not None:
logger.info("Peft from pre-trained model")
model = PeftModel.from_pretrained(model, training_args.peft_path, device_map=device_map, is_trainable=True)
else:
logger.info("Init new peft model")
target_modules = training_args.trainable.split(',')
modules_to_save = training_args.modules_to_save
if modules_to_save is not None:
modules_to_save = modules_to_save.split(',')
lora_rank = training_args.lora_rank
lora_dropout = training_args.lora_dropout
lora_alpha = training_args.lora_alpha
logger.info(f"target_modules: {target_modules}")
logger.info(f"lora_rank: {lora_rank}")
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
target_modules=target_modules,
inference_mode=False,
r=lora_rank, lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
modules_to_save=modules_to_save)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset if training_args.do_train else None,
eval_dataset=eval_dataset if training_args.do_eval else None,
tokenizer=tokenizer,
data_collator=fault_tolerance_data_collator
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model()
metrics = train_result.metrics
max_train_samples = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
)
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
try:
perplexity = math.exp(metrics["eval_loss"])
except OverflowError:
perplexity = float("inf")
metrics["perplexity"] = perplexity
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
if __name__ == "__main__":
main() | ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/run_clm_sft_with_peft.py | Python | #!/usr/bin/env python
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Fine-tuning the library models for causal language modeling (GPT, GPT-2, CTRL, ...) on a text file or a dataset.
Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
https://huggingface.co/models?filter=text-generation
"""
# You can also adapt this script on your own causal language modeling task. Pointers for this are left as comments.
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from pathlib import Path
import datasets
import torch
from build_dataset import build_instruction_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
AutoConfig,
BitsAndBytesConfig,
MixtralForCausalLM,
LlamaTokenizer,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
DataCollatorForSeq2Seq
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import send_example_telemetry
from transformers.utils.versions import require_version
from peft import LoraConfig, TaskType, get_peft_model, PeftModel, prepare_model_for_kbit_training
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
@dataclass
class ModelArguments:
"""
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
"""
model_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."
)
},
)
tokenizer_name_or_path: Optional[str] = field(
default=None,
metadata={
"help": (
"The tokenizer for weights initialization.Don't set if you want to train a model from scratch."
)
},
)
config_overrides: Optional[str] = field(
default=None,
metadata={
"help": (
"Override some existing default config settings when a model is trained from scratch. Example: "
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
)
},
)
config_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
)
tokenizer_name: Optional[str] = field(
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
)
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
use_fast_tokenizer: bool = field(
default=False,
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
)
model_revision: str = field(
default="main",
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": (
"Will use the token generated when running `huggingface-cli login` (necessary to use this script "
"with private models)."
)
},
)
torch_dtype: Optional[str] = field(
default=None,
metadata={
"help": (
"Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
"dtype will be automatically derived from the model's weights."
),
"choices": ["auto", "bfloat16", "float16", "float32"],
},
)
def __post_init__(self):
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path"
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
dataset_dir: Optional[str] = field(
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
)
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
validation_file: Optional[str] = field(
default=None,
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
)
overwrite_cache: bool = field(
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
)
validation_split_percentage: Optional[float] = field(
default=0.05,
metadata={
"help": "The percentage of the train set used as validation set in case there's no validation split"
},
)
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={"help": "The number of processes to use for the preprocessing."},
)
keep_linebreaks: bool = field(
default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."}
)
data_cache_dir: Optional[str] = field(default=None, metadata={"help": "The datasets processed stored"})
max_seq_length: Optional[int] = field(default=1024)
@dataclass
class MyTrainingArguments(TrainingArguments):
trainable : Optional[str] = field(default="q_proj,v_proj")
lora_rank : Optional[int] = field(default=8)
lora_dropout : Optional[float] = field(default=0.1)
lora_alpha : Optional[float] = field(default=32.)
modules_to_save : Optional[str] = field(default=None)
peft_path : Optional[str] = field(default=None)
use_flash_attention_2 : Optional[bool] = field(default=False)
double_quant: Optional[bool] = field(default=True)
quant_type: Optional[str] = field(default="nf4")
load_in_kbits: Optional[int] = field(default=16)
output_router_logits: Optional[bool] = field(default=False)
logger = logging.getLogger(__name__)
def main():
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, MyTrainingArguments))
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
send_example_telemetry("run_clm", model_args, data_args)
# Setup logging
logging.basicConfig(format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO, # if training_args.local_rank in [-1, 0] else logging.WARN,
handlers=[logging.StreamHandler(sys.stdout)],)
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# transformers.tokenization_utils.logging.set_verbosity_warning()
# Log on each process the small summary:
logger.warning(
f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
+ f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16 or training_args.bf16}"
)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f"Output directory ({training_args.output_dir}) already exists and is not empty. "
"Use --overwrite_output_dir to overcome."
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
)
# Set seed before initializing model.
set_seed(training_args.seed)
config_kwargs = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
"output_router_logits": True if training_args.output_router_logits else False
}
if model_args.config_name:
config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
elif model_args.model_name_or_path:
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
else:
config = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch.")
if model_args.config_overrides is not None:
logger.info(f"Overriding config: {model_args.config_overrides}")
config.update_from_string(model_args.config_overrides)
logger.info(f"New config: {config}")
tokenizer_kwargs = {
"cache_dir": model_args.cache_dir,
"use_fast": model_args.use_fast_tokenizer,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.tokenizer_name:
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
elif model_args.tokenizer_name_or_path:
tokenizer = LlamaTokenizer.from_pretrained(model_args.tokenizer_name_or_path, **tokenizer_kwargs)
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
)
if tokenizer.pad_token_id is None:
tokenizer.pad_token = tokenizer.eos_token
data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer)
eval_dataset=None
train_dataset = None
if training_args.do_train:
with training_args.main_process_first(desc="loading and tokenization"):
path = Path(data_args.dataset_dir)
files = [os.path.join(path,file.name) for file in path.glob("*.json")]
logger.info(f"Training files: {' '.join(files)}")
train_dataset = build_instruction_dataset(
data_path=files,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
data_cache_dir = None,
preprocessing_num_workers = data_args.preprocessing_num_workers)
logger.info(f"Num train_samples {len(train_dataset)}")
logger.info("Training example:")
logger.info(tokenizer.decode(train_dataset[0]['input_ids']))
if training_args.do_eval:
with training_args.main_process_first(desc="loading and tokenization"):
files = [data_args.validation_file]
logger.info(f"Evaluation files: {' '.join(files)}")
eval_dataset = build_instruction_dataset(
data_path=files,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
data_cache_dir=None,
preprocessing_num_workers = data_args.preprocessing_num_workers)
logger.info(f"Num eval_samples {len(eval_dataset)}")
logger.info("Evaluation example:")
logger.info(tokenizer.decode(eval_dataset[0]['input_ids']))
torch_dtype = (
model_args.torch_dtype
if model_args.torch_dtype in ["auto", None]
else getattr(torch, model_args.torch_dtype)
)
compute_dtype = (torch.float16 if training_args.fp16 else (torch.bfloat16 if training_args.bf16 else torch.float32))
if training_args.load_in_kbits in [4, 8]:
if training_args.modules_to_save is not None:
load_in_8bit_skip_modules = training_args.modules_to_save.split(',')
else:
load_in_8bit_skip_modules = None
quantization_config = BitsAndBytesConfig(
load_in_4bit=training_args.load_in_kbits == 4,
load_in_8bit=training_args.load_in_kbits == 8,
llm_int8_threshold=6.0,
load_in_8bit_skip_modules=load_in_8bit_skip_modules,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=training_args.double_quant,
bnb_4bit_quant_type=training_args.quant_type # {'fp4', 'nf4'}
)
else:
quantization_config = None
if quantization_config is not None:
logger.info(f"quantization_config:{quantization_config.to_dict()}")
device_map = {"":int(os.environ.get("LOCAL_RANK") or 0)}
model = MixtralForCausalLM.from_pretrained(
model_args.model_name_or_path,
config=config,
cache_dir=model_args.cache_dir,
revision=model_args.model_revision,
use_auth_token=True if model_args.use_auth_token else None,
torch_dtype=torch_dtype,
low_cpu_mem_usage=True,
device_map=device_map,
quantization_config=quantization_config,
attn_implementation="flash_attention_2" if training_args.use_flash_attention_2 else "sdpa"
)
if training_args.load_in_kbits in [4, 8]:
model = prepare_model_for_kbit_training(model, use_gradient_checkpointing=training_args.gradient_checkpointing)
model.config.use_cache = False
model_vocab_size = model.get_input_embeddings().weight.shape[0]
logger.info(f"Model vocab size: {model_vocab_size}")
logger.info(f"len(tokenizer):{len(tokenizer)}")
if model_vocab_size != len(tokenizer):
logger.info(f"Resize model vocab size to {len(tokenizer)}")
model.resize_token_embeddings(len(tokenizer))
if training_args.peft_path is not None:
logger.info("Peft from pre-trained model")
model = PeftModel.from_pretrained(model, training_args.peft_path, device_map=device_map, is_trainable=True)
else:
logger.info("Init new peft model")
target_modules = training_args.trainable.split(',')
modules_to_save = training_args.modules_to_save
if modules_to_save is not None:
modules_to_save = modules_to_save.split(',')
lora_rank = training_args.lora_rank
lora_dropout = training_args.lora_dropout
lora_alpha = training_args.lora_alpha
logger.info(f"target_modules: {target_modules}")
logger.info(f"lora_rank: {lora_rank}")
peft_config = LoraConfig(
task_type=TaskType.CAUSAL_LM,
target_modules=target_modules,
inference_mode=False,
r=lora_rank, lora_alpha=lora_alpha,
lora_dropout=lora_dropout,
modules_to_save=modules_to_save)
model = get_peft_model(model, peft_config)
model.print_trainable_parameters()
# Initialize our Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=tokenizer,
data_collator=data_collator,
)
# Training
if training_args.do_train:
checkpoint = None
if training_args.resume_from_checkpoint is not None:
checkpoint = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
checkpoint = last_checkpoint
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model()
metrics = train_result.metrics
metrics["train_samples"] = len(train_dataset)
trainer.log_metrics("train", metrics)
trainer.save_metrics("train", metrics)
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("*** Evaluate ***")
metrics = trainer.evaluate()
metrics["eval_samples"] =len(eval_dataset)
try:
perplexity = math.exp(metrics["eval_loss"])
except OverflowError:
perplexity = float("inf")
metrics["perplexity"] = perplexity
trainer.log_metrics("eval", metrics)
trainer.save_metrics("eval", metrics)
if __name__ == "__main__":
main() | ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/run_pt.sh | Shell |
## 运行脚本前请仔细阅读wiki(https://github.com/ymcui/Chinese-Mixtral/wiki/pt_scripts_zh)
## Read the wiki(https://github.com/ymcui/Chinese-Mixtral/wiki/pt_scripts_en) carefully before running the script
lr=1e-4
lora_rank=64
lora_alpha=128
lora_trainable="q_proj,v_proj,k_proj,o_proj,gate,w1,w2,w3"
modules_to_save="embed_tokens,lm_head"
lora_dropout=0.05
pretrained_model=path/to/hf/mixtral/dir
dataset_dir=path/to/pt/data/dir
data_cache=temp_data_cache_dir
per_device_train_batch_size=1
gradient_accumulation_steps=8
block_size=1024
output_dir=output_dir
deepspeed_config_file=ds_zero2_no_offload.json
torchrun --nnodes 1 --nproc_per_node 1 run_clm_pt_with_peft.py \
--deepspeed ${deepspeed_config_file} \
--model_name_or_path ${pretrained_model} \
--tokenizer_name_or_path ${pretrained_model} \
--dataset_dir ${dataset_dir} \
--data_cache_dir ${data_cache} \
--validation_split_percentage 0.001 \
--per_device_train_batch_size ${per_device_train_batch_size} \
--do_train \
--seed $RANDOM \
--fp16 \
--num_train_epochs 1 \
--lr_scheduler_type cosine \
--learning_rate ${lr} \
--warmup_ratio 0.05 \
--weight_decay 0.1 \
--logging_strategy steps \
--logging_steps 10 \
--save_strategy steps \
--save_total_limit 3 \
--save_steps 200 \
--gradient_accumulation_steps ${gradient_accumulation_steps} \
--preprocessing_num_workers 8 \
--block_size ${block_size} \
--output_dir ${output_dir} \
--overwrite_output_dir \
--ddp_timeout 30000 \
--logging_first_step True \
--lora_rank ${lora_rank} \
--lora_alpha ${lora_alpha} \
--trainable ${lora_trainable} \
--lora_dropout ${lora_dropout} \
--modules_to_save ${modules_to_save} \
--torch_dtype float16 \
--load_in_kbits 4 \
--gradient_checkpointing \
--ddp_find_unused_parameters False \
--output_router_logits
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
scripts/training/run_sft.sh | Shell |
## 运行脚本前请仔细阅读wiki(https://github.com/ymcui/Chinese-Mixtral/wiki/sft_scripts_zh)
## Read the wiki(https://github.com/ymcui/Chinese-Mixtral/wiki/sft_scripts_en) carefully before running the script
lr=1e-4
lora_rank=64
lora_alpha=128
lora_trainable="q_proj,v_proj,k_proj,o_proj,gate,w1,w2,w3"
modules_to_save="embed_tokens,lm_head"
lora_dropout=0.05
pretrained_model=path/to/hf/chinese-mixtral/dir/or/model_id
dataset_dir=path/to/sft/data/dir
per_device_train_batch_size=1
per_device_eval_batch_size=1
gradient_accumulation_steps=8
max_seq_length=1024
output_dir=output_dir
validation_file=validation_file_name
deepspeed_config_file=ds_zero2_no_offload.json
torchrun --nnodes 1 --nproc_per_node 1 run_clm_sft_with_peft.py \
--deepspeed ${deepspeed_config_file} \
--model_name_or_path ${pretrained_model} \
--tokenizer_name_or_path ${pretrained_model} \
--dataset_dir ${dataset_dir} \
--per_device_train_batch_size ${per_device_train_batch_size} \
--per_device_eval_batch_size ${per_device_eval_batch_size} \
--do_train \
--do_eval \
--seed $RANDOM \
--fp16 \
--num_train_epochs 3 \
--lr_scheduler_type cosine \
--learning_rate ${lr} \
--warmup_ratio 0.05 \
--weight_decay 0.1 \
--logging_strategy steps \
--logging_steps 10 \
--save_strategy steps \
--save_total_limit 3 \
--evaluation_strategy steps \
--eval_steps 100 \
--save_steps 200 \
--gradient_accumulation_steps ${gradient_accumulation_steps} \
--preprocessing_num_workers 8 \
--max_seq_length ${max_seq_length} \
--output_dir ${output_dir} \
--overwrite_output_dir \
--ddp_timeout 30000 \
--logging_first_step True \
--lora_rank ${lora_rank} \
--lora_alpha ${lora_alpha} \
--trainable ${lora_trainable} \
--lora_dropout ${lora_dropout} \
--modules_to_save ${modules_to_save} \
--torch_dtype float16 \
--validation_file ${validation_file} \
--load_in_kbits 4 \
--gradient_checkpointing \
--ddp_find_unused_parameters False \
--output_router_logits
| ymcui/Chinese-Mixtral | 609 | 中文Mixtral混合专家大模型（Chinese Mixtral MoE LLMs） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/classifier_utils.py | Python | from absl import flags
import re
import numpy as np
import tensorflow as tf
from data_utils import SEP_ID, CLS_ID
FLAGS = flags.FLAGS
SEG_ID_A = 0
SEG_ID_B = 1
SEG_ID_CLS = 2
SEG_ID_SEP = 3
SEG_ID_PAD = 4
class PaddingInputExample(object):
"""Fake example so the num input examples is a multiple of the batch size.
When running eval/predict on the TPU, we need to pad the number of examples
to be a multiple of the batch size, because the TPU requires a fixed batch
size. The alternative is to drop the last batch, which is bad because it means
the entire output data won't be generated.
We use this class instead of `None` because treating `None` as padding
battches could cause silent errors.
"""
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
input_ids,
input_mask,
segment_ids,
label_id,
is_real_example=True):
self.input_ids = input_ids
self.input_mask = input_mask
self.segment_ids = segment_ids
self.label_id = label_id
self.is_real_example = is_real_example
def _truncate_seq_pair(tokens_a, tokens_b, max_length):
"""Truncates a sequence pair in place to the maximum length."""
# This is a simple heuristic which will always truncate the longer sequence
# one token at a time. This makes more sense than truncating an equal percent
# of tokens from each, since if one sequence is very short then each token
# that's truncated likely contains more information than a longer sequence.
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_length:
break
if len(tokens_a) > len(tokens_b):
tokens_a.pop()
else:
tokens_b.pop()
def convert_single_example(ex_index, example, label_list, max_seq_length,
tokenize_fn):
"""Converts a single `InputExample` into a single `InputFeatures`."""
if isinstance(example, PaddingInputExample):
return InputFeatures(
input_ids=[0] * max_seq_length,
input_mask=[1] * max_seq_length,
segment_ids=[0] * max_seq_length,
label_id=0,
is_real_example=False)
if label_list is not None:
label_map = {}
for (i, label) in enumerate(label_list):
label_map[label] = i
tokens_a = tokenize_fn(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenize_fn(example.text_b)
if tokens_b:
# Modifies `tokens_a` and `tokens_b` in place so that the total
# length is less than the specified length.
# Account for two [SEP] & one [CLS] with "- 3"
_truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
else:
# Account for one [SEP] & one [CLS] with "- 2"
if len(tokens_a) > max_seq_length - 2:
tokens_a = tokens_a[:max_seq_length - 2]
tokens = []
segment_ids = []
for token in tokens_a:
tokens.append(token)
segment_ids.append(SEG_ID_A)
tokens.append(SEP_ID)
segment_ids.append(SEG_ID_A)
if tokens_b:
for token in tokens_b:
tokens.append(token)
segment_ids.append(SEG_ID_B)
tokens.append(SEP_ID)
segment_ids.append(SEG_ID_B)
tokens.append(CLS_ID)
segment_ids.append(SEG_ID_CLS)
input_ids = tokens
# The mask has 0 for real tokens and 1 for padding tokens. Only real
# tokens are attended to.
input_mask = [0] * len(input_ids)
# Zero-pad up to the sequence length.
if len(input_ids) < max_seq_length:
delta_len = max_seq_length - len(input_ids)
input_ids = [0] * delta_len + input_ids
input_mask = [1] * delta_len + input_mask
segment_ids = [SEG_ID_PAD] * delta_len + segment_ids
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
if label_list is not None:
label_id = label_map[example.label]
else:
label_id = example.label
if ex_index < 5:
tf.logging.info("*** Example ***")
tf.logging.info("guid: %s" % (example.guid))
tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
tf.logging.info("label: {} (id = {})".format(example.label, label_id))
feature = InputFeatures(
input_ids=input_ids,
input_mask=input_mask,
segment_ids=segment_ids,
label_id=label_id)
return feature
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/cmrc2018_evaluate_drcd.py | Python | # -*- coding: utf-8 -*-
'''
Evaluation script for CMRC 2018
version: v5
Note:
v5 formatted output, add usage description
v4 fixed segmentation issues
'''
from __future__ import print_function
from collections import Counter, OrderedDict
import string
import re
import argparse
import json
import sys
reload(sys)
sys.setdefaultencoding('utf8')
import nltk
import pdb
# split Chinese with English
def mixed_segmentation(in_str, rm_punc=False):
in_str = str(in_str).decode('utf-8').lower().strip()
segs_out = []
temp_str = ""
sp_char = ['-',':','_','*','^','/','\\','~','`','+','=',
'，','。','：','？','！','“','”','；','’','《','》','……','·','、',
'「','」','（','）','－','～','『','』']
for char in in_str:
if rm_punc and char in sp_char:
continue
if re.search(ur'[\u4e00-\u9fa5]', char) or char in sp_char:
if temp_str != "":
ss = nltk.word_tokenize(temp_str)
segs_out.extend(ss)
temp_str = ""
segs_out.append(char)
else:
temp_str += char
#handling last part
if temp_str != "":
ss = nltk.word_tokenize(temp_str)
segs_out.extend(ss)
return segs_out
# remove punctuation
def remove_punctuation(in_str):
in_str = str(in_str).decode('utf-8').lower().strip()
sp_char = ['-',':','_','*','^','/','\\','~','`','+','=',
'，','。','：','？','！','“','”','；','’','《','》','……','·','、',
'「','」','（','）','－','～','『','』']
out_segs = []
for char in in_str:
if char in sp_char:
continue
else:
out_segs.append(char)
return ''.join(out_segs)
# find longest common string
def find_lcs(s1, s2):
m = [[0 for i in range(len(s2)+1)] for j in range(len(s1)+1)]
mmax = 0
p = 0
for i in range(len(s1)):
for j in range(len(s2)):
if s1[i] == s2[j]:
m[i+1][j+1] = m[i][j]+1
if m[i+1][j+1] > mmax:
mmax=m[i+1][j+1]
p=i+1
return s1[p-mmax:p], mmax
#
def evaluate(ground_truth_file, prediction_file):
f1 = 0
em = 0
total_count = 0
skip_count = 0
for instance in ground_truth_file["data"]:
#context_id = instance['context_id'].strip()
#context_text = instance['context_text'].strip()
for para in instance["paragraphs"]:
for qas in para['qas']:
total_count += 1
query_id = qas['id'].strip()
query_text = qas['question'].strip()
answers = [x["text"] for x in qas['answers']]
if query_id not in prediction_file:
sys.stderr.write('Unanswered question: {}\n'.format(query_id))
skip_count += 1
continue
prediction = str(prediction_file[query_id]).decode('utf-8')
f1 += calc_f1_score(answers, prediction)
em += calc_em_score(answers, prediction)
f1_score = 100.0 * f1 / total_count
em_score = 100.0 * em / total_count
return f1_score, em_score, total_count, skip_count
def calc_f1_score(answers, prediction):
f1_scores = []
for ans in answers:
ans_segs = mixed_segmentation(ans, rm_punc=True)
prediction_segs = mixed_segmentation(prediction, rm_punc=True)
lcs, lcs_len = find_lcs(ans_segs, prediction_segs)
if lcs_len == 0:
f1_scores.append(0)
continue
precision = 1.0*lcs_len/len(prediction_segs)
recall = 1.0*lcs_len/len(ans_segs)
f1 = (2*precision*recall)/(precision+recall)
f1_scores.append(f1)
return max(f1_scores)
def calc_em_score(answers, prediction):
em = 0
for ans in answers:
ans_ = remove_punctuation(ans)
prediction_ = remove_punctuation(prediction)
if ans_ == prediction_:
em = 1
break
return em
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Evaluation Script for CMRC 2018')
parser.add_argument('dataset_file', help='Official dataset file')
parser.add_argument('prediction_file', help='Your prediction File')
args = parser.parse_args()
ground_truth_file = json.load(open(args.dataset_file, 'rb'))
prediction_file = json.load(open(args.prediction_file, 'rb'))
F1, EM, TOTAL, SKIP = evaluate(ground_truth_file, prediction_file)
AVG = (EM+F1)*0.5
output_result = OrderedDict()
output_result['AVERAGE'] = '%.3f' % AVG
output_result['F1'] = '%.3f' % F1
output_result['EM'] = '%.3f' % EM
output_result['TOTAL'] = TOTAL
output_result['SKIP'] = SKIP
output_result['FILE'] = args.prediction_file
print(json.dumps(output_result))
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/data_utils.py | Python | # -*- coding: utf-8 -*-
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import random
from absl import flags
import absl.logging as _logging # pylint: disable=unused-import
import numpy as np
import tensorflow as tf
from prepro_utils import preprocess_text, encode_ids
import sentencepiece as spm
special_symbols = {
"<unk>" : 0,
"<s>" : 1,
"</s>" : 2,
"<cls>" : 3,
"<sep>" : 4,
"<pad>" : 5,
"<mask>" : 6,
"<eod>" : 7,
"<eop>" : 8,
}
VOCAB_SIZE = 32000
UNK_ID = special_symbols["<unk>"]
CLS_ID = special_symbols["<cls>"]
SEP_ID = special_symbols["<sep>"]
MASK_ID = special_symbols["<mask>"]
EOD_ID = special_symbols["<eod>"]
def _int64_feature(values):
return tf.train.Feature(int64_list=tf.train.Int64List(value=values))
def _float_feature(values):
return tf.train.Feature(float_list=tf.train.FloatList(value=values))
def format_filename(prefix, bsz_per_host, seq_len, bi_data, suffix,
mask_alpha=5, mask_beta=1, reuse_len=None, uncased=False,
fixed_num_predict=None):
"""docs."""
if reuse_len is None:
reuse_len_str = ""
else:
reuse_len_str = "reuse-{}.".format(reuse_len)
if not uncased:
uncased_str = ""
else:
uncased_str = "uncased."
if bi_data:
bi_data_str = "bi"
else:
bi_data_str = "uni"
if fixed_num_predict is not None:
fnp_str = "fnp-{}.".format(fixed_num_predict)
else:
fnp_str = ""
file_name = "{}.bsz-{}.seqlen-{}.{}{}{}.alpha-{}.beta-{}.{}{}".format(
prefix, bsz_per_host, seq_len, reuse_len_str, uncased_str, bi_data_str,
mask_alpha, mask_beta, fnp_str, suffix)
return file_name
def _create_data(idx, input_paths):
# Load sentence-piece model
sp = spm.SentencePieceProcessor()
sp.Load(FLAGS.sp_path)
input_shards = []
total_line_cnt = 0
for input_path in input_paths:
input_data, sent_ids = [], []
sent_id, line_cnt = True, 0
tf.logging.info("Processing %s", input_path)
for line in tf.gfile.Open(input_path):
if line_cnt % 100000 == 0:
tf.logging.info("Loading line %d", line_cnt)
line_cnt += 1
if not line.strip():
if FLAGS.use_eod:
sent_id = not sent_id
cur_sent = [EOD_ID]
else:
continue
else:
if FLAGS.from_raw_text:
cur_sent = preprocess_text(line.strip(), lower=FLAGS.uncased)
cur_sent = encode_ids(sp, cur_sent)
else:
cur_sent = list(map(int, line.strip().split()))
input_data.extend(cur_sent)
sent_ids.extend([sent_id] * len(cur_sent))
sent_id = not sent_id
tf.logging.info("Finish with line %d", line_cnt)
if line_cnt == 0:
continue
input_data = np.array(input_data, dtype=np.int64)
sent_ids = np.array(sent_ids, dtype=np.bool)
total_line_cnt += line_cnt
input_shards.append((input_data, sent_ids))
tf.logging.info("[Task %d] Total number line: %d", idx, total_line_cnt)
tfrecord_dir = os.path.join(FLAGS.save_dir, "tfrecords")
filenames, num_batch = [], 0
# Randomly shuffle input shards (with a fixed but distinct random seed)
np.random.seed(100 * FLAGS.task + FLAGS.pass_id)
perm_indices = np.random.permutation(len(input_shards))
tf.logging.info("Using perm indices %s for pass %d",
perm_indices.tolist(), FLAGS.pass_id)
input_data_list, sent_ids_list = [], []
prev_sent_id = None
for perm_idx in perm_indices:
input_data, sent_ids = input_shards[perm_idx]
# make sure the `send_ids[0] == not prev_sent_id`
if prev_sent_id is not None and sent_ids[0] == prev_sent_id:
sent_ids = np.logical_not(sent_ids)
# append to temporary list
input_data_list.append(input_data)
sent_ids_list.append(sent_ids)
# update `prev_sent_id`
prev_sent_id = sent_ids[-1]
input_data = np.concatenate(input_data_list)
sent_ids = np.concatenate(sent_ids_list)
file_name, cur_num_batch = create_tfrecords(
save_dir=tfrecord_dir,
basename="{}-{}-{}".format(FLAGS.split, idx, FLAGS.pass_id),
data=[input_data, sent_ids],
bsz_per_host=FLAGS.bsz_per_host,
seq_len=FLAGS.seq_len,
bi_data=FLAGS.bi_data,
sp=sp,
)
filenames.append(file_name)
num_batch += cur_num_batch
record_info = {
"filenames": filenames,
"num_batch": num_batch
}
return record_info
def create_data(_):
# Validate FLAGS
assert FLAGS.bsz_per_host % FLAGS.num_core_per_host == 0
if not FLAGS.use_tpu:
FLAGS.num_core_per_host = 1 # forced to be one
# Make workdirs
if not tf.gfile.Exists(FLAGS.save_dir):
tf.gfile.MakeDirs(FLAGS.save_dir)
tfrecord_dir = os.path.join(FLAGS.save_dir, "tfrecords")
if not tf.gfile.Exists(tfrecord_dir):
tf.gfile.MakeDirs(tfrecord_dir)
# Create and dump corpus_info from task 0
if FLAGS.task == 0:
corpus_info = {
"vocab_size": VOCAB_SIZE,
"bsz_per_host": FLAGS.bsz_per_host,
"num_core_per_host": FLAGS.num_core_per_host,
"seq_len": FLAGS.seq_len,
"reuse_len": FLAGS.reuse_len,
"uncased": FLAGS.uncased,
"bi_data": FLAGS.bi_data,
"mask_alpha": FLAGS.mask_alpha,
"mask_beta": FLAGS.mask_beta,
"num_predict": FLAGS.num_predict,
"use_eod": FLAGS.use_eod,
"sp_path": FLAGS.sp_path,
"input_glob": FLAGS.input_glob,
}
corpus_info_path = os.path.join(FLAGS.save_dir, "corpus_info.json")
with tf.gfile.Open(corpus_info_path, "w") as fp:
json.dump(corpus_info, fp)
# Interleavely split the work into FLAGS.num_task splits
file_paths = sorted(tf.gfile.Glob(FLAGS.input_glob))
tf.logging.info("Use glob: %s", FLAGS.input_glob)
tf.logging.info("Find %d files: %s", len(file_paths), file_paths)
task_file_paths = file_paths[FLAGS.task::FLAGS.num_task]
if not task_file_paths:
tf.logging.info("Exit: task %d has no file to process.", FLAGS.task)
return
tf.logging.info("Task %d process %d files: %s",
FLAGS.task, len(task_file_paths), task_file_paths)
record_info = _create_data(FLAGS.task, task_file_paths)
record_prefix = "record_info-{}-{}-{}".format(
FLAGS.split, FLAGS.task, FLAGS.pass_id)
record_name = format_filename(
prefix=record_prefix,
bsz_per_host=FLAGS.bsz_per_host,
seq_len=FLAGS.seq_len,
mask_alpha=FLAGS.mask_alpha,
mask_beta=FLAGS.mask_beta,
reuse_len=FLAGS.reuse_len,
bi_data=FLAGS.bi_data,
suffix="json",
uncased=FLAGS.uncased,
fixed_num_predict=FLAGS.num_predict)
record_info_path = os.path.join(tfrecord_dir, record_name)
with tf.gfile.Open(record_info_path, "w") as fp:
json.dump(record_info, fp)
def batchify(data, bsz_per_host, sent_ids=None):
num_step = len(data) // bsz_per_host
data = data[:bsz_per_host * num_step]
data = data.reshape(bsz_per_host, num_step)
if sent_ids is not None:
sent_ids = sent_ids[:bsz_per_host * num_step]
sent_ids = sent_ids.reshape(bsz_per_host, num_step)
if sent_ids is not None:
return data, sent_ids
return data
def _split_a_and_b(data, sent_ids, begin_idx, tot_len, extend_target=False):
"""Split two segments from `data` starting from the index `begin_idx`."""
data_len = data.shape[0]
if begin_idx + tot_len >= data_len:
tf.logging.info("[_split_a_and_b] returns None: "
"begin_idx %d + tot_len %d >= data_len %d",
begin_idx, tot_len, data_len)
return None
end_idx = begin_idx + 1
cut_points = []
while end_idx < data_len:
if sent_ids[end_idx] != sent_ids[end_idx - 1]:
if end_idx - begin_idx >= tot_len: break
cut_points.append(end_idx)
end_idx += 1
a_begin = begin_idx
if len(cut_points) == 0 or random.random() < 0.5:
label = 0
if len(cut_points) == 0:
a_end = end_idx
else:
a_end = random.choice(cut_points)
b_len = max(1, tot_len - (a_end - a_begin))
# (zihang): `data_len - 1` to account for extend_target
b_begin = random.randint(0, data_len - 1 - b_len)
b_end = b_begin + b_len
while b_begin > 0 and sent_ids[b_begin - 1] == sent_ids[b_begin]:
b_begin -= 1
# (zihang): `data_len - 1` to account for extend_target
while b_end < data_len - 1 and sent_ids[b_end - 1] == sent_ids[b_end]:
b_end += 1
new_begin = a_end
else:
label = 1
a_end = random.choice(cut_points)
b_begin = a_end
b_end = end_idx
new_begin = b_end
while a_end - a_begin + b_end - b_begin > tot_len:
if a_end - a_begin > b_end - b_begin:
# delete the right side only for the LM objective
a_end -= 1
else:
b_end -= 1
ret = [data[a_begin: a_end], data[b_begin: b_end], label, new_begin]
if extend_target:
if a_end >= data_len or b_end >= data_len:
tf.logging.info("[_split_a_and_b] returns None: "
"a_end %d or b_end %d >= data_len %d",
a_end, b_end, data_len)
return None
a_target = data[a_begin + 1: a_end + 1]
b_target = data[b_begin: b_end + 1]
ret.extend([a_target, b_target])
return ret
def _is_start_piece(piece):
special_pieces = set(list('!"#$%&\"()*+,-./:;?@[\\]^_`{|}~'))
if (piece.startswith("▁") or piece.startswith("<")
or piece in special_pieces):
return True
else:
return False
def _sample_mask(sp, seg, reverse=False, max_gram=5, goal_num_predict=None):
"""Sample `goal_num_predict` tokens for partial prediction.
About `mask_beta` tokens are chosen in a context of `mask_alpha` tokens."""
seg_len = len(seg)
mask = np.array([False] * seg_len, dtype=np.bool)
num_predict = 0
ngrams = np.arange(1, max_gram + 1, dtype=np.int64)
pvals = 1. / np.arange(1, max_gram + 1)
pvals /= pvals.sum(keepdims=True)
if reverse:
seg = np.flip(seg, 0)
cur_len = 0
while cur_len < seg_len:
if goal_num_predict is not None and num_predict >= goal_num_predict: break
n = np.random.choice(ngrams, p=pvals)
if goal_num_predict is not None:
n = min(n, goal_num_predict - num_predict)
ctx_size = (n * FLAGS.mask_alpha) // FLAGS.mask_beta
l_ctx = np.random.choice(ctx_size)
r_ctx = ctx_size - l_ctx
# Find the start position of a complete token
beg = cur_len + l_ctx
while beg < seg_len and not _is_start_piece(sp.IdToPiece(seg[beg].item())):
beg += 1
if beg >= seg_len:
break
# Find the end position of the n-gram (start pos of the n+1-th gram)
end = beg + 1
cnt_ngram = 1
while end < seg_len:
if _is_start_piece(sp.IdToPiece(seg[beg].item())):
cnt_ngram += 1
if cnt_ngram > n:
break
end += 1
if end >= seg_len:
break
# Update
mask[beg:end] = True
num_predict += end - beg
cur_len = end + r_ctx
while goal_num_predict is not None and num_predict < goal_num_predict:
i = np.random.randint(seg_len)
if not mask[i]:
mask[i] = True
num_predict += 1
if reverse:
mask = np.flip(mask, 0)
return mask
def create_tfrecords(save_dir, basename, data, bsz_per_host, seq_len,
bi_data, sp):
data, sent_ids = data[0], data[1]
num_core = FLAGS.num_core_per_host
bsz_per_core = bsz_per_host // num_core
if bi_data:
assert bsz_per_host % (2 * FLAGS.num_core_per_host) == 0
fwd_data, fwd_sent_ids = batchify(data, bsz_per_host // 2, sent_ids)
fwd_data = fwd_data.reshape(num_core, 1, bsz_per_core // 2, -1)
fwd_sent_ids = fwd_sent_ids.reshape(num_core, 1, bsz_per_core // 2, -1)
bwd_data = fwd_data[:, :, :, ::-1]
bwd_sent_ids = fwd_sent_ids[:, :, :, ::-1]
data = np.concatenate(
[fwd_data, bwd_data], 1).reshape(bsz_per_host, -1)
sent_ids = np.concatenate(
[fwd_sent_ids, bwd_sent_ids], 1).reshape(bsz_per_host, -1)
else:
data, sent_ids = batchify(data, bsz_per_host, sent_ids)
tf.logging.info("Raw data shape %s.", data.shape)
file_name = format_filename(
prefix=basename,
bsz_per_host=bsz_per_host,
seq_len=seq_len,
bi_data=bi_data,
suffix="tfrecords",
mask_alpha=FLAGS.mask_alpha,
mask_beta=FLAGS.mask_beta,
reuse_len=FLAGS.reuse_len,
uncased=FLAGS.uncased,
fixed_num_predict=FLAGS.num_predict
)
save_path = os.path.join(save_dir, file_name)
record_writer = tf.python_io.TFRecordWriter(save_path)
tf.logging.info("Start writing %s.", save_path)
num_batch = 0
reuse_len = FLAGS.reuse_len
# [sep] x 2 + [cls]
assert reuse_len < seq_len - 3
data_len = data.shape[1]
sep_array = np.array([SEP_ID], dtype=np.int64)
cls_array = np.array([CLS_ID], dtype=np.int64)
i = 0
while i + seq_len <= data_len:
if num_batch % 500 == 0:
tf.logging.info("Processing batch %d", num_batch)
all_ok = True
features = []
for idx in range(bsz_per_host):
inp = data[idx, i: i + reuse_len]
tgt = data[idx, i + 1: i + reuse_len + 1]
results = _split_a_and_b(
data[idx],
sent_ids[idx],
begin_idx=i + reuse_len,
tot_len=seq_len - reuse_len - 3,
extend_target=True)
if results is None:
tf.logging.info("Break out with seq idx %d", i)
all_ok = False
break
# unpack the results
(a_data, b_data, label, _, a_target, b_target) = tuple(results)
# sample ngram spans to predict
reverse = bi_data and (idx // (bsz_per_core // 2)) % 2 == 1
if FLAGS.num_predict is None:
num_predict_0 = num_predict_1 = None
else:
num_predict_1 = FLAGS.num_predict // 2
num_predict_0 = FLAGS.num_predict - num_predict_1
mask_0 = _sample_mask(sp, inp, reverse=reverse,
goal_num_predict=num_predict_0)
mask_1 = _sample_mask(sp, np.concatenate([a_data, sep_array, b_data,
sep_array, cls_array]),
reverse=reverse, goal_num_predict=num_predict_1)
# concatenate data
cat_data = np.concatenate([inp, a_data, sep_array, b_data,
sep_array, cls_array])
seg_id = ([0] * (reuse_len + a_data.shape[0]) + [0] +
[1] * b_data.shape[0] + [1] + [2])
assert cat_data.shape[0] == seq_len
assert mask_0.shape[0] == seq_len // 2
assert mask_1.shape[0] == seq_len // 2
# the last two CLS's are not used, just for padding purposes
tgt = np.concatenate([tgt, a_target, b_target, cls_array, cls_array])
assert tgt.shape[0] == seq_len
is_masked = np.concatenate([mask_0, mask_1], 0)
if FLAGS.num_predict is not None:
assert np.sum(is_masked) == FLAGS.num_predict
feature = {
"input": _int64_feature(cat_data),
"is_masked": _int64_feature(is_masked),
"target": _int64_feature(tgt),
"seg_id": _int64_feature(seg_id),
"label": _int64_feature([label]),
}
features.append(feature)
if all_ok:
assert len(features) == bsz_per_host
for feature in features:
example = tf.train.Example(features=tf.train.Features(feature=feature))
record_writer.write(example.SerializeToString())
num_batch += 1
else:
break
i += reuse_len
record_writer.close()
tf.logging.info("Done writing %s. Num of batches: %d", save_path, num_batch)
return save_path, num_batch
################
# get_input_fn #
################
def _convert_example(example, use_bfloat16):
"""Cast int64 into int32 and float32 to bfloat16 if use_bfloat16."""
for key in list(example.keys()):
val = example[key]
if tf.keras.backend.is_sparse(val):
val = tf.sparse.to_dense(val)
if val.dtype == tf.int64:
val = tf.cast(val, tf.int32)
if use_bfloat16 and val.dtype == tf.float32:
val = tf.cast(val, tf.bfloat16)
example[key] = val
def parse_files_to_dataset(parser, file_names, split, num_batch, num_hosts,
host_id, num_core_per_host, bsz_per_core):
# list of file pathes
num_files = len(file_names)
num_files_per_host = num_files // num_hosts
my_start_file_id = host_id * num_files_per_host
my_end_file_id = (host_id + 1) * num_files_per_host
if host_id == num_hosts - 1:
my_end_file_id = num_files
file_paths = file_names[my_start_file_id: my_end_file_id]
tf.logging.info("Host %d handles %d files", host_id, len(file_paths))
assert split == "train"
dataset = tf.data.Dataset.from_tensor_slices(file_paths)
# file-level shuffle
if len(file_paths) > 1:
dataset = dataset.shuffle(len(file_paths))
# Note: we cannot perform sample-level shuffle here because this will violate
# the consecutive requirement of data stream.
dataset = tf.data.TFRecordDataset(dataset)
# (zihang): since we are doing online preprocessing, the parsed result of
# the same input at each time will be different. Thus, cache processed data
# is not helpful. It will use a lot of memory and lead to contrainer OOM.
# So, change to cache non-parsed raw data instead.
dataset = dataset.cache().map(parser).repeat()
dataset = dataset.batch(bsz_per_core, drop_remainder=True)
dataset = dataset.prefetch(num_core_per_host * bsz_per_core)
return dataset
def _local_perm(inputs, targets, is_masked, perm_size, seq_len):
"""
Sample a permutation of the factorization order, and create an
attention mask accordingly.
Args:
inputs: int64 Tensor in shape [seq_len], input ids.
targets: int64 Tensor in shape [seq_len], target ids.
is_masked: bool Tensor in shape [seq_len]. True means being selected
for partial prediction.
perm_size: the length of longest permutation. Could be set to be reuse_len.
Should not be larger than reuse_len or there will be data leaks.
seq_len: int, sequence length.
"""
# Generate permutation indices
index = tf.range(seq_len, dtype=tf.int64)
index = tf.transpose(tf.reshape(index, [-1, perm_size]))
index = tf.random_shuffle(index)
index = tf.reshape(tf.transpose(index), [-1])
# `perm_mask` and `target_mask`
# non-functional tokens
non_func_tokens = tf.logical_not(tf.logical_or(
tf.equal(inputs, SEP_ID),
tf.equal(inputs, CLS_ID)))
non_mask_tokens = tf.logical_and(tf.logical_not(is_masked), non_func_tokens)
masked_or_func_tokens = tf.logical_not(non_mask_tokens)
# Set the permutation indices of non-masked (& non-funcional) tokens to the
# smallest index (-1):
# (1) they can be seen by all other positions
# (2) they cannot see masked positions, so there won"t be information leak
smallest_index = -tf.ones([seq_len], dtype=tf.int64)
rev_index = tf.where(non_mask_tokens, smallest_index, index)
# Create `target_mask`: non-funcional and maksed tokens
# 1: use mask as input and have loss
# 0: use token (or [SEP], [CLS]) as input and do not have loss
target_tokens = tf.logical_and(masked_or_func_tokens, non_func_tokens)
target_mask = tf.cast(target_tokens, tf.float32)
# Create `perm_mask`
# `target_tokens` cannot see themselves
self_rev_index = tf.where(target_tokens, rev_index, rev_index + 1)
# 1: cannot attend if i <= j and j is not non-masked (masked_or_func_tokens)
# 0: can attend if i > j or j is non-masked
perm_mask = tf.logical_and(
self_rev_index[:, None] <= rev_index[None, :],
masked_or_func_tokens)
perm_mask = tf.cast(perm_mask, tf.float32)
# new target: [next token] for LM and [curr token] (self) for PLM
new_targets = tf.concat([inputs[0: 1], targets[: -1]],
axis=0)
# construct inputs_k
inputs_k = inputs
# construct inputs_q
inputs_q = target_mask
return perm_mask, new_targets, target_mask, inputs_k, inputs_q
def get_dataset(params, num_hosts, num_core_per_host, split, file_names,
num_batch, seq_len, reuse_len, perm_size, mask_alpha,
mask_beta, use_bfloat16=False, num_predict=None):
bsz_per_core = params["batch_size"]
if num_hosts > 1:
host_id = params["context"].current_host
else:
host_id = 0
#### Function used to parse tfrecord
def parser(record):
"""function used to parse tfrecord."""
record_spec = {
"input": tf.FixedLenFeature([seq_len], tf.int64),
"target": tf.FixedLenFeature([seq_len], tf.int64),
"seg_id": tf.FixedLenFeature([seq_len], tf.int64),
"label": tf.FixedLenFeature([1], tf.int64),
"is_masked": tf.FixedLenFeature([seq_len], tf.int64),
}
# retrieve serialized example
example = tf.parse_single_example(
serialized=record,
features=record_spec)
inputs = example.pop("input")
target = example.pop("target")
is_masked = tf.cast(example.pop("is_masked"), tf.bool)
non_reuse_len = seq_len - reuse_len
assert perm_size <= reuse_len and perm_size <= non_reuse_len
perm_mask_0, target_0, target_mask_0, input_k_0, input_q_0 = _local_perm(
inputs[:reuse_len],
target[:reuse_len],
is_masked[:reuse_len],
perm_size,
reuse_len)
perm_mask_1, target_1, target_mask_1, input_k_1, input_q_1 = _local_perm(
inputs[reuse_len:],
target[reuse_len:],
is_masked[reuse_len:],
perm_size,
non_reuse_len)
perm_mask_0 = tf.concat([perm_mask_0, tf.ones([reuse_len, non_reuse_len])],
axis=1)
perm_mask_1 = tf.concat([tf.zeros([non_reuse_len, reuse_len]), perm_mask_1],
axis=1)
perm_mask = tf.concat([perm_mask_0, perm_mask_1], axis=0)
target = tf.concat([target_0, target_1], axis=0)
target_mask = tf.concat([target_mask_0, target_mask_1], axis=0)
input_k = tf.concat([input_k_0, input_k_1], axis=0)
input_q = tf.concat([input_q_0, input_q_1], axis=0)
if num_predict is not None:
indices = tf.range(seq_len, dtype=tf.int64)
bool_target_mask = tf.cast(target_mask, tf.bool)
indices = tf.boolean_mask(indices, bool_target_mask)
##### extra padding due to CLS/SEP introduced after prepro
actual_num_predict = tf.shape(indices)[0]
pad_len = num_predict - actual_num_predict
##### target_mapping
target_mapping = tf.one_hot(indices, seq_len, dtype=tf.float32)
paddings = tf.zeros([pad_len, seq_len], dtype=target_mapping.dtype)
target_mapping = tf.concat([target_mapping, paddings], axis=0)
example["target_mapping"] = tf.reshape(target_mapping,
[num_predict, seq_len])
##### target
target = tf.boolean_mask(target, bool_target_mask)
paddings = tf.zeros([pad_len], dtype=target.dtype)
target = tf.concat([target, paddings], axis=0)
example["target"] = tf.reshape(target, [num_predict])
##### target mask
target_mask = tf.concat(
[tf.ones([actual_num_predict], dtype=tf.float32),
tf.zeros([pad_len], dtype=tf.float32)],
axis=0)
example["target_mask"] = tf.reshape(target_mask, [num_predict])
else:
example["target"] = tf.reshape(target, [seq_len])
example["target_mask"] = tf.reshape(target_mask, [seq_len])
# reshape back to fixed shape
example["perm_mask"] = tf.reshape(perm_mask, [seq_len, seq_len])
example["input_k"] = tf.reshape(input_k, [seq_len])
example["input_q"] = tf.reshape(input_q, [seq_len])
_convert_example(example, use_bfloat16)
for k, v in example.items():
tf.logging.info("%s: %s", k, v)
return example
# Get dataset
dataset = parse_files_to_dataset(
parser=parser,
file_names=file_names,
split=split,
num_batch=num_batch,
num_hosts=num_hosts,
host_id=host_id,
num_core_per_host=num_core_per_host,
bsz_per_core=bsz_per_core)
return dataset
def get_input_fn(
tfrecord_dir,
split,
bsz_per_host,
seq_len,
reuse_len,
bi_data,
num_hosts=1,
num_core_per_host=1,
perm_size=None,
mask_alpha=None,
mask_beta=None,
uncased=False,
num_passes=None,
use_bfloat16=False,
num_predict=None):
# Merge all record infos into a single one
record_glob_base = format_filename(
prefix="record_info-{}-*".format(split),
bsz_per_host=bsz_per_host,
seq_len=seq_len,
bi_data=bi_data,
suffix="json",
mask_alpha=mask_alpha,
mask_beta=mask_beta,
reuse_len=reuse_len,
uncased=uncased,
fixed_num_predict=num_predict)
record_info = {"num_batch": 0, "filenames": []}
tfrecord_dirs = tfrecord_dir.split(",")
tf.logging.info("Use the following tfrecord dirs: %s", tfrecord_dirs)
for idx, record_dir in enumerate(tfrecord_dirs):
record_glob = os.path.join(record_dir, record_glob_base)
tf.logging.info("[%d] Record glob: %s", idx, record_glob)
record_paths = sorted(tf.gfile.Glob(record_glob))
tf.logging.info("[%d] Num of record info path: %d",
idx, len(record_paths))
cur_record_info = {"num_batch": 0, "filenames": []}
for record_info_path in record_paths:
if num_passes is not None:
record_info_name = os.path.basename(record_info_path)
fields = record_info_name.split(".")[0].split("-")
pass_id = int(fields[-1])
if len(fields) == 5 and pass_id >= num_passes:
tf.logging.info("Skip pass %d: %s", pass_id, record_info_name)
continue
with tf.gfile.Open(record_info_path, "r") as fp:
info = json.load(fp)
if num_passes is not None:
eff_num_passes = min(num_passes, len(info["filenames"]))
ratio = eff_num_passes / len(info["filenames"])
cur_record_info["num_batch"] += int(info["num_batch"] * ratio)
cur_record_info["filenames"] += info["filenames"][:eff_num_passes]
else:
cur_record_info["num_batch"] += info["num_batch"]
cur_record_info["filenames"] += info["filenames"]
# overwrite directory for `cur_record_info`
new_filenames = []
for filename in cur_record_info["filenames"]:
basename = os.path.basename(filename)
new_filename = os.path.join(record_dir, basename)
new_filenames.append(new_filename)
cur_record_info["filenames"] = new_filenames
tf.logging.info("[Dir %d] Number of chosen batches: %s",
idx, cur_record_info["num_batch"])
tf.logging.info("[Dir %d] Number of chosen files: %s",
idx, len(cur_record_info["filenames"]))
tf.logging.info(cur_record_info["filenames"])
# add `cur_record_info` to global `record_info`
record_info["num_batch"] += cur_record_info["num_batch"]
record_info["filenames"] += cur_record_info["filenames"]
tf.logging.info("Total number of batches: %d",
record_info["num_batch"])
tf.logging.info("Total number of files: %d",
len(record_info["filenames"]))
tf.logging.info(record_info["filenames"])
def input_fn(params):
"""docs."""
assert params["batch_size"] * num_core_per_host == bsz_per_host
dataset = get_dataset(
params=params,
num_hosts=num_hosts,
num_core_per_host=num_core_per_host,
split=split,
file_names=record_info["filenames"],
num_batch=record_info["num_batch"],
seq_len=seq_len,
reuse_len=reuse_len,
perm_size=perm_size,
mask_alpha=mask_alpha,
mask_beta=mask_beta,
use_bfloat16=use_bfloat16,
num_predict=num_predict)
return dataset
return input_fn, record_info
if __name__ == "__main__":
FLAGS = flags.FLAGS
flags.DEFINE_bool("use_tpu", True, help="whether to use TPUs")
flags.DEFINE_integer("bsz_per_host", 32, help="batch size per host.")
flags.DEFINE_integer("num_core_per_host", 8, help="num TPU cores per host.")
flags.DEFINE_integer("seq_len", 512,
help="Sequence length.")
flags.DEFINE_integer("reuse_len", 256,
help="Number of token that can be reused as memory. "
"Could be half of `seq_len`.")
flags.DEFINE_bool("uncased", True, help="Use uncased inputs or not.")
flags.DEFINE_bool("bi_data", True,
help="whether to create bidirectional data")
flags.DEFINE_integer("mask_alpha", default=6,
help="How many tokens to form a group.")
flags.DEFINE_integer("mask_beta", default=1,
help="How many tokens to mask within each group.")
flags.DEFINE_bool("use_eod", True,
help="whether to append EOD at the end of a doc.")
flags.DEFINE_bool("from_raw_text", True,
help="Whether the input is raw text or encoded ids.")
flags.DEFINE_integer("num_predict", default=85,
help="Num of tokens to predict.")
flags.DEFINE_string("input_glob", "data/example/*.txt",
help="Input file glob.")
flags.DEFINE_string("sp_path", "", help="Path to the sentence piece model.")
flags.DEFINE_string("save_dir", "proc_data/example",
help="Directory for saving the processed data.")
flags.DEFINE_enum("split", "train", ["train", "dev", "test"],
help="Save the data as which split.")
flags.DEFINE_integer("pass_id", 0, help="ID of the current pass."
"Different passes sample different negative segment.")
flags.DEFINE_integer("num_task", 1, help="Number of total tasks.")
flags.DEFINE_integer("task", 0, help="The Task ID. This value is used when "
"using multiple workers to identify each worker.")
tf.logging.set_verbosity(tf.logging.INFO)
tf.app.run(create_data)
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/function_builder.py | Python | """doc."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import functools
import os
import tensorflow as tf
import modeling
import xlnet
def construct_scalar_host_call(
monitor_dict,
model_dir,
prefix="",
reduce_fn=None):
"""
Construct host calls to monitor training progress on TPUs.
"""
metric_names = list(monitor_dict.keys())
def host_call_fn(global_step, *args):
"""actual host call function."""
step = global_step[0]
with tf.contrib.summary.create_file_writer(
logdir=model_dir, filename_suffix=".host_call").as_default():
with tf.contrib.summary.always_record_summaries():
for i, name in enumerate(metric_names):
if reduce_fn is None:
scalar = args[i][0]
else:
scalar = reduce_fn(args[i])
with tf.contrib.summary.record_summaries_every_n_global_steps(
100, global_step=step):
tf.contrib.summary.scalar(prefix + name, scalar, step=step)
return tf.contrib.summary.all_summary_ops()
global_step_tensor = tf.reshape(tf.train.get_or_create_global_step(), [1])
other_tensors = [tf.reshape(monitor_dict[key], [1]) for key in metric_names]
return host_call_fn, [global_step_tensor] + other_tensors
def two_stream_loss(FLAGS, features, labels, mems, is_training):
"""Pretraining loss with two-stream attention Transformer-XL."""
#### Unpack input
mem_name = "mems"
mems = mems.get(mem_name, None)
inp_k = tf.transpose(features["input_k"], [1, 0])
inp_q = tf.transpose(features["input_q"], [1, 0])
seg_id = tf.transpose(features["seg_id"], [1, 0])
inp_mask = None
perm_mask = tf.transpose(features["perm_mask"], [1, 2, 0])
if FLAGS.num_predict is not None:
# [num_predict x tgt_len x bsz]
target_mapping = tf.transpose(features["target_mapping"], [1, 2, 0])
else:
target_mapping = None
# target for LM loss
tgt = tf.transpose(features["target"], [1, 0])
# target mask for LM loss
tgt_mask = tf.transpose(features["target_mask"], [1, 0])
# construct xlnet config and save to model_dir
xlnet_config = xlnet.XLNetConfig(FLAGS=FLAGS)
xlnet_config.to_json(os.path.join(FLAGS.model_dir, "config.json"))
# construct run config from FLAGS
run_config = xlnet.create_run_config(is_training, False, FLAGS)
xlnet_model = xlnet.XLNetModel(
xlnet_config=xlnet_config,
run_config=run_config,
input_ids=inp_k,
seg_ids=seg_id,
input_mask=inp_mask,
mems=mems,
perm_mask=perm_mask,
target_mapping=target_mapping,
inp_q=inp_q)
output = xlnet_model.get_sequence_output()
new_mems = {mem_name: xlnet_model.get_new_memory()}
lookup_table = xlnet_model.get_embedding_table()
initializer = xlnet_model.get_initializer()
with tf.variable_scope("model", reuse=tf.AUTO_REUSE):
# LM loss
lm_loss = modeling.lm_loss(
hidden=output,
target=tgt,
n_token=xlnet_config.n_token,
d_model=xlnet_config.d_model,
initializer=initializer,
lookup_table=lookup_table,
tie_weight=True,
bi_data=run_config.bi_data,
use_tpu=run_config.use_tpu)
#### Quantity to monitor
monitor_dict = {}
if FLAGS.use_bfloat16:
tgt_mask = tf.cast(tgt_mask, tf.float32)
lm_loss = tf.cast(lm_loss, tf.float32)
total_loss = tf.reduce_sum(lm_loss * tgt_mask) / tf.reduce_sum(tgt_mask)
monitor_dict["total_loss"] = total_loss
return total_loss, new_mems, monitor_dict
def get_loss(FLAGS, features, labels, mems, is_training):
"""Pretraining loss with two-stream attention Transformer-XL."""
if FLAGS.use_bfloat16:
with tf.tpu.bfloat16_scope():
return two_stream_loss(FLAGS, features, labels, mems, is_training)
else:
return two_stream_loss(FLAGS, features, labels, mems, is_training)
def get_classification_loss(
FLAGS, features, n_class, is_training):
"""Loss for downstream classification tasks."""
bsz_per_core = tf.shape(features["input_ids"])[0]
inp = tf.transpose(features["input_ids"], [1, 0])
seg_id = tf.transpose(features["segment_ids"], [1, 0])
inp_mask = tf.transpose(features["input_mask"], [1, 0])
label = tf.reshape(features["label_ids"], [bsz_per_core])
xlnet_config = xlnet.XLNetConfig(json_path=FLAGS.model_config_path)
run_config = xlnet.create_run_config(is_training, True, FLAGS)
xlnet_model = xlnet.XLNetModel(
xlnet_config=xlnet_config,
run_config=run_config,
input_ids=inp,
seg_ids=seg_id,
input_mask=inp_mask)
summary = xlnet_model.get_pooled_out(FLAGS.summary_type, FLAGS.use_summ_proj)
with tf.variable_scope("model", reuse=tf.AUTO_REUSE):
if FLAGS.cls_scope is not None and FLAGS.cls_scope:
cls_scope = "classification_{}".format(FLAGS.cls_scope)
else:
cls_scope = "classification_{}".format(FLAGS.task_name.lower())
per_example_loss, logits = modeling.classification_loss(
hidden=summary,
labels=label,
n_class=n_class,
initializer=xlnet_model.get_initializer(),
scope=cls_scope,
return_logits=True)
total_loss = tf.reduce_mean(per_example_loss)
return total_loss, per_example_loss, logits
def get_regression_loss(
FLAGS, features, is_training):
"""Loss for downstream regression tasks."""
bsz_per_core = tf.shape(features["input_ids"])[0]
inp = tf.transpose(features["input_ids"], [1, 0])
seg_id = tf.transpose(features["segment_ids"], [1, 0])
inp_mask = tf.transpose(features["input_mask"], [1, 0])
label = tf.reshape(features["label_ids"], [bsz_per_core])
xlnet_config = xlnet.XLNetConfig(json_path=FLAGS.model_config_path)
run_config = xlnet.create_run_config(is_training, True, FLAGS)
xlnet_model = xlnet.XLNetModel(
xlnet_config=xlnet_config,
run_config=run_config,
input_ids=inp,
seg_ids=seg_id,
input_mask=inp_mask)
summary = xlnet_model.get_pooled_out(FLAGS.summary_type, FLAGS.use_summ_proj)
with tf.variable_scope("model", reuse=tf.AUTO_REUSE):
per_example_loss, logits = modeling.regression_loss(
hidden=summary,
labels=label,
initializer=xlnet_model.get_initializer(),
scope="regression_{}".format(FLAGS.task_name.lower()),
return_logits=True)
total_loss = tf.reduce_mean(per_example_loss)
return total_loss, per_example_loss, logits
def get_qa_outputs(FLAGS, features, is_training):
"""Loss for downstream span-extraction QA tasks such as SQuAD."""
inp = tf.transpose(features["input_ids"], [1, 0])
seg_id = tf.transpose(features["segment_ids"], [1, 0])
inp_mask = tf.transpose(features["input_mask"], [1, 0])
seq_len = tf.shape(inp)[0]
xlnet_config = xlnet.XLNetConfig(json_path=FLAGS.model_config_path)
run_config = xlnet.create_run_config(is_training, True, FLAGS)
xlnet_model = xlnet.XLNetModel(
xlnet_config=xlnet_config,
run_config=run_config,
input_ids=inp,
seg_ids=seg_id,
input_mask=inp_mask)
output = xlnet_model.get_sequence_output()
initializer = xlnet_model.get_initializer()
return_dict = {}
# invalid position mask such as query and special symbols (PAD, SEP, CLS)
p_mask = features["p_mask"]
# logit of the start position
with tf.variable_scope("start_logits"):
start_logits = tf.layers.dense(
output,
1,
kernel_initializer=initializer)
start_logits = tf.transpose(tf.squeeze(start_logits, -1), [1, 0])
start_logits_masked = start_logits * (1 - p_mask) - 1e30 * p_mask
start_log_probs = tf.nn.log_softmax(start_logits_masked, -1)
# logit of the end position
with tf.variable_scope("end_logits"):
if is_training:
# during training, compute the end logits based on the
# ground truth of the start position
start_positions = tf.reshape(features["start_positions"], [-1])
start_index = tf.one_hot(start_positions, depth=seq_len, axis=-1,
dtype=tf.float32)
start_features = tf.einsum("lbh,bl->bh", output, start_index)
start_features = tf.tile(start_features[None], [seq_len, 1, 1])
end_logits = tf.layers.dense(
tf.concat([output, start_features], axis=-1), xlnet_config.d_model,
kernel_initializer=initializer, activation=tf.tanh, name="dense_0")
end_logits = tf.contrib.layers.layer_norm(
end_logits, begin_norm_axis=-1)
end_logits = tf.layers.dense(
end_logits, 1,
kernel_initializer=initializer,
name="dense_1")
end_logits = tf.transpose(tf.squeeze(end_logits, -1), [1, 0])
end_logits_masked = end_logits * (1 - p_mask) - 1e30 * p_mask
end_log_probs = tf.nn.log_softmax(end_logits_masked, -1)
else:
# during inference, compute the end logits based on beam search
start_top_log_probs, start_top_index = tf.nn.top_k(
start_log_probs, k=FLAGS.start_n_top)
start_index = tf.one_hot(start_top_index,
depth=seq_len, axis=-1, dtype=tf.float32)
start_features = tf.einsum("lbh,bkl->bkh", output, start_index)
end_input = tf.tile(output[:, :, None],
[1, 1, FLAGS.start_n_top, 1])
start_features = tf.tile(start_features[None],
[seq_len, 1, 1, 1])
end_input = tf.concat([end_input, start_features], axis=-1)
end_logits = tf.layers.dense(
end_input,
xlnet_config.d_model,
kernel_initializer=initializer,
activation=tf.tanh,
name="dense_0")
end_logits = tf.contrib.layers.layer_norm(end_logits,
begin_norm_axis=-1)
end_logits = tf.layers.dense(
end_logits,
1,
kernel_initializer=initializer,
name="dense_1")
end_logits = tf.reshape(end_logits, [seq_len, -1, FLAGS.start_n_top])
end_logits = tf.transpose(end_logits, [1, 2, 0])
end_logits_masked = end_logits * (
1 - p_mask[:, None]) - 1e30 * p_mask[:, None]
end_log_probs = tf.nn.log_softmax(end_logits_masked, -1)
end_top_log_probs, end_top_index = tf.nn.top_k(
end_log_probs, k=FLAGS.end_n_top)
end_top_log_probs = tf.reshape(
end_top_log_probs,
[-1, FLAGS.start_n_top * FLAGS.end_n_top])
end_top_index = tf.reshape(
end_top_index,
[-1, FLAGS.start_n_top * FLAGS.end_n_top])
if is_training:
return_dict["start_log_probs"] = start_log_probs
return_dict["end_log_probs"] = end_log_probs
else:
return_dict["start_top_log_probs"] = start_top_log_probs
return_dict["start_top_index"] = start_top_index
return_dict["end_top_log_probs"] = end_top_log_probs
return_dict["end_top_index"] = end_top_index
return return_dict
def get_race_loss(FLAGS, features, is_training):
"""Loss for downstream multi-choice QA tasks such as RACE."""
bsz_per_core = tf.shape(features["input_ids"])[0]
def _transform_features(feature):
out = tf.reshape(feature, [bsz_per_core, 4, -1])
out = tf.transpose(out, [2, 0, 1])
out = tf.reshape(out, [-1, bsz_per_core * 4])
return out
inp = _transform_features(features["input_ids"])
seg_id = _transform_features(features["segment_ids"])
inp_mask = _transform_features(features["input_mask"])
label = tf.reshape(features["label_ids"], [bsz_per_core])
xlnet_config = xlnet.XLNetConfig(json_path=FLAGS.model_config_path)
run_config = xlnet.create_run_config(is_training, True, FLAGS)
xlnet_model = xlnet.XLNetModel(
xlnet_config=xlnet_config,
run_config=run_config,
input_ids=inp,
seg_ids=seg_id,
input_mask=inp_mask)
summary = xlnet_model.get_pooled_out(FLAGS.summary_type, FLAGS.use_summ_proj)
with tf.variable_scope("logits"):
logits = tf.layers.dense(summary, 1,
kernel_initializer=xlnet_model.get_initializer())
logits = tf.reshape(logits, [bsz_per_core, 4])
one_hot_target = tf.one_hot(label, 4)
per_example_loss = -tf.reduce_sum(
tf.nn.log_softmax(logits) * one_hot_target, -1)
total_loss = tf.reduce_mean(per_example_loss)
return total_loss, per_example_loss, logits
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/gpu_utils.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import tensorflow as tf
def assign_to_gpu(gpu=0, ps_dev="/device:CPU:0"):
def _assign(op):
node_def = op if isinstance(op, tf.NodeDef) else op.node_def
if node_def.op == "Variable":
return ps_dev
else:
return "/gpu:%d" % gpu
return _assign
def average_grads_and_vars(tower_grads_and_vars):
def average_dense(grad_and_vars):
if len(grad_and_vars) == 1:
return grad_and_vars[0][0]
grad = grad_and_vars[0][0]
for g, _ in grad_and_vars[1:]:
grad += g
return grad / len(grad_and_vars)
def average_sparse(grad_and_vars):
if len(grad_and_vars) == 1:
return grad_and_vars[0][0]
indices = []
values = []
for g, _ in grad_and_vars:
indices += [g.indices]
values += [g.values]
indices = tf.concat(indices, 0)
values = tf.concat(values, 0) / len(grad_and_vars)
return tf.IndexedSlices(values, indices, grad_and_vars[0][0].dense_shape)
average_grads_and_vars = []
for grad_and_vars in zip(*tower_grads_and_vars):
if grad_and_vars[0][0] is None:
grad = None
elif isinstance(grad_and_vars[0][0], tf.IndexedSlices):
grad = average_sparse(grad_and_vars)
else:
grad = average_dense(grad_and_vars)
# Keep in mind that the Variables are redundant because they are shared
# across towers. So .. we will just return the first tower's pointer to
# the Variable.
v = grad_and_vars[0][1]
grad_and_var = (grad, v)
average_grads_and_vars.append(grad_and_var)
return average_grads_and_vars
def load_from_checkpoint(saver, logdir):
sess = tf.get_default_session()
ckpt = tf.train.get_checkpoint_state(logdir)
if ckpt and ckpt.model_checkpoint_path:
if os.path.isabs(ckpt.model_checkpoint_path):
# Restores from checkpoint with absolute path.
saver.restore(sess, ckpt.model_checkpoint_path)
else:
# Restores from checkpoint with relative path.
saver.restore(sess, os.path.join(logdir, ckpt.model_checkpoint_path))
return True
return False
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/model_utils.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import os
import re
import numpy as np
import six
from os.path import join
from six.moves import zip
from absl import flags
import tensorflow as tf
def configure_tpu(FLAGS):
if FLAGS.use_tpu:
tpu_cluster = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
master = tpu_cluster.get_master()
else:
tpu_cluster = None
master = FLAGS.master
session_config = tf.ConfigProto(allow_soft_placement=True)
# Uncomment the following line if you hope to monitor GPU RAM growth
# session_config.gpu_options.allow_growth = True
if FLAGS.use_tpu:
strategy = None
tf.logging.info('Use TPU without distribute strategy.')
elif FLAGS.num_core_per_host == 1:
strategy = None
tf.logging.info('Single device mode.')
else:
strategy = tf.contrib.distribute.MirroredStrategy(
num_gpus=FLAGS.num_core_per_host)
tf.logging.info('Use MirroredStrategy with %d devices.',
strategy.num_replicas_in_sync)
per_host_input = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.contrib.tpu.RunConfig(
master=master,
model_dir=FLAGS.model_dir,
session_config=session_config,
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations,
num_shards=FLAGS.num_hosts * FLAGS.num_core_per_host,
per_host_input_for_training=per_host_input),
keep_checkpoint_max=FLAGS.max_save,
save_checkpoints_secs=None,
save_checkpoints_steps=FLAGS.save_steps,
train_distribute=strategy
)
return run_config
def init_from_checkpoint(FLAGS, global_vars=False):
tvars = tf.global_variables() if global_vars else tf.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if FLAGS.init_checkpoint is not None:
if FLAGS.init_checkpoint.endswith("latest"):
ckpt_dir = os.path.dirname(FLAGS.init_checkpoint)
init_checkpoint = tf.train.latest_checkpoint(ckpt_dir)
else:
init_checkpoint = FLAGS.init_checkpoint
tf.logging.info("Initialize from the ckpt {}".format(init_checkpoint))
(assignment_map, initialized_variable_names
) = get_assignment_map_from_checkpoint(tvars, init_checkpoint)
if FLAGS.use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
# Log customized initialization
tf.logging.info("**** Global Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
return scaffold_fn
def get_train_op(FLAGS, total_loss, grads_and_vars=None):
global_step = tf.train.get_or_create_global_step()
# increase the learning rate linearly
if FLAGS.warmup_steps > 0:
warmup_lr = (tf.cast(global_step, tf.float32)
/ tf.cast(FLAGS.warmup_steps, tf.float32)
* FLAGS.learning_rate)
else:
warmup_lr = 0.0
# decay the learning rate
if FLAGS.decay_method == "poly":
decay_lr = tf.train.polynomial_decay(
FLAGS.learning_rate,
global_step=global_step - FLAGS.warmup_steps,
decay_steps=FLAGS.train_steps - FLAGS.warmup_steps,
end_learning_rate=FLAGS.learning_rate * FLAGS.min_lr_ratio)
elif FLAGS.decay_method == "cos":
decay_lr = tf.train.cosine_decay(
FLAGS.learning_rate,
global_step=global_step - FLAGS.warmup_steps,
decay_steps=FLAGS.train_steps - FLAGS.warmup_steps,
alpha=FLAGS.min_lr_ratio)
else:
raise ValueError(FLAGS.decay_method)
learning_rate = tf.where(global_step < FLAGS.warmup_steps,
warmup_lr, decay_lr)
if (FLAGS.weight_decay > 0 and not FLAGS.use_tpu and
FLAGS.num_core_per_host > 1):
raise ValueError("Do not support `weight_decay > 0` with multi-gpu "
"training so far.")
if FLAGS.weight_decay == 0:
optimizer = tf.train.AdamOptimizer(
learning_rate=learning_rate,
epsilon=FLAGS.adam_epsilon)
else:
optimizer = AdamWeightDecayOptimizer(
learning_rate=learning_rate,
epsilon=FLAGS.adam_epsilon,
exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"],
weight_decay_rate=FLAGS.weight_decay)
if FLAGS.use_tpu:
optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)
if grads_and_vars is None:
grads_and_vars = optimizer.compute_gradients(total_loss)
gradients, variables = zip(*grads_and_vars)
clipped, gnorm = tf.clip_by_global_norm(gradients, FLAGS.clip)
if getattr(FLAGS, "lr_layer_decay_rate", 1.0) != 1.0:
n_layer = 0
for i in range(len(clipped)):
m = re.search(r"model/transformer/layer_(\d+?)/", variables[i].name)
if not m: continue
n_layer = max(n_layer, int(m.group(1)) + 1)
for i in range(len(clipped)):
for l in range(n_layer):
if "model/transformer/layer_{}/".format(l) in variables[i].name:
abs_rate = FLAGS.lr_layer_decay_rate ** (n_layer - 1 - l)
clipped[i] *= abs_rate
tf.logging.info("Apply mult {:.4f} to layer-{} grad of {}".format(
abs_rate, l, variables[i].name))
break
train_op = optimizer.apply_gradients(
zip(clipped, variables), global_step=global_step)
# Manually increment `global_step` for AdamWeightDecayOptimizer
if FLAGS.weight_decay > 0:
new_global_step = global_step + 1
train_op = tf.group(train_op, [global_step.assign(new_global_step)])
return train_op, learning_rate, gnorm
def clean_ckpt(_):
input_ckpt = FLAGS.clean_input_ckpt
output_model_dir = FLAGS.clean_output_model_dir
tf.reset_default_graph()
var_list = tf.contrib.framework.list_variables(input_ckpt)
var_values, var_dtypes = {}, {}
for (name, shape) in var_list:
if not name.startswith("global_step") and "adam" not in name.lower():
var_values[name] = None
tf.logging.info("Include {}".format(name))
else:
tf.logging.info("Exclude {}".format(name))
tf.logging.info("Loading from {}".format(input_ckpt))
reader = tf.contrib.framework.load_checkpoint(input_ckpt)
for name in var_values:
tensor = reader.get_tensor(name)
var_dtypes[name] = tensor.dtype
var_values[name] = tensor
with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
tf_vars = [
tf.get_variable(v, shape=var_values[v].shape, dtype=var_dtypes[v])
for v in var_values
]
placeholders = [tf.placeholder(v.dtype, shape=v.shape) for v in tf_vars]
assign_ops = [tf.assign(v, p) for (v, p) in zip(tf_vars, placeholders)]
global_step = tf.Variable(
0, name="global_step", trainable=False, dtype=tf.int64)
saver = tf.train.Saver(tf.all_variables())
if not tf.gfile.Exists(output_model_dir):
tf.gfile.MakeDirs(output_model_dir)
# Build a model consisting only of variables, set them to the average values.
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for p, assign_op, (name, value) in zip(placeholders, assign_ops,
six.iteritems(var_values)):
sess.run(assign_op, {p: value})
# Use the built saver to save the averaged checkpoint.
saver.save(sess, join(output_model_dir, "model.ckpt"),
global_step=global_step)
def avg_checkpoints(model_dir, output_model_dir, last_k):
tf.reset_default_graph()
checkpoint_state = tf.train.get_checkpoint_state(model_dir)
checkpoints = checkpoint_state.all_model_checkpoint_paths[- last_k:]
var_list = tf.contrib.framework.list_variables(checkpoints[0])
var_values, var_dtypes = {}, {}
for (name, shape) in var_list:
if not name.startswith("global_step"):
var_values[name] = np.zeros(shape)
for checkpoint in checkpoints:
reader = tf.contrib.framework.load_checkpoint(checkpoint)
for name in var_values:
tensor = reader.get_tensor(name)
var_dtypes[name] = tensor.dtype
var_values[name] += tensor
tf.logging.info("Read from checkpoint %s", checkpoint)
for name in var_values: # Average.
var_values[name] /= len(checkpoints)
with tf.variable_scope(tf.get_variable_scope(), reuse=tf.AUTO_REUSE):
tf_vars = [
tf.get_variable(v, shape=var_values[v].shape, dtype=var_dtypes[v])
for v in var_values
]
placeholders = [tf.placeholder(v.dtype, shape=v.shape) for v in tf_vars]
assign_ops = [tf.assign(v, p) for (v, p) in zip(tf_vars, placeholders)]
global_step = tf.Variable(
0, name="global_step", trainable=False, dtype=tf.int64)
saver = tf.train.Saver(tf.all_variables())
# Build a model consisting only of variables, set them to the average values.
with tf.Session() as sess:
sess.run(tf.initialize_all_variables())
for p, assign_op, (name, value) in zip(placeholders, assign_ops,
six.iteritems(var_values)):
sess.run(assign_op, {p: value})
# Use the built saver to save the averaged checkpoint.
saver.save(sess, join(output_model_dir, "model.ckpt"),
global_step=global_step)
def get_assignment_map_from_checkpoint(tvars, init_checkpoint):
"""Compute the union of the current variables and checkpoint variables."""
assignment_map = {}
initialized_variable_names = {}
name_to_variable = collections.OrderedDict()
for var in tvars:
name = var.name
m = re.match("^(.*):\\d+$", name)
if m is not None:
name = m.group(1)
name_to_variable[name] = var
init_vars = tf.train.list_variables(init_checkpoint)
assignment_map = collections.OrderedDict()
for x in init_vars:
(name, var) = (x[0], x[1])
# tf.logging.info('original name: %s', name)
if name not in name_to_variable:
continue
# assignment_map[name] = name
assignment_map[name] = name_to_variable[name]
initialized_variable_names[name] = 1
initialized_variable_names[name + ":0"] = 1
return (assignment_map, initialized_variable_names)
class AdamWeightDecayOptimizer(tf.train.Optimizer):
"""A basic Adam optimizer that includes "correct" L2 weight decay."""
def __init__(self,
learning_rate,
weight_decay_rate=0.0,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6,
exclude_from_weight_decay=None,
include_in_weight_decay=["r_s_bias", "r_r_bias", "r_w_bias"],
name="AdamWeightDecayOptimizer"):
"""Constructs a AdamWeightDecayOptimizer."""
super(AdamWeightDecayOptimizer, self).__init__(False, name)
self.learning_rate = learning_rate
self.weight_decay_rate = weight_decay_rate
self.beta_1 = beta_1
self.beta_2 = beta_2
self.epsilon = epsilon
self.exclude_from_weight_decay = exclude_from_weight_decay
self.include_in_weight_decay = include_in_weight_decay
def apply_gradients(self, grads_and_vars, global_step=None, name=None):
"""See base class."""
assignments = []
for (grad, param) in grads_and_vars:
if grad is None or param is None:
continue
param_name = self._get_variable_name(param.name)
m = tf.get_variable(
name=param_name + "/adam_m",
shape=param.shape.as_list(),
dtype=tf.float32,
trainable=False,
initializer=tf.zeros_initializer())
v = tf.get_variable(
name=param_name + "/adam_v",
shape=param.shape.as_list(),
dtype=tf.float32,
trainable=False,
initializer=tf.zeros_initializer())
# Standard Adam update.
next_m = (
tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad))
next_v = (
tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2,
tf.square(grad)))
update = next_m / (tf.sqrt(next_v) + self.epsilon)
# Just adding the square of the weights to the loss function is *not*
# the correct way of using L2 regularization/weight decay with Adam,
# since that will interact with the m and v parameters in strange ways.
#
# Instead we want ot decay the weights in a manner that doesn't interact
# with the m/v parameters. This is equivalent to adding the square
# of the weights to the loss with plain (non-momentum) SGD.
if self._do_use_weight_decay(param_name):
update += self.weight_decay_rate * param
update_with_lr = self.learning_rate * update
next_param = param - update_with_lr
assignments.extend(
[param.assign(next_param),
m.assign(next_m),
v.assign(next_v)])
return tf.group(*assignments, name=name)
def _do_use_weight_decay(self, param_name):
"""Whether to use L2 weight decay for `param_name`."""
if not self.weight_decay_rate:
return False
for r in self.include_in_weight_decay:
if re.search(r, param_name) is not None:
return True
if self.exclude_from_weight_decay:
for r in self.exclude_from_weight_decay:
if re.search(r, param_name) is not None:
tf.logging.info('Adam WD excludes {}'.format(param_name))
return False
return True
def _get_variable_name(self, param_name):
"""Get the variable name from the tensor name."""
m = re.match("^(.*):\\d+$", param_name)
if m is not None:
param_name = m.group(1)
return param_name
if __name__ == "__main__":
flags.DEFINE_string("clean_input_ckpt", "", "input ckpt for cleaning")
flags.DEFINE_string("clean_output_model_dir", "", "output dir for cleaned ckpt")
FLAGS = flags.FLAGS
tf.app.run(clean_ckpt)
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/modeling.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
def gelu(x):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
x: float Tensor to perform activation.
Returns:
`x` with the GELU activation applied.
"""
cdf = 0.5 * (1.0 + tf.tanh(
(np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))
return x * cdf
def embedding_lookup(x, n_token, d_embed, initializer, use_tpu=True,
scope='embedding', reuse=None, dtype=tf.float32):
"""TPU and GPU embedding_lookup function."""
with tf.variable_scope(scope, reuse=reuse):
lookup_table = tf.get_variable('lookup_table', [n_token, d_embed],
dtype=dtype, initializer=initializer)
if use_tpu:
one_hot_idx = tf.one_hot(x, n_token, dtype=dtype)
if one_hot_idx.shape.ndims == 2:
return tf.einsum('in,nd->id', one_hot_idx, lookup_table), lookup_table
else:
return tf.einsum('ibn,nd->ibd', one_hot_idx, lookup_table), lookup_table
else:
return tf.nn.embedding_lookup(lookup_table, x), lookup_table
def positional_embedding(pos_seq, inv_freq, bsz=None):
sinusoid_inp = tf.einsum('i,d->id', pos_seq, inv_freq)
pos_emb = tf.concat([tf.sin(sinusoid_inp), tf.cos(sinusoid_inp)], -1)
pos_emb = pos_emb[:, None, :]
if bsz is not None:
pos_emb = tf.tile(pos_emb, [1, bsz, 1])
return pos_emb
def positionwise_ffn(inp, d_model, d_inner, dropout, kernel_initializer,
activation_type='relu', scope='ff', is_training=True,
reuse=None):
"""Position-wise Feed-forward Network."""
if activation_type == 'relu':
activation = tf.nn.relu
elif activation_type == 'gelu':
activation = gelu
else:
raise ValueError('Unsupported activation type {}'.format(activation_type))
output = inp
with tf.variable_scope(scope, reuse=reuse):
output = tf.layers.dense(output, d_inner, activation=activation,
kernel_initializer=kernel_initializer,
name='layer_1')
output = tf.layers.dropout(output, dropout, training=is_training,
name='drop_1')
output = tf.layers.dense(output, d_model,
kernel_initializer=kernel_initializer,
name='layer_2')
output = tf.layers.dropout(output, dropout, training=is_training,
name='drop_2')
output = tf.contrib.layers.layer_norm(output + inp, begin_norm_axis=-1,
scope='LayerNorm')
return output
def head_projection(h, d_model, n_head, d_head, kernel_initializer, name):
"""Project hidden states to a specific head with a 4D-shape."""
proj_weight = tf.get_variable('{}/kernel'.format(name),
[d_model, n_head, d_head], dtype=h.dtype,
initializer=kernel_initializer)
head = tf.einsum('ibh,hnd->ibnd', h, proj_weight)
return head
def post_attention(h, attn_vec, d_model, n_head, d_head, dropout, is_training,
kernel_initializer, residual=True):
"""Post-attention processing."""
# post-attention projection (back to `d_model`)
proj_o = tf.get_variable('o/kernel', [d_model, n_head, d_head],
dtype=h.dtype, initializer=kernel_initializer)
attn_out = tf.einsum('ibnd,hnd->ibh', attn_vec, proj_o)
attn_out = tf.layers.dropout(attn_out, dropout, training=is_training)
if residual:
output = tf.contrib.layers.layer_norm(attn_out + h, begin_norm_axis=-1,
scope='LayerNorm')
else:
output = tf.contrib.layers.layer_norm(attn_out, begin_norm_axis=-1,
scope='LayerNorm')
return output
def abs_attn_core(q_head, k_head, v_head, attn_mask, dropatt, is_training,
scale):
"""Core absolute positional attention operations."""
attn_score = tf.einsum('ibnd,jbnd->ijbn', q_head, k_head)
attn_score *= scale
if attn_mask is not None:
attn_score = attn_score - 1e30 * attn_mask
# attention probability
attn_prob = tf.nn.softmax(attn_score, 1)
attn_prob = tf.layers.dropout(attn_prob, dropatt, training=is_training)
# attention output
attn_vec = tf.einsum('ijbn,jbnd->ibnd', attn_prob, v_head)
return attn_vec
def rel_attn_core(q_head, k_head_h, v_head_h, k_head_r, seg_embed, seg_mat,
r_w_bias, r_r_bias, r_s_bias, attn_mask, dropatt, is_training,
scale):
"""Core relative positional attention operations."""
# content based attention score
ac = tf.einsum('ibnd,jbnd->ijbn', q_head + r_w_bias, k_head_h)
# position based attention score
bd = tf.einsum('ibnd,jbnd->ijbn', q_head + r_r_bias, k_head_r)
bd = rel_shift(bd, klen=tf.shape(ac)[1])
# segment based attention score
if seg_mat is None:
ef = 0
else:
ef = tf.einsum('ibnd,snd->ibns', q_head + r_s_bias, seg_embed)
ef = tf.einsum('ijbs,ibns->ijbn', seg_mat, ef)
# merge attention scores and perform masking
attn_score = (ac + bd + ef) * scale
if attn_mask is not None:
# attn_score = attn_score * (1 - attn_mask) - 1e30 * attn_mask
attn_score = attn_score - 1e30 * attn_mask
# attention probability
attn_prob = tf.nn.softmax(attn_score, 1)
attn_prob = tf.layers.dropout(attn_prob, dropatt, training=is_training)
# attention output
attn_vec = tf.einsum('ijbn,jbnd->ibnd', attn_prob, v_head_h)
return attn_vec
def rel_shift(x, klen=-1):
"""perform relative shift to form the relative attention score."""
x_size = tf.shape(x)
x = tf.reshape(x, [x_size[1], x_size[0], x_size[2], x_size[3]])
x = tf.slice(x, [1, 0, 0, 0], [-1, -1, -1, -1])
x = tf.reshape(x, [x_size[0], x_size[1] - 1, x_size[2], x_size[3]])
x = tf.slice(x, [0, 0, 0, 0], [-1, klen, -1, -1])
return x
def _create_mask(qlen, mlen, dtype=tf.float32, same_length=False):
"""create causal attention mask."""
attn_mask = tf.ones([qlen, qlen], dtype=dtype)
mask_u = tf.matrix_band_part(attn_mask, 0, -1)
mask_dia = tf.matrix_band_part(attn_mask, 0, 0)
attn_mask_pad = tf.zeros([qlen, mlen], dtype=dtype)
ret = tf.concat([attn_mask_pad, mask_u - mask_dia], 1)
if same_length:
mask_l = tf.matrix_band_part(attn_mask, -1, 0)
ret = tf.concat([ret[:, :qlen] + mask_l - mask_dia, ret[:, qlen:]], 1)
return ret
def _cache_mem(curr_out, prev_mem, mem_len, reuse_len=None):
"""cache hidden states into memory."""
if mem_len is None or mem_len == 0:
return None
else:
if reuse_len is not None and reuse_len > 0:
curr_out = curr_out[:reuse_len]
if prev_mem is None:
new_mem = curr_out[-mem_len:]
else:
new_mem = tf.concat([prev_mem, curr_out], 0)[-mem_len:]
return tf.stop_gradient(new_mem)
def relative_positional_encoding(qlen, klen, d_model, clamp_len, attn_type,
bi_data, bsz=None, dtype=None):
"""create relative positional encoding."""
freq_seq = tf.range(0, d_model, 2.0)
if dtype is not None and dtype != tf.float32:
freq_seq = tf.cast(freq_seq, dtype=dtype)
inv_freq = 1 / (10000 ** (freq_seq / d_model))
if attn_type == 'bi':
# beg, end = klen - 1, -qlen
beg, end = klen, -qlen
elif attn_type == 'uni':
# beg, end = klen - 1, -1
beg, end = klen, -1
else:
raise ValueError('Unknown `attn_type` {}.'.format(attn_type))
if bi_data:
fwd_pos_seq = tf.range(beg, end, -1.0)
bwd_pos_seq = tf.range(-beg, -end, 1.0)
if dtype is not None and dtype != tf.float32:
fwd_pos_seq = tf.cast(fwd_pos_seq, dtype=dtype)
bwd_pos_seq = tf.cast(bwd_pos_seq, dtype=dtype)
if clamp_len > 0:
fwd_pos_seq = tf.clip_by_value(fwd_pos_seq, -clamp_len, clamp_len)
bwd_pos_seq = tf.clip_by_value(bwd_pos_seq, -clamp_len, clamp_len)
if bsz is not None:
# With bi_data, the batch size should be divisible by 2.
assert bsz%2 == 0
fwd_pos_emb = positional_embedding(fwd_pos_seq, inv_freq, bsz//2)
bwd_pos_emb = positional_embedding(bwd_pos_seq, inv_freq, bsz//2)
else:
fwd_pos_emb = positional_embedding(fwd_pos_seq, inv_freq)
bwd_pos_emb = positional_embedding(bwd_pos_seq, inv_freq)
pos_emb = tf.concat([fwd_pos_emb, bwd_pos_emb], axis=1)
else:
fwd_pos_seq = tf.range(beg, end, -1.0)
if dtype is not None and dtype != tf.float32:
fwd_pos_seq = tf.cast(fwd_pos_seq, dtype=dtype)
if clamp_len > 0:
fwd_pos_seq = tf.clip_by_value(fwd_pos_seq, -clamp_len, clamp_len)
pos_emb = positional_embedding(fwd_pos_seq, inv_freq, bsz)
return pos_emb
def multihead_attn(q, k, v, attn_mask, d_model, n_head, d_head, dropout,
dropatt, is_training, kernel_initializer, residual=True,
scope='abs_attn', reuse=None):
"""Standard multi-head attention with absolute positional embedding."""
scale = 1 / (d_head ** 0.5)
with tf.variable_scope(scope, reuse=reuse):
# attention heads
q_head = head_projection(
q, d_model, n_head, d_head, kernel_initializer, 'q')
k_head = head_projection(
k, d_model, n_head, d_head, kernel_initializer, 'k')
v_head = head_projection(
v, d_model, n_head, d_head, kernel_initializer, 'v')
# attention vector
attn_vec = abs_attn_core(q_head, k_head, v_head, attn_mask, dropatt,
is_training, scale)
# post processing
output = post_attention(v, attn_vec, d_model, n_head, d_head, dropout,
is_training, kernel_initializer, residual)
return output
def rel_multihead_attn(h, r, r_w_bias, r_r_bias, seg_mat, r_s_bias, seg_embed,
attn_mask, mems, d_model, n_head, d_head, dropout,
dropatt, is_training, kernel_initializer,
scope='rel_attn', reuse=None):
"""Multi-head attention with relative positional encoding."""
scale = 1 / (d_head ** 0.5)
with tf.variable_scope(scope, reuse=reuse):
if mems is not None and mems.shape.ndims > 1:
cat = tf.concat([mems, h], 0)
else:
cat = h
# content heads
q_head_h = head_projection(
h, d_model, n_head, d_head, kernel_initializer, 'q')
k_head_h = head_projection(
cat, d_model, n_head, d_head, kernel_initializer, 'k')
v_head_h = head_projection(
cat, d_model, n_head, d_head, kernel_initializer, 'v')
# positional heads
k_head_r = head_projection(
r, d_model, n_head, d_head, kernel_initializer, 'r')
# core attention ops
attn_vec = rel_attn_core(
q_head_h, k_head_h, v_head_h, k_head_r, seg_embed, seg_mat, r_w_bias,
r_r_bias, r_s_bias, attn_mask, dropatt, is_training, scale)
# post processing
output = post_attention(h, attn_vec, d_model, n_head, d_head, dropout,
is_training, kernel_initializer)
return output
def two_stream_rel_attn(h, g, r, mems, r_w_bias, r_r_bias, seg_mat, r_s_bias,
seg_embed, attn_mask_h, attn_mask_g, target_mapping,
d_model, n_head, d_head, dropout, dropatt, is_training,
kernel_initializer, scope='rel_attn'):
"""Two-stream attention with relative positional encoding."""
scale = 1 / (d_head ** 0.5)
with tf.variable_scope(scope, reuse=False):
# content based attention score
if mems is not None and mems.shape.ndims > 1:
cat = tf.concat([mems, h], 0)
else:
cat = h
# content-based key head
k_head_h = head_projection(
cat, d_model, n_head, d_head, kernel_initializer, 'k')
# content-based value head
v_head_h = head_projection(
cat, d_model, n_head, d_head, kernel_initializer, 'v')
# position-based key head
k_head_r = head_projection(
r, d_model, n_head, d_head, kernel_initializer, 'r')
##### h-stream
# content-stream query head
q_head_h = head_projection(
h, d_model, n_head, d_head, kernel_initializer, 'q')
# core attention ops
attn_vec_h = rel_attn_core(
q_head_h, k_head_h, v_head_h, k_head_r, seg_embed, seg_mat, r_w_bias,
r_r_bias, r_s_bias, attn_mask_h, dropatt, is_training, scale)
# post processing
output_h = post_attention(h, attn_vec_h, d_model, n_head, d_head, dropout,
is_training, kernel_initializer)
with tf.variable_scope(scope, reuse=True):
##### g-stream
# query-stream query head
q_head_g = head_projection(
g, d_model, n_head, d_head, kernel_initializer, 'q')
# core attention ops
if target_mapping is not None:
q_head_g = tf.einsum('mbnd,mlb->lbnd', q_head_g, target_mapping)
attn_vec_g = rel_attn_core(
q_head_g, k_head_h, v_head_h, k_head_r, seg_embed, seg_mat, r_w_bias,
r_r_bias, r_s_bias, attn_mask_g, dropatt, is_training, scale)
attn_vec_g = tf.einsum('lbnd,mlb->mbnd', attn_vec_g, target_mapping)
else:
attn_vec_g = rel_attn_core(
q_head_g, k_head_h, v_head_h, k_head_r, seg_embed, seg_mat, r_w_bias,
r_r_bias, r_s_bias, attn_mask_g, dropatt, is_training, scale)
# post processing
output_g = post_attention(g, attn_vec_g, d_model, n_head, d_head, dropout,
is_training, kernel_initializer)
return output_h, output_g
def transformer_xl(inp_k, n_token, n_layer, d_model, n_head,
d_head, d_inner, dropout, dropatt, attn_type,
bi_data, initializer, is_training, mem_len=None,
inp_q=None, mems=None,
same_length=False, clamp_len=-1, untie_r=False,
use_tpu=True, input_mask=None,
perm_mask=None, seg_id=None, reuse_len=None,
ff_activation='relu', target_mapping=None,
use_bfloat16=False, scope='transformer', **kwargs):
"""
Defines a Transformer-XL computation graph with additional
support for XLNet.
Args:
inp_k: int32 Tensor in shape [len, bsz], the input token IDs.
seg_id: int32 Tensor in shape [len, bsz], the input segment IDs.
input_mask: float32 Tensor in shape [len, bsz], the input mask.
0 for real tokens and 1 for padding.
mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
from previous batches. The length of the list equals n_layer.
If None, no memory is used.
perm_mask: float32 Tensor in shape [len, len, bsz].
If perm_mask[i, j, k] = 0, i attend to j in batch k;
if perm_mask[i, j, k] = 1, i does not attend to j in batch k.
If None, each position attends to all the others.
target_mapping: float32 Tensor in shape [num_predict, len, bsz].
If target_mapping[i, j, k] = 1, the i-th predict in batch k is
on the j-th token.
Only used during pretraining for partial prediction.
Set to None during finetuning.
inp_q: float32 Tensor in shape [len, bsz].
1 for tokens with losses and 0 for tokens without losses.
Only used during pretraining for two-stream attention.
Set to None during finetuning.
n_layer: int, the number of layers.
d_model: int, the hidden size.
n_head: int, the number of attention heads.
d_head: int, the dimension size of each attention head.
d_inner: int, the hidden size in feed-forward layers.
ff_activation: str, "relu" or "gelu".
untie_r: bool, whether to untie the biases in attention.
n_token: int, the vocab size.
is_training: bool, whether in training mode.
use_tpu: bool, whether TPUs are used.
use_bfloat16: bool, use bfloat16 instead of float32.
dropout: float, dropout rate.
dropatt: float, dropout rate on attention probabilities.
init: str, the initialization scheme, either "normal" or "uniform".
init_range: float, initialize the parameters with a uniform distribution
in [-init_range, init_range]. Only effective when init="uniform".
init_std: float, initialize the parameters with a normal distribution
with mean 0 and stddev init_std. Only effective when init="normal".
mem_len: int, the number of tokens to cache.
reuse_len: int, the number of tokens in the currect batch to be cached
and reused in the future.
bi_data: bool, whether to use bidirectional input pipeline.
Usually set to True during pretraining and False during finetuning.
clamp_len: int, clamp all relative distances larger than clamp_len.
-1 means no clamping.
same_length: bool, whether to use the same attention length for each token.
summary_type: str, "last", "first", "mean", or "attn". The method
to pool the input to get a vector representation.
initializer: A tf initializer.
scope: scope name for the computation graph.
"""
tf.logging.info('memory input {}'.format(mems))
tf_float = tf.bfloat16 if use_bfloat16 else tf.float32
tf.logging.info('Use float type {}'.format(tf_float))
new_mems = []
with tf.variable_scope(scope):
if untie_r:
r_w_bias = tf.get_variable('r_w_bias', [n_layer, n_head, d_head],
dtype=tf_float, initializer=initializer)
r_r_bias = tf.get_variable('r_r_bias', [n_layer, n_head, d_head],
dtype=tf_float, initializer=initializer)
else:
r_w_bias = tf.get_variable('r_w_bias', [n_head, d_head],
dtype=tf_float, initializer=initializer)
r_r_bias = tf.get_variable('r_r_bias', [n_head, d_head],
dtype=tf_float, initializer=initializer)
bsz = tf.shape(inp_k)[1]
qlen = tf.shape(inp_k)[0]
mlen = tf.shape(mems[0])[0] if mems is not None else 0
klen = mlen + qlen
##### Attention mask
# causal attention mask
if attn_type == 'uni':
attn_mask = _create_mask(qlen, mlen, tf_float, same_length)
attn_mask = attn_mask[:, :, None, None]
elif attn_type == 'bi':
attn_mask = None
else:
raise ValueError('Unsupported attention type: {}'.format(attn_type))
# data mask: input mask & perm mask
if input_mask is not None and perm_mask is not None:
data_mask = input_mask[None] + perm_mask
elif input_mask is not None and perm_mask is None:
data_mask = input_mask[None]
elif input_mask is None and perm_mask is not None:
data_mask = perm_mask
else:
data_mask = None
if data_mask is not None:
# all mems can be attended to
mems_mask = tf.zeros([tf.shape(data_mask)[0], mlen, bsz],
dtype=tf_float)
data_mask = tf.concat([mems_mask, data_mask], 1)
if attn_mask is None:
attn_mask = data_mask[:, :, :, None]
else:
attn_mask += data_mask[:, :, :, None]
if attn_mask is not None:
attn_mask = tf.cast(attn_mask > 0, dtype=tf_float)
if attn_mask is not None:
non_tgt_mask = -tf.eye(qlen, dtype=tf_float)
non_tgt_mask = tf.concat([tf.zeros([qlen, mlen], dtype=tf_float),
non_tgt_mask], axis=-1)
non_tgt_mask = tf.cast((attn_mask + non_tgt_mask[:, :, None, None]) > 0,
dtype=tf_float)
else:
non_tgt_mask = None
##### Word embedding
word_emb_k, lookup_table = embedding_lookup(
x=inp_k,
n_token=n_token,
d_embed=d_model,
initializer=initializer,
use_tpu=use_tpu,
dtype=tf_float,
scope='word_embedding')
if inp_q is not None:
with tf.variable_scope('mask_emb'):
mask_emb = tf.get_variable('mask_emb', [1, 1, d_model], dtype=tf_float)
if target_mapping is not None:
word_emb_q = tf.tile(mask_emb, [tf.shape(target_mapping)[0], bsz, 1])
else:
inp_q_ext = inp_q[:, :, None]
word_emb_q = inp_q_ext * mask_emb + (1 - inp_q_ext) * word_emb_k
output_h = tf.layers.dropout(word_emb_k, dropout, training=is_training)
if inp_q is not None:
output_g = tf.layers.dropout(word_emb_q, dropout, training=is_training)
##### Segment embedding
if seg_id is not None:
if untie_r:
r_s_bias = tf.get_variable('r_s_bias', [n_layer, n_head, d_head],
dtype=tf_float, initializer=initializer)
else:
# default case (tie)
r_s_bias = tf.get_variable('r_s_bias', [n_head, d_head],
dtype=tf_float, initializer=initializer)
seg_embed = tf.get_variable('seg_embed', [n_layer, 2, n_head, d_head],
dtype=tf_float, initializer=initializer)
# Convert `seg_id` to one-hot `seg_mat`
mem_pad = tf.zeros([mlen, bsz], dtype=tf.int32)
cat_ids = tf.concat([mem_pad, seg_id], 0)
# `1` indicates not in the same segment [qlen x klen x bsz]
seg_mat = tf.cast(
tf.logical_not(tf.equal(seg_id[:, None], cat_ids[None, :])),
tf.int32)
seg_mat = tf.one_hot(seg_mat, 2, dtype=tf_float)
else:
seg_mat = None
##### Positional encoding
pos_emb = relative_positional_encoding(
qlen, klen, d_model, clamp_len, attn_type, bi_data,
bsz=bsz, dtype=tf_float)
pos_emb = tf.layers.dropout(pos_emb, dropout, training=is_training)
##### Attention layers
if mems is None:
mems = [None] * n_layer
for i in range(n_layer):
# cache new mems
new_mems.append(_cache_mem(output_h, mems[i], mem_len, reuse_len))
# segment bias
if seg_id is None:
r_s_bias_i = None
seg_embed_i = None
else:
r_s_bias_i = r_s_bias if not untie_r else r_s_bias[i]
seg_embed_i = seg_embed[i]
with tf.variable_scope('layer_{}'.format(i)):
if inp_q is not None:
output_h, output_g = two_stream_rel_attn(
h=output_h,
g=output_g,
r=pos_emb,
r_w_bias=r_w_bias if not untie_r else r_w_bias[i],
r_r_bias=r_r_bias if not untie_r else r_r_bias[i],
seg_mat=seg_mat,
r_s_bias=r_s_bias_i,
seg_embed=seg_embed_i,
attn_mask_h=non_tgt_mask,
attn_mask_g=attn_mask,
mems=mems[i],
target_mapping=target_mapping,
d_model=d_model,
n_head=n_head,
d_head=d_head,
dropout=dropout,
dropatt=dropatt,
is_training=is_training,
kernel_initializer=initializer)
reuse = True
else:
reuse = False
output_h = rel_multihead_attn(
h=output_h,
r=pos_emb,
r_w_bias=r_w_bias if not untie_r else r_w_bias[i],
r_r_bias=r_r_bias if not untie_r else r_r_bias[i],
seg_mat=seg_mat,
r_s_bias=r_s_bias_i,
seg_embed=seg_embed_i,
attn_mask=non_tgt_mask,
mems=mems[i],
d_model=d_model,
n_head=n_head,
d_head=d_head,
dropout=dropout,
dropatt=dropatt,
is_training=is_training,
kernel_initializer=initializer,
reuse=reuse)
if inp_q is not None:
output_g = positionwise_ffn(
inp=output_g,
d_model=d_model,
d_inner=d_inner,
dropout=dropout,
kernel_initializer=initializer,
activation_type=ff_activation,
is_training=is_training)
output_h = positionwise_ffn(
inp=output_h,
d_model=d_model,
d_inner=d_inner,
dropout=dropout,
kernel_initializer=initializer,
activation_type=ff_activation,
is_training=is_training,
reuse=reuse)
if inp_q is not None:
output = tf.layers.dropout(output_g, dropout, training=is_training)
else:
output = tf.layers.dropout(output_h, dropout, training=is_training)
return output, new_mems, lookup_table
def lm_loss(hidden, target, n_token, d_model, initializer, lookup_table=None,
tie_weight=False, bi_data=True, use_tpu=False):
"""doc."""
with tf.variable_scope('lm_loss'):
if tie_weight:
assert lookup_table is not None, \
'lookup_table cannot be None for tie_weight'
softmax_w = lookup_table
else:
softmax_w = tf.get_variable('weight', [n_token, d_model],
dtype=hidden.dtype, initializer=initializer)
softmax_b = tf.get_variable('bias', [n_token], dtype=hidden.dtype,
initializer=tf.zeros_initializer())
logits = tf.einsum('ibd,nd->ibn', hidden, softmax_w) + softmax_b
if use_tpu:
one_hot_target = tf.one_hot(target, n_token, dtype=logits.dtype)
loss = -tf.reduce_sum(tf.nn.log_softmax(logits) * one_hot_target, -1)
else:
loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=target,
logits=logits)
return loss
def summarize_sequence(summary_type, hidden, d_model, n_head, d_head, dropout,
dropatt, input_mask, is_training, initializer,
scope=None, reuse=None, use_proj=True):
"""
Different classification tasks may not may not share the same parameters
to summarize the sequence features.
If shared, one can keep the `scope` to the default value `None`.
Otherwise, one should specify a different `scope` for each task.
"""
with tf.variable_scope(scope, 'sequnece_summary', reuse=reuse):
if summary_type == 'last':
summary = hidden[-1]
elif summary_type == 'first':
summary = hidden[0]
elif summary_type == 'mean':
summary = tf.reduce_mean(hidden, axis=0)
elif summary_type == 'attn':
bsz = tf.shape(hidden)[1]
summary_bias = tf.get_variable('summary_bias', [d_model],
dtype=hidden.dtype,
initializer=initializer)
summary_bias = tf.tile(summary_bias[None, None], [1, bsz, 1])
if input_mask is not None:
input_mask = input_mask[None, :, :, None]
summary = multihead_attn(summary_bias, hidden, hidden, input_mask,
d_model, n_head, d_head, dropout, dropatt,
is_training, initializer, residual=False)
summary = summary[0]
else:
raise ValueError('Unsupported summary type {}'.format(summary_type))
# use another projection as in BERT
if use_proj:
summary = tf.layers.dense(
summary,
d_model,
activation=tf.tanh,
kernel_initializer=initializer,
name='summary')
# dropout
summary = tf.layers.dropout(
summary, dropout, training=is_training,
name='dropout')
return summary
def classification_loss(hidden, labels, n_class, initializer, scope, reuse=None,
return_logits=False):
"""
Different classification tasks should use different scope names to ensure
different dense layers (parameters) are used to produce the logits.
An exception will be in transfer learning, where one hopes to transfer
the classification weights.
"""
with tf.variable_scope(scope, reuse=reuse):
logits = tf.layers.dense(
hidden,
n_class,
kernel_initializer=initializer,
name='logit')
one_hot_target = tf.one_hot(labels, n_class, dtype=hidden.dtype)
loss = -tf.reduce_sum(tf.nn.log_softmax(logits) * one_hot_target, -1)
if return_logits:
return loss, logits
return loss
def regression_loss(hidden, labels, initializer, scope, reuse=None,
return_logits=False):
with tf.variable_scope(scope, reuse=reuse):
logits = tf.layers.dense(
hidden,
1,
kernel_initializer=initializer,
name='logit')
logits = tf.squeeze(logits, axis=-1)
loss = tf.square(logits - labels)
if return_logits:
return loss, logits
return loss
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/prepro_utils.py | Python | # coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import unicodedata
import six
from functools import partial
SPIECE_UNDERLINE = '▁'
def printable_text(text):
"""Returns text encoded in a way suitable for print or `tf.logging`."""
# These functions want `str` for both Python2 and Python3, but in one case
# it's a Unicode string and in the other it's a byte string.
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text
elif isinstance(text, unicode):
return text.encode("utf-8")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def print_(*args):
new_args = []
for arg in args:
if isinstance(arg, list):
s = [printable_text(i) for i in arg]
s = ' '.join(s)
new_args.append(s)
else:
new_args.append(printable_text(arg))
print(*new_args)
def preprocess_text(inputs, lower=False, remove_space=True, keep_accents=False):
if remove_space:
outputs = ' '.join(inputs.strip().split())
else:
outputs = inputs
outputs = outputs.replace("``", '"').replace("''", '"')
if six.PY2 and isinstance(outputs, str):
outputs = outputs.decode('utf-8')
if not keep_accents:
outputs = unicodedata.normalize('NFKD', outputs)
outputs = ''.join([c for c in outputs if not unicodedata.combining(c)])
if lower:
outputs = outputs.lower()
return outputs
def encode_pieces(sp_model, text, return_unicode=True, sample=False):
# return_unicode is used only for py2
# note(zhiliny): in some systems, sentencepiece only accepts str for py2
if six.PY2 and isinstance(text, unicode):
text = text.encode('utf-8')
if not sample:
pieces = sp_model.EncodeAsPieces(text)
else:
pieces = sp_model.SampleEncodeAsPieces(text, 64, 0.1)
new_pieces = []
for piece in pieces:
if len(piece) > 1 and piece[-1] == ',' and piece[-2].isdigit():
cur_pieces = sp_model.EncodeAsPieces(
piece[:-1].replace(SPIECE_UNDERLINE, ''))
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0]) == 1:
cur_pieces = cur_pieces[1:]
else:
cur_pieces[0] = cur_pieces[0][1:]
cur_pieces.append(piece[-1])
new_pieces.extend(cur_pieces)
else:
new_pieces.append(piece)
# note(zhiliny): convert back to unicode for py2
if six.PY2 and return_unicode:
ret_pieces = []
for piece in new_pieces:
if isinstance(piece, str):
piece = piece.decode('utf-8')
ret_pieces.append(piece)
new_pieces = ret_pieces
return new_pieces
def encode_ids(sp_model, text, sample=False):
pieces = encode_pieces(sp_model, text, return_unicode=False, sample=sample)
ids = [sp_model.PieceToId(piece) for piece in pieces]
return ids
if __name__ == '__main__':
import sentencepiece as spm
sp = spm.SentencePieceProcessor()
sp.load('sp10m.uncased.v3.model')
print_(u'I was born in 2000, and this is falsé.')
print_(u'ORIGINAL', sp.EncodeAsPieces(u'I was born in 2000, and this is falsé.'))
print_(u'OURS', encode_pieces(sp, u'I was born in 2000, and this is falsé.'))
print(encode_ids(sp, u'I was born in 2000, and this is falsé.'))
print_('')
prepro_func = partial(preprocess_text, lower=True)
print_(prepro_func('I was born in 2000, and this is falsé.'))
print_('ORIGINAL', sp.EncodeAsPieces(prepro_func('I was born in 2000, and this is falsé.')))
print_('OURS', encode_pieces(sp, prepro_func('I was born in 2000, and this is falsé.')))
print(encode_ids(sp, prepro_func('I was born in 2000, and this is falsé.')))
print_('')
print_('I was born in 2000, and this is falsé.')
print_('ORIGINAL', sp.EncodeAsPieces('I was born in 2000, and this is falsé.'))
print_('OURS', encode_pieces(sp, 'I was born in 2000, and this is falsé.'))
print(encode_ids(sp, 'I was born in 2000, and this is falsé.'))
print_('')
print_('I was born in 92000, and this is falsé.')
print_('ORIGINAL', sp.EncodeAsPieces('I was born in 92000, and this is falsé.'))
print_('OURS', encode_pieces(sp, 'I was born in 92000, and this is falsé.'))
print(encode_ids(sp, 'I was born in 92000, and this is falsé.'))
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/run_classifier.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from os.path import join
from absl import flags
import os
import sys
import csv
import collections
import numpy as np
import time
import math
import json
import random
from copy import copy
from collections import defaultdict as dd
import absl.logging as _logging # pylint: disable=unused-import
import tensorflow as tf
import sentencepiece as spm
from data_utils import SEP_ID, VOCAB_SIZE, CLS_ID
import model_utils
import function_builder
from classifier_utils import PaddingInputExample
from classifier_utils import convert_single_example
from prepro_utils import preprocess_text, encode_ids
# Model
flags.DEFINE_string("model_config_path", default=None,
help="Model config path.")
flags.DEFINE_float("dropout", default=0.1,
help="Dropout rate.")
flags.DEFINE_float("dropatt", default=0.1,
help="Attention dropout rate.")
flags.DEFINE_integer("clamp_len", default=-1,
help="Clamp length")
flags.DEFINE_string("summary_type", default="last",
help="Method used to summarize a sequence into a compact vector.")
flags.DEFINE_bool("use_summ_proj", default=True,
help="Whether to use projection for summarizing sequences.")
flags.DEFINE_bool("use_bfloat16", False,
help="Whether to use bfloat16.")
# Parameter initialization
flags.DEFINE_enum("init", default="normal",
enum_values=["normal", "uniform"],
help="Initialization method.")
flags.DEFINE_float("init_std", default=0.02,
help="Initialization std when init is normal.")
flags.DEFINE_float("init_range", default=0.1,
help="Initialization std when init is uniform.")
# I/O paths
flags.DEFINE_bool("overwrite_data", default=False,
help="If False, will use cached data if available.")
flags.DEFINE_string("init_checkpoint", default=None,
help="checkpoint path for initializing the model. "
"Could be a pretrained model or a finetuned model.")
flags.DEFINE_string("output_dir", default="",
help="Output dir for TF records.")
flags.DEFINE_string("spiece_model_file", default="",
help="Sentence Piece model path.")
flags.DEFINE_string("model_dir", default="",
help="Directory for saving the finetuned model.")
flags.DEFINE_string("data_dir", default="",
help="Directory for input data.")
# TPUs and machines
flags.DEFINE_bool("use_tpu", default=False, help="whether to use TPU.")
flags.DEFINE_integer("num_hosts", default=1, help="How many TPU hosts.")
flags.DEFINE_integer("num_core_per_host", default=8,
help="8 for TPU v2 and v3-8, 16 for larger TPU v3 pod. In the context "
"of GPU training, it refers to the number of GPUs used.")
flags.DEFINE_string("tpu_job_name", default=None, help="TPU worker job name.")
flags.DEFINE_string("tpu", default=None, help="TPU name.")
flags.DEFINE_string("tpu_zone", default=None, help="TPU zone.")
flags.DEFINE_string("gcp_project", default=None, help="gcp project.")
flags.DEFINE_string("master", default=None, help="master")
flags.DEFINE_integer("iterations", default=1000,
help="number of iterations per TPU training loop.")
# training
flags.DEFINE_bool("do_train", default=False, help="whether to do training")
flags.DEFINE_integer("train_steps", default=1000,
help="Number of training steps")
flags.DEFINE_integer("num_train_epochs", default=0,
help="Number of training steps")
flags.DEFINE_integer("warmup_steps", default=0, help="number of warmup steps")
flags.DEFINE_float("learning_rate", default=1e-5, help="initial learning rate")
flags.DEFINE_float("lr_layer_decay_rate", 1.0,
"Top layer: lr[L] = FLAGS.learning_rate."
"Low layer: lr[l-1] = lr[l] * lr_layer_decay_rate.")
flags.DEFINE_float("min_lr_ratio", default=0.0,
help="min lr ratio for cos decay.")
flags.DEFINE_float("clip", default=1.0, help="Gradient clipping")
flags.DEFINE_integer("max_save", default=0,
help="Max number of checkpoints to save. Use 0 to save all.")
flags.DEFINE_integer("save_steps", default=None,
help="Save the model for every save_steps. "
"If None, not to save any model.")
flags.DEFINE_integer("train_batch_size", default=8,
help="Batch size for training")
flags.DEFINE_float("weight_decay", default=0.00, help="Weight decay rate")
flags.DEFINE_float("adam_epsilon", default=1e-8, help="Adam epsilon")
flags.DEFINE_string("decay_method", default="poly", help="poly or cos")
# evaluation
flags.DEFINE_bool("do_eval", default=False, help="whether to do eval")
flags.DEFINE_bool("do_predict", default=False, help="whether to do prediction")
flags.DEFINE_float("predict_threshold", default=0,
help="Threshold for binary prediction.")
flags.DEFINE_string("eval_split", default="dev", help="could be dev or test")
flags.DEFINE_integer("eval_batch_size", default=128,
help="batch size for evaluation")
flags.DEFINE_integer("predict_batch_size", default=128,
help="batch size for prediction.")
flags.DEFINE_string("predict_dir", default=None,
help="Dir for saving prediction files.")
flags.DEFINE_bool("eval_all_ckpt", default=False,
help="Eval all ckpts. If False, only evaluate the last one.")
flags.DEFINE_string("predict_ckpt", default=None,
help="Ckpt path for do_predict. If None, use the last one.")
# task specific
flags.DEFINE_string("task_name", default=None, help="Task name")
flags.DEFINE_integer("max_seq_length", default=128, help="Max sequence length")
flags.DEFINE_integer("shuffle_buffer", default=2048,
help="Buffer size used for shuffle.")
flags.DEFINE_integer("num_passes", default=1,
help="Num passes for processing training data. "
"This is use to batch data without loss for TPUs.")
flags.DEFINE_bool("uncased", default=False,
help="Use uncased.")
flags.DEFINE_string("cls_scope", default=None,
help="Classifier layer scope.")
flags.DEFINE_bool("is_regression", default=False,
help="Whether it's a regression task.")
FLAGS = flags.FLAGS
class InputExample(object):
"""A single training/test example for simple sequence classification."""
def __init__(self, guid, text_a, text_b=None, label=None):
"""Constructs a InputExample.
Args:
guid: Unique id for the example.
text_a: string. The untokenized text of the first sequence. For single
sequence tasks, only this sequence must be specified.
text_b: (Optional) string. The untokenized text of the second sequence.
Only must be specified for sequence pair tasks.
label: (Optional) string. The label of the example. This should be
specified for train and dev examples, but not for test examples.
"""
self.guid = guid
self.text_a = text_a
self.text_b = text_b
self.label = label
class DataProcessor(object):
"""Base class for data converters for sequence classification data sets."""
def get_train_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the train set."""
raise NotImplementedError()
def get_dev_examples(self, data_dir):
"""Gets a collection of `InputExample`s for the dev set."""
raise NotImplementedError()
def get_test_examples(self, data_dir):
"""Gets a collection of `InputExample`s for prediction."""
raise NotImplementedError()
def get_labels(self):
"""Gets the list of labels for this data set."""
raise NotImplementedError()
@classmethod
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with tf.gfile.Open(input_file, "r") as f:
reader = csv.reader(f, delimiter="\t", quotechar=quotechar)
lines = []
for line in reader:
if len(line) == 0: continue
lines.append(line)
return lines
class GLUEProcessor(DataProcessor):
def __init__(self):
self.train_file = "train.tsv"
self.dev_file = "dev.tsv"
self.test_file = "test.tsv"
self.label_column = None
self.text_a_column = None
self.text_b_column = None
self.contains_header = True
self.test_text_a_column = None
self.test_text_b_column = None
self.test_contains_header = True
def get_train_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, self.train_file)), "train")
def get_dev_examples(self, data_dir):
"""See base class."""
return self._create_examples(
self._read_tsv(os.path.join(data_dir, self.dev_file)), "dev")
def get_test_examples(self, data_dir):
"""See base class."""
if self.test_text_a_column is None:
self.test_text_a_column = self.text_a_column
if self.test_text_b_column is None:
self.test_text_b_column = self.text_b_column
return self._create_examples(
self._read_tsv(os.path.join(data_dir, self.test_file)), "test")
def get_labels(self):
"""See base class."""
return ["0", "1"]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0 and self.contains_header and set_type != "test":
continue
if i == 0 and self.test_contains_header and set_type == "test":
continue
guid = "%s-%s" % (set_type, i)
a_column = (self.text_a_column if set_type != "test" else
self.test_text_a_column)
b_column = (self.text_b_column if set_type != "test" else
self.test_text_b_column)
# there are some incomplete lines in QNLI
if len(line) <= a_column:
tf.logging.warning('Incomplete line, ignored.')
continue
text_a = line[a_column]
if b_column is not None:
if len(line) <= b_column:
tf.logging.warning('Incomplete line, ignored.')
continue
text_b = line[b_column]
else:
text_b = None
if set_type == "test":
label = self.get_labels()[0]
else:
if len(line) <= self.label_column:
tf.logging.warning('Incomplete line, ignored.')
continue
label = line[self.label_column]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
class Yelp5Processor(DataProcessor):
def get_train_examples(self, data_dir):
return self._create_examples(os.path.join(data_dir, "train.csv"))
def get_dev_examples(self, data_dir):
return self._create_examples(os.path.join(data_dir, "test.csv"))
def get_labels(self):
"""See base class."""
return ["1", "2", "3", "4", "5"]
def _create_examples(self, input_file):
"""Creates examples for the training and dev sets."""
examples = []
with tf.gfile.Open(input_file) as f:
reader = csv.reader(f)
for i, line in enumerate(reader):
label = line[0]
text_a = line[1].replace('""', '"').replace('\\"', '"')
examples.append(
InputExample(guid=str(i), text_a=text_a, text_b=None, label=label))
return examples
class ImdbProcessor(DataProcessor):
def get_labels(self):
return ["neg", "pos"]
def get_train_examples(self, data_dir):
return self._create_examples(os.path.join(data_dir, "train"))
def get_dev_examples(self, data_dir):
return self._create_examples(os.path.join(data_dir, "test"))
def _create_examples(self, data_dir):
examples = []
for label in ["neg", "pos"]:
cur_dir = os.path.join(data_dir, label)
for filename in tf.gfile.ListDirectory(cur_dir):
if not filename.endswith("txt"): continue
path = os.path.join(cur_dir, filename)
with tf.gfile.Open(path) as f:
text = f.read().strip().replace("<br />", " ")
examples.append(InputExample(
guid="unused_id", text_a=text, text_b=None, label=label))
return examples
class MnliMatchedProcessor(GLUEProcessor):
def __init__(self):
super(MnliMatchedProcessor, self).__init__()
self.dev_file = "dev_matched.tsv"
self.test_file = "test_matched.tsv"
self.label_column = -1
self.text_a_column = 8
self.text_b_column = 9
def get_labels(self):
return ["contradiction", "entailment", "neutral"]
class XnliProcessor(DataProcessor):
def __init__(self):
self.language = "zh"
def get_train_examples(self, data_dir, set_type="train"):
"""See base class."""
train_file = os.path.join(data_dir, "multinli",
"multinli.train.%s.tsv" % self.language)
lines = self._read_tsv(train_file)
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = line[0].replace(' ','')
text_b = line[1].replace(' ','')
label = line[2]
if label == "contradictory":
label = "contradiction"
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def get_devtest_examples(self, data_dir, set_type="dev"):
"""See base class."""
devtest_file = os.path.join(data_dir, "xnli."+set_type+".tsv")
tf.logging.info("using file %s" % devtest_file)
lines = self._read_tsv(devtest_file)
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
language = line[0]
if language != self.language:
continue
text_a = line[6].replace(' ','')
text_b = line[7].replace(' ','')
label = line[1]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def get_labels(self):
"""See base class."""
return ["contradiction", "entailment", "neutral"]
class CSCProcessor(DataProcessor):
def get_labels(self):
return ["0", "1"]
def get_train_examples(self, data_dir):
set_type = "train"
input_file = os.path.join(data_dir, set_type+".tsv")
tf.logging.info("using file %s" % input_file)
lines = self._read_tsv(input_file)
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = line[1]
label = line[0]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
def get_devtest_examples(self, data_dir, set_type="dev"):
input_file = os.path.join(data_dir, set_type+".tsv")
tf.logging.info("using file %s" % input_file)
lines = self._read_tsv(input_file)
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = line[1]
label = line[0]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
class CSVProcessor(DataProcessor):
def _read_tsv(cls, input_file, quotechar=None):
"""Reads a tab separated value file."""
with tf.gfile.Open(input_file, "r") as f:
reader = csv.reader(f)
lines = []
for line in reader:
if len(line) == 0: continue
lines.append(line)
return lines
def get_labels(self):
return ["0", "1"]
def get_train_examples(self, data_dir):
set_type = "train"
input_file = os.path.join(data_dir, set_type + ".csv")
tf.logging.info("using file %s" % input_file)
lines = self._read_tsv(input_file)
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = line[0]
label = line[1]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
def get_devtest_examples(self, data_dir, set_type="dev"):
input_file = os.path.join(data_dir, set_type + ".csv")
tf.logging.info("using file %s" % input_file)
lines = self._read_tsv(input_file)
examples = []
for (i, line) in enumerate(lines):
if i == 0:
continue
guid = "%s-%s" % (set_type, i)
text_a = line[0]
label = line[1]
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=None, label=label))
return examples
class MnliMismatchedProcessor(MnliMatchedProcessor):
def __init__(self):
super(MnliMismatchedProcessor, self).__init__()
self.dev_file = "dev_mismatched.tsv"
self.test_file = "test_mismatched.tsv"
class StsbProcessor(GLUEProcessor):
def __init__(self):
super(StsbProcessor, self).__init__()
self.label_column = 9
self.text_a_column = 7
self.text_b_column = 8
def get_labels(self):
return [0.0]
def _create_examples(self, lines, set_type):
"""Creates examples for the training and dev sets."""
examples = []
for (i, line) in enumerate(lines):
if i == 0 and self.contains_header and set_type != "test":
continue
if i == 0 and self.test_contains_header and set_type == "test":
continue
guid = "%s-%s" % (set_type, i)
a_column = (self.text_a_column if set_type != "test" else
self.test_text_a_column)
b_column = (self.text_b_column if set_type != "test" else
self.test_text_b_column)
# there are some incomplete lines in QNLI
if len(line) <= a_column:
tf.logging.warning('Incomplete line, ignored.')
continue
text_a = line[a_column]
if b_column is not None:
if len(line) <= b_column:
tf.logging.warning('Incomplete line, ignored.')
continue
text_b = line[b_column]
else:
text_b = None
if set_type == "test":
label = self.get_labels()[0]
else:
if len(line) <= self.label_column:
tf.logging.warning('Incomplete line, ignored.')
continue
label = float(line[self.label_column])
examples.append(
InputExample(guid=guid, text_a=text_a, text_b=text_b, label=label))
return examples
def file_based_convert_examples_to_features(
examples, label_list, max_seq_length, tokenize_fn, output_file,
num_passes=1):
"""Convert a set of `InputExample`s to a TFRecord file."""
# do not create duplicated records
if tf.gfile.Exists(output_file) and not FLAGS.overwrite_data:
tf.logging.info("Do not overwrite tfrecord {} exists.".format(output_file))
return
tf.logging.info("Create new tfrecord {}.".format(output_file))
writer = tf.python_io.TFRecordWriter(output_file)
if num_passes > 1:
examples *= num_passes
for (ex_index, example) in enumerate(examples):
if ex_index % 10000 == 0:
tf.logging.info("Writing example {} of {}".format(ex_index,
len(examples)))
feature = convert_single_example(ex_index, example, label_list,
max_seq_length, tokenize_fn)
def create_int_feature(values):
f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
return f
def create_float_feature(values):
f = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
return f
features = collections.OrderedDict()
features["input_ids"] = create_int_feature(feature.input_ids)
features["input_mask"] = create_float_feature(feature.input_mask)
features["segment_ids"] = create_int_feature(feature.segment_ids)
if label_list is not None:
features["label_ids"] = create_int_feature([feature.label_id])
else:
features["label_ids"] = create_float_feature([float(feature.label_id)])
features["is_real_example"] = create_int_feature(
[int(feature.is_real_example)])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
writer.write(tf_example.SerializeToString())
writer.close()
def file_based_input_fn_builder(input_file, seq_length, is_training,
drop_remainder):
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
name_to_features = {
"input_ids": tf.FixedLenFeature([seq_length], tf.int64),
"input_mask": tf.FixedLenFeature([seq_length], tf.float32),
"segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
"label_ids": tf.FixedLenFeature([], tf.int64),
"is_real_example": tf.FixedLenFeature([], tf.int64),
}
if FLAGS.is_regression:
name_to_features["label_ids"] = tf.FixedLenFeature([], tf.float32)
tf.logging.info("Input tfrecord file {}".format(input_file))
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.cast(t, tf.int32)
example[name] = t
return example
def input_fn(params, input_context=None):
"""The actual input function."""
if FLAGS.use_tpu:
batch_size = params["batch_size"]
elif is_training:
batch_size = FLAGS.train_batch_size
elif FLAGS.do_eval:
batch_size = FLAGS.eval_batch_size
else:
batch_size = FLAGS.predict_batch_size
d = tf.data.TFRecordDataset(input_file)
# Shard the dataset to difference devices
if input_context is not None:
tf.logging.info("Input pipeline id %d out of %d",
input_context.input_pipeline_id, input_context.num_replicas_in_sync)
d = d.shard(input_context.num_input_pipelines,
input_context.input_pipeline_id)
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
if is_training:
d = d.shuffle(buffer_size=FLAGS.shuffle_buffer)
d = d.repeat()
d = d.apply(
tf.contrib.data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
drop_remainder=drop_remainder))
return d
return input_fn
def get_model_fn(n_class):
def model_fn(features, labels, mode, params):
#### Training or Evaluation
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
#### Get loss from inputs
if FLAGS.is_regression:
(total_loss, per_example_loss, logits
) = function_builder.get_regression_loss(FLAGS, features, is_training)
else:
(total_loss, per_example_loss, logits
) = function_builder.get_classification_loss(
FLAGS, features, n_class, is_training)
#### Check model parameters
num_params = sum([np.prod(v.shape) for v in tf.trainable_variables()])
tf.logging.info('#params: {}'.format(num_params))
#### load pretrained models
scaffold_fn = model_utils.init_from_checkpoint(FLAGS)
#### Evaluation mode
if mode == tf.estimator.ModeKeys.EVAL:
assert FLAGS.num_hosts == 1
def metric_fn(per_example_loss, label_ids, logits, is_real_example):
predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
eval_input_dict = {
'labels': label_ids,
'predictions': predictions,
'weights': is_real_example
}
accuracy = tf.metrics.accuracy(**eval_input_dict)
loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
return {
'eval_accuracy': accuracy,
'eval_loss': loss}
def regression_metric_fn(
per_example_loss, label_ids, logits, is_real_example):
loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
pearsonr = tf.contrib.metrics.streaming_pearson_correlation(
logits, label_ids, weights=is_real_example)
return {'eval_loss': loss, 'eval_pearsonr': pearsonr}
is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
#### Constucting evaluation TPUEstimatorSpec with new cache.
label_ids = tf.reshape(features['label_ids'], [-1])
if FLAGS.is_regression:
metric_fn = regression_metric_fn
else:
metric_fn = metric_fn
metric_args = [per_example_loss, label_ids, logits, is_real_example]
if FLAGS.use_tpu:
eval_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=(metric_fn, metric_args),
scaffold_fn=scaffold_fn)
else:
eval_spec = tf.estimator.EstimatorSpec(
mode=mode,
loss=total_loss,
eval_metric_ops=metric_fn(*metric_args))
return eval_spec
elif mode == tf.estimator.ModeKeys.PREDICT:
label_ids = tf.reshape(features["label_ids"], [-1])
predictions = {
"logits": logits,
"labels": label_ids,
"is_real": features["is_real_example"]
}
if FLAGS.use_tpu:
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
else:
output_spec = tf.estimator.EstimatorSpec(
mode=mode, predictions=predictions)
return output_spec
#### Configuring the optimizer
train_op, learning_rate, _ = model_utils.get_train_op(FLAGS, total_loss)
monitor_dict = {}
monitor_dict["lr"] = learning_rate
#### Constucting training TPUEstimatorSpec with new cache.
if FLAGS.use_tpu:
#### Creating host calls
if not FLAGS.is_regression:
label_ids = tf.reshape(features['label_ids'], [-1])
predictions = tf.argmax(logits, axis=-1, output_type=label_ids.dtype)
is_correct = tf.equal(predictions, label_ids)
accuracy = tf.reduce_mean(tf.cast(is_correct, tf.float32))
monitor_dict["accuracy"] = accuracy
host_call = function_builder.construct_scalar_host_call(
monitor_dict=monitor_dict,
model_dir=FLAGS.model_dir,
prefix="train/",
reduce_fn=tf.reduce_mean)
else:
host_call = None
train_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode, loss=total_loss, train_op=train_op, host_call=host_call,
scaffold_fn=scaffold_fn)
else:
train_spec = tf.estimator.EstimatorSpec(
mode=mode, loss=total_loss, train_op=train_op)
return train_spec
return model_fn
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
#### Validate flags
if FLAGS.save_steps is not None:
FLAGS.iterations = min(FLAGS.iterations, FLAGS.save_steps)
if FLAGS.do_predict:
predict_dir = FLAGS.predict_dir
if not tf.gfile.Exists(predict_dir):
tf.gfile.MakeDirs(predict_dir)
processors = {
"mnli_matched": MnliMatchedProcessor,
"mnli_mismatched": MnliMismatchedProcessor,
'sts-b': StsbProcessor,
'imdb': ImdbProcessor,
"yelp5": Yelp5Processor,
"xnli": XnliProcessor,
"csc": CSCProcessor,
"csv": CSVProcessor,
}
if not FLAGS.do_train and not FLAGS.do_eval and not FLAGS.do_predict:
raise ValueError(
"At least one of `do_train`, `do_eval, `do_predict` or "
"`do_submit` must be True.")
if not tf.gfile.Exists(FLAGS.output_dir):
tf.gfile.MakeDirs(FLAGS.output_dir)
task_name = FLAGS.task_name.lower()
if task_name not in processors:
raise ValueError("Task not found: %s" % (task_name))
processor = processors[task_name]()
label_list = processor.get_labels() if not FLAGS.is_regression else None
sp = spm.SentencePieceProcessor()
sp.Load(FLAGS.spiece_model_file)
def tokenize_fn(text):
text = preprocess_text(text, lower=FLAGS.uncased)
return encode_ids(sp, text)
run_config = model_utils.configure_tpu(FLAGS)
model_fn = get_model_fn(len(label_list) if label_list is not None else None)
spm_basename = os.path.basename(FLAGS.spiece_model_file)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
if FLAGS.use_tpu:
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
predict_batch_size=FLAGS.predict_batch_size,
eval_batch_size=FLAGS.eval_batch_size)
else:
estimator = tf.estimator.Estimator(
model_fn=model_fn,
config=run_config)
if FLAGS.do_train:
train_file_base = "{}.len-{}.train.tf_record".format(
spm_basename, FLAGS.max_seq_length)
train_file = os.path.join(FLAGS.output_dir, train_file_base)
tf.logging.info("Use tfrecord file {}".format(train_file))
train_examples = processor.get_train_examples(FLAGS.data_dir)
np.random.shuffle(train_examples)
tf.logging.info("Num of train samples: {}".format(len(train_examples)))
file_based_convert_examples_to_features(
train_examples, label_list, FLAGS.max_seq_length, tokenize_fn,
train_file, FLAGS.num_passes)
# here we use epoch number to calculate total train_steps
FLAGS.train_steps = int(len(train_examples) * FLAGS.num_train_epochs / FLAGS.train_batch_size)
FLAGS.warmup_steps = int(0.1 * FLAGS.train_steps)
train_input_fn = file_based_input_fn_builder(
input_file=train_file,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True)
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.train_steps)
if FLAGS.do_eval or FLAGS.do_predict:
eval_examples = processor.get_devtest_examples(FLAGS.data_dir, FLAGS.eval_split)
tf.logging.info("Num of eval samples: {}".format(len(eval_examples)))
if FLAGS.do_eval:
# TPU requires a fixed batch size for all batches, therefore the number
# of examples must be a multiple of the batch size, or else examples
# will get dropped. So we pad with fake examples which are ignored
# later on. These do NOT count towards the metric (all tf.metrics
# support a per-instance weight, and these get a weight of 0.0).
#
# Modified in XL: We also adopt the same mechanism for GPUs.
while len(eval_examples) % FLAGS.eval_batch_size != 0:
eval_examples.append(PaddingInputExample())
eval_file_base = "{}.len-{}.{}.eval.tf_record".format(
spm_basename, FLAGS.max_seq_length, FLAGS.eval_split)
eval_file = os.path.join(FLAGS.output_dir, eval_file_base)
file_based_convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenize_fn,
eval_file)
assert len(eval_examples) % FLAGS.eval_batch_size == 0
eval_steps = int(len(eval_examples) // FLAGS.eval_batch_size)
eval_input_fn = file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=True)
# Filter out all checkpoints in the directory
steps_and_files = []
filenames = tf.gfile.ListDirectory(FLAGS.model_dir)
for filename in filenames:
if filename.endswith(".index"):
ckpt_name = filename[:-6]
tf.logging.info(f"ckpt_name: {ckpt_name}")
cur_filename = join(FLAGS.model_dir, ckpt_name)
step = cur_filename.split("-")[-1]
if step.isdigit():
global_step = int(step)
tf.logging.info("Add {} to eval list.".format(cur_filename))
steps_and_files.append([global_step, cur_filename])
steps_and_files = sorted(steps_and_files, key=lambda x: x[0])
# Decide whether to evaluate all ckpts
if not FLAGS.eval_all_ckpt:
steps_and_files = steps_and_files[-1:]
eval_results = []
for global_step, filename in sorted(steps_and_files, key=lambda x: x[0]):
ret = estimator.evaluate(
input_fn=eval_input_fn,
steps=eval_steps,
checkpoint_path=filename)
ret["step"] = global_step
ret["path"] = filename
eval_results.append(ret)
tf.logging.info("=" * 80)
log_str = "Eval result | "
for key, val in sorted(ret.items(), key=lambda x: x[0]):
log_str += "{} {} | ".format(key, val)
tf.logging.info(log_str)
key_name = "eval_pearsonr" if FLAGS.is_regression else "eval_accuracy"
eval_results.sort(key=lambda x: x[key_name], reverse=True)
tf.logging.info("=" * 80)
log_str = "Best result | "
for key, val in sorted(eval_results[0].items(), key=lambda x: x[0]):
log_str += "{} {} | ".format(key, val)
tf.logging.info(log_str)
if FLAGS.do_predict:
eval_file_base = "{}.len-{}.{}.predict.tf_record".format(
spm_basename, FLAGS.max_seq_length, FLAGS.eval_split)
eval_file = os.path.join(FLAGS.output_dir, eval_file_base)
file_based_convert_examples_to_features(
eval_examples, label_list, FLAGS.max_seq_length, tokenize_fn,
eval_file)
pred_input_fn = file_based_input_fn_builder(
input_file=eval_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=False)
predict_results = []
with tf.gfile.Open(os.path.join(predict_dir, "{}.tsv".format(
task_name)), "w") as fout:
fout.write("index\tprediction\n")
for pred_cnt, result in enumerate(estimator.predict(
input_fn=pred_input_fn,
yield_single_examples=True,
checkpoint_path=FLAGS.predict_ckpt)):
if pred_cnt % 1000 == 0:
tf.logging.info("Predicting submission for example: {}".format(
pred_cnt))
logits = [float(x) for x in result["logits"].flat]
predict_results.append(logits)
if len(logits) == 1:
label_out = logits[0]
elif len(logits) == 2:
if logits[1] - logits[0] > FLAGS.predict_threshold:
label_out = label_list[1]
else:
label_out = label_list[0]
elif len(logits) > 2:
max_index = np.argmax(np.array(logits, dtype=np.float32))
label_out = label_list[max_index]
else:
raise NotImplementedError
fout.write("{}\t{}\n".format(pred_cnt, label_out))
predict_json_path = os.path.join(predict_dir, "{}.logits.json".format(
task_name))
with tf.gfile.Open(predict_json_path, "w") as fp:
json.dump(predict_results, fp, indent=4)
if __name__ == "__main__":
tf.app.run()
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/run_cmrc_drcd.py | Python | # coding=utf-8
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import flags
import absl.logging as _logging # pylint: disable=unused-import
import collections
import os
import time
import math
import json
import six
import random
import gc
import numpy as np
if six.PY2:
import cPickle as pickle
else:
import pickle
import tensorflow as tf
import sentencepiece as spm
from prepro_utils import preprocess_text, encode_ids, encode_pieces, printable_text
import function_builder
import model_utils
import squad_utils
from data_utils import SEP_ID, CLS_ID, VOCAB_SIZE
SPIECE_UNDERLINE = u'▁'
SEG_ID_P = 0
SEG_ID_Q = 1
SEG_ID_CLS = 2
SEG_ID_PAD = 3
# Preprocessing
flags.DEFINE_bool("do_prepro", default=False,
help="Perform preprocessing only.")
flags.DEFINE_integer("num_proc", default=1,
help="Number of preprocessing processes.")
flags.DEFINE_integer("proc_id", default=0,
help="Process id for preprocessing.")
# Model
flags.DEFINE_string("model_config_path", default=None,
help="Model config path.")
flags.DEFINE_float("dropout", default=0.1,
help="Dropout rate.")
flags.DEFINE_float("dropatt", default=0.1,
help="Attention dropout rate.")
flags.DEFINE_integer("clamp_len", default=-1,
help="Clamp length.")
flags.DEFINE_string("summary_type", default="last",
help="Method used to summarize a sequence into a vector.")
flags.DEFINE_bool("use_bfloat16", default=False,
help="Whether to use bfloat16.")
# Parameter initialization
flags.DEFINE_enum("init", default="normal",
enum_values=["normal", "uniform"],
help="Initialization method.")
flags.DEFINE_float("init_std", default=0.02,
help="Initialization std when init is normal.")
flags.DEFINE_float("init_range", default=0.1,
help="Initialization std when init is uniform.")
# I/O paths
flags.DEFINE_bool("overwrite_data", default=False,
help="If False, will use cached data if available.")
flags.DEFINE_string("init_checkpoint", default=None,
help="checkpoint path for initializing the model. "
"Could be a pretrained model or a finetuned model.")
flags.DEFINE_bool("init_global_vars", default=False,
help="If true, init all global vars. If false, init "
"trainable vars only.")
flags.DEFINE_string("output_dir", default="",
help="Output dir for TF records.")
flags.DEFINE_string("predict_dir", default="",
help="Dir for predictions.")
flags.DEFINE_string("spiece_model_file", default="",
help="Sentence Piece model path.")
flags.DEFINE_string("model_dir", default="",
help="Directory for saving the finetuned model.")
flags.DEFINE_string("train_file", default="",
help="Path of train file.")
flags.DEFINE_string("predict_file", default="",
help="Path of prediction file.")
# Data preprocessing config
flags.DEFINE_integer("max_seq_length",
default=512, help="Max sequence length")
flags.DEFINE_integer("max_query_length",
default=64, help="Max query length")
flags.DEFINE_integer("doc_stride",
default=128, help="Doc stride")
flags.DEFINE_integer("max_answer_length",
default=64, help="Max answer length")
flags.DEFINE_bool("uncased", default=False, help="Use uncased data.")
# TPUs and machines
flags.DEFINE_bool("use_tpu", default=False, help="whether to use TPU.")
flags.DEFINE_integer("num_hosts", default=1, help="How many TPU hosts.")
flags.DEFINE_integer("num_core_per_host", default=8,
help="8 for TPU v2 and v3-8, 16 for larger TPU v3 pod. In the context "
"of GPU training, it refers to the number of GPUs used.")
flags.DEFINE_string("tpu_job_name", default=None, help="TPU worker job name.")
flags.DEFINE_string("tpu", default=None, help="TPU name.")
flags.DEFINE_string("tpu_zone", default=None, help="TPU zone.")
flags.DEFINE_string("gcp_project", default=None, help="gcp project.")
flags.DEFINE_string("master", default=None, help="master")
flags.DEFINE_integer("iterations", default=1000,
help="number of iterations per TPU training loop.")
# Training
flags.DEFINE_bool("do_train", default=True, help="whether to do training")
flags.DEFINE_integer("train_batch_size", default=48,
help="batch size for training")
flags.DEFINE_integer("train_steps", default=8000,
help="Number of training steps")
flags.DEFINE_integer("warmup_steps", default=0, help="number of warmup steps")
flags.DEFINE_integer("save_steps", default=None,
help="Save the model for every save_steps. "
"If None, not to save any model.")
flags.DEFINE_integer("max_save", default=5,
help="Max number of checkpoints to save. "
"Use 0 to save all.")
flags.DEFINE_integer("shuffle_buffer", default=2048,
help="Buffer size used for shuffle.")
# Optimization
flags.DEFINE_float("learning_rate", default=3e-5, help="initial learning rate")
flags.DEFINE_float("min_lr_ratio", default=0.0,
help="min lr ratio for cos decay.")
flags.DEFINE_float("clip", default=1.0, help="Gradient clipping")
flags.DEFINE_float("weight_decay", default=0.00, help="Weight decay rate")
flags.DEFINE_float("adam_epsilon", default=1e-6, help="Adam epsilon")
flags.DEFINE_string("decay_method", default="poly", help="poly or cos")
flags.DEFINE_float("lr_layer_decay_rate", default=0.75,
help="Top layer: lr[L] = FLAGS.learning_rate."
"Lower layers: lr[l-1] = lr[l] * lr_layer_decay_rate.")
# Eval / Prediction
flags.DEFINE_bool("do_predict", default=False, help="whether to do predict")
flags.DEFINE_integer("predict_batch_size", default=32,
help="batch size for prediction")
flags.DEFINE_integer("n_best_size", default=5,
help="n best size for predictions")
flags.DEFINE_integer("start_n_top", default=5, help="Beam size for span start.")
flags.DEFINE_integer("end_n_top", default=5, help="Beam size for span end.")
flags.DEFINE_string("target_eval_key", default="best_f1",
help="Use has_ans_f1 for Model I.")
FLAGS = flags.FLAGS
class SquadExample(object):
"""A single training/test example for simple sequence classification.
For examples without an answer, the start and end position are -1.
"""
def __init__(self,
qas_id,
question_text,
paragraph_text,
orig_answer_text=None,
start_position=None,
is_impossible=False):
self.qas_id = qas_id
self.question_text = question_text
self.paragraph_text = paragraph_text
self.orig_answer_text = orig_answer_text
self.start_position = start_position
self.is_impossible = is_impossible
def __str__(self):
return self.__repr__()
def __repr__(self):
s = ""
s += "qas_id: %s" % (printable_text(self.qas_id))
s += ", question_text: %s" % (
printable_text(self.question_text))
s += ", paragraph_text: [%s]" % (" ".join(self.paragraph_text))
if self.start_position:
s += ", start_position: %d" % (self.start_position)
if self.start_position:
s += ", is_impossible: %r" % (self.is_impossible)
return s
class InputFeatures(object):
"""A single set of features of data."""
def __init__(self,
unique_id,
example_index,
doc_span_index,
tok_start_to_orig_index,
tok_end_to_orig_index,
token_is_max_context,
input_ids,
input_mask,
p_mask,
segment_ids,
paragraph_len,
cls_index,
start_position=None,
end_position=None,
is_impossible=None):
self.unique_id = unique_id
self.example_index = example_index
self.doc_span_index = doc_span_index
self.tok_start_to_orig_index = tok_start_to_orig_index
self.tok_end_to_orig_index = tok_end_to_orig_index
self.token_is_max_context = token_is_max_context
self.input_ids = input_ids
self.input_mask = input_mask
self.p_mask = p_mask
self.segment_ids = segment_ids
self.paragraph_len = paragraph_len
self.cls_index = cls_index
self.start_position = start_position
self.end_position = end_position
self.is_impossible = is_impossible
def read_squad_examples(input_file, is_training):
"""Read a SQuAD json file into a list of SquadExample."""
with tf.gfile.Open(input_file, "r") as reader:
input_data = json.load(reader)["data"]
examples = []
for entry in input_data:
for paragraph in entry["paragraphs"]:
paragraph_text = paragraph["context"]
for qa in paragraph["qas"]:
qas_id = qa["id"]
question_text = qa["question"]
start_position = None
orig_answer_text = None
is_impossible = False
if is_training:
if "is_impossible" in qa:
is_impossible = qa["is_impossible"]
else:
is_impossible = False
if (len(qa["answers"]) != 1) and (not is_impossible):
raise ValueError(
"For training, each question should have exactly 1 answer.")
if not is_impossible:
answer = qa["answers"][0]
orig_answer_text = answer["text"]
start_position = answer["answer_start"]
else:
start_position = -1
orig_answer_text = ""
example = SquadExample(
qas_id=qas_id,
question_text=question_text,
paragraph_text=paragraph_text,
orig_answer_text=orig_answer_text,
start_position=start_position,
is_impossible=is_impossible)
examples.append(example)
return examples
def _convert_index(index, pos, M=None, is_start=True):
if pos >= len(index):
pos = len(index) - 1
if index[pos] is not None:
return index[pos]
N = len(index)
rear = pos
while rear < N - 1 and index[rear] is None:
rear += 1
front = pos
while front > 0 and index[front] is None:
front -= 1
assert index[front] is not None or index[rear] is not None
if index[front] is None:
if index[rear] >= 1:
if is_start:
return 0
else:
return index[rear] - 1
return index[rear]
if index[rear] is None:
if M is not None and index[front] < M - 1:
if is_start:
return index[front] + 1
else:
return M - 1
return index[front]
if is_start:
if index[rear] > index[front] + 1:
return index[front] + 1
else:
return index[rear]
else:
if index[rear] > index[front] + 1:
return index[rear] - 1
else:
return index[front]
def convert_examples_to_features(examples, sp_model, max_seq_length,
doc_stride, max_query_length, is_training,
output_fn):
"""Loads a data file into a list of `InputBatch`s."""
cnt_pos, cnt_neg = 0, 0
unique_id = 1000000000
max_N, max_M = 1024, 1024
f = np.zeros((max_N, max_M), dtype=np.float32)
for (example_index, example) in enumerate(examples):
if example_index % 100 == 0:
tf.logging.info('Converting {}/{} pos {} neg {}'.format(
example_index, len(examples), cnt_pos, cnt_neg))
query_tokens = encode_ids(
sp_model,
preprocess_text(example.question_text, lower=FLAGS.uncased))
if len(query_tokens) > max_query_length:
query_tokens = query_tokens[0:max_query_length]
paragraph_text = example.paragraph_text
para_tokens = encode_pieces(
sp_model,
preprocess_text(example.paragraph_text, lower=FLAGS.uncased))
chartok_to_tok_index = []
tok_start_to_chartok_index = []
tok_end_to_chartok_index = []
char_cnt = 0
for i, token in enumerate(para_tokens):
chartok_to_tok_index.extend([i] * len(token))
tok_start_to_chartok_index.append(char_cnt)
char_cnt += len(token)
tok_end_to_chartok_index.append(char_cnt - 1)
tok_cat_text = ''.join(para_tokens).replace(SPIECE_UNDERLINE, ' ')
N, M = len(paragraph_text), len(tok_cat_text)
if N > max_N or M > max_M:
max_N = max(N, max_N)
max_M = max(M, max_M)
f = np.zeros((max_N, max_M), dtype=np.float32)
gc.collect()
g = {}
def _lcs_match(max_dist):
f.fill(0)
g.clear()
### longest common sub sequence
# f[i, j] = max(f[i - 1, j], f[i, j - 1], f[i - 1, j - 1] + match(i, j))
for i in range(N):
# note(zhiliny):
# unlike standard LCS, this is specifically optimized for the setting
# because the mismatch between sentence pieces and original text will
# be small
for j in range(i - max_dist, i + max_dist):
if j >= M or j < 0: continue
if i > 0:
g[(i, j)] = 0
f[i, j] = f[i - 1, j]
if j > 0 and f[i, j - 1] > f[i, j]:
g[(i, j)] = 1
f[i, j] = f[i, j - 1]
f_prev = f[i - 1, j - 1] if i > 0 and j > 0 else 0
if (preprocess_text(paragraph_text[i], lower=FLAGS.uncased,
remove_space=False)
== tok_cat_text[j]
and f_prev + 1 > f[i, j]):
g[(i, j)] = 2
f[i, j] = f_prev + 1
max_dist = abs(N - M) + 5
for _ in range(2):
_lcs_match(max_dist)
if f[N - 1, M - 1] > 0.8 * N: break
max_dist *= 2
orig_to_chartok_index = [None] * N
chartok_to_orig_index = [None] * M
i, j = N - 1, M - 1
while i >= 0 and j >= 0:
if (i, j) not in g: break
if g[(i, j)] == 2:
orig_to_chartok_index[i] = j
chartok_to_orig_index[j] = i
i, j = i - 1, j - 1
elif g[(i, j)] == 1:
j = j - 1
else:
i = i - 1
if all(v is None for v in orig_to_chartok_index) or f[N - 1, M - 1] < 0.8 * N:
print('MISMATCH DETECTED!')
continue
tok_start_to_orig_index = []
tok_end_to_orig_index = []
for i in range(len(para_tokens)):
start_chartok_pos = tok_start_to_chartok_index[i]
end_chartok_pos = tok_end_to_chartok_index[i]
start_orig_pos = _convert_index(chartok_to_orig_index, start_chartok_pos,
N, is_start=True)
end_orig_pos = _convert_index(chartok_to_orig_index, end_chartok_pos,
N, is_start=False)
tok_start_to_orig_index.append(start_orig_pos)
tok_end_to_orig_index.append(end_orig_pos)
if not is_training:
tok_start_position = tok_end_position = None
if is_training and example.is_impossible:
tok_start_position = -1
tok_end_position = -1
if is_training and not example.is_impossible:
start_position = example.start_position
end_position = start_position + len(example.orig_answer_text) - 1
start_chartok_pos = _convert_index(orig_to_chartok_index, start_position,
is_start=True)
tok_start_position = chartok_to_tok_index[start_chartok_pos]
end_chartok_pos = _convert_index(orig_to_chartok_index, end_position,
is_start=False)
tok_end_position = chartok_to_tok_index[end_chartok_pos]
assert tok_start_position <= tok_end_position
def _piece_to_id(x):
if six.PY2 and isinstance(x, unicode):
x = x.encode('utf-8')
return sp_model.PieceToId(x)
all_doc_tokens = list(map(_piece_to_id, para_tokens))
# The -3 accounts for [CLS], [SEP] and [SEP]
max_tokens_for_doc = max_seq_length - len(query_tokens) - 3
# We can have documents that are longer than the maximum sequence length.
# To deal with this we do a sliding window approach, where we take chunks
# of the up to our max length with a stride of `doc_stride`.
_DocSpan = collections.namedtuple( # pylint: disable=invalid-name
"DocSpan", ["start", "length"])
doc_spans = []
start_offset = 0
while start_offset < len(all_doc_tokens):
length = len(all_doc_tokens) - start_offset
if length > max_tokens_for_doc:
length = max_tokens_for_doc
doc_spans.append(_DocSpan(start=start_offset, length=length))
if start_offset + length == len(all_doc_tokens):
break
start_offset += min(length, doc_stride)
for (doc_span_index, doc_span) in enumerate(doc_spans):
tokens = []
token_is_max_context = {}
segment_ids = []
p_mask = []
cur_tok_start_to_orig_index = []
cur_tok_end_to_orig_index = []
for i in range(doc_span.length):
split_token_index = doc_span.start + i
cur_tok_start_to_orig_index.append(
tok_start_to_orig_index[split_token_index])
cur_tok_end_to_orig_index.append(
tok_end_to_orig_index[split_token_index])
is_max_context = _check_is_max_context(doc_spans, doc_span_index,
split_token_index)
token_is_max_context[len(tokens)] = is_max_context
tokens.append(all_doc_tokens[split_token_index])
segment_ids.append(SEG_ID_P)
p_mask.append(0)
paragraph_len = len(tokens)
tokens.append(SEP_ID)
segment_ids.append(SEG_ID_P)
p_mask.append(1)
# note(zhiliny): we put P before Q
# because during pretraining, B is always shorter than A
for token in query_tokens:
tokens.append(token)
segment_ids.append(SEG_ID_Q)
p_mask.append(1)
tokens.append(SEP_ID)
segment_ids.append(SEG_ID_Q)
p_mask.append(1)
cls_index = len(segment_ids)
tokens.append(CLS_ID)
segment_ids.append(SEG_ID_CLS)
p_mask.append(0)
input_ids = tokens
# The mask has 0 for real tokens and 1 for padding tokens. Only real
# tokens are attended to.
input_mask = [0] * len(input_ids)
# Zero-pad up to the sequence length.
while len(input_ids) < max_seq_length:
input_ids.append(0)
input_mask.append(1)
segment_ids.append(SEG_ID_PAD)
p_mask.append(1)
assert len(input_ids) == max_seq_length
assert len(input_mask) == max_seq_length
assert len(segment_ids) == max_seq_length
assert len(p_mask) == max_seq_length
span_is_impossible = example.is_impossible
start_position = None
end_position = None
if is_training and not span_is_impossible:
# For training, if our document chunk does not contain an annotation
# we throw it out, since there is nothing to predict.
doc_start = doc_span.start
doc_end = doc_span.start + doc_span.length - 1
out_of_span = False
if not (tok_start_position >= doc_start and
tok_end_position <= doc_end):
out_of_span = True
if out_of_span:
# continue
start_position = 0
end_position = 0
span_is_impossible = True
else:
# note(zhiliny): we put P before Q, so doc_offset should be zero.
# doc_offset = len(query_tokens) + 2
doc_offset = 0
start_position = tok_start_position - doc_start + doc_offset
end_position = tok_end_position - doc_start + doc_offset
if is_training and span_is_impossible:
start_position = cls_index
end_position = cls_index
if example_index < 20:
tf.logging.info("*** Example ***")
tf.logging.info("unique_id: %s" % (unique_id))
tf.logging.info("example_index: %s" % (example_index))
tf.logging.info("doc_span_index: %s" % (doc_span_index))
tf.logging.info("tok_start_to_orig_index: %s" % " ".join(
[str(x) for x in cur_tok_start_to_orig_index]))
tf.logging.info("tok_end_to_orig_index: %s" % " ".join(
[str(x) for x in cur_tok_end_to_orig_index]))
tf.logging.info("token_is_max_context: %s" % " ".join([
"%d:%s" % (x, y) for (x, y) in six.iteritems(token_is_max_context)
]))
tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
tf.logging.info(
"input_mask: %s" % " ".join([str(x) for x in input_mask]))
tf.logging.info(
"segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
if is_training and span_is_impossible:
tf.logging.info("impossible example span")
if is_training and not span_is_impossible:
pieces = [sp_model.IdToPiece(token) for token in
tokens[start_position: (end_position + 1)]]
answer_text = sp_model.DecodePieces(pieces)
tf.logging.info("start_position: %d" % (start_position))
tf.logging.info("end_position: %d" % (end_position))
tf.logging.info(
"answer: %s" % (printable_text(answer_text)))
# note(zhiliny): With multi processing,
# the example_index is actually the index within the current process
# therefore we use example_index=None to avoid being used in the future.
# The current code does not use example_index of training data.
if is_training:
feat_example_index = None
else:
feat_example_index = example_index
feature = InputFeatures(
unique_id=unique_id,
example_index=feat_example_index,
doc_span_index=doc_span_index,
tok_start_to_orig_index=cur_tok_start_to_orig_index,
tok_end_to_orig_index=cur_tok_end_to_orig_index,
token_is_max_context=token_is_max_context,
input_ids=input_ids,
input_mask=input_mask,
p_mask=p_mask,
segment_ids=segment_ids,
paragraph_len=paragraph_len,
cls_index=cls_index,
start_position=start_position,
end_position=end_position,
is_impossible=span_is_impossible)
# Run callback
output_fn(feature)
unique_id += 1
if span_is_impossible:
cnt_neg += 1
else:
cnt_pos += 1
tf.logging.info("Total number of instances: {} = pos {} neg {}".format(
cnt_pos + cnt_neg, cnt_pos, cnt_neg))
def _check_is_max_context(doc_spans, cur_span_index, position):
"""Check if this is the 'max context' doc span for the token."""
# Because of the sliding window approach taken to scoring documents, a single
# token can appear in multiple documents. E.g.
# Doc: the man went to the store and bought a gallon of milk
# Span A: the man went to the
# Span B: to the store and bought
# Span C: and bought a gallon of
# ...
#
# Now the word 'bought' will have two scores from spans B and C. We only
# want to consider the score with "maximum context", which we define as
# the *minimum* of its left and right context (the *sum* of left and
# right context will always be the same, of course).
#
# In the example the maximum context for 'bought' would be span C since
# it has 1 left context and 3 right context, while span B has 4 left context
# and 0 right context.
best_score = None
best_span_index = None
for (span_index, doc_span) in enumerate(doc_spans):
end = doc_span.start + doc_span.length - 1
if position < doc_span.start:
continue
if position > end:
continue
num_left_context = position - doc_span.start
num_right_context = end - position
score = min(num_left_context, num_right_context) + 0.01 * doc_span.length
if best_score is None or score > best_score:
best_score = score
best_span_index = span_index
return cur_span_index == best_span_index
class FeatureWriter(object):
"""Writes InputFeature to TF example file."""
def __init__(self, filename, is_training):
self.filename = filename
self.is_training = is_training
self.num_features = 0
self._writer = tf.python_io.TFRecordWriter(filename)
def process_feature(self, feature):
"""Write a InputFeature to the TFRecordWriter as a tf.train.Example."""
self.num_features += 1
def create_int_feature(values):
feature = tf.train.Feature(
int64_list=tf.train.Int64List(value=list(values)))
return feature
def create_float_feature(values):
f = tf.train.Feature(float_list=tf.train.FloatList(value=list(values)))
return f
features = collections.OrderedDict()
features["unique_ids"] = create_int_feature([feature.unique_id])
features["input_ids"] = create_int_feature(feature.input_ids)
features["input_mask"] = create_float_feature(feature.input_mask)
features["p_mask"] = create_float_feature(feature.p_mask)
features["segment_ids"] = create_int_feature(feature.segment_ids)
features["cls_index"] = create_int_feature([feature.cls_index])
if self.is_training:
features["start_positions"] = create_int_feature([feature.start_position])
features["end_positions"] = create_int_feature([feature.end_position])
impossible = 0
if feature.is_impossible:
impossible = 1
features["is_impossible"] = create_float_feature([impossible])
tf_example = tf.train.Example(features=tf.train.Features(feature=features))
self._writer.write(tf_example.SerializeToString())
def close(self):
self._writer.close()
RawResult = collections.namedtuple("RawResult",
["unique_id", "start_top_log_probs", "start_top_index",
"end_top_log_probs", "end_top_index"])
_PrelimPrediction = collections.namedtuple( # pylint: disable=invalid-name
"PrelimPrediction",
["feature_index", "start_index", "end_index",
"start_log_prob", "end_log_prob"])
_NbestPrediction = collections.namedtuple( # pylint: disable=invalid-name
"NbestPrediction", ["text", "start_log_prob", "end_log_prob"])
def write_predictions(all_examples, all_features, all_results, n_best_size,
max_answer_length, output_prediction_file,
output_nbest_file,
orig_data):
"""Write final predictions to the json file and log-odds of null if needed."""
tf.logging.info("Writing predictions to: %s" % (output_prediction_file))
# tf.logging.info("Writing nbest to: %s" % (output_nbest_file))
example_index_to_features = collections.defaultdict(list)
for feature in all_features:
example_index_to_features[feature.example_index].append(feature)
unique_id_to_result = {}
for result in all_results:
unique_id_to_result[result.unique_id] = result
all_predictions = collections.OrderedDict()
all_nbest_json = collections.OrderedDict()
scores_diff_json = collections.OrderedDict()
for (example_index, example) in enumerate(all_examples):
features = example_index_to_features[example_index]
prelim_predictions = []
# keep track of the minimum score of null start+end of position 0
for (feature_index, feature) in enumerate(features):
result = unique_id_to_result[feature.unique_id]
for i in range(FLAGS.start_n_top):
for j in range(FLAGS.end_n_top):
start_log_prob = result.start_top_log_probs[i]
start_index = result.start_top_index[i]
j_index = i * FLAGS.end_n_top + j
end_log_prob = result.end_top_log_probs[j_index]
end_index = result.end_top_index[j_index]
# We could hypothetically create invalid predictions, e.g., predict
# that the start of the span is in the question. We throw out all
# invalid predictions.
if start_index >= feature.paragraph_len - 1:
continue
if end_index >= feature.paragraph_len - 1:
continue
if not feature.token_is_max_context.get(start_index, False):
continue
if end_index < start_index:
continue
length = end_index - start_index + 1
if length > max_answer_length:
continue
prelim_predictions.append(
_PrelimPrediction(
feature_index=feature_index,
start_index=start_index,
end_index=end_index,
start_log_prob=start_log_prob,
end_log_prob=end_log_prob))
prelim_predictions = sorted(
prelim_predictions,
key=lambda x: (x.start_log_prob + x.end_log_prob),
reverse=True)
seen_predictions = {}
nbest = []
for pred in prelim_predictions:
if len(nbest) >= n_best_size:
break
feature = features[pred.feature_index]
tok_start_to_orig_index = feature.tok_start_to_orig_index
tok_end_to_orig_index = feature.tok_end_to_orig_index
start_orig_pos = tok_start_to_orig_index[pred.start_index]
end_orig_pos = tok_end_to_orig_index[pred.end_index]
paragraph_text = example.paragraph_text
final_text = paragraph_text[start_orig_pos: end_orig_pos + 1].strip()
if final_text in seen_predictions:
continue
seen_predictions[final_text] = True
nbest.append(
_NbestPrediction(
text=final_text,
start_log_prob=pred.start_log_prob,
end_log_prob=pred.end_log_prob))
# In very rare edge cases we could have no valid predictions. So we
# just create a nonce prediction in this case to avoid failure.
if not nbest:
nbest.append(
_NbestPrediction(text="", start_log_prob=-1e6,
end_log_prob=-1e6))
total_scores = []
best_non_null_entry = None
for entry in nbest:
total_scores.append(entry.start_log_prob + entry.end_log_prob)
if not best_non_null_entry:
best_non_null_entry = entry
probs = _compute_softmax(total_scores)
nbest_json = []
for (i, entry) in enumerate(nbest):
output = collections.OrderedDict()
output["text"] = entry.text
output["probability"] = probs[i]
output["start_log_prob"] = entry.start_log_prob
output["end_log_prob"] = entry.end_log_prob
nbest_json.append(output)
assert len(nbest_json) >= 1
assert best_non_null_entry is not None
score_diff = 0 #score_null
scores_diff_json[example.qas_id] = score_diff
# note(zhiliny): always predict best_non_null_entry
# and the evaluation script will search for the best threshold
all_predictions[example.qas_id] = best_non_null_entry.text
all_nbest_json[example.qas_id] = nbest_json
with tf.gfile.GFile(output_prediction_file, "w") as writer:
writer.write(json.dumps(all_predictions, indent=4) + "\n")
with tf.gfile.GFile(output_nbest_file, "w") as writer:
writer.write(json.dumps(all_nbest_json, indent=4) + "\n")
qid_to_has_ans = squad_utils.make_qid_to_has_ans(orig_data)
has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
exact_raw, f1_raw = squad_utils.get_raw_scores(orig_data, all_predictions)
out_eval = {}
squad_utils.find_all_best_thresh_v2(out_eval, all_predictions, exact_raw, f1_raw,
scores_diff_json, qid_to_has_ans)
return out_eval
def _get_best_indexes(logits, n_best_size):
"""Get the n-best logits from a list."""
index_and_score = sorted(enumerate(logits), key=lambda x: x[1], reverse=True)
best_indexes = []
for i in range(len(index_and_score)):
if i >= n_best_size:
break
best_indexes.append(index_and_score[i][0])
return best_indexes
def _compute_softmax(scores):
"""Compute softmax probability over raw logits."""
if not scores:
return []
max_score = None
for score in scores:
if max_score is None or score > max_score:
max_score = score
exp_scores = []
total_sum = 0.0
for score in scores:
x = math.exp(score - max_score)
exp_scores.append(x)
total_sum += x
probs = []
for score in exp_scores:
probs.append(score / total_sum)
return probs
def input_fn_builder(input_glob, seq_length, is_training, drop_remainder,
num_hosts, num_threads=8):
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
name_to_features = {
"unique_ids": tf.FixedLenFeature([], tf.int64),
"input_ids": tf.FixedLenFeature([seq_length], tf.int64),
"input_mask": tf.FixedLenFeature([seq_length], tf.float32),
"segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
"cls_index": tf.FixedLenFeature([], tf.int64),
"p_mask": tf.FixedLenFeature([seq_length], tf.float32)
}
if is_training:
name_to_features["start_positions"] = tf.FixedLenFeature([], tf.int64)
name_to_features["end_positions"] = tf.FixedLenFeature([], tf.int64)
name_to_features["is_impossible"] = tf.FixedLenFeature([], tf.float32)
tf.logging.info("Input tfrecord file glob {}".format(input_glob))
global_input_paths = tf.gfile.Glob(input_glob)
tf.logging.info("Find {} input paths {}".format(
len(global_input_paths), global_input_paths))
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.cast(t, tf.int32)
example[name] = t
return example
def input_fn(params):
"""The actual input function."""
if FLAGS.use_tpu:
batch_size = params["batch_size"]
elif is_training:
batch_size = FLAGS.train_batch_size
else:
batch_size = FLAGS.predict_batch_size
# Split tfrecords across hosts
if num_hosts > 1:
host_id = params["context"].current_host
num_files = len(global_input_paths)
if num_files >= num_hosts:
num_files_per_host = (num_files + num_hosts - 1) // num_hosts
my_start_file_id = host_id * num_files_per_host
my_end_file_id = min((host_id + 1) * num_files_per_host, num_files)
input_paths = global_input_paths[my_start_file_id: my_end_file_id]
tf.logging.info("Host {} handles {} files".format(host_id,
len(input_paths)))
else:
input_paths = global_input_paths
if len(input_paths) == 1:
d = tf.data.TFRecordDataset(input_paths[0])
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
if is_training:
d = d.shuffle(buffer_size=FLAGS.shuffle_buffer)
d = d.repeat()
else:
d = tf.data.Dataset.from_tensor_slices(input_paths)
# file level shuffle
d = d.shuffle(len(input_paths)).repeat()
# `cycle_length` is the number of parallel files that get read.
cycle_length = min(num_threads, len(input_paths))
d = d.apply(
tf.contrib.data.parallel_interleave(
tf.data.TFRecordDataset,
sloppy=is_training,
cycle_length=cycle_length))
if is_training:
# sample level shuffle
d = d.shuffle(buffer_size=FLAGS.shuffle_buffer)
d = d.apply(
tf.contrib.data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
num_parallel_batches=num_threads,
drop_remainder=drop_remainder))
d = d.prefetch(1024)
return d
return input_fn
def get_model_fn():
def model_fn(features, labels, mode, params):
#### Training or Evaluation
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
#### Get loss from inputs
outputs = function_builder.get_qa_outputs(FLAGS, features, is_training)
#### Check model parameters
num_params = sum([np.prod(v.shape) for v in tf.trainable_variables()])
tf.logging.info('#params: {}'.format(num_params))
scaffold_fn = None
#### Evaluation mode
if mode == tf.estimator.ModeKeys.PREDICT:
if FLAGS.init_checkpoint:
tf.logging.info("init_checkpoint not being used in predict mode.")
predictions = {
"unique_ids": features["unique_ids"],
"start_top_index": outputs["start_top_index"],
"start_top_log_probs": outputs["start_top_log_probs"],
"end_top_index": outputs["end_top_index"],
"end_top_log_probs": outputs["end_top_log_probs"]
}
if FLAGS.use_tpu:
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode, predictions=predictions, scaffold_fn=scaffold_fn)
else:
output_spec = tf.estimator.EstimatorSpec(
mode=mode, predictions=predictions)
return output_spec
### Compute loss
seq_length = tf.shape(features["input_ids"])[1]
def compute_loss(log_probs, positions):
one_hot_positions = tf.one_hot(
positions, depth=seq_length, dtype=tf.float32)
loss = - tf.reduce_sum(one_hot_positions * log_probs, axis=-1)
loss = tf.reduce_mean(loss)
return loss
start_loss = compute_loss(
outputs["start_log_probs"], features["start_positions"])
end_loss = compute_loss(
outputs["end_log_probs"], features["end_positions"])
total_loss = (start_loss + end_loss) * 0.5
#### Configuring the optimizer
train_op, learning_rate, _ = model_utils.get_train_op(FLAGS, total_loss)
monitor_dict = {}
monitor_dict["lr"] = learning_rate
#### load pretrained models
scaffold_fn = model_utils.init_from_checkpoint(FLAGS)
#### Constucting training TPUEstimatorSpec with new cache.
if FLAGS.use_tpu:
host_call = function_builder.construct_scalar_host_call(
monitor_dict=monitor_dict,
model_dir=FLAGS.model_dir,
prefix="train/",
reduce_fn=tf.reduce_mean)
train_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode, loss=total_loss, train_op=train_op, host_call=host_call,
scaffold_fn=scaffold_fn)
else:
train_spec = tf.estimator.EstimatorSpec(
mode=mode, loss=total_loss, train_op=train_op)
return train_spec
return model_fn
def _get_spm_basename():
spm_basename = os.path.basename(FLAGS.spiece_model_file)
return spm_basename
def preprocess():
sp_model = spm.SentencePieceProcessor()
sp_model.Load(FLAGS.spiece_model_file)
spm_basename = _get_spm_basename()
train_rec_file = os.path.join(
FLAGS.output_dir,
"{}.{}.slen-{}.qlen-{}.train.tf_record".format(
spm_basename, FLAGS.proc_id, FLAGS.max_seq_length,
FLAGS.max_query_length))
tf.logging.info("Read examples from {}".format(FLAGS.train_file))
train_examples = read_squad_examples(FLAGS.train_file, is_training=True)
train_examples = train_examples[FLAGS.proc_id::FLAGS.num_proc]
# Pre-shuffle the input to avoid having to make a very large shuffle
# buffer in the `input_fn`.
random.shuffle(train_examples)
tf.logging.info("Write to {}".format(train_rec_file))
train_writer = FeatureWriter(
filename=train_rec_file,
is_training=True)
convert_examples_to_features(
examples=train_examples,
sp_model=sp_model,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=True,
output_fn=train_writer.process_feature)
train_writer.close()
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
if not tf.gfile.Exists(FLAGS.output_dir):
tf.gfile.MakeDirs(FLAGS.output_dir)
if FLAGS.do_prepro:
preprocess()
return
#### Validate flags
if FLAGS.save_steps is not None:
FLAGS.iterations = min(FLAGS.iterations, FLAGS.save_steps)
if not FLAGS.do_train and not FLAGS.do_predict:
raise ValueError(
"At least one of `do_train` and `do_predict` must be True.")
if FLAGS.do_predict and not tf.gfile.Exists(FLAGS.predict_dir):
tf.gfile.MakeDirs(FLAGS.predict_dir)
sp_model = spm.SentencePieceProcessor()
sp_model.Load(FLAGS.spiece_model_file)
### TPU Configuration
run_config = model_utils.configure_tpu(FLAGS)
model_fn = get_model_fn()
spm_basename = _get_spm_basename()
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
if FLAGS.use_tpu:
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
predict_batch_size=FLAGS.predict_batch_size)
else:
estimator = tf.estimator.Estimator(
model_fn=model_fn,
config=run_config)
if FLAGS.do_train:
train_rec_glob = os.path.join(
FLAGS.output_dir,
"{}.*.slen-{}.qlen-{}.train.tf_record".format(
spm_basename, FLAGS.max_seq_length,
FLAGS.max_query_length))
train_input_fn = input_fn_builder(
input_glob=train_rec_glob,
seq_length=FLAGS.max_seq_length,
is_training=True,
drop_remainder=True,
num_hosts=FLAGS.num_hosts)
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.train_steps)
if FLAGS.do_predict:
for eval_set in ['dev','test','challenge']:
new_predict_file = FLAGS.predict_file + "_" + eval_set + ".json"
eval_examples = read_squad_examples(new_predict_file, is_training=False)
with tf.gfile.Open(new_predict_file) as f:
orig_data = json.load(f)["data"]
eval_rec_file = os.path.join(
FLAGS.output_dir,
"{}.slen-{}.qlen-{}.{}.tf_record".format(
spm_basename, FLAGS.max_seq_length, FLAGS.max_query_length, eval_set))
eval_feature_file = os.path.join(
FLAGS.output_dir,
"{}.slen-{}.qlen-{}.{}.features.pkl".format(
spm_basename, FLAGS.max_seq_length, FLAGS.max_query_length, eval_set))
if tf.gfile.Exists(eval_rec_file) and tf.gfile.Exists(
eval_feature_file) and not FLAGS.overwrite_data:
tf.logging.info("Loading eval features from {}".format(eval_feature_file))
with tf.gfile.Open(eval_feature_file, 'rb') as fin:
eval_features = pickle.load(fin)
else:
eval_writer = FeatureWriter(filename=eval_rec_file, is_training=False)
eval_features = []
def append_feature(feature):
eval_features.append(feature)
eval_writer.process_feature(feature)
convert_examples_to_features(
examples=eval_examples,
sp_model=sp_model,
max_seq_length=FLAGS.max_seq_length,
doc_stride=FLAGS.doc_stride,
max_query_length=FLAGS.max_query_length,
is_training=False,
output_fn=append_feature)
eval_writer.close()
with tf.gfile.Open(eval_feature_file, 'wb') as fout:
pickle.dump(eval_features, fout)
eval_input_fn = input_fn_builder(
input_glob=eval_rec_file,
seq_length=FLAGS.max_seq_length,
is_training=False,
drop_remainder=False,
num_hosts=1)
cur_results = []
for result in estimator.predict(
input_fn=eval_input_fn,
yield_single_examples=True):
if len(cur_results) % 1000 == 0:
tf.logging.info("Processing example: %d" % (len(cur_results)))
unique_id = int(result["unique_ids"])
start_top_log_probs = (
[float(x) for x in result["start_top_log_probs"].flat])
start_top_index = [int(x) for x in result["start_top_index"].flat]
end_top_log_probs = (
[float(x) for x in result["end_top_log_probs"].flat])
end_top_index = [int(x) for x in result["end_top_index"].flat]
cur_results.append(
RawResult(
unique_id=unique_id,
start_top_log_probs=start_top_log_probs,
start_top_index=start_top_index,
end_top_log_probs=end_top_log_probs,
end_top_index=end_top_index))
output_prediction_file = os.path.join(
FLAGS.predict_dir, eval_set+"_predictions.json")
output_nbest_file = os.path.join(
FLAGS.predict_dir, eval_set+"_nbest_predictions.json")
ret = write_predictions(eval_examples, eval_features, cur_results,
FLAGS.n_best_size, FLAGS.max_answer_length,
output_prediction_file,
output_nbest_file,
orig_data)
# Log current result
tf.logging.info("=" * 80)
log_str = "Result | "
for key, val in ret.items():
log_str += "{} {} | ".format(key, val)
tf.logging.info(log_str)
tf.logging.info("=" * 80)
if __name__ == "__main__":
tf.app.run()
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/squad_utils.py | Python | """Official evaluation script for SQuAD version 2.0.
In addition to basic functionality, we also compute additional statistics and
plot precision-recall curves if an additional na_prob.json file is provided.
This file is expected to map question ID's to the model's predicted probability
that a question is unanswerable.
"""
import argparse
import collections
import json
import numpy as np
import os
import re
import string
import sys
OPTS = None
def parse_args():
parser = argparse.ArgumentParser('Official evaluation script for SQuAD version 2.0.')
parser.add_argument('data_file', metavar='data.json', help='Input data JSON file.')
parser.add_argument('pred_file', metavar='pred.json', help='Model predictions.')
parser.add_argument('--out-file', '-o', metavar='eval.json',
help='Write accuracy metrics to file (default is stdout).')
parser.add_argument('--na-prob-file', '-n', metavar='na_prob.json',
help='Model estimates of probability of no answer.')
parser.add_argument('--na-prob-thresh', '-t', type=float, default=1.0,
help='Predict "" if no-answer probability exceeds this (default = 1.0).')
parser.add_argument('--out-image-dir', '-p', metavar='out_images', default=None,
help='Save precision-recall curves to directory.')
parser.add_argument('--verbose', '-v', action='store_true')
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
return parser.parse_args()
def make_qid_to_has_ans(dataset):
qid_to_has_ans = {}
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
qid_to_has_ans[qa['id']] = bool(qa['answers'])
return qid_to_has_ans
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
return re.sub(regex, ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def get_tokens(s):
if not s: return []
return normalize_answer(s).split()
def compute_exact(a_gold, a_pred):
return int(normalize_answer(a_gold) == normalize_answer(a_pred))
def compute_f1(a_gold, a_pred):
gold_toks = get_tokens(a_gold)
pred_toks = get_tokens(a_pred)
common = collections.Counter(gold_toks) & collections.Counter(pred_toks)
num_same = sum(common.values())
if len(gold_toks) == 0 or len(pred_toks) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks)
if num_same == 0:
return 0
precision = 1.0 * num_same / len(pred_toks)
recall = 1.0 * num_same / len(gold_toks)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def get_raw_scores(dataset, preds):
exact_scores = {}
f1_scores = {}
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
qid = qa['id']
gold_answers = [a['text'] for a in qa['answers']
if normalize_answer(a['text'])]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
gold_answers = ['']
if qid not in preds:
print('Missing prediction for %s' % qid)
continue
a_pred = preds[qid]
# Take max over all gold answers
exact_scores[qid] = max(compute_exact(a, a_pred) for a in gold_answers)
f1_scores[qid] = max(compute_f1(a, a_pred) for a in gold_answers)
return exact_scores, f1_scores
def apply_no_ans_threshold(scores, na_probs, qid_to_has_ans, na_prob_thresh):
new_scores = {}
for qid, s in scores.items():
pred_na = na_probs[qid] > na_prob_thresh
if pred_na:
new_scores[qid] = float(not qid_to_has_ans[qid])
else:
new_scores[qid] = s
return new_scores
def make_eval_dict(exact_scores, f1_scores, qid_list=None):
if not qid_list:
total = len(exact_scores)
return collections.OrderedDict([
('exact', 100.0 * sum(exact_scores.values()) / total),
('f1', 100.0 * sum(f1_scores.values()) / total),
('total', total),
])
else:
total = len(qid_list)
return collections.OrderedDict([
('exact', 100.0 * sum(exact_scores[k] for k in qid_list) / total),
('f1', 100.0 * sum(f1_scores[k] for k in qid_list) / total),
('total', total),
])
def merge_eval(main_eval, new_eval, prefix):
for k in new_eval:
main_eval['%s_%s' % (prefix, k)] = new_eval[k]
def plot_pr_curve(precisions, recalls, out_image, title):
plt.step(recalls, precisions, color='b', alpha=0.2, where='post')
plt.fill_between(recalls, precisions, step='post', alpha=0.2, color='b')
plt.xlabel('Recall')
plt.ylabel('Precision')
plt.xlim([0.0, 1.05])
plt.ylim([0.0, 1.05])
plt.title(title)
plt.savefig(out_image)
plt.clf()
def make_precision_recall_eval(scores, na_probs, num_true_pos, qid_to_has_ans,
out_image=None, title=None):
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
true_pos = 0.0
cur_p = 1.0
cur_r = 0.0
precisions = [1.0]
recalls = [0.0]
avg_prec = 0.0
for i, qid in enumerate(qid_list):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
cur_p = true_pos / float(i+1)
cur_r = true_pos / float(num_true_pos)
if i == len(qid_list) - 1 or na_probs[qid] != na_probs[qid_list[i+1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(cur_p)
recalls.append(cur_r)
if out_image:
plot_pr_curve(precisions, recalls, out_image, title)
return {'ap': 100.0 * avg_prec}
def run_precision_recall_analysis(main_eval, exact_raw, f1_raw, na_probs,
qid_to_has_ans, out_image_dir):
if out_image_dir and not os.path.exists(out_image_dir):
os.makedirs(out_image_dir)
num_true_pos = sum(1 for v in qid_to_has_ans.values() if v)
if num_true_pos == 0:
return
pr_exact = make_precision_recall_eval(
exact_raw, na_probs, num_true_pos, qid_to_has_ans,
out_image=os.path.join(out_image_dir, 'pr_exact.png'),
title='Precision-Recall curve for Exact Match score')
pr_f1 = make_precision_recall_eval(
f1_raw, na_probs, num_true_pos, qid_to_has_ans,
out_image=os.path.join(out_image_dir, 'pr_f1.png'),
title='Precision-Recall curve for F1 score')
oracle_scores = {k: float(v) for k, v in qid_to_has_ans.items()}
pr_oracle = make_precision_recall_eval(
oracle_scores, na_probs, num_true_pos, qid_to_has_ans,
out_image=os.path.join(out_image_dir, 'pr_oracle.png'),
title='Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)')
merge_eval(main_eval, pr_exact, 'pr_exact')
merge_eval(main_eval, pr_f1, 'pr_f1')
merge_eval(main_eval, pr_oracle, 'pr_oracle')
def histogram_na_prob(na_probs, qid_list, image_dir, name):
if not qid_list:
return
x = [na_probs[k] for k in qid_list]
weights = np.ones_like(x) / float(len(x))
plt.hist(x, weights=weights, bins=20, range=(0.0, 1.0))
plt.xlabel('Model probability of no-answer')
plt.ylabel('Proportion of dataset')
plt.title('Histogram of no-answer probability: %s' % name)
plt.savefig(os.path.join(image_dir, 'na_prob_hist_%s.png' % name))
plt.clf()
def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for i, qid in enumerate(qid_list):
if qid not in scores: continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if preds[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
return 100.0 * best_score / len(scores), best_thresh
def find_best_thresh_v2(preds, scores, na_probs, qid_to_has_ans):
num_no_ans = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k])
cur_score = num_no_ans
best_score = cur_score
best_thresh = 0.0
qid_list = sorted(na_probs, key=lambda k: na_probs[k])
for i, qid in enumerate(qid_list):
if qid not in scores: continue
if qid_to_has_ans[qid]:
diff = scores[qid]
else:
if preds[qid]:
diff = -1
else:
diff = 0
cur_score += diff
if cur_score > best_score:
best_score = cur_score
best_thresh = na_probs[qid]
has_ans_score, has_ans_cnt = 0, 0
for qid in qid_list:
if not qid_to_has_ans[qid]: continue
has_ans_cnt += 1
if qid not in scores: continue
has_ans_score += scores[qid]
return 100.0 * best_score / len(scores), best_thresh, 1.0 * has_ans_score / has_ans_cnt
def find_all_best_thresh(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh = find_best_thresh(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh = find_best_thresh(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval['best_exact'] = best_exact
main_eval['best_exact_thresh'] = exact_thresh
main_eval['best_f1'] = best_f1
main_eval['best_f1_thresh'] = f1_thresh
def find_all_best_thresh_v2(main_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans):
best_exact, exact_thresh, has_ans_exact = find_best_thresh_v2(preds, exact_raw, na_probs, qid_to_has_ans)
best_f1, f1_thresh, has_ans_f1 = find_best_thresh_v2(preds, f1_raw, na_probs, qid_to_has_ans)
main_eval['best_exact'] = best_exact
main_eval['best_exact_thresh'] = exact_thresh
main_eval['best_f1'] = best_f1
main_eval['best_f1_thresh'] = f1_thresh
main_eval['has_ans_exact'] = has_ans_exact
main_eval['has_ans_f1'] = has_ans_f1
def main():
with open(OPTS.data_file) as f:
dataset_json = json.load(f)
dataset = dataset_json['data']
with open(OPTS.pred_file) as f:
preds = json.load(f)
new_orig_data = []
for article in dataset:
for p in article['paragraphs']:
for qa in p['qas']:
if qa['id'] in preds:
new_para = {'qas': [qa]}
new_article = {'paragraphs': [new_para]}
new_orig_data.append(new_article)
dataset = new_orig_data
if OPTS.na_prob_file:
with open(OPTS.na_prob_file) as f:
na_probs = json.load(f)
else:
na_probs = {k: 0.0 for k in preds}
qid_to_has_ans = make_qid_to_has_ans(dataset) # maps qid to True/False
has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
exact_raw, f1_raw = get_raw_scores(dataset, preds)
exact_thresh = apply_no_ans_threshold(exact_raw, na_probs, qid_to_has_ans,
OPTS.na_prob_thresh)
f1_thresh = apply_no_ans_threshold(f1_raw, na_probs, qid_to_has_ans,
OPTS.na_prob_thresh)
out_eval = make_eval_dict(exact_thresh, f1_thresh)
if has_ans_qids:
has_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=has_ans_qids)
merge_eval(out_eval, has_ans_eval, 'HasAns')
if no_ans_qids:
no_ans_eval = make_eval_dict(exact_thresh, f1_thresh, qid_list=no_ans_qids)
merge_eval(out_eval, no_ans_eval, 'NoAns')
if OPTS.na_prob_file:
find_all_best_thresh(out_eval, preds, exact_raw, f1_raw, na_probs, qid_to_has_ans)
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(out_eval, exact_raw, f1_raw, na_probs,
qid_to_has_ans, OPTS.out_image_dir)
histogram_na_prob(na_probs, has_ans_qids, OPTS.out_image_dir, 'hasAns')
histogram_na_prob(na_probs, no_ans_qids, OPTS.out_image_dir, 'noAns')
if OPTS.out_file:
with open(OPTS.out_file, 'w') as f:
json.dump(out_eval, f)
else:
print(json.dumps(out_eval, indent=2))
if __name__ == '__main__':
OPTS = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
main()
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/summary.py | Python | # -*- coding: utf-8 -*-
'''
print summary
'''
from __future__ import print_function
from collections import Counter, OrderedDict
import string
import re
import argparse
import json
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
import pdb
import os
import math
import numpy as np
import collections
from prettytable import PrettyTable
def print_summary():
lscmd = os.popen('ls '+sys.argv[1]+'/result.*').read()
result_list = lscmd.split()
num_args = len(result_list)
assert num_args==2 or num_args==3
dev_input_file = open(sys.argv[1]+'/result.dev', 'rb')
test_input_file = open(sys.argv[1]+'/result.test', 'rb')
if num_args==2:
print_table = PrettyTable(['#','DEV-AVG','DEV-EM','DEV-F1','TEST-AVG','TEST-EM','TEST-F1','FILE'])
elif num_args==3:
chl_input_file = open(sys.argv[1]+'/result.challenge', 'rb')
print_table = PrettyTable(['#','DEV-AVG','DEV-EM','DEV-F1','TEST-AVG','TEST-EM','TEST-F1','CHL-AVG','CHL-EM','CHL-F1','FILE'])
# style set
print_table.align['FILE'] = 'l'
print_table.float_format = '2.3'
# data fill
dev_avg = []
dev_em = []
dev_f1 = []
dev_file = []
for dline in dev_input_file.readlines():
dline = dline.strip()
if re.search('^{', dline):
ddict = json.loads(dline)
dev_avg.append(float(ddict['AVERAGE']))
dev_em.append(float(ddict['EM']))
dev_f1.append(float(ddict['F1']))
dev_file.append(ddict['FILE'])
test_avg = []
test_em = []
test_f1 = []
test_file = []
for dline in test_input_file.readlines():
dline = dline.strip()
if re.search('^{', dline):
ddict = json.loads(dline)
test_avg.append(float(ddict['AVERAGE']))
test_em.append(float(ddict['EM']))
test_f1.append(float(ddict['F1']))
test_file.append(ddict['FILE'])
if num_args==3:
chl_avg = []
chl_em = []
chl_f1 = []
chl_file = []
for dline in chl_input_file.readlines():
dline = dline.strip()
if re.search('^{', dline):
ddict = json.loads(dline)
chl_avg.append(float(ddict['AVERAGE']))
chl_em.append(float(ddict['EM']))
chl_f1.append(float(ddict['F1']))
chl_file.append(ddict['FILE'])
# print
if num_args == 2:
min_len = min(len(dev_avg),len(test_avg))
for k in range(min_len):
print_table.add_row([k+1, dev_avg[k], dev_em[k], dev_f1[k], test_avg[k], test_em[k], test_f1[k], dev_file[k]])
elif num_args == 3:
min_len = min(len(dev_avg),len(test_avg),len(chl_avg))
for k in range(min_len):
print_table.add_row([k+1, dev_avg[k], dev_em[k], dev_f1[k], test_avg[k], test_em[k], test_f1[k], chl_avg[k], chl_em[k], chl_f1[k], dev_file[k]])
if len(sys.argv)==3:
sk = sys.argv[2].upper()
print('sort key detected: {}'.format(sk))
print(print_table.get_string(sortby=sk, reversesort=True))
else:
print(print_table)
if num_args == 2:
summary_table = PrettyTable(['#','DEV-AVG','DEV-EM','DEV-F1','TEST-AVG','TEST-EM','TEST-F1','FILE'])
summary_table.add_row(["M", np.max(dev_avg), np.max(dev_em), np.max(dev_f1),
np.max(test_avg), np.max(test_em), np.max(test_f1),"-"])
summary_table.add_row(["A", np.mean(dev_avg), np.mean(dev_em), np.mean(dev_f1),
np.mean(test_avg), np.mean(test_em), np.mean(test_f1),"-"])
summary_table.add_row(["D", np.std(dev_avg), np.std(dev_em), np.std(dev_f1),
np.std(test_avg), np.std(test_em), np.std(test_f1),"-"])
elif num_args == 3:
summary_table = PrettyTable(['#','DEV-AVG','DEV-EM','DEV-F1','TEST-AVG','TEST-EM','TEST-F1','CHL-AVG','CHL-EM','CHL-F1','FILE'])
summary_table.add_row(["M", np.max(dev_avg), np.max(dev_em), np.max(dev_f1),
np.max(test_avg), np.max(test_em), np.max(test_f1),
np.max(chl_avg), np.max(chl_em), np.max(chl_f1), "-"])
summary_table.add_row(["A", np.mean(dev_avg), np.mean(dev_em), np.mean(dev_f1),
np.mean(test_avg), np.mean(test_em), np.mean(test_f1),
np.mean(chl_avg), np.mean(chl_em), np.mean(chl_f1), "-"])
summary_table.add_row(["D", np.std(dev_avg), np.std(dev_em), np.std(dev_f1),
np.std(test_avg), np.std(test_em), np.std(test_f1),
np.std(chl_avg), np.std(chl_em), np.std(chl_f1), "-"])
# style set
summary_table.align['FILE'] = 'l'
summary_table.float_format = '2.3'
print(summary_table)
return 0
if __name__ == '__main__':
print_summary()
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/tpu_estimator.py | Python | # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===================================================================
"""TPUEstimator class."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import os
import signal
import sys
import threading
import time
import numpy as np
import six
from six.moves import queue as Queue # pylint: disable=redefined-builtin
from six.moves import xrange # pylint: disable=redefined-builtin
from tensorflow.contrib.tpu.proto import compilation_result_pb2 as tpu_compilation_result
from tensorflow.contrib.tpu.python.tpu import tensor_tracer
from tensorflow.contrib.tpu.python.ops import tpu_ops
from tensorflow.contrib.tpu.python.tpu import error_handling
from tensorflow.contrib.tpu.python.tpu import session_support
from tensorflow.contrib.tpu.python.tpu import tpu
from tensorflow.contrib.tpu.python.tpu import tpu_config
from tensorflow.contrib.tpu.python.tpu import tpu_context
from tensorflow.contrib.tpu.python.tpu import tpu_feed
from tensorflow.contrib.tpu.python.tpu import training_loop
from tensorflow.contrib.tpu.python.tpu import util as util_lib
from tensorflow.contrib.training.python.training import hparam
from tensorflow.core.framework import variable_pb2
from tensorflow.core.framework.summary_pb2 import Summary
from tensorflow.core.protobuf import config_pb2
from tensorflow.python.client import session as tf_session
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.data.util import nest as data_nest
from tensorflow.python.estimator import estimator as estimator_lib
from tensorflow.python.estimator import model_fn as model_fn_lib
from tensorflow.python.estimator.export import export_output as export_output_lib
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import check_ops
from tensorflow.python.ops import control_flow_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import state_ops
from tensorflow.python.ops import summary_ops_v2 as contrib_summary
from tensorflow.python.ops import variable_scope
from tensorflow.python.ops import variables
from tensorflow.python.platform import tf_logging as logging
from tensorflow.python.saved_model import tag_constants
from tensorflow.python.summary import summary
from tensorflow.python.training import basic_session_run_hooks
from tensorflow.python.training import evaluation
from tensorflow.python.training import session_run_hook
from tensorflow.python.training import training
from tensorflow.python.training import training_util
from tensorflow.python.util import function_utils
from tensorflow.python.util import nest
from tensorflow.python.util import tf_inspect
_INITIAL_LOSS = 1e7
_ZERO_LOSS = 0.
_TPU_ESTIMATOR = 'custom_tpu_estimator'
_ITERATIONS_PER_LOOP_VAR = 'iterations_per_loop'
_BATCH_SIZE_KEY = 'batch_size'
_CTX_KEY = 'context'
_USE_TPU_KEY = 'use_tpu'
_CROSS_REPLICA_SUM_OP = 'CrossReplicaSum'
_ONE_GIGABYTE = 1024 * 1024 * 1024
_TPU_ENQUEUE_OPS = '_tpu_enqueue_ops'
_TPU_TRAIN_OP = '_tpu_train_op'
_REWRITE_FOR_INFERENCE_MODE = '_rewrite_for_inference'
# Ideally _USE_TPU_KEY should be reserved as well. However there are already
# models that make use of this key, thus it can not be reserved now to prevent
# breakage. In the long run, we would like to mitigate this by migrating models
# off of using _USE_TPU_KEY.
_RESERVED_PARAMS_KEYS = [_BATCH_SIZE_KEY, _CTX_KEY]
# TODO(b/65703635): Flip the value and remove all dead code. Currently, this is
# only used for per-core based deployments. For per-host based pipelines, if a
# user returns a Dataset instance it will be automatically wrapped in a
# tf.while_loop (This can be disabled by returning features and labels
# explicitly).
_WRAP_INPUT_FN_INTO_WHILE_LOOP = False
ops.register_proto_function(
'{}_{}'.format(_TPU_ESTIMATOR, _ITERATIONS_PER_LOOP_VAR),
proto_type=variable_pb2.VariableDef,
to_proto=resource_variable_ops._to_proto_fn, # pylint: disable=protected-access
from_proto=resource_variable_ops._from_proto_fn) # pylint: disable=protected-access
def _is_iterable(obj):
"""A Python 2 and 3 compatible util to check whether `obj` is iterable."""
try:
iter(obj)
return True
except TypeError:
return False
def _create_global_step(graph):
graph = graph or ops.get_default_graph()
if training.get_global_step(graph) is not None:
raise ValueError('"global_step" already exists.')
# Create in proper graph and base name_scope.
with graph.as_default() as g, g.name_scope(None):
return variable_scope.get_variable(
ops.GraphKeys.GLOBAL_STEP,
shape=[],
dtype=dtypes.int64,
initializer=init_ops.zeros_initializer(),
trainable=False,
use_resource=True,
collections=[ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP])
def _create_or_get_iterations_per_loop():
"""Creates or gets the iterations_per_loop variable.
In TPUEstimator, the user provided computation, the model_fn, is wrapped
inside a tf.while_loop for peak performance. The iterations of the loop are
specified by this variable, which adjusts its value on the CPU after each TPU
program execution and before the next TPU execution.
The purpose of using a variable, rather then a constant, is to allow
TPUEstimator adapt the TPU training iterations according to the final steps
specified by users. For example, if the user sets the iterations_per_loop as 4
in TPUConfig and steps as 10 in TPUEstimator.train(), the iterations_per_loop
variable will have the following value before each TPU training.
- 1-th TPU execution: iterations_per_loop = 4
- 2-th TPU execution: iterations_per_loop = 4
- 3-th TPU execution: iterations_per_loop = 2
As model_fn increases the global step once per train_op invocation, the global
step is 10 after all TPU executions, matching the steps=10 inputs passed in by
users.
Returns:
A TF non-trainable resource variable.
Raises:
RuntimeError: If multi iterations_per_loop variables were found.
"""
graph = ops.get_default_graph()
collection_name = '{}_{}'.format(_TPU_ESTIMATOR, _ITERATIONS_PER_LOOP_VAR)
iter_vars = graph.get_collection(collection_name)
if len(iter_vars) == 1:
return iter_vars[0]
elif len(iter_vars) > 1:
raise RuntimeError('Multiple iterations_per_loop_var in collection.')
with ops.colocate_with(training_util.get_global_step()):
with variable_scope.variable_scope(
_TPU_ESTIMATOR, reuse=variable_scope.AUTO_REUSE):
return variable_scope.get_variable(
_ITERATIONS_PER_LOOP_VAR,
initializer=init_ops.zeros_initializer(),
shape=[],
dtype=dtypes.int32,
trainable=False,
collections=[collection_name, ops.GraphKeys.LOCAL_VARIABLES],
use_resource=True)
def _sync_variables_ops(ctx):
"""Create varriables synchronization ops.
Gets the variables back from TPU nodes. This means the variables updated
by TPU will now be *synced* to host memory.
In BROADCAST mode, we skip this sync since the variables are ususally too
big to transmit via RPC.
Args:
ctx: A `_InternalTPUContext` instance with mode.
Returns:
A list of sync ops.
"""
if not ctx.is_input_broadcast_with_iterators():
return [
array_ops.check_numerics(v.read_value(),
'Gradient for %s is NaN' % v.name).op
for v in variables.trainable_variables()
]
else:
return [control_flow_ops.no_op()]
def _increase_eval_step_op(iterations_per_loop):
"""Returns an op to increase the eval step for TPU evaluation.
Args:
iterations_per_loop: Tensor. The number of eval steps running in TPU system
before returning to CPU host for each `Session.run`.
Returns:
An operation
"""
eval_step = evaluation._get_or_create_eval_step() # pylint: disable=protected-access
# Estimator evaluate increases 1 by default. So, we increase the difference.
return state_ops.assign_add(
eval_step,
math_ops.cast(iterations_per_loop - 1, dtype=eval_step.dtype),
use_locking=True)
def _extract_key_names(tensor_or_dict):
if isinstance(tensor_or_dict, dict):
return sorted(tensor_or_dict.keys())
return []
class _SIGNAL(object):
"""Signal used to control the thread of infeed/outfeed.
All preserved signals must be negative numbers. Positive numbers are used to
indicate the number of iterations for next training/evaluation loop.
"""
NEXT_BATCH = -1
STOP = -2
class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=protected-access
"""Ops and objects returned from a `model_fn` and passed to `TPUEstimator`.
See `EstimatorSpec` for `mode`, `predictions`, `loss`, `train_op`, and
`export_outputs`.
For evaluation, `eval_metrics `is a tuple of `metric_fn` and `tensors`, where
`metric_fn` runs on CPU to generate metrics and `tensors` represents the
`Tensor`s transferred from TPU system to CPU host and passed to `metric_fn`.
To be precise, TPU evaluation expects a slightly different signature from the
`tf.estimator.Estimator`. While `EstimatorSpec.eval_metric_ops` expects a
dict, `TPUEstimatorSpec.eval_metrics` is a tuple of `metric_fn` and `tensors`.
The `tensors` could be a list of `Tensor`s or dict of names to `Tensor`s. The
`tensors` usually specify the model logits, which are transferred back from
TPU system to CPU host. All tensors must have be batch-major, i.e., the batch
size is the first dimension. Once all tensors are available at CPU host from
all shards, they are concatenated (on CPU) and passed as positional arguments
to the `metric_fn` if `tensors` is list or keyword arguments if `tensors` is
a dict. `metric_fn` takes the `tensors` and returns a dict from metric string
name to the result of calling a metric function, namely a `(metric_tensor,
update_op)` tuple. See `TPUEstimator` for MNIST example how to specify the
`eval_metrics`.
`scaffold_fn` is a function running on CPU to generate the `Scaffold`. This
function should not capture any Tensors in `model_fn`.
`host_call` is a tuple of a `function` and a list or dictionary of `tensors`
to pass to that function and returns a list of Tensors. `host_call` currently
works for train() and evaluate(). The Tensors returned by the function is
executed on the CPU on every step, so there is communication overhead when
sending tensors from TPU to CPU. To reduce the overhead, try reducing the
size of the tensors. The `tensors` are concatenated along their major (batch)
dimension, and so must be >= rank 1. The `host_call` is useful for writing
summaries with `tf.contrib.summary.create_file_writer`.
"""
def __new__(cls,
mode,
predictions=None,
loss=None,
train_op=None,
eval_metrics=None,
export_outputs=None,
scaffold_fn=None,
host_call=None,
training_hooks=None,
evaluation_hooks=None,
prediction_hooks=None):
"""Creates a validated `TPUEstimatorSpec` instance."""
host_calls = {}
if eval_metrics is not None:
host_calls['eval_metrics'] = eval_metrics
if host_call is not None:
host_calls['host_call'] = host_call
_OutfeedHostCall.validate(host_calls)
training_hooks = tuple(training_hooks or [])
evaluation_hooks = tuple(evaluation_hooks or [])
prediction_hooks = tuple(prediction_hooks or [])
for hook in training_hooks + evaluation_hooks + prediction_hooks:
if not isinstance(hook, session_run_hook.SessionRunHook):
raise TypeError('All hooks must be SessionRunHook instances, given: {}'
.format(hook))
return super(TPUEstimatorSpec, cls).__new__(
cls,
mode=mode,
predictions=predictions,
loss=loss,
train_op=train_op,
eval_metrics=eval_metrics,
export_outputs=export_outputs,
scaffold_fn=scaffold_fn,
host_call=host_call,
training_hooks=training_hooks,
evaluation_hooks=evaluation_hooks,
prediction_hooks=prediction_hooks)
def as_estimator_spec(self):
"""Creates an equivalent `EstimatorSpec` used by CPU train/eval."""
host_calls = {}
if self.eval_metrics is not None:
host_calls['eval_metrics'] = self.eval_metrics
if self.host_call is not None:
host_calls['host_call'] = self.host_call
host_call_ret = _OutfeedHostCall.create_cpu_hostcall(host_calls)
eval_metric_ops = None
if self.eval_metrics is not None:
eval_metric_ops = host_call_ret['eval_metrics']
hooks = None
if self.host_call is not None:
hooks = [_OutfeedHostCallHook(host_call_ret['host_call'])]
if tensor_tracer.TensorTracer.is_enabled():
tt = tensor_tracer.TensorTracer()
tracing_calls = tt.trace_cpu(ops.get_default_graph())
tracing_call_ret = _OutfeedHostCall.create_cpu_hostcall(tracing_calls)
tracing_functions = tracing_call_ret.values()
if tracing_functions:
if hooks:
hooks.extend([_OutfeedHostCallHook(tracing_functions)])
else:
hooks = [_OutfeedHostCallHook(tracing_functions)]
hooks = tuple(hooks or [])
scaffold = self.scaffold_fn() if self.scaffold_fn else None
return model_fn_lib.EstimatorSpec(
mode=self.mode,
predictions=self.predictions,
loss=self.loss,
train_op=self.train_op,
eval_metric_ops=eval_metric_ops,
export_outputs=self.export_outputs,
scaffold=scaffold,
training_hooks=self.training_hooks + hooks,
evaluation_hooks=self.evaluation_hooks + hooks,
prediction_hooks=self.prediction_hooks + hooks)
class _OpQueueContext(object):
"""Manages work queue and thread for a infeed/outfeed thread."""
def __init__(self, name, target, args):
self._name = name
self._queue = Queue.Queue()
args = (self,) + args
self._thread = threading.Thread(name=name, target=target, args=args)
self._thread.daemon = True
self._thread.start()
def stop(self):
self._queue.put(_SIGNAL.STOP)
def send_next_batch_signal(self, iterations):
self._queue.put(iterations)
def read_iteration_counts(self):
while True:
iterations = self._queue.get(block=True)
logging.debug('%s read iterations %s', self._name, iterations)
if iterations == _SIGNAL.STOP:
logging.info('%s received shutdown signal, stopping.', self._name)
return
yield iterations
def join(self):
logging.info('Shutting down %s thread.', self._name)
self.stop()
self._thread.join()
class _OpSignalOnceQueueContext(_OpQueueContext):
"""Manages work queue and thread for a infeed/outfeed thread.
This subclass only signals once.
"""
def __init__(self, name, target, args):
super(_OpSignalOnceQueueContext, self).__init__(name, target, args)
self._has_signaled = False
def send_next_batch_signal(self, iterations):
if not self._has_signaled:
self._queue.put(iterations)
self._has_signaled = True
class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook):
"""A Session hook setting up the TPU initialization, infeed, and outfeed.
This hook does two major things:
1. initialize and shutdown TPU system.
2. launch and join the threads for infeed enqueue and (optional) outfeed
dequeue.
"""
def __init__(self,
ctx,
enqueue_ops,
dequeue_ops,
tpu_compile_op,
run_infeed_loop_on_coordinator=True,
rendezvous=None,
master=None,
session_config=None):
self._master_job = ctx.master_job
self._enqueue_ops = enqueue_ops
self._dequeue_ops = dequeue_ops
self._rendezvous = rendezvous
self._master = master
self._session_config = session_config
self._run_infeed_loop_on_coordinator = run_infeed_loop_on_coordinator
self._initial_infeed_sleep_secs = (
ctx.config.tpu_config.initial_infeed_sleep_secs)
self._feed_error = None
self._finished = False
self._should_initialize_tpu = True
self._tpu_compile_op = tpu_compile_op
def begin(self):
logging.info('TPU job name %s', self._master_job)
self._iterations_per_loop_var = _create_or_get_iterations_per_loop()
self._init_ops = []
if self._should_initialize_tpu:
self._finalize_ops = [tpu.shutdown_system(job=self._master_job)]
else:
self._finalize_ops = []
summary_writer_init_ops = contrib_summary.summary_writer_initializer_op()
self._init_ops.extend(summary_writer_init_ops)
# Get all the writer resources from the initializer, so we know what to
# flush.
for op in summary_writer_init_ops:
self._finalize_ops.append(contrib_summary.flush(writer=op.inputs[0]))
def _run_infeed(self, queue_ctx, session):
logging.info('Starting infeed thread controller.')
if self._initial_infeed_sleep_secs:
logging.info('Infeed thread sleeping for %d seconds.',
self._initial_infeed_sleep_secs)
time.sleep(self._initial_infeed_sleep_secs)
logging.info('Infeed thread starting after sleep')
with self._rendezvous.catch_errors(source='infeed', session=session):
if self._run_infeed_loop_on_coordinator:
for count, steps in enumerate(queue_ctx.read_iteration_counts()):
for i in xrange(steps):
logging.debug('Infeed enqueue for iteration (%d, %d)', count, i)
session.run(self._enqueue_ops)
else:
for _ in queue_ctx.read_iteration_counts():
session.run(self._enqueue_ops)
logging.info('Infeed thread finished, shutting down.')
def _run_outfeed(self, queue_ctx, session):
logging.info('Starting outfeed thread controller.')
with self._rendezvous.catch_errors(source='outfeed', session=session):
for count, steps in enumerate(queue_ctx.read_iteration_counts()):
for i in xrange(steps):
logging.debug('Outfeed dequeue for iteration (%d, %d)', count, i)
session.run(self._dequeue_ops)
logging.info('Outfeed thread finished, shutting down.')
def _create_infeed_controller(self, name, target, args):
return _OpQueueContext(name=name, target=target, args=args)
def _assertCompilationSucceeded(self, result, coord):
proto = tpu_compilation_result.CompilationResultProto()
proto.ParseFromString(result)
if proto.status_error_message:
logging.error('Compilation failed: {}'.format(proto.status_error_message))
coord.request_stop()
else:
logging.info('Compilation succeeded')
def after_create_session(self, session, coord):
if self._should_initialize_tpu:
logging.info('Init TPU system')
start = time.time()
with ops.Graph().as_default():
with tf_session.Session(
self._master, config=self._session_config) as sess:
sess.run(tpu.initialize_system(job=self._master_job))
logging.info('Initialized TPU in %d seconds', time.time() - start)
session.run(self._init_ops,
options=config_pb2.RunOptions(timeout_in_ms=5 * 60 * 1000))
if os.environ.get('TPU_SPLIT_COMPILE_AND_EXECUTE', '') == '1':
logging.info('Compiling user program: this may take a while...')
self._assertCompilationSucceeded(session.run(self._tpu_compile_op), coord)
self._infeed_controller = self._create_infeed_controller(
name='InfeedController', target=self._run_infeed, args=(session,))
self._outfeed_controller = _OpQueueContext(
name='OutfeedController', target=self._run_outfeed, args=(session,))
# Enable the worker watchdog to terminate workers on coordinator exit.
watchdog_timeout = int(os.environ.get('TF_TPU_WATCHDOG_TIMEOUT', '0'))
if watchdog_timeout > 0:
session_support.start_worker_watchdog(session,
shutdown_timeout=watchdog_timeout)
def before_run(self, run_context):
self._feed_error = None
iterations = run_context.session.run(self._iterations_per_loop_var)
logging.info('Enqueue next (%d) batch(es) of data to infeed.', iterations)
self._infeed_controller.send_next_batch_signal(iterations)
logging.info('Dequeue next (%d) batch(es) of data from outfeed.',
iterations)
self._outfeed_controller.send_next_batch_signal(iterations)
def end(self, session):
self._finished = True
logging.info('Stop infeed thread controller')
self._infeed_controller.join()
self._rendezvous.record_done('infeed')
logging.info('Stop output thread controller')
self._outfeed_controller.join()
self._rendezvous.record_done('outfeed')
logging.info('Shutdown TPU system.')
session.run(self._finalize_ops)
class TPUInfeedOutfeedSessionHookForPrediction(TPUInfeedOutfeedSessionHook):
def __init__(self, ctx, enqueue_ops, dequeue_ops, tpu_compile_op,
rendezvous=None, master=None, session_config=None):
super(TPUInfeedOutfeedSessionHookForPrediction, self).__init__(
ctx,
enqueue_ops,
dequeue_ops,
tpu_compile_op=tpu_compile_op,
run_infeed_loop_on_coordinator=False,
rendezvous=rendezvous,
master=master,
session_config=session_config)
def _create_infeed_controller(self, name, target, args):
return _OpSignalOnceQueueContext(name=name, target=target, args=args)
class _TPUStopAtStepHook(session_run_hook.SessionRunHook):
"""Hook that requests stop at a specified step.
This hook is similar to the `session_run_hook._StopAfterNEvalsHook` with
following differences for TPU training:
1. This hook sets the variable for iterations_per_loop, which is used by
`TPUInfeedOutfeedSessionHook` to control the iterations for infeed/outfeed.
As the hook execution order is not guaranteed, the variable update is
handled in `after_create_session` and `after_run` as
`TPUInfeedOutfeedSessionHook` reads the variable value in `before_run`.
2. For each training loop (session.run), the global step could be increased
multiple times on TPU. The global step tensor value will be explicitly read
again in `after_run` to ensure the latest value is retrieved to avoid race
condition.
"""
def __init__(self, iterations, num_steps=None, last_step=None):
"""Initializes a `StopAtStepHook`.
Args:
iterations: The number of iterations to run optimizer per training loop.
num_steps: Number of steps to execute.
last_step: Step after which to stop.
Raises:
ValueError: If one of the arguments is invalid.
"""
if num_steps is None and last_step is None:
raise ValueError('One of num_steps or last_step must be specified.')
if num_steps is not None and last_step is not None:
raise ValueError('Only one of num_steps or last_step can be specified.')
self._num_steps = num_steps
self._last_step = last_step
self._iterations = iterations
def _next_iterations(self, global_step, last_step):
gap = last_step - global_step
return min(gap, self._iterations)
def begin(self):
self._global_step_tensor = training_util.get_global_step()
if self._global_step_tensor is None:
raise RuntimeError('Global step should be created.')
self._iterations_per_loop_var = _create_or_get_iterations_per_loop()
def after_create_session(self, session, coord):
global_step = session.run(self._global_step_tensor)
if self._last_step is None:
self._last_step = global_step + self._num_steps
iterations = self._next_iterations(global_step, self._last_step)
self._iterations_per_loop_var.load(iterations, session=session)
def after_run(self, run_context, run_values):
# Global step cannot be retrieved via SessionRunArgs and before_run due to
# race condition.
global_step = run_context.session.run(self._global_step_tensor)
if global_step >= self._last_step:
run_context.request_stop()
else:
iterations = self._next_iterations(global_step, self._last_step)
self._iterations_per_loop_var.load(
iterations, session=run_context.session)
class _SetEvalIterationsHook(session_run_hook.SessionRunHook):
"""Hook that requests stop at a specified step."""
def __init__(self, num_steps):
"""Initializes a `_SetEvalIterationsHook`.
Args:
num_steps: Number of steps to execute.
"""
self._num_steps = num_steps
def begin(self):
self._iterations_per_loop_var = _create_or_get_iterations_per_loop()
def after_create_session(self, session, coord):
self._iterations_per_loop_var.load(self._num_steps, session=session)
class _StoppingPredictHook(session_run_hook.SessionRunHook):
"""Hook that requests stop according to the stopping signal in prediction."""
def __init__(self, scalar_stopping_signal):
self._scalar_stopping_signal = scalar_stopping_signal
def begin(self):
self._iterations_per_loop_var = _create_or_get_iterations_per_loop()
def after_create_session(self, session, coord):
# This is not necessary as we do not run infeed enqueue and outfeed dequeue
# in side threads for prediction model. But it makes the
# TPUInfeedOutfeedSessionHook prints nice message.
self._iterations_per_loop_var.load(1, session=session)
def before_run(self, run_context):
return session_run_hook.SessionRunArgs(self._scalar_stopping_signal)
def after_run(self, run_context, run_values):
_ = run_context
scalar_stopping_signal = run_values.results
if _StopSignals.should_stop(scalar_stopping_signal):
# NOTE(xiejw): In prediction, stopping signals are inserted for each
# batch. And we append one more batch to signal the system it should stop.
# The data flow might look like
#
# batch 0: images, labels, stop = 0 (user provided)
# batch 1: images, labels, stop = 0 (user provided)
# ...
# batch 99: images, labels, stop = 0 (user provided)
# batch 100: images, labels, stop = 1 (TPUEstimator appended)
#
# where the final batch (id = 100) is appended by TPUEstimator, so we
# should drop it before returning the predictions to user.
# To achieve that, we throw the OutOfRangeError in after_run. Once
# Monitored Session sees this error in SessionRunHook.after_run, the
# "current" prediction, i.e., batch with id=100, will be discarded
# immediately
raise errors.OutOfRangeError(None, None, 'Stopped by stopping signal.')
def generate_per_core_enqueue_ops_fn_for_host(
ctx, input_fn, inputs_structure_recorder, host_device, host_id):
"""Generates infeed enqueue ops for per-core input_fn on a single host."""
captured_infeed_queue = _CapturedObject()
tpu_ordinal_function_impl = ctx.tpu_ordinal_function(host_id)
def enqueue_ops_fn():
"""A fn returns enqueue_ops."""
num_cores_per_host = ctx.num_of_cores_per_host
per_host_sharded_inputs = []
for core_ordinal in range(num_cores_per_host):
with ops.name_scope('ordinal_%d' % (core_ordinal)):
user_context = tpu_context.TPUContext(
internal_ctx=ctx,
input_device=host_device,
invocation_index=host_id * ctx.num_of_cores_per_host + core_ordinal)
inputs = _Inputs.from_input_fn(input_fn(user_context))
if inputs.is_dataset:
raise TypeError(
'`input_fn` returning `Dataset` is not yet supported in '
'per-Core input pipeline deployment yet. Please set '
'TPUConfig.per_host_input_for_training to True or return '
'`features` and `labels` from `input_fn`')
features, labels = inputs.features_and_labels()
inputs_structure_recorder.validate_and_record_structure(
features, labels)
flattened_inputs = (
inputs_structure_recorder.flatten_features_and_labels(
features, labels))
per_host_sharded_inputs.append(flattened_inputs)
infeed_queue = tpu_feed.InfeedQueue(
number_of_tuple_elements=len(per_host_sharded_inputs[0]))
captured_infeed_queue.capture(infeed_queue)
per_host_enqueue_ops = infeed_queue.generate_enqueue_ops(
per_host_sharded_inputs, tpu_ordinal_function=tpu_ordinal_function_impl)
return per_host_enqueue_ops
return enqueue_ops_fn, captured_infeed_queue
def generate_per_host_enqueue_ops_fn_for_host(
ctx, input_fn, inputs_structure_recorder, batch_axis, device, host_id):
"""Generates infeed enqueue ops for per-host input_fn on a single host."""
captured_infeed_queue = _CapturedObject()
dataset_initializer = None
with ops.device(device):
user_context = tpu_context.TPUContext(
internal_ctx=ctx, input_device=device, invocation_index=host_id)
inputs = _Inputs.from_input_fn(input_fn(user_context))
is_dataset = inputs.is_dataset
if ctx.mode == model_fn_lib.ModeKeys.PREDICT:
if not is_dataset:
raise TypeError(
'For mode PREDICT, `input_fn` must return `Dataset` instead of '
'`features` and `labels`.')
if batch_axis is not None:
raise TypeError('For mode PREDICT, batch_axis is not supported yet.')
inputs = _InputsWithStoppingSignals(
dataset=inputs.dataset,
batch_size=ctx.batch_size_for_input_fn,
add_padding=True)
if is_dataset:
dataset_initializer = inputs.dataset_initializer()
tpu_ordinal_function_impl = ctx.tpu_ordinal_function(host_id)
def enqueue_ops_fn():
"""A Fn returning the TPU infeed enqueue ops.
By providing as a Fn, it can be invoked inside the tf.while_loop such that
the input pipeline for multiple iterations can be executed by one
Session.run call.
Returns:
list of dict of ops.
"""
with ops.device(device):
num_of_replicas_per_host = ctx.num_of_replicas_per_host
# Convert user input to features and labels. If the user returns a
# dataset, it is initialized and the features and labels extracted via
# `dataset.iterator.get_next()`
features, labels = inputs.features_and_labels()
signals = inputs.signals()
inputs_structure_recorder.validate_and_record_structure(features, labels)
unsharded_tensor_list = (
inputs_structure_recorder.flatten_features_and_labels(
features, labels, signals))
infeed_queue = tpu_feed.InfeedQueue(
tuple_types=[t.dtype for t in unsharded_tensor_list],
tuple_shapes=[t.shape for t in unsharded_tensor_list],
shard_dimensions=batch_axis)
captured_infeed_queue.capture(infeed_queue)
infeed_queue.set_number_of_shards(num_of_replicas_per_host)
per_host_enqueue_ops = (
infeed_queue.split_inputs_and_generate_enqueue_ops(
unsharded_tensor_list,
placement_function=lambda x: device,
tpu_ordinal_function=tpu_ordinal_function_impl))
if signals is None:
return per_host_enqueue_ops
else:
return {
'ops': per_host_enqueue_ops,
'signals': signals,
}
return enqueue_ops_fn, captured_infeed_queue, dataset_initializer
def generate_per_host_v2_enqueue_ops_fn_for_host(
ctx, input_fn, inputs_structure_recorder, device, host_id):
"""Generates infeed enqueue ops for per-host input_fn on a single host."""
captured_infeed_queue = _CapturedObject()
dataset_initializer = None
with ops.device(device):
user_context = tpu_context.TPUContext(
internal_ctx=ctx, input_device=device, invocation_index=host_id)
inputs = _Inputs.from_input_fn(input_fn(user_context))
is_dataset = inputs.is_dataset
if not is_dataset:
raise TypeError('`input_fn` must return a `Dataset` for the PER_HOST_V2 '
'input pipeline configuration.')
if ctx.mode == model_fn_lib.ModeKeys.PREDICT:
inputs = _InputsWithStoppingSignals(
dataset=inputs.dataset,
batch_size=ctx.batch_size_for_input_fn,
add_padding=True,
num_invocations_per_step=ctx.num_of_replicas_per_host)
dataset_initializer = inputs.dataset_initializer()
tpu_ordinal_function_impl = ctx.tpu_ordinal_function(host_id)
def enqueue_ops_fn():
"""Generates the per_host enqueue ops."""
control_deps = []
per_host_sharded_inputs = []
num_replicas_per_host = ctx.num_of_replicas_per_host
cached_signals = None
with ops.device(device):
if not inputs.is_dataset:
raise TypeError('`input_fn` must return a `Dataset` for this mode.')
for _ in range(num_replicas_per_host):
# Use control dependencies to ensure a deterministic ordering.
with ops.control_dependencies(control_deps):
features, labels = inputs.features_and_labels() # Calls get_next()
signals = inputs.signals()
# All the replicas share the replica 0's stopping singal.
# This avoids inconsistent state among different model replcias.
if cached_signals:
signals['stopping'] = cached_signals['stopping']
else:
cached_signals = signals
inputs_structure_recorder.validate_and_record_structure(
features, labels)
flattened_inputs = (
inputs_structure_recorder.flatten_features_and_labels(
features, labels, signals))
control_deps.extend(flattened_inputs)
per_host_sharded_inputs.append(flattened_inputs)
if inputs_structure_recorder.flattened_input_dims:
input_partition_dims = inputs_structure_recorder.flattened_input_dims
if signals:
input_partition_dims += [None] * len(signals)
# pylint: disable=protected-access
infeed_queue = tpu_feed._PartitionedInfeedQueue(
number_of_tuple_elements=len(per_host_sharded_inputs[0]),
host_id=host_id,
input_partition_dims=input_partition_dims,
device_assignment=ctx.device_assignment)
per_host_enqueue_ops = infeed_queue.generate_enqueue_ops(
per_host_sharded_inputs)
else:
infeed_queue = tpu_feed.InfeedQueue(
number_of_tuple_elements=len(per_host_sharded_inputs[0]))
per_host_enqueue_ops = infeed_queue.generate_enqueue_ops(
per_host_sharded_inputs,
tpu_ordinal_function=tpu_ordinal_function_impl)
captured_infeed_queue.capture(infeed_queue)
if signals is None:
return per_host_enqueue_ops
else:
return {
'ops': per_host_enqueue_ops,
'signals': signals,
}
return enqueue_ops_fn, captured_infeed_queue, dataset_initializer
def generate_broadcast_enqueue_ops_fn(ctx, input_fn, inputs_structure_recorder,
num_hosts):
"""Generates infeed enqueue ops for one input_fn on all the hosts."""
captured_infeed_queue = _CapturedObject()
dataset_initializer = None
device_0 = ctx.tpu_host_placement_function(host_id=0)
with ops.device(device_0):
user_context = tpu_context.TPUContext(
internal_ctx=ctx, input_device=device_0, invocation_index=0)
inputs = _Inputs.from_input_fn(input_fn(user_context))
is_dataset = inputs.is_dataset
if ctx.mode == model_fn_lib.ModeKeys.PREDICT:
if not is_dataset:
raise TypeError(
'For mode PREDICT, `input_fn` must return `Dataset` instead of '
'`features` and `labels`.')
inputs = _InputsWithStoppingSignals(
dataset=inputs.dataset,
batch_size=ctx.batch_size_for_input_fn,
add_padding=True)
if is_dataset:
dataset_initializer = inputs.dataset_initializer()
num_replicas_per_host = ctx.num_of_replicas_per_host
def tpu_ordinal_function_impl(replica_id):
if ctx.device_assignment:
return ctx.device_assignment.tpu_ordinal(replica=replica_id)
else:
return replica_id % num_replicas_per_host
def device_function_impl(replica_id):
return ctx.tpu_host_placement_function(replica_id=replica_id)
def enqueue_ops_fn():
"""Generates enqueue ops for all the hosts."""
broadcasted_inputs = []
flattened_inputs = None # Cache result from input_fn.
signals = None
for host_id in xrange(num_hosts):
with ops.device(ctx.tpu_host_placement_function(host_id=host_id)):
for _ in xrange(ctx.num_of_replicas_per_host):
# Note: input_fn is only called once at host 0 for the first replica.
# The features and labels returned from that invocation are
# broadcasted to other replicas(including the replicas on other
# hosts).
if flattened_inputs is None:
features, labels = inputs.features_and_labels() # Calls get_next()
signals = inputs.signals()
inputs_structure_recorder.validate_and_record_structure(
features, labels)
flattened_inputs = (
inputs_structure_recorder.flatten_features_and_labels(
features, labels, signals))
broadcasted_inputs.append(flattened_inputs)
infeed_queue = tpu_feed.InfeedQueue(
number_of_tuple_elements=len(broadcasted_inputs[0]))
captured_infeed_queue.capture(infeed_queue)
enqueue_ops = infeed_queue.generate_enqueue_ops(
broadcasted_inputs,
tpu_ordinal_function=tpu_ordinal_function_impl,
placement_function=device_function_impl)
if signals is None:
return enqueue_ops
else:
return {
'ops': enqueue_ops,
'signals': signals,
}
return enqueue_ops_fn, captured_infeed_queue, dataset_initializer
class _InputPipeline(object):
"""`_InputPipeline` handles invoking `input_fn` and piping to infeed queue.
`_InputPipeline` abstracts the per-core/per-host `input_fn` invocation from
call site. To be precise, based on the configuration in
`_InternalTPUContext`, it invokes `input_fn` for all cores (usually
multi-host TPU training) or for one host (usually for single-host TPU
evaluation), and sends all `features` and `labels` returned by `input_fn` to
TPU infeed. For per-core invocation, `features` and `labels` are piped to
infeed directly, one tuple for each core. For per-host invocation, `features`
and `labels` are split at host (with respect to `batch_axis`) and piped to all
cores accordingly.
In addition, flatten/unflatten are handled by `_InputPipeline` also. Model
inputs returned by the `input_fn` can have one of the following forms:
1. features
2. (features, labels)
3. ((arbitrarily nested structure of features), labels)
Internally, form 1 is reformed to `(features, None)` as features and labels
are passed separately to underlying methods. For TPU training, TPUEstimator
may expect multiple `features` and `labels` tuples one for each core.
TPUEstimator allows various different structures for inputs (namely `features`
and `labels`). Both `features` and `labels` can be any nested sturcture
supported by TF nest (namely, dict, tuples, namedtuples or any nested
structure of such of Tensors). `labels` could be `None` as well.
These are flattened before they are passed to the infeed/outfeed library
as that expectes flattend lists.
"""
class InputsStructureRecorder(object):
"""The recorder to record inputs structure."""
def __init__(self, input_partition_dims=None):
# Holds the structure of inputs
self._feature_structure = {}
self._flattened_input_dims = None
if input_partition_dims:
# This should have been validated in TPUConfig.
assert len(input_partition_dims) <= 2, 'must have 1 or 2 elements.'
if len(input_partition_dims) == 2:
self._feature_dims, self._label_dims = input_partition_dims
else:
self._feature_dims = input_partition_dims[0]
self._label_dims = None
assert self._feature_dims is not None, ('input_partition_dims[0] must '
'not be None')
else:
self._feature_dims = None
self._label_dims = None
# Internal state.
self._initialized = False
@property
def flattened_input_dims(self):
assert self._initialized, 'InputsStructureRecorder is not initialized.'
return self._flattened_input_dims
def has_labels(self):
return 'labels' in self._feature_structure
def _flatten_input_dims(self, feature_dims, feature_dims_names, label_dims,
label_dims_names, label_names, has_labels):
"""Flatten input dims with the same order as flattened input tensors."""
flattened_input_dims = []
if feature_dims_names:
# We need a fixed ordering for matching the tensors in features.
flattened_input_dims.extend(
[feature_dims[name] for name in feature_dims_names])
else:
flattened_input_dims.append(feature_dims)
if label_dims_names:
# We need a fixed ordering for matching the tensors in labels.
flattened_input_dims.extend(
[label_dims[name] for name in label_dims_names])
else:
if label_names:
num_tensors_in_label = len(label_names)
else:
num_tensors_in_label = int(has_labels)
# Setting `None` in input_partition_dims[1] will apply `None` to
# all the tensors in labels, regardless of internal structure.
flattened_input_dims.extend([label_dims] * num_tensors_in_label)
return flattened_input_dims
def validate_and_record_structure(self, features, labels):
"""Validates and records the structure of `features` and `labels`."""
# Extract structure.
has_labels = labels is not None
feature_names = _extract_key_names(features)
label_names = _extract_key_names(labels)
if not self._initialized:
# Record structure.
self._initialized = True
if self._feature_dims is not None:
feature_dims_names = _extract_key_names(self._feature_dims)
if feature_dims_names != feature_names:
raise ValueError(
'TPUConfig.input_partition_dims[0] mismatched feature'
' keys. Expected {}, got {}'.format(feature_names,
feature_dims_names))
label_dims_names = _extract_key_names(self._label_dims)
if self._label_dims is not None and label_dims_names != label_names:
raise ValueError(
'TPUConfig.input_partition_dims[1] mismatched label'
' keys. Expected {}, got {}'.format(label_names,
label_dims_names))
self._flattened_input_dims = self._flatten_input_dims(
self._feature_dims, feature_dims_names, self._label_dims,
label_dims_names, label_names, has_labels)
def flatten_features_and_labels(self, features, labels, signals=None):
"""Flattens the `features` and `labels` to a single tensor list."""
self._feature_structure['features'] = features
if labels is not None:
self._feature_structure['labels'] = labels
if signals is not None:
self._feature_structure['signals'] = signals
return data_nest.flatten(self._feature_structure)
def unflatten_features_and_labels(self, flattened_inputs):
"""Restores the flattened inputs to original features and labels form.
Args:
flattened_inputs: Flattened inputs for each shard.
Returns:
A tuple of (`features`, `labels`), where `labels` could be None.
Each one, if present, should have identical structure (single tensor vs
dict) as the one returned by input_fn.
Raises:
ValueError: If the number of expected tensors from `flattened_inputs`
mismatches the recorded structure.
"""
unflattened_inputs = data_nest.pack_sequence_as(self._feature_structure,
flattened_inputs)
return _Inputs(
unflattened_inputs['features'],
unflattened_inputs.get('labels'),
signals=unflattened_inputs.get('signals'))
def __init__(self, input_fn, batch_axis, ctx):
"""Constructor.
Args:
input_fn: input fn for train or eval.
batch_axis: A python tuple of int values describing how each tensor
produced by the Estimator `input_fn` should be split across the TPU
compute shards.
ctx: A `_InternalTPUContext` instance with mode.
Raises:
ValueError: If both `sharded_features` and `num_cores` are `None`.
"""
self._inputs_structure_recorder = _InputPipeline.InputsStructureRecorder(
ctx.input_partition_dims)
self._sharded_per_core = ctx.is_input_sharded_per_core()
self._input_fn = input_fn
self._infeed_queue = None
self._ctx = ctx
self._batch_axis = batch_axis
def generate_infeed_enqueue_ops_and_dequeue_fn(self):
"""Generates infeed enqueue ops and dequeue_fn."""
# While tf.while_loop is called, the body function, which invokes
# `enqueue_fn` passed in, is called to construct the graph. So, input_fn
# structure is recorded.
enqueue_ops, all_hooks, run_infeed_loop_on_coordinator = (
self._invoke_input_fn_and_record_structure())
self._validate_input_pipeline()
def dequeue_fn():
"""dequeue_fn is used by TPU to retrieve the tensors."""
# In the model-parallel case, both the host-side and device-side
# computations must agree on the core on which infeed takes place. We
# choose to perform infeed on logical core 0 of each replica.
values = self._infeed_queue.generate_dequeue_op(tpu_device=0)
# The unflatten process uses the structure information recorded above.
return self._inputs_structure_recorder.unflatten_features_and_labels(
values)
return (enqueue_ops, dequeue_fn, all_hooks, run_infeed_loop_on_coordinator)
def _invoke_input_fn_and_record_structure(self):
"""Deploys the input pipeline and record input structure."""
enqueue_ops = []
infeed_queues = []
all_dataset_initializers = []
num_hosts = self._ctx.num_hosts
tpu_host_placement_fn = self._ctx.tpu_host_placement_function
run_infeed_loop_on_coordinator = True
if self._sharded_per_core:
# Per-Core input pipeline deployment.
# Invoke input pipeline for each core and placed on the corresponding
# host.
for host_id in range(num_hosts):
host_device = tpu_host_placement_fn(host_id=host_id)
with ops.device(host_device):
with ops.name_scope('input_pipeline_task%d' % (host_id)):
enqueue_ops_fn, captured_infeed_queue = (
generate_per_core_enqueue_ops_fn_for_host(
self._ctx, self._input_fn, self._inputs_structure_recorder,
host_device, host_id))
if _WRAP_INPUT_FN_INTO_WHILE_LOOP:
run_infeed_loop_on_coordinator = False
enqueue_ops.append(
_wrap_computation_in_while_loop(
device=host_device, op_fn=enqueue_ops_fn))
else:
enqueue_ops.append(enqueue_ops_fn())
# Infeed_queue_getter must be called after enqueue_ops_fn is called.
infeed_queues.append(captured_infeed_queue.get())
elif self._ctx.is_input_broadcast_with_iterators():
# Only calls input_fn in host 0.
host_device = tpu_host_placement_fn(host_id=0)
enqueue_ops_fn, captured_infeed_queue, dataset_initializer = (
generate_broadcast_enqueue_ops_fn(self._ctx, self._input_fn,
self._inputs_structure_recorder,
num_hosts))
if dataset_initializer:
all_dataset_initializers.append(dataset_initializer)
run_infeed_loop_on_coordinator = False
wrap_fn = (
_wrap_computation_in_while_loop
if self._ctx.mode != model_fn_lib.ModeKeys.PREDICT else
_wrap_computation_in_while_loop_with_stopping_signals)
enqueue_ops.append(wrap_fn(device=host_device, op_fn=enqueue_ops_fn))
else:
enqueue_ops.append(enqueue_ops_fn())
infeed_queues.append(captured_infeed_queue.get())
else:
for host_id in range(num_hosts):
host_device = tpu_host_placement_fn(host_id=host_id)
with ops.device(host_device):
with ops.name_scope('input_pipeline_task%d' % (host_id)):
if self._ctx.is_input_per_host_with_iterators():
enqueue_ops_fn, captured_infeed_queue, dataset_initializer = (
generate_per_host_v2_enqueue_ops_fn_for_host(
self._ctx, self._input_fn,
self._inputs_structure_recorder, host_device, host_id))
else:
enqueue_ops_fn, captured_infeed_queue, dataset_initializer = (
generate_per_host_enqueue_ops_fn_for_host(
self._ctx, self._input_fn,
self._inputs_structure_recorder, self._batch_axis,
host_device, host_id))
# NOTE(xiejw): We dispatch here based on the return type of the
# users `input_fn`.
#
# 1. If input_fn returns a Dataset instance, we initialize the
# iterator outside of tf.while_loop, and call the iterator.get_next
# inside tf.while_loop. This should be always safe.
#
# 2. If input_fn returns (features, labels), it is too late to wrap
# them inside tf.while_loop, as resource initialization cannot be
# handled in TF control flow properly. In this case, we will use
# python loop to enqueue the data into TPU system. This may be
# slow compared to the previous case.
if dataset_initializer:
all_dataset_initializers.append(dataset_initializer)
run_infeed_loop_on_coordinator = False
wrap_fn = (
_wrap_computation_in_while_loop
if self._ctx.mode != model_fn_lib.ModeKeys.PREDICT else
_wrap_computation_in_while_loop_with_stopping_signals)
enqueue_ops.append(
wrap_fn(device=host_device, op_fn=enqueue_ops_fn))
else:
enqueue_ops.append(enqueue_ops_fn())
infeed_queues.append(captured_infeed_queue.get())
# infeed_queue is used to generate dequeue ops. The only thing it uses for
# dequeue is dtypes and types. So, any one can be used. Here, grab the
# first one.
self._infeed_queue = infeed_queues[0]
return enqueue_ops, [
util_lib.MultiHostDatasetInitializerHook(all_dataset_initializers)
], run_infeed_loop_on_coordinator
def _validate_input_pipeline(self):
"""Validates the input pipeline.
Perform some sanity checks to log user friendly information. We should
error out to give users better error message. But, if
_WRAP_INPUT_FN_INTO_WHILE_LOOP is False (legacy behavior), we cannot break
user code, so, log a warning.
Raises:
RuntimeError: If the validation failed.
"""
if ops.get_default_graph().get_collection(ops.GraphKeys.QUEUE_RUNNERS):
err_msg = ('Input pipeline contains one or more QueueRunners. '
'It could be slow and not scalable. Please consider '
'converting your input pipeline to use `tf.data` instead (see '
'https://www.tensorflow.org/guide/datasets for '
'instructions.')
if _WRAP_INPUT_FN_INTO_WHILE_LOOP:
raise RuntimeError(err_msg)
else:
logging.warn(err_msg)
class _ModelFnWrapper(object):
"""A `model_fn` wrapper.
This makes calling model_fn on CPU and TPU easier and more consistent and
performs necessary check and mutation required by TPU training and evaluation.
In addition, this wrapper manages converting the `model_fn` to a single TPU
train and eval step.
"""
def __init__(self, model_fn, train_cache_fn, eval_cache_fn, config, params, ctx):
self._model_fn = model_fn
self._train_cache_fn = train_cache_fn
self._eval_cache_fn = eval_cache_fn
self._config = config
self._params = params
self._ctx = ctx
def call_without_tpu(self, features, labels, is_export_mode):
return self._call_model_fn(features, labels, is_export_mode=is_export_mode)
def convert_to_single_tpu_train_step(self, dequeue_fn):
"""Converts user provided model_fn` as a single train step on TPU.
The user provided `model_fn` takes input tuple
(features, labels) and produces the EstimatorSpec with train_op and loss for
train `mode`. This usually represents a single train computation on CPU.
For TPU training, a train (computation) step is first wrapped in a
tf.while_loop control flow to repeat for many times and then replicated to
all TPU shards. Besides the input should be taken from TPU infeed rather
than input pipeline (input_fn) directly. To fit TPU loop and replicate
pattern, the original train computation should be reformed, which is the
returned `train_step`.
Args:
dequeue_fn: The function to retrieve inputs, features and labels, from TPU
infeed dequeue channel.
Returns:
A tuple of train_fn, host_calls, and captured scaffold_fn. The train_fn
representing the train step for TPU.
"""
host_call = _OutfeedHostCall(self._ctx)
captured_scaffold_fn = _CapturedObject()
captured_training_hooks = _CapturedObject()
def train_step(loss, *cache):
"""Training step function for use inside a while loop."""
del loss # unused; required in function signature.
inputs = dequeue_fn()
features, labels = inputs.features_and_labels()
# Consume the current cache
estimator_spec = self._verify_estimator_spec(
self._call_model_fn(features, labels, cache=cache))
# Retrieve the new returned cache
"""
`cache` consists of a list of tensors, potentially empty (of length 0)
"""
cache = estimator_spec.cache
loss, train_op = estimator_spec.loss, estimator_spec.train_op
if isinstance(estimator_spec, model_fn_lib._TPUEstimatorSpec): # pylint: disable=protected-access
captured_scaffold_fn.capture(estimator_spec.scaffold_fn)
else:
captured_scaffold_fn.capture(None)
captured_training_hooks.capture(estimator_spec.training_hooks)
tracing_ops = []
if tensor_tracer.TensorTracer.is_enabled():
tt = tensor_tracer.TensorTracer()
loss, tracing_ops = tt.trace_tpu(ops.get_default_graph(), loss,
self._ctx.num_replicas)
# We must run train_op to update the variables prior to running the
# outfeed.
with ops.control_dependencies([train_op]+tracing_ops):
host_call_outfeed_ops = []
if (isinstance(estimator_spec, model_fn_lib._TPUEstimatorSpec) # pylint: disable=protected-access
and estimator_spec.host_call is not None):
host_call.record({'host_call': estimator_spec.host_call})
host_call_outfeed_ops = host_call.create_enqueue_op()
with ops.control_dependencies(host_call_outfeed_ops):
return [array_ops.identity(loss)] + cache
return (train_step, host_call, captured_scaffold_fn,
captured_training_hooks)
def convert_to_single_tpu_eval_step(self, dequeue_fn):
"""Converts user provided model_fn` as a single eval step on TPU.
Similar to training, the user provided `model_fn` takes input tuple
(features, labels) and produces the TPUEstimatorSpec with eval_metrics for
eval `mode`. This usually represents a single evaluation computation on CPU.
For TPU evaluation, a eval (computation) step is first wrapped in a
tf.while_loop control flow to repeat for many times and then replicated to
all TPU shards. Besides the input and output are slightly different. Input,
features and labels, should be taken from TPU infeed rather than input
pipeline (input_fn) directly. Output is managed in two stages. First, the
model outputs as the result of evaluation computation, usually model logits,
should be transferred from TPU system to CPU. Then, all model outputs are
concatenated first on CPU and sent to the metric_fn for metrics computation.
To fit TPU evaluation pattern, the original eval computation should be
reformed, which is the returned `eval_step`.
Args:
dequeue_fn: The function to retrieve inputs, features and labels, from TPU
infeed dequeue channel.
Returns:
A tuple of eval_fn, host_calls, and captured scaffold_fn. The eval_fn
representing the eval step for TPU.
"""
host_calls = _OutfeedHostCall(self._ctx)
captured_scaffold_fn = _CapturedObject()
captured_eval_hooks = _CapturedObject()
def eval_step(total_loss, *cache):
"""Evaluation step function for use inside a while loop."""
inputs = dequeue_fn()
features, labels = inputs.features_and_labels()
# Consume the current cache
tpu_estimator_spec = self._call_model_fn(features, labels, cache=cache)
if not isinstance(tpu_estimator_spec, model_fn_lib._TPUEstimatorSpec): # pylint: disable=protected-access
raise RuntimeError(
'estimator_spec used by TPU evaluation must have type'
'`TPUEstimatorSpec`. Got {}'.format(type(tpu_estimator_spec)))
# Retrieve the new returned cache
cache = tpu_estimator_spec.cache
loss = tpu_estimator_spec.loss
captured_scaffold_fn.capture(tpu_estimator_spec.scaffold_fn)
captured_eval_hooks.capture(tpu_estimator_spec.evaluation_hooks)
to_record = {}
if tpu_estimator_spec.eval_metrics:
to_record['eval_metrics'] = tpu_estimator_spec.eval_metrics
if tpu_estimator_spec.host_call is not None:
# We assume that evaluate won't update global step, so we don't wrap
# this host_call.
to_record['host_call'] = tpu_estimator_spec.host_call
host_calls.record(to_record)
with ops.control_dependencies(host_calls.create_enqueue_op()):
return [math_ops.add(total_loss, loss)] + cache
return eval_step, host_calls, captured_scaffold_fn, captured_eval_hooks
def convert_to_single_tpu_predict_step(self, dequeue_fn):
"""Converts user provided model_fn` as a single predict step on TPU.
Args:
dequeue_fn: The function to retrieve inputs, features and labels, from TPU
infeed dequeue channel.
Returns:
A tuple of predict_fn, host_calls, and captured scaffold_fn. The
predict_fn representing the predict step for TPU.
"""
host_calls = _OutfeedHostCall(self._ctx)
captured_scaffold_fn = _CapturedObject()
captured_predict_hooks = _CapturedObject()
def predict_step(unused_scalar_stopping_signal):
"""Evaluation step function for use inside a while loop."""
inputs = dequeue_fn()
features, labels = inputs.features_and_labels()
stopping_signals = inputs.signals()
assert stopping_signals is not None, (
'Internal Error: `signals` is missing.')
tpu_estimator_spec = self._call_model_fn(
features, labels, is_export_mode=False)
if not isinstance(tpu_estimator_spec, model_fn_lib._TPUEstimatorSpec): # pylint: disable=protected-access
raise RuntimeError(
'estimator_spec used by TPU prediction must have type'
'`TPUEstimatorSpec`. Got {}'.format(type(tpu_estimator_spec)))
self._verify_tpu_spec_predictions(tpu_estimator_spec.predictions)
captured_scaffold_fn.capture(tpu_estimator_spec.scaffold_fn)
captured_predict_hooks.capture(tpu_estimator_spec.prediction_hooks)
to_record = {}
identity_fn = lambda **kwargs: kwargs
to_record['predictions'] = [identity_fn, tpu_estimator_spec.predictions]
to_record['signals'] = [identity_fn, stopping_signals]
if tpu_estimator_spec.host_call is not None:
to_record['host_call'] = tpu_estimator_spec.host_call
host_calls.record(to_record)
with ops.control_dependencies(host_calls.create_enqueue_op()):
return _StopSignals.as_scalar_stopping_signal(stopping_signals)
return (predict_step, host_calls, captured_scaffold_fn,
captured_predict_hooks)
def _verify_tpu_spec_predictions(self, predictions):
"""Validates TPUEstimatorSpec.predictions dict."""
# TODO(xiejw): Adds validation for prediction dictionrary.
# TODO(xiejw): Adds support for single tensor as predictions.
if not isinstance(predictions, dict):
raise TypeError('TPUEstimatorSpec.predictions must be dict of Tensors.')
for (key, tensor) in predictions.items():
if tensor.shape.dims[0].value is None:
raise ValueError(
'The tensor with key ({}) in TPUEstimatorSpec.predictions has '
'dynamic shape (should be static). Tensor: {}'.format(key, tensor))
return predictions
def _validate_model_features_and_labels(self, features, labels,
is_export_mode):
"""Validates that the features and labels for the model function are valid.
A valid features/labels object is the one with:
- Type: A tensor or any nested structure of tensors supported by TF nest,
namely nested dictionary, tuple, namedtuple, or sequence of tensors.
- Static shape if is_export_mode is False.
Args:
features: the features that would be input to the model function.
labels: the labels that would be input to the model function.
is_export_mode: boolean value specifying if in export mode.
Raises:
TypeError: If features/labels are not of the correct type.
ValueError: If features/labels have dynamic shape.
"""
def validate(obj, obj_name):
"""Helper validate function."""
if is_export_mode or self._ctx.is_running_on_cpu(is_export_mode):
return
if isinstance(obj, ops.Tensor):
if not obj.get_shape().is_fully_defined():
raise ValueError(
'The {} to the model returned by input_fn must have static shape.'
' Tensor: {}'.format(obj_name, obj))
else:
for tensor in data_nest.flatten(obj):
if not tensor.get_shape().is_fully_defined():
raise ValueError(
('The {} to the model returned by input_fn must have static '
'shape. Tensor: {}').format(obj_name, tensor))
validate(features, 'features')
if labels is not None:
validate(labels, 'labels')
def _call_model_fn(self, features, labels, cache=None, is_export_mode=False):
"""Calls the model_fn with required parameters."""
self._validate_model_features_and_labels(features, labels, is_export_mode)
model_fn_args = function_utils.fn_args(self._model_fn)
kwargs = {}
# Makes deep copy with `config` and params` in case user mutates them.
config = copy.deepcopy(self._config)
params = copy.deepcopy(self._params)
if 'labels' in model_fn_args:
kwargs['labels'] = labels
elif labels is not None:
raise ValueError(
'model_fn does not take labels, but input_fn returns labels.')
if 'mode' in model_fn_args:
kwargs['mode'] = self._ctx.mode
if 'config' in model_fn_args:
kwargs['config'] = config
if 'params' in model_fn_args:
kwargs['params'] = params
if cache is not None:
params['cache'] = cache
if 'params' not in model_fn_args:
raise ValueError('model_fn ({}) does not include params argument, '
'required by TPUEstimator to pass batch size as '
'params[\'batch_size\']'.format(self._model_fn))
if is_export_mode:
batch_size_for_model_fn = None
else:
batch_size_for_model_fn = self._ctx.batch_size_for_model_fn
if batch_size_for_model_fn is not None:
_add_item_to_params(params, _BATCH_SIZE_KEY, batch_size_for_model_fn)
running_on_cpu = self._ctx.is_running_on_cpu(is_export_mode)
_add_item_to_params(params, _USE_TPU_KEY, not running_on_cpu)
if not running_on_cpu:
user_context = tpu_context.TPUContext(
internal_ctx=self._ctx, call_from_input_fn=False)
_add_item_to_params(params, _CTX_KEY, user_context)
estimator_spec = self._model_fn(features=features, **kwargs)
if (running_on_cpu and
isinstance(estimator_spec, model_fn_lib._TPUEstimatorSpec)): # pylint: disable=protected-access
# The estimator_spec will be passed to `Estimator` directly, which expects
# type `EstimatorSpec`.
return estimator_spec.as_estimator_spec()
else:
return estimator_spec
def _verify_estimator_spec(self, estimator_spec):
"""Validates the estimator_spec."""
if isinstance(estimator_spec, model_fn_lib._TPUEstimatorSpec): # pylint: disable=protected-access
return estimator_spec
err_msg = '{} returned by EstimatorSpec is not supported in TPUEstimator.'
if estimator_spec.training_chief_hooks:
raise ValueError(
err_msg.format('training_chief_hooks') + 'If you want' +
' to pass training hooks, please pass via training_hooks.')
if estimator_spec.scaffold:
logging.warning('EstimatorSpec.Scaffold is ignored by TPU train/eval. '
'Please use TPUEstimatorSpec.')
return estimator_spec
class _OutfeedHostCall(object):
"""Support for `eval_metrics` and `host_call` in TPUEstimatorSpec."""
def __init__(self, ctx):
self._ctx = ctx
self._names = []
# All of these are dictionaries of lists keyed on the name.
self._host_fns = {}
self._tensor_keys = collections.defaultdict(list)
self._tensors = collections.defaultdict(list)
self._tensor_dtypes = collections.defaultdict(list)
self._tensor_shapes = collections.defaultdict(list)
@staticmethod
def validate(host_calls):
"""Validates the `eval_metrics` and `host_call` in `TPUEstimatorSpec`."""
for name, host_call in host_calls.items():
if not isinstance(host_call, (tuple, list)):
raise ValueError('{} should be tuple or list'.format(name))
if len(host_call) != 2:
raise ValueError('{} should have two elements.'.format(name))
if not callable(host_call[0]):
raise TypeError('{}[0] should be callable.'.format(name))
if not isinstance(host_call[1], (tuple, list, dict)):
raise ValueError('{}[1] should be tuple or list, or dict.'.format(name))
if isinstance(host_call[1], (tuple, list)):
fullargspec = tf_inspect.getfullargspec(host_call[0])
fn_args = function_utils.fn_args(host_call[0])
# wrapped_hostcall_with_global_step uses varargs, so we allow that.
if fullargspec.varargs is None and len(host_call[1]) != len(fn_args):
raise RuntimeError(
'In TPUEstimatorSpec.{}, length of tensors {} does not match '
'method args of the function, which takes {}.'.format(
name, len(host_call[1]), len(fn_args)))
@staticmethod
def create_cpu_hostcall(host_calls):
"""Runs on the host_call on CPU instead of TPU when use_tpu=False."""
_OutfeedHostCall.validate(host_calls)
ret = {}
for name, host_call in host_calls.items():
host_fn, tensors = host_call
if isinstance(tensors, (tuple, list)):
ret[name] = host_fn(*tensors)
else:
# Must be dict.
try:
ret[name] = host_fn(**tensors)
except TypeError as e:
logging.warning(
'Exception while calling %s: %s. It is likely the tensors '
'(%s[1]) do not match the '
'function\'s arguments', name, e, name)
raise e
return ret
def record(self, host_calls):
"""Records the host_call structure."""
for name, host_call in host_calls.items():
host_fn, tensor_list_or_dict = host_call
self._names.append(name)
self._host_fns[name] = host_fn
if isinstance(tensor_list_or_dict, dict):
for (key, tensor) in six.iteritems(tensor_list_or_dict):
self._tensor_keys[name].append(key)
self._tensors[name].append(tensor)
self._tensor_dtypes[name].append(tensor.dtype)
self._tensor_shapes[name].append(tensor.shape)
else:
# List or tuple.
self._tensor_keys[name] = None
for tensor in tensor_list_or_dict:
self._tensors[name].append(tensor)
self._tensor_dtypes[name].append(tensor.dtype)
self._tensor_shapes[name].append(tensor.shape)
def create_enqueue_op(self):
"""Create the op to enqueue the recorded host_calls.
Returns:
A list of enqueue ops, which is empty if there are no host calls.
"""
if not self._names:
return []
tensors = []
# TODO(jhseu): Consider deduping tensors.
for name in self._names:
tensors.extend(self._tensors[name])
with ops.device(tpu.core(0)):
return [tpu_ops.outfeed_enqueue_tuple(tensors)]
def create_tpu_hostcall(self):
"""Sends the tensors through outfeed and runs the host_fn on CPU.
The tensors are concatenated along dimension 0 to form a global tensor
across all shards. The concatenated function is passed to the host_fn and
executed on the first host.
Returns:
A dictionary mapping name to the return type of the host_call by that
name.
Raises:
RuntimeError: If outfeed tensor is scalar.
"""
if not self._names:
return {}
ret = {}
# For each i, dequeue_ops[i] is a list containing the tensors from all
# shards. This list is concatenated later.
dequeue_ops = []
tensor_dtypes = []
tensor_shapes = []
for name in self._names:
for _ in self._tensors[name]:
dequeue_ops.append([])
for dtype in self._tensor_dtypes[name]:
tensor_dtypes.append(dtype)
for shape in self._tensor_shapes[name]:
tensor_shapes.append(shape)
# Outfeed ops execute on each replica's first logical core. Note: we must
# constraint it such that we have at most one outfeed dequeue and enqueue
# per replica.
for i in xrange(self._ctx.num_replicas):
host_device, ordinal_id = self._ctx.device_for_replica(i)
with ops.device(host_device):
outfeed_tensors = tpu_ops.outfeed_dequeue_tuple(
dtypes=tensor_dtypes,
shapes=tensor_shapes,
device_ordinal=ordinal_id)
for j, item in enumerate(outfeed_tensors):
dequeue_ops[j].append(item)
# Deconstruct dequeue ops.
dequeue_ops_by_name = {}
pos = 0
for name in self._names:
dequeue_ops_by_name[name] = dequeue_ops[pos:pos +
len(self._tensors[name])]
pos += len(self._tensors[name])
# It is assumed evaluation always happens on single host TPU system. So,
# place all ops on tpu host if possible.
#
# TODO(jhseu): Evaluate whether this is right for summaries.
with ops.device(self._ctx.tpu_host_placement_function(replica_id=0)):
for name in self._names:
dequeue_ops = dequeue_ops_by_name[name]
for i, item in enumerate(dequeue_ops):
if dequeue_ops[i][0].shape.ndims == 0:
raise RuntimeError(
'All tensors outfed from TPU should preserve batch size '
'dimension, but got scalar {}'.format(dequeue_ops[i][0]))
# TODO(xiejw): Allow users to specify the axis for batch size
# dimension.
dequeue_ops[i] = array_ops.concat(dequeue_ops[i], axis=0)
if self._tensor_keys[name] is not None:
# The user-provided eval_metrics[1] is a dict.
dequeue_ops = dict(zip(self._tensor_keys[name], dequeue_ops))
try:
ret[name] = self._host_fns[name](**dequeue_ops)
except TypeError as e:
logging.warning(
'Exception while calling %s: %s. It is likely the tensors '
'(%s[1]) do not match the '
'function\'s arguments', name, e, name)
raise e
else:
ret[name] = self._host_fns[name](*dequeue_ops)
return ret
class _OutfeedHostCallHook(session_run_hook.SessionRunHook):
"""Hook to run host calls when use_tpu=False."""
def __init__(self, tensors):
self._tensors = tensors
def begin(self):
# We duplicate this code from the TPUInfeedOutfeedSessionHook rather than
# create a separate hook to guarantee execution order, because summaries
# need to be initialized before the outfeed thread starts.
# TODO(jhseu): Make a wrapper hook instead?
self._init_ops = contrib_summary.summary_writer_initializer_op()
# Get all the writer resources from the initializer, so we know what to
# flush.
self._finalize_ops = []
for op in self._init_ops:
self._finalize_ops.append(contrib_summary.flush(writer=op.inputs[0]))
def after_create_session(self, session, coord):
session.run(self._init_ops)
def before_run(self, run_context):
return basic_session_run_hooks.SessionRunArgs(self._tensors)
def end(self, session):
session.run(self._finalize_ops)
class ExamplesPerSecondHook(basic_session_run_hooks.StepCounterHook):
"""Calculate and report global_step/sec and examples/sec during runtime."""
def __init__(self,
batch_size,
every_n_steps=100,
every_n_secs=None,
output_dir=None,
summary_writer=None):
self._batch_size = batch_size
super(ExamplesPerSecondHook, self).__init__(
every_n_steps=every_n_steps,
every_n_secs=every_n_secs,
output_dir=output_dir,
summary_writer=summary_writer)
def _log_and_record(self, elapsed_steps, elapsed_time, global_step):
global_step_per_sec = elapsed_steps / elapsed_time
examples_per_sec = self._batch_size * global_step_per_sec
if self._summary_writer is not None:
global_step_summary = Summary(value=[
Summary.Value(tag='global_step/sec', simple_value=global_step_per_sec)
])
example_summary = Summary(value=[
Summary.Value(tag='examples/sec', simple_value=examples_per_sec)
])
self._summary_writer.add_summary(global_step_summary, global_step)
self._summary_writer.add_summary(example_summary, global_step)
logging.info('global_step/sec: %g', global_step_per_sec)
logging.info('examples/sec: %g', examples_per_sec)
class InstallSignalHandlerHook(session_run_hook.SessionRunHook):
"""Change SIGINT (CTRL^C) handler to force quit the process.
The default behavior often results in hanging processes.
The original handler is restored after training/evaluation.
"""
def __init__(self):
self._signal_fn = signal.getsignal(signal.SIGINT)
def before_run(self, run_context):
signal.signal(signal.SIGINT, signal.SIG_DFL)
def end(self, session):
signal.signal(signal.SIGINT, self._signal_fn)
class TPUEstimator(estimator_lib.Estimator):
"""Estimator with TPU support.
TPUEstimator also supports training on CPU and GPU. You don't need to define
a separate `tf.estimator.Estimator`.
TPUEstimator handles many of the details of running on TPU devices, such as
replicating inputs and models for each core, and returning to host
periodically to run hooks.
TPUEstimator transforms a global batch size in params to a per-shard batch
size when calling the `input_fn` and `model_fn`. Users should specify
global batch size in constructor, and then get the batch size for each shard
in `input_fn` and `model_fn` by `params['batch_size']`.
- For training, `model_fn` gets per-core batch size; `input_fn` may get
per-core or per-host batch size depending on `per_host_input_for_training`
in `TPUConfig` (See docstring for TPUConfig for details).
- For evaluation and prediction, `model_fn` gets per-core batch size and
`input_fn` get per-host batch size.
Evaluation
==========
`model_fn` should return `TPUEstimatorSpec`, which expects the `eval_metrics`
for TPU evaluation. However, if eval_on_tpu is False, `model_fn` must return
`EstimatorSpec` and the evaluation will execute on CPU or GPU; in this case
the following discussion on TPU evaluation does not apply.
`TPUEstimatorSpec.eval_metrics` is a tuple of `metric_fn` and `tensors`, where
`tensors` could be a list of any nested structure of `Tensor`s (See
`TPUEstimatorSpec` for details). `metric_fn` takes the `tensors` and returns
a dict from metric string name to the result of calling a metric function,
namely a `(metric_tensor, update_op)` tuple.
One can set `use_tpu` to `False` for testing. All training, evaluation, and
predict will be executed on CPU. `input_fn` and `model_fn` will receive
`train_batch_size` or `eval_batch_size` unmodified as `params['batch_size']`.
Current limitations:
--------------------
1. TPU evaluation only works on a single host (one TPU worker) except
BROADCAST mode.
2. `input_fn` for evaluation should **NOT** raise an end-of-input exception
(`OutOfRangeError` or `StopIteration`). And all evaluation steps and all
batches should have the same size.
Example (MNIST):
----------------
```
# The metric Fn which runs on CPU.
def metric_fn(labels, logits):
predictions = tf.argmax(logits, 1)
return {
'accuracy': tf.metrics.precision(
labels=labels, predictions=predictions),
}
# Your model Fn which runs on TPU (eval_metrics is list in this example)
def model_fn(features, labels, mode, config, params):
...
logits = ...
if mode = tf.estimator.ModeKeys.EVAL:
return tpu_estimator.TPUEstimatorSpec(
mode=mode,
loss=loss,
eval_metrics=(metric_fn, [labels, logits]))
# or specify the eval_metrics tensors as dict.
def model_fn(features, labels, mode, config, params):
...
final_layer_output = ...
if mode = tf.estimator.ModeKeys.EVAL:
return tpu_estimator.TPUEstimatorSpec(
mode=mode,
loss=loss,
eval_metrics=(metric_fn, {
'labels': labels,
'logits': final_layer_output,
}))
```
Prediction
==========
Prediction on TPU is an experimental feature to support large batch inference.
It is not designed for latency-critical system. In addition, due to some
usability issues, for prediction with small dataset, CPU `.predict`, i.e.,
creating a new `TPUEstimator` instance with `use_tpu=False`, might be more
convenient.
Note: In contrast to TPU training/evaluation, the `input_fn` for prediction
*should* raise an end-of-input exception (`OutOfRangeError` or
`StopIteration`), which serves as the stopping signal to `TPUEstimator`. To be
precise, the ops created by `input_fn` produce one batch of the data.
The `predict()` API processes one batch at a time. When reaching the end of
the data source, an end-of-input exception should be raised by one of these
operations. The user usually does not need to do this manually. As long as the
dataset is not repeated forever, the `tf.data` API will raise an end-of-input
exception automatically after the last batch has been produced.
Note: Estimator.predict returns a Python generator. Please consume all the
data from the generator so that TPUEstimator can shutdown the TPU system
properly for user.
Current limitations:
--------------------
1. TPU prediction only works on a single host (one TPU worker).
2. `input_fn` must return a `Dataset` instance rather than `features`. In
fact, .train() and .evaluate() also support Dataset as return value.
Example (MNIST):
----------------
```
height = 32
width = 32
total_examples = 100
def predict_input_fn(params):
batch_size = params['batch_size']
images = tf.random_uniform(
[total_examples, height, width, 3], minval=-1, maxval=1)
dataset = tf.data.Dataset.from_tensor_slices(images)
dataset = dataset.map(lambda images: {'image': images})
dataset = dataset.batch(batch_size)
return dataset
def model_fn(features, labels, params, mode):
# Generate predictions, called 'output', from features['image']
if mode == tf.estimator.ModeKeys.PREDICT:
return tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
predictions={
'predictions': output,
'is_padding': features['is_padding']
})
tpu_est = TPUEstimator(
model_fn=model_fn,
...,
predict_batch_size=16)
# Fully consume the generator so that TPUEstimator can shutdown the TPU
# system.
for item in tpu_est.predict(input_fn=input_fn):
# Filter out item if the `is_padding` is 1.
# Process the 'predictions'
```
Exporting
=========
`export_savedmodel` exports 2 metagraphs, one with `tag_constants.SERVING`,
and another with `tag_constants.SERVING` and `tag_constants.TPU`.
At serving time, these tags are used to select metagraph to load.
Before running the graph on TPU, TPU system needs to be initialized. If
TensorFlow Serving model-server is used, this is done automatically. If
not, please call `session.run(tpu.initialize_system())`.
`tpu.outside_compilation` can be used to wrap TPU incompatible ops in
`model_fn`.
Example:
----------------
```
def model_fn(features, labels, mode, config, params):
...
logits = ...
export_outputs = {
'logits': export_output_lib.PredictOutput(
{'logits': logits})
}
def host_call(logits):
class_ids = math_ops.argmax(logits)
classes = string_ops.as_string(class_ids)
export_outputs['classes'] =
export_output_lib.ClassificationOutput(classes=classes)
tpu.outside_compilation(host_call, logits)
...
```
"""
def __init__(self,
model_fn=None,
train_cache_fn=None,
eval_cache_fn=None,
model_dir=None,
config=None,
params=None,
use_tpu=True,
train_batch_size=None,
eval_batch_size=None,
predict_batch_size=None,
batch_axis=None,
eval_on_tpu=True,
export_to_tpu=True,
warm_start_from=None):
"""Constructs an `TPUEstimator` instance.
Args:
model_fn: Model function as required by `Estimator` which returns
EstimatorSpec or TPUEstimatorSpec. `training_hooks`, 'evaluation_hooks',
and `prediction_hooks` must not capure any TPU Tensor inside the
model_fn.
model_dir: Directory to save model parameters, graph and etc. This can
also be used to load checkpoints from the directory into a estimator to
continue training a previously saved model. If `None`, the model_dir in
`config` will be used if set. If both are set, they must be same. If
both are `None`, a temporary directory will be used.
config: An `tpu_config.RunConfig` configuration object. Cannot be `None`.
params: An optional `dict` of hyper parameters that will be passed into
`input_fn` and `model_fn`. Keys are names of parameters, values are
basic python types. There are reserved keys for `TPUEstimator`,
including 'batch_size'.
use_tpu: A bool indicating whether TPU support is enabled. Currently, -
TPU training and evaluation respect this bit, but eval_on_tpu can
override execution of eval. See below. - Predict still happens on CPU.
train_batch_size: An int representing the global training batch size.
TPUEstimator transforms this global batch size to a per-shard batch
size, as params['batch_size'], when calling `input_fn` and `model_fn`.
Cannot be `None` if `use_tpu` is `True`. Must be divisible by total
number of replicas.
eval_batch_size: An int representing evaluation batch size. Must be
divisible by total number of replicas.
predict_batch_size: An int representing the prediction batch size. Must be
divisible by total number of replicas.
batch_axis: A python tuple of int values describing how each tensor
produced by the Estimator `input_fn` should be split across the TPU
compute shards. For example, if your input_fn produced (images, labels)
where the images tensor is in `HWCN` format, your shard dimensions would
be [3, 0], where 3 corresponds to the `N` dimension of your images
Tensor, and 0 corresponds to the dimension along which to split the
labels to match up with the corresponding images. If None is supplied,
and per_host_input_for_training is True, batches will be sharded based
on the major dimension. If tpu_config.per_host_input_for_training is
False or `PER_HOST_V2`, batch_axis is ignored.
eval_on_tpu: If False, evaluation runs on CPU or GPU. In this case, the
model_fn must return `EstimatorSpec` when called with `mode` as `EVAL`.
export_to_tpu: If True, `export_savedmodel()` exports a metagraph for
serving on TPU besides the one on CPU.
warm_start_from: Optional string filepath to a checkpoint or SavedModel to
warm-start from, or a `tf.estimator.WarmStartSettings` object to fully
configure warm-starting. If the string filepath is provided instead of
a `WarmStartSettings`, then all variables are warm-started, and it is
assumed that vocabularies and Tensor names are unchanged.
Raises:
ValueError: `params` has reserved keys already.
"""
if config is None or not isinstance(config, tpu_config.RunConfig):
raise ValueError(
'`config` must be provided with type `tpu_config.RunConfig`')
if params is not None and any(k in params for k in _RESERVED_PARAMS_KEYS):
raise ValueError('{} are reserved keys but existed in params {}.'.format(
_RESERVED_PARAMS_KEYS, params))
if use_tpu:
# Perform some very basic validations. More validations will be found in
# _InternalTPUContext.
if train_batch_size is None:
raise ValueError('`train_batch_size` cannot be `None`')
util_lib.check_positive_integer(train_batch_size, 'train_batch_size')
if (config.tpu_config.per_host_input_for_training is
tpu_config.InputPipelineConfig.PER_SHARD_V1 and
config.tpu_config.num_cores_per_replica):
raise ValueError(
'Model parallelism only supports per host input for training. '
'Please adjust TPURunconfig.per_host_input_for_training.')
if eval_batch_size is not None:
util_lib.check_positive_integer(eval_batch_size, 'eval_batch_size')
if predict_batch_size is not None:
util_lib.check_positive_integer(predict_batch_size,
'predict_batch_size')
# Verifies the model_fn signature according to Estimator framework.
estimator_lib._verify_model_fn_args(model_fn, params) # pylint: disable=protected-access
# We cannot store config and params in this constructor as parent
# constructor might change them, such as assigning a temp dir for
# config.model_dir.
model_function = self._augment_model_fn(
model_fn,
train_cache_fn,
eval_cache_fn,
batch_axis)
# Overwrite log_step_count_steps to disable TensorLoggingHook and
# StepCounterHook from being created in Estimator. TPUEstimator already
# added equivalent hooks in _augment_model_fn above.
self._log_every_n_steps = config.log_step_count_steps
config = config.replace(log_step_count_steps=None)
# Passing non-None params as wrapped model_fn has it.
params = params or {}
super(TPUEstimator, self).__init__(
model_fn=model_function,
model_dir=model_dir,
config=config,
params=params,
warm_start_from=warm_start_from)
self._iterations_per_training_loop = (
self._config.tpu_config.iterations_per_loop)
# All properties passed to _InternalTPUContext are immutable.
# pylint: disable=protected-access
self._ctx = tpu_context._get_tpu_context(
self._config, train_batch_size, eval_batch_size, predict_batch_size,
use_tpu, eval_on_tpu)
self._export_to_tpu = export_to_tpu
self._is_input_fn_invoked = None
self._rendezvous = {}
def _add_meta_graph_for_mode(self,
builder,
input_receiver_fn_map,
checkpoint_path,
save_variables=True,
mode=model_fn_lib.ModeKeys.PREDICT,
export_tags=None,
check_variables=True):
if self._export_to_tpu and mode != model_fn_lib.ModeKeys.PREDICT:
raise NotImplementedError(
'TPUEstimator only handles mode PREDICT for exporting '
'when `export_to_tpu` is `True`; '
'got {}.'.format(mode))
(super(TPUEstimator, self)._add_meta_graph_for_mode(
builder,
input_receiver_fn_map,
checkpoint_path,
save_variables,
mode=mode,
export_tags=export_tags,
check_variables=check_variables))
if self._export_to_tpu:
input_receiver_fn_map = {
_REWRITE_FOR_INFERENCE_MODE: input_receiver_fn_map[mode]
}
export_tags = [tag_constants.SERVING, tag_constants.TPU]
mode = _REWRITE_FOR_INFERENCE_MODE
# See b/110052256 for why `check_variables` is `False`.
(super(TPUEstimator, self)._add_meta_graph_for_mode(
builder,
input_receiver_fn_map,
checkpoint_path,
save_variables=False,
mode=mode,
export_tags=export_tags,
check_variables=False))
def _call_model_fn(self, features, labels, mode, config):
if mode == _REWRITE_FOR_INFERENCE_MODE:
return self._call_model_fn_for_inference(features, labels, mode, config)
else:
return super(TPUEstimator, self)._call_model_fn(features, labels, mode,
config)
def _call_model_fn_for_inference(self, features, labels, mode, config):
"""Wraps `_call_model_fn` for `export_savedmodel`."""
if mode != _REWRITE_FOR_INFERENCE_MODE:
raise ValueError('mode must be {}; '
'got {}.'.format(_REWRITE_FOR_INFERENCE_MODE, mode))
capture = _CapturedObject()
def computation():
"""Compute tpu tensors used in export_outputs.
Passed to rewrite_for_inference so that model_fn will be called under
the rewriting contexts. Only tpu tensors are returned, but export_outputs
and scaffold are captured.
Returns:
A list of Tensors used in export_outputs and not marked for
outside_compilation.
"""
# We should only call model fn once and it should be inside `computation`
# so that building the graph will happen under `rewrite_for_inference`.
mode = model_fn_lib.ModeKeys.PREDICT
estimator_spec = self._call_model_fn(features, labels, mode, config)
# We pick the TPU tensors out from `export_output` and later return them
# from `computation` for rewriting.
tensors_dict = collections.OrderedDict(
(k, _export_output_to_tensors(v))
for k, v in six.iteritems(estimator_spec.export_outputs))
tensors = nest.flatten(tensors_dict)
tpu_tensors = [t for t in tensors if t is not None]
# We cannot return anything other than `tpu_tensors` here so we capture
# the rest for later use.
capture.capture((estimator_spec, tensors_dict, tensors))
return tpu_tensors
tpu_tensors_on_cpu = tpu.rewrite_for_inference(computation)
estimator_spec, tensors_dict, tensors = capture.get()
# Reconstruct `tensors`, but with `tpu_tensors` replaced with
# `tpu_tensors_on_cpu`.
new_tensors = []
for t in tensors:
if t is None:
new_tensors.append(None)
else:
new_tensors.append(tpu_tensors_on_cpu.pop(0))
# Reconstruct `tensors_dict`.
new_tensors_dict = nest.pack_sequence_as(tensors_dict, new_tensors)
# Reconstruct `export_outputs`.
export_outputs = estimator_spec.export_outputs
new_export_outputs = collections.OrderedDict(
(k, _clone_export_output_with_tensors(export_outputs[k], v))
for k, v in six.iteritems(new_tensors_dict))
return estimator_spec._replace(export_outputs=new_export_outputs)
def _create_global_step(self, graph):
"""Creates a global step suitable for TPUs.
Args:
graph: The graph in which to create the global step.
Returns:
A global step `Tensor`.
Raises:
ValueError: if the global step tensor is already defined.
"""
return _create_global_step(graph)
def _convert_train_steps_to_hooks(self, steps, max_steps):
with self._ctx.with_mode(model_fn_lib.ModeKeys.TRAIN) as ctx:
if ctx.is_running_on_cpu():
return super(TPUEstimator, self)._convert_train_steps_to_hooks(
steps, max_steps)
# On TPU.
if steps is None and max_steps is None:
raise ValueError(
'For TPU training, one of `steps` or `max_steps` must be set. '
'Cannot be both `None`.')
# Estimator.train has explicit positiveness check.
if steps is not None:
util_lib.check_positive_integer(steps, 'Train steps')
if max_steps is not None:
util_lib.check_positive_integer(max_steps, 'Train max_steps')
return [
_TPUStopAtStepHook(self._iterations_per_training_loop, steps, max_steps)
]
def _convert_eval_steps_to_hooks(self, steps):
with self._ctx.with_mode(model_fn_lib.ModeKeys.EVAL) as ctx:
if ctx.is_running_on_cpu():
return super(TPUEstimator, self)._convert_eval_steps_to_hooks(steps)
if steps is None:
raise ValueError('Evaluate `steps` must be set on TPU. Cannot be `None`.')
util_lib.check_positive_integer(steps, 'Eval steps')
return [
evaluation._StopAfterNEvalsHook( # pylint: disable=protected-access
num_evals=steps),
_SetEvalIterationsHook(steps)
]
def _call_input_fn(self, input_fn, mode):
"""Calls the input function.
Args:
input_fn: The input function.
mode: ModeKeys
Returns:
In TPU mode, returns an input_fn to be called later in model_fn.
Otherwise, calls the input_fn and returns either fatures or
(features, labels).
Raises:
ValueError: if input_fn takes invalid arguments or does not have `params`.
"""
input_fn_args = function_utils.fn_args(input_fn)
config = self.config # a deep copy.
kwargs = {}
if 'params' in input_fn_args:
kwargs['params'] = self.params # a deep copy.
else:
raise ValueError('input_fn ({}) does not include params argument, '
'required by TPUEstimator to pass batch size as '
'params["batch_size"]'.format(input_fn))
if 'config' in input_fn_args:
kwargs['config'] = config
if 'mode' in input_fn_args:
kwargs['mode'] = mode
# Records the fact input_fn has been invoked.
self._is_input_fn_invoked = True
with self._ctx.with_mode(mode) as ctx:
# Setting the batch size in params first. This helps user to have same
# input_fn for use_tpu=True/False.
batch_size_for_input_fn = ctx.batch_size_for_input_fn
if batch_size_for_input_fn is not None:
_add_item_to_params(kwargs['params'], _BATCH_SIZE_KEY,
batch_size_for_input_fn)
# For export_savedmodel, input_fn is never passed to Estimator. So,
# `is_export_mode` must be False.
if ctx.is_running_on_cpu(is_export_mode=False):
with ops.device('/device:CPU:0'):
return input_fn(**kwargs)
# For TPU computation, input_fn should be invoked in a tf.while_loop for
# performance. While constructing the tf.while_loop, the structure of
# inputs returned by the `input_fn` needs to be recorded. The structure
# includes whether features or labels is dict or single Tensor, dict keys,
# tensor shapes, and dtypes. The recorded structure is used to create the
# infeed dequeue ops, which must be wrapped and passed as a Fn, called
# inside the TPU computation, as the TPU computation is wrapped inside a
# tf.while_loop also. So, we either pass input_fn to model_fn or pass
# dequeue_fn to model_fn. Here, `input_fn` is passed directly as
# `features` in `model_fn` signature.
def _input_fn(ctx):
_add_item_to_params(kwargs['params'], _CTX_KEY, ctx)
return input_fn(**kwargs)
return _input_fn
def _validate_features_in_predict_input(self, result):
"""Skip the validation.
For TPUEstimator, we do not need to check the result type. `_InputPipeline`
has stronger check. Parent class's check generates confusing warning msg.
Args:
result: `features` returned by input_fn.
"""
pass
def train(self,
input_fn,
hooks=None,
steps=None,
max_steps=None,
saving_listeners=None):
rendezvous = error_handling.ErrorRendezvous(num_sources=3)
self._rendezvous[model_fn_lib.ModeKeys.TRAIN] = rendezvous
try:
return super(TPUEstimator, self).train(
input_fn=input_fn,
hooks=hooks,
steps=steps,
max_steps=max_steps,
saving_listeners=saving_listeners)
except Exception: # pylint: disable=broad-except
rendezvous.record_error('training_loop', sys.exc_info())
finally:
rendezvous.record_done('training_loop')
rendezvous.raise_errors()
def evaluate(self,
input_fn,
steps=None,
hooks=None,
checkpoint_path=None,
name=None):
rendezvous = error_handling.ErrorRendezvous(num_sources=3)
self._rendezvous[model_fn_lib.ModeKeys.EVAL] = rendezvous
try:
return super(TPUEstimator, self).evaluate(
input_fn,
steps=steps,
hooks=hooks,
checkpoint_path=checkpoint_path,
name=name)
except Exception: # pylint: disable=broad-except
rendezvous.record_error('evaluation_loop', sys.exc_info())
finally:
rendezvous.record_done('evaluation_loop')
rendezvous.raise_errors()
def predict(self,
input_fn,
predict_keys=None,
hooks=None,
checkpoint_path=None,
yield_single_examples=True):
rendezvous = error_handling.ErrorRendezvous(num_sources=3)
self._rendezvous[model_fn_lib.ModeKeys.PREDICT] = rendezvous
try:
for result in super(TPUEstimator, self).predict(
input_fn=input_fn,
predict_keys=predict_keys,
hooks=hooks,
checkpoint_path=checkpoint_path,
yield_single_examples=yield_single_examples):
yield result
except Exception: # pylint: disable=broad-except
rendezvous.record_error('prediction_loop', sys.exc_info())
finally:
rendezvous.record_done('prediction_loop')
rendezvous.raise_errors()
rendezvous.record_done('prediction_loop')
rendezvous.raise_errors()
def _augment_model_fn(self, model_fn, train_cache_fn, eval_cache_fn, batch_axis):
"""Returns a new model_fn, which wraps the TPU support."""
def _model_fn(features, labels, mode, config, params):
"""A Estimator `model_fn` for TPUEstimator."""
with self._ctx.with_mode(mode) as ctx:
model_fn_wrapper = _ModelFnWrapper(model_fn, train_cache_fn,
eval_cache_fn, config, params, ctx)
# `input_fn` is called in `train()`, `evaluate()`, and `predict()`,
# but not in `export_savedmodel()`.
if self._is_input_fn_invoked:
is_export_mode = False
else:
is_export_mode = True
# Clear the bit.
self._is_input_fn_invoked = None
# examples_hook is added to training_hooks for both CPU and TPU
# execution.
if self._log_every_n_steps is not None:
examples_hook = ExamplesPerSecondHook(
ctx.global_batch_size,
output_dir=self.model_dir,
every_n_steps=self._log_every_n_steps)
if ctx.is_running_on_cpu(is_export_mode=is_export_mode):
logging.info('Running %s on CPU', mode)
estimator_spec = model_fn_wrapper.call_without_tpu(
features, labels, is_export_mode=is_export_mode)
if self._log_every_n_steps is not None:
estimator_spec = estimator_spec._replace(
training_hooks=estimator_spec.training_hooks + (examples_hook,))
return estimator_spec
assert labels is None, '`labels` passed to `model_fn` must be `None`.'
# TPUEstimator._call_input_fn passes `input_fn` as features to here.
assert callable(features), '`input_fn` is not callable.'
input_fn = features
input_holders = _InputPipeline(input_fn, batch_axis, ctx)
enqueue_ops, dequeue_fn, input_hooks, run_infeed_loop_on_coordinator = (
input_holders.generate_infeed_enqueue_ops_and_dequeue_fn())
graph = ops.get_default_graph()
for enqueue_op in enqueue_ops:
if isinstance(enqueue_op, list):
graph.get_collection_ref(_TPU_ENQUEUE_OPS).extend(enqueue_op)
else:
graph.add_to_collection(_TPU_ENQUEUE_OPS, enqueue_op)
if mode == model_fn_lib.ModeKeys.TRAIN:
compile_op, loss, host_call, scaffold, training_hooks = (
_train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn))
host_ops = host_call.create_tpu_hostcall()
if host_ops is None:
host_ops = []
shutdown_hooks = []
shutdown_mode = os.environ.get('TF_TPU_GRACEFUL_SHUTDOWN_MODE',
'shutdown_worker')
if shutdown_mode:
if shutdown_mode == 'shutdown_worker':
finalizer_hooks = [
session_support.ShutdownLameWorkers(timeout_ms=60 * 1000),
]
elif shutdown_mode == 'shutdown_computation':
finalizer_hooks = [
session_support.RestartComputation(timeout_ms=60 * 1000),
]
else:
raise ValueError(
'Unknown TF_TPU_GRACEFUL_SHUTDOWN_MODE "%s"' % shutdown_mode)
shutdown_hooks.append(
session_support.GracefulShutdownHook(
checkpoint_prefix=self.model_dir + '/model.ckpt',
on_shutdown_hooks=finalizer_hooks))
with ops.control_dependencies([loss]):
global_step = array_ops.identity(training.get_global_step())
hooks = input_hooks + shutdown_hooks
hooks.extend([
TPUInfeedOutfeedSessionHook(
ctx,
enqueue_ops,
host_ops,
tpu_compile_op=compile_op,
run_infeed_loop_on_coordinator=(
run_infeed_loop_on_coordinator),
rendezvous=self._rendezvous[mode],
master=self._config.master,
session_config=self._session_config,
),
InstallSignalHandlerHook()
])
if self._log_every_n_steps is not None:
logging_hook_frequency = ( # Divide and round up
(self._log_every_n_steps +
self._config.tpu_config.iterations_per_loop - 1) //
self._config.tpu_config.iterations_per_loop)
hooks.append(
training.LoggingTensorHook({
'loss': array_ops.identity(loss),
'step': global_step,
},
every_n_iter=logging_hook_frequency))
examples_hook._set_steps_per_run( # pylint: disable=protected-access
self._config.tpu_config.iterations_per_loop)
hooks.append(examples_hook)
if training_hooks:
hooks.extend(training_hooks)
chief_hooks = []
if (self._config.save_checkpoints_secs or
self._config.save_checkpoints_steps):
checkpoint_hook = training.CheckpointSaverHook(
self.model_dir,
save_secs=self._config.save_checkpoints_secs,
save_steps=self._config.save_checkpoints_steps,
scaffold=scaffold)
checkpoint_hook._set_steps_per_run( # pylint: disable=protected-access
self._config.tpu_config.iterations_per_loop)
chief_hooks.append(checkpoint_hook)
summary.scalar(model_fn_lib.LOSS_METRIC_KEY, loss)
with ops.control_dependencies([loss]):
update_ops = _sync_variables_ops(ctx)
# Validate the TPU training graph to catch basic errors
_validate_tpu_training_graph()
train_op = control_flow_ops.group(*update_ops)
graph.add_to_collection(_TPU_TRAIN_OP, train_op)
return model_fn_lib.EstimatorSpec(
mode,
loss=loss,
training_chief_hooks=chief_hooks,
training_hooks=hooks,
train_op=train_op,
scaffold=scaffold)
if mode == model_fn_lib.ModeKeys.EVAL:
compile_op, total_loss, host_calls, scaffold, eval_hooks = (
_eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn))
iterations_per_loop_var = _create_or_get_iterations_per_loop()
mean_loss = math_ops.div(
total_loss,
math_ops.cast(iterations_per_loop_var, dtype=total_loss.dtype))
with ops.control_dependencies([mean_loss]):
# After TPU evaluation computation is done (the mean_loss tensor),
# reads all variables back from TPU and updates the eval step
# counter properly
internal_ops_to_run = _sync_variables_ops(ctx)
internal_ops_to_run.append(
_increase_eval_step_op(iterations_per_loop_var))
host_call_ret = host_calls.create_tpu_hostcall()
eval_metric_ops = {}
eval_update_ops = []
eval_metrics = host_call_ret.get('eval_metrics', {})
if eval_metrics:
# Creates a dummy metric update_op for all metrics. Estimator
# expects all metrics in `eval_metric_ops` have update_op and calls
# them one by one. The real metric update_ops are invoked in a
# separated thread. So, here give Estimator the dummy op for all
# metrics.
with ops.control_dependencies(internal_ops_to_run):
dummy_update_op = control_flow_ops.no_op()
for k, v in eval_metrics.items():
eval_metric_ops[k] = (v[0], dummy_update_op)
eval_update_ops.append(v[1])
else:
# If no eval metrics are passed, create an identity node for the
# loss and add `internal_ops_to_run` to its dependencies. So
# `internal_ops_to_run` can be executed.
with ops.control_dependencies(internal_ops_to_run):
mean_loss = array_ops.identity(mean_loss)
if 'host_call' not in host_call_ret:
host_ops = []
else:
host_ops = host_call_ret['host_call']
hooks = [
TPUInfeedOutfeedSessionHook(
ctx,
enqueue_ops,
eval_update_ops + host_ops,
tpu_compile_op=compile_op,
run_infeed_loop_on_coordinator=(
run_infeed_loop_on_coordinator),
rendezvous=self._rendezvous[mode],
master=self._config.evaluation_master,
session_config=self._session_config,
)] + input_hooks
if eval_hooks:
hooks.extend(eval_hooks)
return model_fn_lib.EstimatorSpec(
mode,
loss=mean_loss,
evaluation_hooks=hooks,
eval_metric_ops=eval_metric_ops,
scaffold=scaffold)
# Predict
assert mode == model_fn_lib.ModeKeys.PREDICT
(compile_op, dummy_predict_op, host_calls,
scaffold, prediction_hooks) = _predict_on_tpu_system(
ctx, model_fn_wrapper, dequeue_fn)
with ops.control_dependencies([dummy_predict_op]):
internal_ops_to_run = _sync_variables_ops(ctx)
with ops.control_dependencies(internal_ops_to_run):
dummy_predict_op = control_flow_ops.no_op()
# In train and evaluation, the main TPU program is passed to monitored
# training session to run. Infeed enqueue and outfeed dequeue are
# executed in side threads. This is not the configuration for
# prediction mode.
#
# For prediction, the Estimator executes the EstimatorSpec.predictions
# directly and yield the element (via generator) to call site. So, the
# outfeed based prediction must be passed to MonitoredSession directly.
# Other parts of the TPU execution are organized as follows.
#
# 1. All outfeed based Tensors must be grouped with predictions Tensors
# to form a single invocation. This avoid the issue we might trigger
# multiple outfeeds incorrectly. To achieve this, `host_call` is
# placed in control_dependencies of `stopping_signals`, and
# `stopping_signals` is passed into _StoppingPredictHook, which sets
# the `stopping_signals` as SessionRunArgs. MonitoredSession merges
# all SessionRunArgs with the fetch in session.run together.
#
# 2. The TPU program (dummy_predict_op) and enqueue_ops (infeed Enqueue)
# are grouped together. They will be launched once and only once in
# side threads and they quit naturally according to the SAME stopping
# condition.
enqueue_ops.append(dummy_predict_op)
host_call_ret = host_calls.create_tpu_hostcall()
if 'host_call' not in host_call_ret:
host_ops = []
else:
host_ops = host_call_ret['host_call']
predictions = host_call_ret['predictions']
_verify_cross_hosts_transfer_size(
predictions,
message=(
'The estimated size for TPUEstimatorSpec.predictions is too '
'large.'))
signals = host_call_ret['signals']
with ops.control_dependencies(host_ops):
host_ops = [] # Empty, we do do not need it anymore.
scalar_stopping_signal = _StopSignals.as_scalar_stopping_signal(
signals)
predictions = _PaddingSignals.slice_tensor_or_dict(
predictions, signals)
hooks = [
_StoppingPredictHook(scalar_stopping_signal),
TPUInfeedOutfeedSessionHookForPrediction(
ctx, enqueue_ops, host_ops, rendezvous=self._rendezvous[mode],
tpu_compile_op=compile_op,
master=self._config.master,
session_config=self._session_config),
] + input_hooks
if prediction_hooks:
hooks.extend(prediction_hooks)
return model_fn_lib.EstimatorSpec(
mode,
prediction_hooks=hooks,
predictions=predictions,
scaffold=scaffold)
return _model_fn
def _export_output_to_tensors(export_output):
"""Get a list of `Tensors` used in `export_output`.
Args:
export_output: an `ExportOutput` object such as `ClassificationOutput`,
`RegressionOutput`, or `PredictOutput`.
Returns:
a list of tensors used in export_output.
Raises:
ValueError: if `export_output` is not one of `ClassificationOutput`,
`RegressionOutput`, or `PredictOutput`.
"""
if isinstance(export_output, export_output_lib.ClassificationOutput):
return [export_output.scores, export_output.classes]
elif isinstance(export_output, export_output_lib.RegressionOutput):
return [export_output.value]
elif isinstance(export_output, export_output_lib.PredictOutput):
return list(export_output.outputs.values())
else:
raise ValueError(
'`export_output` must be have type `ClassificationOutput`, '
'`RegressionOutput`, or `PredictOutput`; got {}.'.format(export_output))
def _clone_export_output_with_tensors(export_output, tensors):
"""Clones `export_output` but with new `tensors`.
Args:
export_output: an `ExportOutput` object such as `ClassificationOutput`,
`RegressionOutput`, or `PredictOutput`.
tensors: a list of `Tensors` used to construct a new `export_output`.
Returns:
A dict similar to `export_output` but with `tensors`.
Raises:
ValueError: if `export_output` is not one of `ClassificationOutput`,
`RegressionOutput`, or `PredictOutput`.
"""
if isinstance(export_output, export_output_lib.ClassificationOutput):
if len(tensors) != 2:
raise ValueError('tensors must be of length 2; '
'got {}.'.format(len(tensors)))
return export_output_lib.ClassificationOutput(*tensors)
elif isinstance(export_output, export_output_lib.RegressionOutput):
if len(tensors) != 1:
raise ValueError('tensors must be of length 1; '
'got {}'.format(len(tensors)))
return export_output_lib.RegressionOutput(*tensors)
elif isinstance(export_output, export_output_lib.PredictOutput):
return export_output_lib.PredictOutput(
dict(zip(export_output.outputs.keys(), tensors)))
else:
raise ValueError(
'`export_output` must be have type `ClassificationOutput`, '
'`RegressionOutput`, or `PredictOutput`; got {}.'.format(export_output))
def _eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn):
"""Executes `model_fn_wrapper` multiple times on all TPU shards."""
iterations_per_loop_var = _create_or_get_iterations_per_loop()
(single_tpu_eval_step, host_calls, captured_scaffold_fn, captured_eval_hooks
) = model_fn_wrapper.convert_to_single_tpu_eval_step(dequeue_fn)
def multi_tpu_eval_steps_on_single_shard():
loop_vars = [_ZERO_LOSS]
if model_fn_wrapper._eval_cache_fn is not None:
batch_size = ctx.global_batch_size
num_shards = ctx._config._tpu_config.num_shards
loop_vars += model_fn_wrapper._eval_cache_fn(batch_size // num_shards)
return training_loop.repeat(
iterations_per_loop_var,
single_tpu_eval_step,
loop_vars)
compile_op, ret = tpu.split_compile_and_shard(
multi_tpu_eval_steps_on_single_shard,
inputs=[],
num_shards=ctx.num_replicas,
outputs_from_all_shards=False,
device_assignment=ctx.device_assignment)
loss = ret[0]
scaffold = _get_scaffold(captured_scaffold_fn)
return compile_op, loss, host_calls, scaffold, captured_eval_hooks.get()
def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn):
"""Executes `model_fn_wrapper` multiple times on all TPU shards."""
iterations_per_loop_var = _create_or_get_iterations_per_loop()
(single_tpu_train_step, host_call, captured_scaffold_fn,
captured_training_hooks) = (
model_fn_wrapper.convert_to_single_tpu_train_step(dequeue_fn))
def multi_tpu_train_steps_on_single_shard():
loop_vars = [_INITIAL_LOSS]
if model_fn_wrapper._train_cache_fn is not None:
batch_size = ctx.global_batch_size
num_shards = ctx._config._tpu_config.num_shards
loop_vars += model_fn_wrapper._train_cache_fn(batch_size // num_shards)
return training_loop.repeat(
iterations_per_loop_var,
single_tpu_train_step,
loop_vars)
compile_op, ret = tpu.split_compile_and_shard(
multi_tpu_train_steps_on_single_shard,
inputs=[],
num_shards=ctx.num_replicas,
outputs_from_all_shards=False,
device_assignment=ctx.device_assignment)
loss = ret[0]
scaffold = _get_scaffold(captured_scaffold_fn)
return compile_op, loss, host_call, scaffold, captured_training_hooks.get()
def _predict_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn):
"""Executes `model_fn_wrapper` multiple times on all TPU shards."""
(single_tpu_predict_step, host_calls, captured_scaffold_fn,
captured_predict_hooks
) = model_fn_wrapper.convert_to_single_tpu_predict_step(dequeue_fn)
def multi_tpu_predict_steps_on_single_shard():
def cond(scalar_stopping_signal):
return math_ops.logical_not(
_StopSignals.should_stop(scalar_stopping_signal))
inputs = [_StopSignals.NON_STOPPING_SIGNAL]
outputs = training_loop.while_loop(
cond, single_tpu_predict_step, inputs=inputs, name=b'loop')
return outputs
(compile_op, dummy_predict_op,) = tpu.split_compile_and_shard(
multi_tpu_predict_steps_on_single_shard,
inputs=[],
num_shards=ctx.num_replicas,
outputs_from_all_shards=False,
device_assignment=ctx.device_assignment)
dummy_predict_op = dummy_predict_op[0]
scaffold = _get_scaffold(captured_scaffold_fn)
return (compile_op, dummy_predict_op, host_calls, scaffold,
captured_predict_hooks.get())
def _wrap_computation_in_while_loop(device, op_fn):
"""Wraps the ops generated by `op_fn` in tf.while_loop."""
def computation(i):
with ops.control_dependencies(op_fn()):
return i + 1
iterations_per_loop_var = _create_or_get_iterations_per_loop()
# By setting parallel_iterations=1, the parallel execution in while_loop is
# basically turned off.
with ops.device(device):
iterations = array_ops.identity(iterations_per_loop_var)
return control_flow_ops.while_loop(
lambda i: i < iterations,
computation, [constant_op.constant(0)],
parallel_iterations=1)
def _wrap_computation_in_while_loop_with_stopping_signals(device, op_fn):
"""Wraps the ops generated by `op_fn` in tf.while_loop."""
def cond(scalar_stopping_signal):
return math_ops.logical_not(
_StopSignals.should_stop(scalar_stopping_signal))
def computation(unused_scalar_stopping_signal):
return_value = op_fn()
execute_ops = return_value['ops']
signals = return_value['signals']
with ops.control_dependencies(execute_ops):
return _StopSignals.as_scalar_stopping_signal(signals)
# By setting parallel_iterations=1, the parallel execution in while_loop is
# basically turned off.
with ops.device(device):
return control_flow_ops.while_loop(
cond,
computation, [_StopSignals.NON_STOPPING_SIGNAL],
parallel_iterations=1)
def _validate_tpu_training_graph():
"""Validate graph before running distributed training.
Raises:
ValueError: If the graph seems invalid for running on device
"""
operations = ops.get_default_graph().get_operations()
# Check if there is atleast one CrossReplicaSum operation in the graph
# This should be introduced by using the CrossShardOptimizer wrapper
cross_replica_sum_ops = [
o for o in operations if o.type == _CROSS_REPLICA_SUM_OP
]
if not cross_replica_sum_ops:
raise ValueError(
'CrossShardOptimizer must be used for model training on TPUs.')
class _CapturedObject(object):
"""A placeholder to capture an object.
This is useful when we need to capture a Python object in the Tensorflow
control flow body function and use it outside the control flow.
"""
def __init__(self):
self._object = None
self._captured = False
def capture(self, o):
if self._captured:
raise RuntimeError(
'InternalError: Object can capture only once. Please file bug.')
self._captured = True
self._object = o
def get(self):
if not self._captured:
raise RuntimeError(
'InternalError: Object is not captured properly before `get`. '
'Please file bug.')
return self._object
def _get_scaffold(captured_scaffold_fn):
"""Retrieves the Scaffold from `captured_scaffold_fn`."""
with _CapturingContext(message='Inside scaffold_fn'):
scaffold_fn = captured_scaffold_fn.get()
if scaffold_fn:
scaffold = scaffold_fn()
if scaffold is None:
raise ValueError(
'TPUEstimatorSpec.scaffold_fn returns None, which is not allowed')
else:
scaffold = None
if scaffold:
wrapped_finalize = scaffold.finalize
def _finalize():
with _CapturingContext('Inside Scaffold.finalize'):
wrapped_finalize()
scaffold.finalize = _finalize
return scaffold
class _CapturingContext(control_flow_ops.ControlFlowContext):
"""Tracks references to Tensors defined in TPU replication."""
def __init__(self, message):
control_flow_ops.ControlFlowContext.__init__(self)
self._message = message
def to_control_flow_context_def(self, context_def, export_scope=None):
# pylint: disable=useless-super-delegation
# NOTE(slebedev): the method is required by `ControlFlowContext`.
super(_CapturingContext, self).to_control_flow_context_def(
context_def, export_scope)
def AddOp(self, op): # pylint: disable=invalid-name
for c in op.inputs:
if tpu._TPU_REPLICATE_ATTR in c.op.node_def.attr: # pylint: disable=protected-access
raise ValueError('{}: Op {} depends on TPU computation {}, '
'which is not allowed.'.format(self._message, op, c))
def __enter__(self):
# pylint: disable=protected-access
self._g = ops.get_default_graph()
self._old = self._g._get_control_flow_context()
self._g._set_control_flow_context(self)
# pylint: enable=protected-access
def __exit__(self, _, __, ___): # pylint: disable=invalid-name
self._g._set_control_flow_context(self._old) # pylint: disable=protected-access
class _Inputs(object):
"""A data structure representing the input_fn returned values.
This also supports the returned value from input_fn as `Dataset`.
"""
def __init__(self, features=None, labels=None, dataset=None, signals=None):
if dataset is not None and (features is not None or labels is not None or
signals is not None):
raise RuntimeError('Internal Error: Either (features and labels) or '
'dataset should be provided, not both. Please file '
'bug')
self._features = features
self._labels = labels
self._signals = signals
self._dataset = dataset
self._iterator = None
@staticmethod
def from_input_fn(return_values):
"""Returns an `_Inputs` instance according to `input_fn` return value."""
if isinstance(return_values, dataset_ops.DatasetV2):
dataset = return_values
return _Inputs(dataset=dataset)
features, labels = _Inputs._parse_inputs(return_values)
return _Inputs(features, labels)
@staticmethod
def _parse_inputs(return_values):
if isinstance(return_values, tuple):
features, labels = return_values
else:
features, labels = return_values, None
return features, labels
@property
def is_dataset(self):
"""Returns True if the return value from input_fn is Dataset."""
return self._dataset is not None
def dataset_initializer(self):
"""Returns the dataset's initializer.
The initializer must be run before calling `features_and_labels`.
"""
self._iterator = dataset_ops.make_initializable_iterator(self._dataset)
return self._iterator.initializer
def features_and_labels(self):
"""Gets `features` and `labels`."""
if self.is_dataset:
if self._iterator is None:
raise RuntimeError('Internal error: Must run dataset_initializer '
'before calling features_and_labels(). Please file '
'a bug!')
return _Inputs._parse_inputs(self._iterator.get_next())
return (self._features, self._labels)
def signals(self):
return self._signals
@property
def dataset(self):
return self._dataset
class _InputsWithStoppingSignals(_Inputs):
"""Inputs with `_StopSignals` inserted into the dataset."""
def __init__(self,
dataset,
batch_size,
add_padding=False,
num_invocations_per_step=1):
assert dataset is not None
user_provided_dataset = dataset.map(
_InputsWithStoppingSignals.insert_stopping_signal(
stop=False, batch_size=batch_size, add_padding=add_padding))
if num_invocations_per_step == 1:
final_batch_dataset = dataset.take(1).map(
_InputsWithStoppingSignals.insert_stopping_signal(
stop=True, batch_size=batch_size, add_padding=add_padding))
else:
# We append (2 * num_invocations_per_step - 1) batches for exhausting the
# user_provided_dataset and stop properly.
# For example, if num_invocations_per_step is 2, we append 3 additional
# padding batches: b1, b2, b3.
# If user_provided_dataset contains two batches: a1, a2
# Step 1: [a1, a2]
# Step 2: [b1, b2] -> STOP
# If user_provided_dataset contains three batches: a1, a2, a3.
# The training loops:
# Step 1: [a1, a2]
# Step 2: [a3, b1]
# Step 3: [b2, b3] -> STOP.
final_batch_dataset = dataset.take(1).map(
_InputsWithStoppingSignals.insert_stopping_signal(
stop=True, batch_size=batch_size, add_padding=add_padding))
final_batch_dataset = final_batch_dataset.repeat(
2 * num_invocations_per_step - 1)
def _set_mask(data_dict):
signals = data_dict['signals']
signals['padding_mask'] = array_ops.ones_like(signals['padding_mask'])
data_dict['signals'] = signals
return data_dict
# Mask out the extra batch.
final_batch_dataset = final_batch_dataset.map(_set_mask)
dataset = user_provided_dataset.concatenate(final_batch_dataset).prefetch(2)
super(_InputsWithStoppingSignals, self).__init__(dataset=dataset)
self._current_inputs = None
def features_and_labels(self):
if self._current_inputs is not None:
raise RuntimeError(
'Internal Error: The previous inputs have not been properly '
'consumed. First call features_and_labels, then call signals.')
inputs_with_signals = self._iterator.get_next()
features = inputs_with_signals['features']
labels = inputs_with_signals.get('labels')
self._current_inputs = inputs_with_signals
return features, labels
def signals(self):
"""Returns the `Signals` from `_Inputs`."""
if self._current_inputs is None:
raise RuntimeError(
'Internal Error: The current inputs have not been properly '
'generated. First call features_and_labels, then call signals.')
signals = self._current_inputs['signals']
self._current_inputs = None
return signals
@staticmethod
def insert_stopping_signal(stop, batch_size, add_padding=False):
"""Inserts stopping_signal into dataset via _map_fn.
Here we change the data structure in the dataset, such that the return value
is a dictionary now and `features`, `labels`, and `signals` are three
distinguished keys in that dict. This provides a better structure, which
eases the process to decompose the inputs (see `features_and_labels`).
Args:
stop: bool, state of current stopping signals.
batch_size: int, batch size.
add_padding: bool, whether to pad the tensor to full batch size.
Returns:
A map_fn passed to dataset.map API.
"""
def _map_fn(*args):
"""The map fn to insert signals."""
if len(args) == 1:
# Unpack the single Tensor/dict argument as features. This is required
# for the input_fn returns no labels.
args = args[0]
features, labels = _Inputs._parse_inputs(args)
new_input_dict = {}
if add_padding:
padding_mask, features, labels = (
_PaddingSignals.pad_features_and_labels(features, labels,
batch_size))
new_input_dict['features'] = features
if labels is not None:
new_input_dict['labels'] = labels
else:
new_input_dict['features'] = features
if labels is not None:
new_input_dict['labels'] = labels
padding_mask = None
new_input_dict['signals'] = _StopSignals(
stop=stop, batch_size=batch_size,
padding_mask=padding_mask).as_dict()
return new_input_dict
return _map_fn
class _StopSignals(object):
"""Signals class holding all logic to handle TPU stopping condition."""
NON_STOPPING_SIGNAL = False
STOPPING_SIGNAL = True
def __init__(self, stop, batch_size, padding_mask=None):
self._stop = stop
self._batch_size = batch_size
self._padding_mask = padding_mask
def as_dict(self):
"""Returns the signals as Python dict."""
shape = [self._batch_size, 1]
dtype = dtypes.bool
if self._stop:
stopping = array_ops.ones(shape=shape, dtype=dtype)
else:
stopping = array_ops.zeros(shape=shape, dtype=dtype)
signals = {'stopping': stopping}
if self._padding_mask is not None:
signals['padding_mask'] = self._padding_mask
return signals
@staticmethod
def as_scalar_stopping_signal(signals):
return array_ops.identity(signals['stopping'][0][0])
@staticmethod
def should_stop(scalar_stopping_signal):
"""Detects whether scalar_stopping_signal indicates stopping."""
if isinstance(scalar_stopping_signal, ops.Tensor):
# STOPPING_SIGNAL is a constant True. Here, the logical_and is just the TF
# way to express the bool check whether scalar_stopping_signal is True.
return math_ops.logical_and(scalar_stopping_signal,
_StopSignals.STOPPING_SIGNAL)
else:
# For non Tensor case, it is used in SessionRunHook. So, we cannot modify
# the graph anymore. Here, we use pure Python.
return bool(scalar_stopping_signal)
class _PaddingSignals(object):
"""Signals class holding all logic to handle padding."""
@staticmethod
def pad_features_and_labels(features, labels, batch_size):
"""Pads out the batch dimension of features and labels."""
real_batch_size = array_ops.shape(
_PaddingSignals._find_any_tensor(features))[0]
batch_size_tensor = constant_op.constant(batch_size, dtypes.int32)
check_greater = check_ops.assert_greater_equal(
batch_size_tensor,
real_batch_size,
data=(batch_size_tensor, real_batch_size),
message='The real batch size should not be greater than batch_size.')
with ops.control_dependencies([check_greater]):
missing_count = batch_size_tensor - real_batch_size
def pad_single_tensor(tensor):
"""Pads out the batch dimension of a tensor to the complete batch_size."""
rank = len(tensor.shape)
assert rank > 0
padding = array_ops.stack([[0, missing_count]] + [[0, 0]] * (rank - 1))
padded_shape = (batch_size,) + tuple(tensor.shape[1:])
padded_tensor = array_ops.pad(tensor, padding)
padded_tensor.set_shape(padded_shape)
return padded_tensor
def nest_pad(tensor_or_dict):
return nest.map_structure(pad_single_tensor, tensor_or_dict)
features = nest_pad(features)
if labels is not None:
labels = nest_pad(labels)
padding_mask = _PaddingSignals._padding_mask(real_batch_size, missing_count,
batch_size)
return padding_mask, features, labels
@staticmethod
def slice_tensor_or_dict(tensor_or_dict, signals):
"""Slice the real Tensors according to padding mask in signals."""
padding_mask = signals['padding_mask']
batch_size = array_ops.shape(padding_mask)[0]
def verify_batch_size(tensor):
check_batch_size = math_ops.equal(batch_size, tensor.shape[0])
with ops.control_dependencies([check_batch_size]):
return array_ops.identity(tensor)
def slice_single_tensor(tensor):
rank = len(tensor.shape)
assert rank > 0
real_batch_size = batch_size - math_ops.reduce_sum(padding_mask)
return verify_batch_size(tensor)[0:real_batch_size]
# As we split the Tensors to all TPU cores and concat them back, it is
# important to ensure the real data is placed before padded ones, i.e.,
# order is preserved. By that, the sliced padding mask should have all 0's.
# If this assertion failed, # the slice logic here would not hold.
sliced_padding_mask = slice_single_tensor(padding_mask)
assert_padding_mask = math_ops.equal(
math_ops.reduce_sum(sliced_padding_mask), 0)
with ops.control_dependencies([assert_padding_mask]):
should_stop = _StopSignals.should_stop(
_StopSignals.as_scalar_stopping_signal(signals))
is_full_batch = math_ops.equal(math_ops.reduce_sum(padding_mask), 0)
def slice_fn(tensor):
# If the current batch is full batch or part of stopping signals, we do
# not need to slice to save performance.
return control_flow_ops.cond(
math_ops.logical_or(should_stop, is_full_batch),
(lambda: verify_batch_size(tensor)),
(lambda: slice_single_tensor(tensor)))
return nest.map_structure(slice_fn, tensor_or_dict)
@staticmethod
def _find_any_tensor(batch_features):
tensors = [
x for x in nest.flatten(batch_features) if isinstance(x, ops.Tensor)
]
if not tensors:
raise ValueError('Cannot find any Tensor in features dict.')
return tensors[0]
@staticmethod
def _padding_mask(real_batch_size, missing_count, batch_size):
padding_mask = array_ops.concat([
array_ops.zeros((real_batch_size,), dtype=dtypes.int32),
array_ops.ones((missing_count,), dtype=dtypes.int32)
],
axis=0)
padding_mask.set_shape((batch_size,))
return padding_mask
def _verify_cross_hosts_transfer_size(tensor_dict, message):
total_size = 0
tensor_structure = {}
for key, tensor in tensor_dict.items():
shape = tensor.shape
size = np.product(shape) * tensor.dtype.size
tensor_structure[key] = shape
total_size += size
if total_size >= _ONE_GIGABYTE:
raise ValueError(
'{} The transfer size is larger than the protobuf limit. Please '
'consider to use Tensors with smaller shapes or reduce batch '
'size. Given:\n'
'{}'.format(
message, '\n'.join([
' -- Key: {}, Shape: {}'.format(k, v)
for k, v in tensor_structure.items()
])))
def _add_item_to_params(params, key, value):
"""Adds a new item into `params`."""
if isinstance(params, hparam.HParams):
# For HParams, we need to use special API.
if key in params:
params.set_hparam(key, value)
else:
params.add_hparam(key, value)
else:
# Now params is Python dict.
params[key] = value
def export_estimator_savedmodel(estimator,
export_dir_base,
serving_input_receiver_fn,
assets_extra=None,
as_text=False,
checkpoint_path=None,
strip_default_attrs=False):
"""Export `Estimator` trained model for TPU inference.
Args:
estimator: `Estimator` with which model has been trained.
export_dir_base: A string containing a directory in which to create
timestamped subdirectories containing exported SavedModels.
serving_input_receiver_fn: A function that takes no argument and returns a
`ServingInputReceiver` or `TensorServingInputReceiver`.
assets_extra: A dict specifying how to populate the assets.extra directory
within the exported SavedModel, or `None` if no extra assets are needed.
as_text: whether to write the SavedModel proto in text format.
checkpoint_path: The checkpoint path to export. If `None` (the default),
the most recent checkpoint found within the model directory is chosen.
strip_default_attrs: Boolean. If `True`, default-valued attributes will be
removed from the NodeDefs.
Returns:
The string path to the exported directory.
"""
# `TPUEstimator` requires `tpu_config.RunConfig`, so we cannot use
# `estimator.config`.
config = tpu_config.RunConfig(model_dir=estimator.model_dir)
est = TPUEstimator(
estimator._model_fn, # pylint: disable=protected-access
config=config,
params=estimator.params,
use_tpu=True,
train_batch_size=2048, # Does not matter.
eval_batch_size=2048, # Does not matter.
)
return est.export_savedmodel(export_dir_base, serving_input_receiver_fn,
assets_extra, as_text, checkpoint_path,
strip_default_attrs)
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/xlnet.py | Python | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import json
import os
import tensorflow as tf
import modeling
def _get_initializer(FLAGS):
"""Get variable intializer."""
if FLAGS.init == "uniform":
initializer = tf.initializers.random_uniform(
minval=-FLAGS.init_range,
maxval=FLAGS.init_range,
seed=None)
elif FLAGS.init == "normal":
initializer = tf.initializers.random_normal(
stddev=FLAGS.init_std,
seed=None)
else:
raise ValueError("Initializer {} not supported".format(FLAGS.init))
return initializer
class XLNetConfig(object):
"""XLNetConfig contains hyperparameters that are specific to a model checkpoint;
i.e., these hyperparameters should be the same between
pretraining and finetuning.
The following hyperparameters are defined:
n_layer: int, the number of layers.
d_model: int, the hidden size.
n_head: int, the number of attention heads.
d_head: int, the dimension size of each attention head.
d_inner: int, the hidden size in feed-forward layers.
ff_activation: str, "relu" or "gelu".
untie_r: bool, whether to untie the biases in attention.
n_token: int, the vocab size.
"""
def __init__(self, FLAGS=None, json_path=None):
"""Constructing an XLNetConfig.
One of FLAGS or json_path should be provided."""
assert FLAGS is not None or json_path is not None
self.keys = ["n_layer", "d_model", "n_head", "d_head", "d_inner",
"ff_activation", "untie_r", "n_token"]
if FLAGS is not None:
self.init_from_flags(FLAGS)
if json_path is not None:
self.init_from_json(json_path)
def init_from_flags(self, FLAGS):
for key in self.keys:
setattr(self, key, getattr(FLAGS, key))
def init_from_json(self, json_path):
with tf.gfile.Open(json_path) as f:
json_data = json.load(f)
for key in self.keys:
setattr(self, key, json_data[key])
def to_json(self, json_path):
"""Save XLNetConfig to a json file."""
json_data = {}
for key in self.keys:
json_data[key] = getattr(self, key)
json_dir = os.path.dirname(json_path)
if not tf.gfile.Exists(json_dir):
tf.gfile.MakeDirs(json_dir)
with tf.gfile.Open(json_path, "w") as f:
json.dump(json_data, f, indent=4, sort_keys=True)
def create_run_config(is_training, is_finetune, FLAGS):
kwargs = dict(
is_training=is_training,
use_tpu=FLAGS.use_tpu,
use_bfloat16=FLAGS.use_bfloat16,
dropout=FLAGS.dropout,
dropatt=FLAGS.dropatt,
init=FLAGS.init,
init_range=FLAGS.init_range,
init_std=FLAGS.init_std,
clamp_len=FLAGS.clamp_len)
if not is_finetune:
kwargs.update(dict(
mem_len=FLAGS.mem_len,
reuse_len=FLAGS.reuse_len,
bi_data=FLAGS.bi_data,
clamp_len=FLAGS.clamp_len,
same_length=FLAGS.same_length))
return RunConfig(**kwargs)
class RunConfig(object):
"""RunConfig contains hyperparameters that could be different
between pretraining and finetuning.
These hyperparameters can also be changed from run to run.
We store them separately from XLNetConfig for flexibility.
"""
def __init__(self, is_training, use_tpu, use_bfloat16, dropout, dropatt,
init="normal", init_range=0.1, init_std=0.02, mem_len=None,
reuse_len=None, bi_data=False, clamp_len=-1, same_length=False):
"""
Args:
is_training: bool, whether in training mode.
use_tpu: bool, whether TPUs are used.
use_bfloat16: bool, use bfloat16 instead of float32.
dropout: float, dropout rate.
dropatt: float, dropout rate on attention probabilities.
init: str, the initialization scheme, either "normal" or "uniform".
init_range: float, initialize the parameters with a uniform distribution
in [-init_range, init_range]. Only effective when init="uniform".
init_std: float, initialize the parameters with a normal distribution
with mean 0 and stddev init_std. Only effective when init="normal".
mem_len: int, the number of tokens to cache.
reuse_len: int, the number of tokens in the currect batch to be cached
and reused in the future.
bi_data: bool, whether to use bidirectional input pipeline.
Usually set to True during pretraining and False during finetuning.
clamp_len: int, clamp all relative distances larger than clamp_len.
-1 means no clamping.
same_length: bool, whether to use the same attention length for each token.
"""
self.init = init
self.init_range = init_range
self.init_std = init_std
self.is_training = is_training
self.dropout = dropout
self.dropatt = dropatt
self.use_tpu = use_tpu
self.use_bfloat16 = use_bfloat16
self.mem_len = mem_len
self.reuse_len = reuse_len
self.bi_data = bi_data
self.clamp_len = clamp_len
self.same_length = same_length
class XLNetModel(object):
"""A wrapper of the XLNet model used during both pretraining and finetuning."""
def __init__(self, xlnet_config, run_config, input_ids, seg_ids, input_mask,
mems=None, perm_mask=None, target_mapping=None, inp_q=None,
**kwargs):
"""
Args:
xlnet_config: XLNetConfig,
run_config: RunConfig,
input_ids: int32 Tensor in shape [len, bsz], the input token IDs.
seg_ids: int32 Tensor in shape [len, bsz], the input segment IDs.
input_mask: float32 Tensor in shape [len, bsz], the input mask.
0 for real tokens and 1 for padding.
mems: a list of float32 Tensors in shape [mem_len, bsz, d_model], memory
from previous batches. The length of the list equals n_layer.
If None, no memory is used.
perm_mask: float32 Tensor in shape [len, len, bsz].
If perm_mask[i, j, k] = 0, i attend to j in batch k;
if perm_mask[i, j, k] = 1, i does not attend to j in batch k.
If None, each position attends to all the others.
target_mapping: float32 Tensor in shape [num_predict, len, bsz].
If target_mapping[i, j, k] = 1, the i-th predict in batch k is
on the j-th token.
Only used during pretraining for partial prediction.
Set to None during finetuning.
inp_q: float32 Tensor in shape [len, bsz].
1 for tokens with losses and 0 for tokens without losses.
Only used during pretraining for two-stream attention.
Set to None during finetuning.
"""
initializer = _get_initializer(run_config)
tfm_args = dict(
n_token=xlnet_config.n_token,
initializer=initializer,
attn_type="bi",
n_layer=xlnet_config.n_layer,
d_model=xlnet_config.d_model,
n_head=xlnet_config.n_head,
d_head=xlnet_config.d_head,
d_inner=xlnet_config.d_inner,
ff_activation=xlnet_config.ff_activation,
untie_r=xlnet_config.untie_r,
is_training=run_config.is_training,
use_bfloat16=run_config.use_bfloat16,
use_tpu=run_config.use_tpu,
dropout=run_config.dropout,
dropatt=run_config.dropatt,
mem_len=run_config.mem_len,
reuse_len=run_config.reuse_len,
bi_data=run_config.bi_data,
clamp_len=run_config.clamp_len,
same_length=run_config.same_length
)
input_args = dict(
inp_k=input_ids,
seg_id=seg_ids,
input_mask=input_mask,
mems=mems,
perm_mask=perm_mask,
target_mapping=target_mapping,
inp_q=inp_q)
tfm_args.update(input_args)
with tf.variable_scope("model", reuse=tf.AUTO_REUSE):
(self.output, self.new_mems, self.lookup_table
) = modeling.transformer_xl(**tfm_args)
self.input_mask = input_mask
self.initializer = initializer
self.xlnet_config = xlnet_config
self.run_config = run_config
def get_pooled_out(self, summary_type, use_summ_proj=True):
"""
Args:
summary_type: str, "last", "first", "mean", or "attn". The method
to pool the input to get a vector representation.
use_summ_proj: bool, whether to use a linear projection during pooling.
Returns:
float32 Tensor in shape [bsz, d_model], the pooled representation.
"""
xlnet_config = self.xlnet_config
run_config = self.run_config
with tf.variable_scope("model", reuse=tf.AUTO_REUSE):
summary = modeling.summarize_sequence(
summary_type=summary_type,
hidden=self.output,
d_model=xlnet_config.d_model,
n_head=xlnet_config.n_head,
d_head=xlnet_config.d_head,
dropout=run_config.dropout,
dropatt=run_config.dropatt,
is_training=run_config.is_training,
input_mask=self.input_mask,
initializer=self.initializer,
use_proj=use_summ_proj)
return summary
def get_sequence_output(self):
"""
Returns:
float32 Tensor in shape [len, bsz, d_model]. The last layer hidden
representation of XLNet.
"""
return self.output
def get_new_memory(self):
"""
Returns:
list of float32 Tensors in shape [mem_len, bsz, d_model], the new
memory that concatenates the previous memory with the current input
representations.
The length of the list equals n_layer.
"""
return self.new_mems
def get_embedding_table(self):
"""
Returns:
float32 Tensor in shape [n_token, d_model]. The embedding lookup table.
Used for tying embeddings between input and output layers.
"""
return self.lookup_table
def get_initializer(self):
"""
Returns:
A tf initializer. Used to initialize variables in layers on top of XLNet.
"""
return self.initializer
| ymcui/Chinese-XLNet | 1,650 | Pre-Trained Chinese XLNet（中文XLNet预训练模型） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/modeling.py | Python | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""The main BERT model and related functions."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import copy
import json
import math
import re
import six
import tensorflow as tf
class BertConfig(object):
"""Configuration for `BertModel`."""
def __init__(self,
vocab_size,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
hidden_act="gelu",
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
max_position_embeddings=512,
type_vocab_size=16,
initializer_range=0.02):
"""Constructs BertConfig.
Args:
vocab_size: Vocabulary size of `inputs_ids` in `BertModel`.
hidden_size: Size of the encoder layers and the pooler layer.
num_hidden_layers: Number of hidden layers in the Transformer encoder.
num_attention_heads: Number of attention heads for each attention layer in
the Transformer encoder.
intermediate_size: The size of the "intermediate" (i.e., feed-forward)
layer in the Transformer encoder.
hidden_act: The non-linear activation function (function or string) in the
encoder and pooler.
hidden_dropout_prob: The dropout probability for all fully connected
layers in the embeddings, encoder, and pooler.
attention_probs_dropout_prob: The dropout ratio for the attention
probabilities.
max_position_embeddings: The maximum sequence length that this model might
ever be used with. Typically set this to something large just in case
(e.g., 512 or 1024 or 2048).
type_vocab_size: The vocabulary size of the `token_type_ids` passed into
`BertModel`.
initializer_range: The stdev of the truncated_normal_initializer for
initializing all weight matrices.
"""
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.hidden_act = hidden_act
self.intermediate_size = intermediate_size
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_probs_dropout_prob = attention_probs_dropout_prob
self.max_position_embeddings = max_position_embeddings
self.type_vocab_size = type_vocab_size
self.initializer_range = initializer_range
@classmethod
def from_dict(cls, json_object):
"""Constructs a `BertConfig` from a Python dictionary of parameters."""
config = BertConfig(vocab_size=None)
for (key, value) in six.iteritems(json_object):
config.__dict__[key] = value
return config
@classmethod
def from_json_file(cls, json_file):
"""Constructs a `BertConfig` from a json file of parameters."""
with tf.gfile.GFile(json_file, "r") as reader:
text = reader.read()
return cls.from_dict(json.loads(text))
def to_dict(self):
"""Serializes this instance to a Python dictionary."""
output = copy.deepcopy(self.__dict__)
return output
def to_json_string(self):
"""Serializes this instance to a JSON string."""
return json.dumps(self.to_dict(), indent=2, sort_keys=True) + "\n"
class BertModel(object):
"""BERT model ("Bidirectional Embedding Representations from a Transformer").
Example usage:
```python
# Already been converted into WordPiece token ids
input_ids = tf.constant([[31, 51, 99], [15, 5, 0]])
input_mask = tf.constant([[1, 1, 1], [1, 1, 0]])
token_type_ids = tf.constant([[0, 0, 1], [0, 2, 0]])
config = modeling.BertConfig(vocab_size=32000, hidden_size=512,
num_hidden_layers=8, num_attention_heads=6, intermediate_size=1024)
model = modeling.BertModel(config=config, is_training=True,
input_ids=input_ids, input_mask=input_mask, token_type_ids=token_type_ids)
label_embeddings = tf.get_variable(...)
pooled_output = model.get_pooled_output()
logits = tf.matmul(pooled_output, label_embeddings)
...
```
"""
def __init__(self,
config,
is_training,
input_ids,
input_mask=None,
token_type_ids=None,
use_one_hot_embeddings=True,
scope=None,
embedding_trainable=True):
"""Constructor for BertModel.
Args:
config: `BertConfig` instance.
is_training: bool. rue for training model, false for eval model. Controls
whether dropout will be applied.
input_ids: int32 Tensor of shape [batch_size, seq_length].
input_mask: (optional) int32 Tensor of shape [batch_size, seq_length].
token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
use_one_hot_embeddings: (optional) bool. Whether to use one-hot word
embeddings or tf.embedding_lookup() for the word embeddings. On the TPU,
it is must faster if this is True, on the CPU or GPU, it is faster if
this is False.
scope: (optional) variable scope. Defaults to "bert".
# embedding_trainable: indicate if the embedding matrix is trainable (default: True)
Raises:
ValueError: The config is invalid or one of the input tensor shapes
is invalid.
"""
config = copy.deepcopy(config)
if not is_training:
config.hidden_dropout_prob = 0.0
config.attention_probs_dropout_prob = 0.0
input_shape = get_shape_list(input_ids, expected_rank=2)
batch_size = input_shape[0]
seq_length = input_shape[1]
if input_mask is None:
input_mask = tf.ones(shape=[batch_size, seq_length], dtype=tf.int32)
if token_type_ids is None:
token_type_ids = tf.zeros(shape=[batch_size, seq_length], dtype=tf.int32)
with tf.variable_scope(scope, default_name="bert"):
with tf.variable_scope("embeddings"):
# Perform embedding lookup on the word ids.
(self.embedding_output, self.embedding_table) = embedding_lookup(
input_ids=input_ids,
vocab_size=config.vocab_size,
embedding_size=config.hidden_size,
initializer_range=config.initializer_range,
word_embedding_name="word_embeddings",
use_one_hot_embeddings=use_one_hot_embeddings,
trainable=embedding_trainable)
# Add positional embeddings and token type embeddings, then layer
# normalize and perform dropout.
self.embedding_output = embedding_postprocessor(
input_tensor=self.embedding_output,
use_token_type=True,
token_type_ids=token_type_ids,
token_type_vocab_size=config.type_vocab_size,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=config.initializer_range,
max_position_embeddings=config.max_position_embeddings,
dropout_prob=config.hidden_dropout_prob)
with tf.variable_scope("encoder"):
# This converts a 2D mask of shape [batch_size, seq_length] to a 3D
# mask of shape [batch_size, seq_length, seq_length] which is used
# for the attention scores.
self.attention_mask = create_attention_mask_from_input_mask(
input_ids, input_mask)
# Run the stacked transformer.
# `sequence_output` shape = [batch_size, seq_length, hidden_size].
self.all_encoder_layers = transformer_model(
input_tensor=self.embedding_output,
attention_mask=self.attention_mask,
hidden_size=config.hidden_size,
num_hidden_layers=config.num_hidden_layers,
num_attention_heads=config.num_attention_heads,
intermediate_size=config.intermediate_size,
intermediate_act_fn=get_activation(config.hidden_act),
hidden_dropout_prob=config.hidden_dropout_prob,
attention_probs_dropout_prob=config.attention_probs_dropout_prob,
initializer_range=config.initializer_range,
do_return_all_layers=True)
self.sequence_output = self.all_encoder_layers[-1]
# The "pooler" converts the encoded sequence tensor of shape
# [batch_size, seq_length, hidden_size] to a tensor of shape
# [batch_size, hidden_size]. This is necessary for segment-level
# (or segment-pair-level) classification tasks where we need a fixed
# dimensional representation of the segment.
with tf.variable_scope("pooler"):
# We "pool" the model by simply taking the hidden state corresponding
# to the first token. We assume that this has been pre-trained
first_token_tensor = tf.squeeze(self.sequence_output[:, 0:1, :], axis=1)
self.pooled_output = tf.layers.dense(
first_token_tensor,
config.hidden_size,
activation=tf.tanh,
kernel_initializer=create_initializer(config.initializer_range))
def get_attention_mask(self):
return self.attention_mask
def get_pooled_output(self):
return self.pooled_output
def get_sequence_output(self):
"""Gets final hidden layer of encoder.
Returns:
float Tensor of shape [batch_size, seq_length, hidden_size] corresponding
to the final hidden of the transformer encoder.
"""
return self.sequence_output
def get_all_encoder_layers(self):
return self.all_encoder_layers
def get_embedding_output(self):
"""Gets output of the embedding lookup (i.e., input to the transformer).
Returns:
float Tensor of shape [batch_size, seq_length, hidden_size] corresponding
to the output of the embedding layer, after summing the word
embeddings with the positional embeddings and the token type embeddings,
then performing layer normalization. This is the input to the transformer.
"""
return self.embedding_output
def get_embedding_table(self):
return self.embedding_table
def gelu(input_tensor):
"""Gaussian Error Linear Unit.
This is a smoother version of the RELU.
Original paper: https://arxiv.org/abs/1606.08415
Args:
input_tensor: float Tensor to perform activation.
Returns:
`input_tensor` with the GELU activation applied.
"""
cdf = 0.5 * (1.0 + tf.erf(input_tensor / tf.sqrt(2.0)))
return input_tensor * cdf
def get_activation(activation_string):
"""Maps a string to a Python function, e.g., "relu" => `tf.nn.relu`.
Args:
activation_string: String name of the activation function.
Returns:
A Python function corresponding to the activation function. If
`activation_string` is None, empty, or "linear", this will return None.
If `activation_string` is not a string, it will return `activation_string`.
Raises:
ValueError: The `activation_string` does not correspond to a known
activation.
"""
# We assume that anything that"s not a string is already an activation
# function, so we just return it.
if not isinstance(activation_string, six.string_types):
return activation_string
if not activation_string:
return None
act = activation_string.lower()
if act == "linear":
return None
elif act == "relu":
return tf.nn.relu
elif act == "gelu":
return gelu
elif act == "tanh":
return tf.tanh
else:
raise ValueError("Unsupported activation: %s" % act)
def get_assignment_map_from_checkpoint(tvars, init_checkpoint):
"""Compute the union of the current variables and checkpoint variables."""
assignment_map = {}
initialized_variable_names = {}
name_to_variable = collections.OrderedDict()
for var in tvars:
name = var.name
m = re.match("^(.*):\\d+$", name)
if m is not None:
name = m.group(1)
name_to_variable[name] = var
init_vars = tf.train.list_variables(init_checkpoint)
assignment_map = collections.OrderedDict()
for x in init_vars:
(name, var) = (x[0], x[1])
if name not in name_to_variable:
continue
assignment_map[name] = name
initialized_variable_names[name] = 1
initialized_variable_names[name + ":0"] = 1
return (assignment_map, initialized_variable_names)
def dropout(input_tensor, dropout_prob):
"""Perform dropout.
Args:
input_tensor: float Tensor.
dropout_prob: Python float. The probability of dropping out a value (NOT of
*keeping* a dimension as in `tf.nn.dropout`).
Returns:
A version of `input_tensor` with dropout applied.
"""
if dropout_prob is None or dropout_prob == 0.0:
return input_tensor
output = tf.nn.dropout(input_tensor, 1.0 - dropout_prob)
return output
def layer_norm(input_tensor, name=None):
"""Run layer normalization on the last dimension of the tensor."""
return tf.contrib.layers.layer_norm(
inputs=input_tensor, begin_norm_axis=-1, begin_params_axis=-1, scope=name)
def layer_norm_and_dropout(input_tensor, dropout_prob, name=None):
"""Runs layer normalization followed by dropout."""
output_tensor = layer_norm(input_tensor, name)
output_tensor = dropout(output_tensor, dropout_prob)
return output_tensor
def create_initializer(initializer_range=0.02):
"""Creates a `truncated_normal_initializer` with the given range."""
return tf.truncated_normal_initializer(stddev=initializer_range)
def embedding_lookup(input_ids,
vocab_size,
embedding_size=128,
initializer_range=0.02,
word_embedding_name="word_embeddings",
use_one_hot_embeddings=False,
trainable=True):
"""Looks up words embeddings for id tensor.
Args:
input_ids: int32 Tensor of shape [batch_size, seq_length] containing word
ids.
vocab_size: int. Size of the embedding vocabulary.
embedding_size: int. Width of the word embeddings.
initializer_range: float. Embedding initialization range.
word_embedding_name: string. Name of the embedding table.
use_one_hot_embeddings: bool. If True, use one-hot method for word
embeddings. If False, use `tf.nn.embedding_lookup()`. One hot is better
for TPUs.
Returns:
float Tensor of shape [batch_size, seq_length, embedding_size].
"""
# This function assumes that the input is of shape [batch_size, seq_length,
# num_inputs].
#
# If the input is a 2D tensor of shape [batch_size, seq_length], we
# reshape to [batch_size, seq_length, 1].
if input_ids.shape.ndims == 2:
input_ids = tf.expand_dims(input_ids, axis=[-1])
embedding_table = tf.get_variable(
name=word_embedding_name,
shape=[vocab_size, embedding_size],
initializer=create_initializer(initializer_range),
trainable=trainable)
if use_one_hot_embeddings:
flat_input_ids = tf.reshape(input_ids, [-1])
one_hot_input_ids = tf.one_hot(flat_input_ids, depth=vocab_size)
output = tf.matmul(one_hot_input_ids, embedding_table)
else:
output = tf.nn.embedding_lookup(embedding_table, input_ids)
input_shape = get_shape_list(input_ids)
output = tf.reshape(output,
input_shape[0:-1] + [input_shape[-1] * embedding_size])
return (output, embedding_table)
def embedding_postprocessor(input_tensor,
use_token_type=False,
token_type_ids=None,
token_type_vocab_size=16,
token_type_embedding_name="token_type_embeddings",
use_position_embeddings=True,
position_embedding_name="position_embeddings",
initializer_range=0.02,
max_position_embeddings=512,
dropout_prob=0.1):
"""Performs various post-processing on a word embedding tensor.
Args:
input_tensor: float Tensor of shape [batch_size, seq_length,
embedding_size].
use_token_type: bool. Whether to add embeddings for `token_type_ids`.
token_type_ids: (optional) int32 Tensor of shape [batch_size, seq_length].
Must be specified if `use_token_type` is True.
token_type_vocab_size: int. The vocabulary size of `token_type_ids`.
token_type_embedding_name: string. The name of the embedding table variable
for token type ids.
use_position_embeddings: bool. Whether to add position embeddings for the
position of each token in the sequence.
position_embedding_name: string. The name of the embedding table variable
for positional embeddings.
initializer_range: float. Range of the weight initialization.
max_position_embeddings: int. Maximum sequence length that might ever be
used with this model. This can be longer than the sequence length of
input_tensor, but cannot be shorter.
dropout_prob: float. Dropout probability applied to the final output tensor.
Returns:
float tensor with same shape as `input_tensor`.
Raises:
ValueError: One of the tensor shapes or input values is invalid.
"""
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
width = input_shape[2]
output = input_tensor
if use_token_type:
if token_type_ids is None:
raise ValueError("`token_type_ids` must be specified if"
"`use_token_type` is True.")
token_type_table = tf.get_variable(
name=token_type_embedding_name,
shape=[token_type_vocab_size, width],
initializer=create_initializer(initializer_range))
# This vocab will be small so we always do one-hot here, since it is always
# faster for a small vocabulary.
flat_token_type_ids = tf.reshape(token_type_ids, [-1])
one_hot_ids = tf.one_hot(flat_token_type_ids, depth=token_type_vocab_size)
token_type_embeddings = tf.matmul(one_hot_ids, token_type_table)
token_type_embeddings = tf.reshape(token_type_embeddings,
[batch_size, seq_length, width])
output += token_type_embeddings
if use_position_embeddings:
assert_op = tf.assert_less_equal(seq_length, max_position_embeddings)
with tf.control_dependencies([assert_op]):
full_position_embeddings = tf.get_variable(
name=position_embedding_name,
shape=[max_position_embeddings, width],
initializer=create_initializer(initializer_range))
# Since the position embedding table is a learned variable, we create it
# using a (long) sequence length `max_position_embeddings`. The actual
# sequence length might be shorter than this, for faster training of
# tasks that do not have long sequences.
#
# So `full_position_embeddings` is effectively an embedding table
# for position [0, 1, 2, ..., max_position_embeddings-1], and the current
# sequence has positions [0, 1, 2, ... seq_length-1], so we can just
# perform a slice.
position_embeddings = tf.slice(full_position_embeddings, [0, 0],
[seq_length, -1])
num_dims = len(output.shape.as_list())
# Only the last two dimensions are relevant (`seq_length` and `width`), so
# we broadcast among the first dimensions, which is typically just
# the batch size.
position_broadcast_shape = []
for _ in range(num_dims - 2):
position_broadcast_shape.append(1)
position_broadcast_shape.extend([seq_length, width])
position_embeddings = tf.reshape(position_embeddings,
position_broadcast_shape)
output += position_embeddings
output = layer_norm_and_dropout(output, dropout_prob)
return output
def create_attention_mask_from_input_mask(from_tensor, to_mask):
"""Create 3D attention mask from a 2D tensor mask.
Args:
from_tensor: 2D or 3D Tensor of shape [batch_size, from_seq_length, ...].
to_mask: int32 Tensor of shape [batch_size, to_seq_length].
Returns:
float Tensor of shape [batch_size, from_seq_length, to_seq_length].
"""
from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])
batch_size = from_shape[0]
from_seq_length = from_shape[1]
to_shape = get_shape_list(to_mask, expected_rank=2)
to_seq_length = to_shape[1]
to_mask = tf.cast(
tf.reshape(to_mask, [batch_size, 1, to_seq_length]), tf.float32)
# We don't assume that `from_tensor` is a mask (although it could be). We
# don't actually care if we attend *from* padding tokens (only *to* padding)
# tokens so we create a tensor of all ones.
#
# `broadcast_ones` = [batch_size, from_seq_length, 1]
broadcast_ones = tf.ones(
shape=[batch_size, from_seq_length, 1], dtype=tf.float32)
# Here we broadcast along two dimensions to create the mask.
mask = broadcast_ones * to_mask
return mask
def attention_layer(from_tensor,
to_tensor,
attention_mask=None,
num_attention_heads=1,
size_per_head=512,
query_act=None,
key_act=None,
value_act=None,
attention_probs_dropout_prob=0.0,
initializer_range=0.02,
do_return_2d_tensor=False,
batch_size=None,
from_seq_length=None,
to_seq_length=None):
"""Performs multi-headed attention from `from_tensor` to `to_tensor`.
This is an implementation of multi-headed attention based on "Attention
is all you Need". If `from_tensor` and `to_tensor` are the same, then
this is self-attention. Each timestep in `from_tensor` attends to the
corresponding sequence in `to_tensor`, and returns a fixed-with vector.
This function first projects `from_tensor` into a "query" tensor and
`to_tensor` into "key" and "value" tensors. These are (effectively) a list
of tensors of length `num_attention_heads`, where each tensor is of shape
[batch_size, seq_length, size_per_head].
Then, the query and key tensors are dot-producted and scaled. These are
softmaxed to obtain attention probabilities. The value tensors are then
interpolated by these probabilities, then concatenated back to a single
tensor and returned.
In practice, the multi-headed attention are done with transposes and
reshapes rather than actual separate tensors.
Args:
from_tensor: float Tensor of shape [batch_size, from_seq_length,
from_width].
to_tensor: float Tensor of shape [batch_size, to_seq_length, to_width].
attention_mask: (optional) int32 Tensor of shape [batch_size,
from_seq_length, to_seq_length]. The values should be 1 or 0. The
attention scores will effectively be set to -infinity for any positions in
the mask that are 0, and will be unchanged for positions that are 1.
num_attention_heads: int. Number of attention heads.
size_per_head: int. Size of each attention head.
query_act: (optional) Activation function for the query transform.
key_act: (optional) Activation function for the key transform.
value_act: (optional) Activation function for the value transform.
attention_probs_dropout_prob: (optional) float. Dropout probability of the
attention probabilities.
initializer_range: float. Range of the weight initializer.
do_return_2d_tensor: bool. If True, the output will be of shape [batch_size
* from_seq_length, num_attention_heads * size_per_head]. If False, the
output will be of shape [batch_size, from_seq_length, num_attention_heads
* size_per_head].
batch_size: (Optional) int. If the input is 2D, this might be the batch size
of the 3D version of the `from_tensor` and `to_tensor`.
from_seq_length: (Optional) If the input is 2D, this might be the seq length
of the 3D version of the `from_tensor`.
to_seq_length: (Optional) If the input is 2D, this might be the seq length
of the 3D version of the `to_tensor`.
Returns:
float Tensor of shape [batch_size, from_seq_length,
num_attention_heads * size_per_head]. (If `do_return_2d_tensor` is
true, this will be of shape [batch_size * from_seq_length,
num_attention_heads * size_per_head]).
Raises:
ValueError: Any of the arguments or tensor shapes are invalid.
"""
def transpose_for_scores(input_tensor, batch_size, num_attention_heads,
seq_length, width):
output_tensor = tf.reshape(
input_tensor, [batch_size, seq_length, num_attention_heads, width])
output_tensor = tf.transpose(output_tensor, [0, 2, 1, 3])
return output_tensor
from_shape = get_shape_list(from_tensor, expected_rank=[2, 3])
to_shape = get_shape_list(to_tensor, expected_rank=[2, 3])
if len(from_shape) != len(to_shape):
raise ValueError(
"The rank of `from_tensor` must match the rank of `to_tensor`.")
if len(from_shape) == 3:
batch_size = from_shape[0]
from_seq_length = from_shape[1]
to_seq_length = to_shape[1]
elif len(from_shape) == 2:
if (batch_size is None or from_seq_length is None or to_seq_length is None):
raise ValueError(
"When passing in rank 2 tensors to attention_layer, the values "
"for `batch_size`, `from_seq_length`, and `to_seq_length` "
"must all be specified.")
# Scalar dimensions referenced here:
# B = batch size (number of sequences)
# F = `from_tensor` sequence length
# T = `to_tensor` sequence length
# N = `num_attention_heads`
# H = `size_per_head`
from_tensor_2d = reshape_to_matrix(from_tensor)
to_tensor_2d = reshape_to_matrix(to_tensor)
# `query_layer` = [B*F, N*H]
query_layer = tf.layers.dense(
from_tensor_2d,
num_attention_heads * size_per_head,
activation=query_act,
name="query",
kernel_initializer=create_initializer(initializer_range))
# `key_layer` = [B*T, N*H]
key_layer = tf.layers.dense(
to_tensor_2d,
num_attention_heads * size_per_head,
activation=key_act,
name="key",
kernel_initializer=create_initializer(initializer_range))
# `value_layer` = [B*T, N*H]
value_layer = tf.layers.dense(
to_tensor_2d,
num_attention_heads * size_per_head,
activation=value_act,
name="value",
kernel_initializer=create_initializer(initializer_range))
# `query_layer` = [B, N, F, H]
query_layer = transpose_for_scores(query_layer, batch_size,
num_attention_heads, from_seq_length,
size_per_head)
# `key_layer` = [B, N, T, H]
key_layer = transpose_for_scores(key_layer, batch_size, num_attention_heads,
to_seq_length, size_per_head)
# Take the dot product between "query" and "key" to get the raw
# attention scores.
# `attention_scores` = [B, N, F, T]
attention_scores = tf.matmul(query_layer, key_layer, transpose_b=True)
attention_scores = tf.multiply(attention_scores,
1.0 / math.sqrt(float(size_per_head)))
if attention_mask is not None:
# `attention_mask` = [B, 1, F, T]
attention_mask = tf.expand_dims(attention_mask, axis=[1])
# Since attention_mask is 1.0 for positions we want to attend and 0.0 for
# masked positions, this operation will create a tensor which is 0.0 for
# positions we want to attend and -10000.0 for masked positions.
adder = (1.0 - tf.cast(attention_mask, tf.float32)) * -10000.0
# Since we are adding it to the raw scores before the softmax, this is
# effectively the same as removing these entirely.
attention_scores += adder
# Normalize the attention scores to probabilities.
# `attention_probs` = [B, N, F, T]
attention_probs = tf.nn.softmax(attention_scores)
# This is actually dropping out entire tokens to attend to, which might
# seem a bit unusual, but is taken from the original Transformer paper.
attention_probs = dropout(attention_probs, attention_probs_dropout_prob)
# `value_layer` = [B, T, N, H]
value_layer = tf.reshape(
value_layer,
[batch_size, to_seq_length, num_attention_heads, size_per_head])
# `value_layer` = [B, N, T, H]
value_layer = tf.transpose(value_layer, [0, 2, 1, 3])
# `context_layer` = [B, N, F, H]
context_layer = tf.matmul(attention_probs, value_layer)
# `context_layer` = [B, F, N, H]
context_layer = tf.transpose(context_layer, [0, 2, 1, 3])
if do_return_2d_tensor:
# `context_layer` = [B*F, N*V]
context_layer = tf.reshape(
context_layer,
[batch_size * from_seq_length, num_attention_heads * size_per_head])
else:
# `context_layer` = [B, F, N*V]
context_layer = tf.reshape(
context_layer,
[batch_size, from_seq_length, num_attention_heads * size_per_head])
return context_layer
def transformer_model(input_tensor,
attention_mask=None,
hidden_size=768,
num_hidden_layers=12,
num_attention_heads=12,
intermediate_size=3072,
intermediate_act_fn=gelu,
hidden_dropout_prob=0.1,
attention_probs_dropout_prob=0.1,
initializer_range=0.02,
do_return_all_layers=False):
"""Multi-headed, multi-layer Transformer from "Attention is All You Need".
This is almost an exact implementation of the original Transformer encoder.
See the original paper:
https://arxiv.org/abs/1706.03762
Also see:
https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py
Args:
input_tensor: float Tensor of shape [batch_size, seq_length, hidden_size].
attention_mask: (optional) int32 Tensor of shape [batch_size, seq_length,
seq_length], with 1 for positions that can be attended to and 0 in
positions that should not be.
hidden_size: int. Hidden size of the Transformer.
num_hidden_layers: int. Number of layers (blocks) in the Transformer.
num_attention_heads: int. Number of attention heads in the Transformer.
intermediate_size: int. The size of the "intermediate" (a.k.a., feed
forward) layer.
intermediate_act_fn: function. The non-linear activation function to apply
to the output of the intermediate/feed-forward layer.
hidden_dropout_prob: float. Dropout probability for the hidden layers.
attention_probs_dropout_prob: float. Dropout probability of the attention
probabilities.
initializer_range: float. Range of the initializer (stddev of truncated
normal).
do_return_all_layers: Whether to also return all layers or just the final
layer.
Returns:
float Tensor of shape [batch_size, seq_length, hidden_size], the final
hidden layer of the Transformer.
Raises:
ValueError: A Tensor shape or parameter is invalid.
"""
if hidden_size % num_attention_heads != 0:
raise ValueError(
"The hidden size (%d) is not a multiple of the number of attention "
"heads (%d)" % (hidden_size, num_attention_heads))
attention_head_size = int(hidden_size / num_attention_heads)
input_shape = get_shape_list(input_tensor, expected_rank=3)
batch_size = input_shape[0]
seq_length = input_shape[1]
input_width = input_shape[2]
# The Transformer performs sum residuals on all layers so the input needs
# to be the same as the hidden size.
if input_width != hidden_size:
raise ValueError("The width of the input tensor (%d) != hidden size (%d)" %
(input_width, hidden_size))
# We keep the representation as a 2D tensor to avoid re-shaping it back and
# forth from a 3D tensor to a 2D tensor. Re-shapes are normally free on
# the GPU/CPU but may not be free on the TPU, so we want to minimize them to
# help the optimizer.
prev_output = reshape_to_matrix(input_tensor)
all_layer_outputs = []
for layer_idx in range(num_hidden_layers):
with tf.variable_scope("layer_%d" % layer_idx):
layer_input = prev_output
with tf.variable_scope("attention"):
attention_heads = []
with tf.variable_scope("self"):
attention_head = attention_layer(
from_tensor=layer_input,
to_tensor=layer_input,
attention_mask=attention_mask,
num_attention_heads=num_attention_heads,
size_per_head=attention_head_size,
attention_probs_dropout_prob=attention_probs_dropout_prob,
initializer_range=initializer_range,
do_return_2d_tensor=True,
batch_size=batch_size,
from_seq_length=seq_length,
to_seq_length=seq_length)
attention_heads.append(attention_head)
attention_output = None
if len(attention_heads) == 1:
attention_output = attention_heads[0]
else:
# In the case where we have other sequences, we just concatenate
# them to the self-attention head before the projection.
attention_output = tf.concat(attention_heads, axis=-1)
# Run a linear projection of `hidden_size` then add a residual
# with `layer_input`.
with tf.variable_scope("output"):
attention_output = tf.layers.dense(
attention_output,
hidden_size,
kernel_initializer=create_initializer(initializer_range))
attention_output = dropout(attention_output, hidden_dropout_prob)
attention_output = layer_norm(attention_output + layer_input)
# The activation is only applied to the "intermediate" hidden layer.
with tf.variable_scope("intermediate"):
intermediate_output = tf.layers.dense(
attention_output,
intermediate_size,
activation=intermediate_act_fn,
kernel_initializer=create_initializer(initializer_range))
# Down-project back to `hidden_size` then add the residual.
with tf.variable_scope("output"):
layer_output = tf.layers.dense(
intermediate_output,
hidden_size,
kernel_initializer=create_initializer(initializer_range))
layer_output = dropout(layer_output, hidden_dropout_prob)
layer_output = layer_norm(layer_output + attention_output)
prev_output = layer_output
all_layer_outputs.append(layer_output)
if do_return_all_layers:
final_outputs = []
for layer_output in all_layer_outputs:
final_output = reshape_from_matrix(layer_output, input_shape)
final_outputs.append(final_output)
return final_outputs
else:
final_output = reshape_from_matrix(prev_output, input_shape)
return final_output
def get_shape_list(tensor, expected_rank=None, name=None):
"""Returns a list of the shape of tensor, preferring static dimensions.
Args:
tensor: A tf.Tensor object to find the shape of.
expected_rank: (optional) int. The expected rank of `tensor`. If this is
specified and the `tensor` has a different rank, and exception will be
thrown.
name: Optional name of the tensor for the error message.
Returns:
A list of dimensions of the shape of tensor. All static dimensions will
be returned as python integers, and dynamic dimensions will be returned
as tf.Tensor scalars.
"""
if name is None:
name = tensor.name
if expected_rank is not None:
assert_rank(tensor, expected_rank, name)
shape = tensor.shape.as_list()
non_static_indexes = []
for (index, dim) in enumerate(shape):
if dim is None:
non_static_indexes.append(index)
if not non_static_indexes:
return shape
dyn_shape = tf.shape(tensor)
for index in non_static_indexes:
shape[index] = dyn_shape[index]
return shape
def reshape_to_matrix(input_tensor):
"""Reshapes a >= rank 2 tensor to a rank 2 tensor (i.e., a matrix)."""
ndims = input_tensor.shape.ndims
if ndims < 2:
raise ValueError("Input tensor must have at least rank 2. Shape = %s" %
(input_tensor.shape))
if ndims == 2:
return input_tensor
width = input_tensor.shape[-1]
output_tensor = tf.reshape(input_tensor, [-1, width])
return output_tensor
def reshape_from_matrix(output_tensor, orig_shape_list):
"""Reshapes a rank 2 tensor back to its original rank >= 2 tensor."""
if len(orig_shape_list) == 2:
return output_tensor
output_shape = get_shape_list(output_tensor)
orig_dims = orig_shape_list[0:-1]
width = output_shape[-1]
return tf.reshape(output_tensor, orig_dims + [width])
def assert_rank(tensor, expected_rank, name=None):
"""Raises an exception if the tensor rank is not of the expected rank.
Args:
tensor: A tf.Tensor to check the rank of.
expected_rank: Python integer or list of integers, expected rank.
name: Optional name of the tensor for the error message.
Raises:
ValueError: If the expected shape doesn't match the actual shape.
"""
if name is None:
name = tensor.name
expected_rank_dict = {}
if isinstance(expected_rank, six.integer_types):
expected_rank_dict[expected_rank] = True
else:
for x in expected_rank:
expected_rank_dict[x] = True
actual_rank = tensor.shape.ndims
if actual_rank not in expected_rank_dict:
scope_name = tf.get_variable_scope().name
raise ValueError(
"For the tensor `%s` in scope `%s`, the actual rank "
"`%d` (shape = %s) is not equal to the expected rank `%s`" %
(name, scope_name, actual_rank, str(tensor.shape), str(expected_rank)))
| ymcui/LERT | 221 | LERT: A Linguistically-motivated Pre-trained Language Model（语言学信息增强的预训练模型LERT） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/optimization.py | Python | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Functions and classes related to optimization (weight updates)."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import re
import tensorflow as tf
def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, use_tpu):
"""Creates an optimizer training op."""
global_step = tf.train.get_or_create_global_step()
learning_rate = tf.constant(value=init_lr, shape=[], dtype=tf.float32)
# Implements linear decay of the learning rate.
learning_rate = tf.train.polynomial_decay(
learning_rate,
global_step,
num_train_steps,
end_learning_rate=0.0,
power=1.0,
cycle=False)
# Implements linear warmup. I.e., if global_step < num_warmup_steps, the
# learning rate will be `global_step/num_warmup_steps * init_lr`.
if num_warmup_steps:
global_steps_int = tf.cast(global_step, tf.int32)
warmup_steps_int = tf.constant(num_warmup_steps, dtype=tf.int32)
global_steps_float = tf.cast(global_steps_int, tf.float32)
warmup_steps_float = tf.cast(warmup_steps_int, tf.float32)
warmup_percent_done = global_steps_float / warmup_steps_float
warmup_learning_rate = init_lr * warmup_percent_done
is_warmup = tf.cast(global_steps_int < warmup_steps_int, tf.float32)
learning_rate = (
(1.0 - is_warmup) * learning_rate + is_warmup * warmup_learning_rate)
# It is recommended that you use this optimizer for fine tuning, since this
# is how the model was trained (note that the Adam m/v variables are NOT
# loaded from init_checkpoint.)
optimizer = AdamWeightDecayOptimizer(
learning_rate=learning_rate,
weight_decay_rate=0.01,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6,
exclude_from_weight_decay=["LayerNorm", "layer_norm", "bias"])
if use_tpu:
optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer)
tvars = tf.trainable_variables()
grads = tf.gradients(loss, tvars)
# This is how the model was pre-trained.
(grads, _) = tf.clip_by_global_norm(grads, clip_norm=1.0)
train_op = optimizer.apply_gradients(
zip(grads, tvars), global_step=global_step)
# Normally the global step update is done inside of `apply_gradients`.
# However, `AdamWeightDecayOptimizer` doesn't do this. But if you use
# a different optimizer, you should probably take this line out.
new_global_step = global_step + 1
train_op = tf.group(train_op, [global_step.assign(new_global_step)])
return train_op
class AdamWeightDecayOptimizer(tf.train.Optimizer):
"""A basic Adam optimizer that includes "correct" L2 weight decay."""
def __init__(self,
learning_rate,
weight_decay_rate=0.0,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-6,
exclude_from_weight_decay=None,
name="AdamWeightDecayOptimizer"):
"""Constructs a AdamWeightDecayOptimizer."""
super(AdamWeightDecayOptimizer, self).__init__(False, name)
self.learning_rate = learning_rate
self.weight_decay_rate = weight_decay_rate
self.beta_1 = beta_1
self.beta_2 = beta_2
self.epsilon = epsilon
self.exclude_from_weight_decay = exclude_from_weight_decay
def apply_gradients(self, grads_and_vars, global_step=None, name=None):
"""See base class."""
assignments = []
for (grad, param) in grads_and_vars:
if grad is None or param is None:
continue
param_name = self._get_variable_name(param.name)
m = tf.get_variable(
name=param_name + "/adam_m",
shape=param.shape.as_list(),
dtype=tf.float32,
trainable=False,
initializer=tf.zeros_initializer())
v = tf.get_variable(
name=param_name + "/adam_v",
shape=param.shape.as_list(),
dtype=tf.float32,
trainable=False,
initializer=tf.zeros_initializer())
# Standard Adam update.
next_m = (
tf.multiply(self.beta_1, m) + tf.multiply(1.0 - self.beta_1, grad))
next_v = (
tf.multiply(self.beta_2, v) + tf.multiply(1.0 - self.beta_2,
tf.square(grad)))
update = next_m / (tf.sqrt(next_v) + self.epsilon)
# Just adding the square of the weights to the loss function is *not*
# the correct way of using L2 regularization/weight decay with Adam,
# since that will interact with the m and v parameters in strange ways.
#
# Instead we want ot decay the weights in a manner that doesn't interact
# with the m/v parameters. This is equivalent to adding the square
# of the weights to the loss with plain (non-momentum) SGD.
if self._do_use_weight_decay(param_name):
update += self.weight_decay_rate * param
update_with_lr = self.learning_rate * update
next_param = param - update_with_lr
assignments.extend(
[param.assign(next_param),
m.assign(next_m),
v.assign(next_v)])
return tf.group(*assignments, name=name)
def _do_use_weight_decay(self, param_name):
"""Whether to use L2 weight decay for `param_name`."""
if not self.weight_decay_rate:
return False
if self.exclude_from_weight_decay:
for r in self.exclude_from_weight_decay:
if re.search(r, param_name) is not None:
return False
return True
def _get_variable_name(self, param_name):
"""Get the variable name from the tensor name."""
m = re.match("^(.*):\\d+$", param_name)
if m is not None:
param_name = m.group(1)
return param_name
| ymcui/LERT | 221 | LERT: A Linguistically-motivated Pre-trained Language Model（语言学信息增强的预训练模型LERT） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/run.pretrain.sh | Shell | #!/bin/bash
set -ex
TPU_NAME="your-tpu-name"
TPU_ZONE="your-tpu-zone"
DATA_DIR=./your-path-to-tfrecords
MODEL_DIR=./your-path-to-model-saving
CONFIG_FILE=./your-path-to-config-file
# run pretraining
python run_pretraining.py \
--input_file=${DATA_DIR}/tf_examples.tfrecord.* \
--output_dir=${MODEL_DIR} \
--do_train=True \
--bert_config_file=${CONFIG_FILE} \
--train_batch_size=1024 \
--eval_batch_size=1024 \
--max_seq_length=512 \
--max_predictions_per_seq=75 \
--num_train_steps=2000000 \
--num_warmup_steps=10000 \
--save_checkpoints_steps=50000 \
--learning_rate=1e-4 \
--do_lower_case=True \
--use_tpu=True \
--tpu_name=${TPU_NAME} \
--tpu_zone=${TPU_ZONE} | ymcui/LERT | 221 | LERT: A Linguistically-motivated Pre-trained Language Model（语言学信息增强的预训练模型LERT） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/run_pretraining.py | Python | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Run masked LM/next sentence masked_lm pre-training for BERT."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import modeling
import optimization
import tensorflow as tf
flags = tf.flags
FLAGS = flags.FLAGS
## Required parameters
flags.DEFINE_string(
"bert_config_file", None,
"The config json file corresponding to the pre-trained BERT model. "
"This specifies the model architecture.")
flags.DEFINE_string(
"input_file", None,
"Input TF example files (can be a glob or comma separated).")
flags.DEFINE_string(
"output_dir", None,
"The output directory where the model checkpoints will be written.")
## Other parameters
flags.DEFINE_string(
"init_checkpoint", None,
"Initial checkpoint (usually from a pre-trained BERT model).")
flags.DEFINE_integer(
"max_seq_length", 128,
"The maximum total input sequence length after WordPiece tokenization. "
"Sequences longer than this will be truncated, and sequences shorter "
"than this will be padded. Must match data generation.")
flags.DEFINE_integer(
"max_predictions_per_seq", 20,
"Maximum number of masked LM predictions per sequence. "
"Must match data generation.")
flags.DEFINE_bool("do_train", False, "Whether to run training.")
flags.DEFINE_bool("do_eval", False, "Whether to run eval on the dev set.")
flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.")
flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.")
flags.DEFINE_integer("save_checkpoints_steps", 50000,
"How often to save the model checkpoint.")
flags.DEFINE_integer("iterations_per_loop", 1000,
"How many steps to make in each estimator call.")
flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.")
flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
tf.flags.DEFINE_string(
"tpu_name", None,
"The Cloud TPU to use for training. This should be either the name "
"used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
"url.")
tf.flags.DEFINE_string(
"tpu_zone", None,
"[Optional] GCE zone where the Cloud TPU is located in. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
tf.flags.DEFINE_string(
"gcp_project", None,
"[Optional] Project name for the Cloud TPU-enabled project. If not "
"specified, we will attempt to automatically detect the GCE project from "
"metadata.")
tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
flags.DEFINE_integer(
"num_tpu_cores", 8,
"Only used if `use_tpu` is True. Total number of TPU cores to use.")
tf.get_logger().propagate = False
# LINGUISTIC FEATURES USED IN THE PAPER
POS_LIST = ["POS-n", "POS-v", "POS-wp", "POS-u", "POS-d", "POS-a", "POS-m", "POS-p", "POS-r", "POS-ns", "POS-c", "POS-q", "POS-nt", "POS-nh", "POS-nd", "POS-j", "POS-i", "POS-b", "POS-ni", "POS-nz", "POS-nl", "POS-z", "POS-k", "POS-ws", "POS-o", "POS-h", "POS-e", "POS-%"]
NER_LIST = ["NER-O", "NER-S-Ns", "NER-S-Nh", "NER-B-Ni", "NER-E-Ni", "NER-I-Ni", "NER-S-Ni", "NER-B-Ns", "NER-E-Ns", "NER-I-Ns", "NER-B-Nh", "NER-E-Nh", "NER-I-Nh"]
DEP_LIST = ["DEP-ATT", "DEP-WP", "DEP-ADV", "DEP-VOB", "DEP-SBV", "DEP-COO", "DEP-RAD", "DEP-HED", "DEP-POB", "DEP-CMP", "DEP-LAD", "DEP-FOB", "DEP-DBL", "DEP-IOB"]
def model_fn_builder(bert_config, init_checkpoint, learning_rate,
num_train_steps, num_warmup_steps, use_tpu,
use_one_hot_embeddings):
"""Returns `model_fn` closure for TPUEstimator."""
def model_fn(features, labels, mode, params): # pylint: disable=unused-argument
"""The `model_fn` for TPUEstimator."""
tf.logging.info("*** Features ***")
for name in sorted(features.keys()):
tf.logging.info(" name = %s, shape = %s" % (name, features[name].shape))
input_ids = features["input_ids"]
input_mask = features["input_mask"]
segment_ids = features["segment_ids"]
masked_lm_positions = features["masked_lm_positions"]
masked_lm_ids = features["masked_lm_ids"]
masked_lm_weights = features["masked_lm_weights"]
class_labels = [features["pos_labels"], features["ner_labels"], features["dep_labels"]]
is_training = (mode == tf.estimator.ModeKeys.TRAIN)
model = modeling.BertModel(
config=bert_config,
is_training=is_training,
input_ids=input_ids,
input_mask=input_mask,
token_type_ids=segment_ids,
use_one_hot_embeddings=use_one_hot_embeddings)
(masked_lm_loss,
masked_lm_example_loss, masked_lm_log_probs) = get_masked_lm_output(
bert_config, model.get_sequence_output(), model.get_embedding_table(),
masked_lm_positions, masked_lm_ids, class_labels, masked_lm_weights)
total_loss = masked_lm_loss
tvars = tf.trainable_variables()
initialized_variable_names = {}
scaffold_fn = None
if init_checkpoint:
(assignment_map, initialized_variable_names
) = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
if use_tpu:
def tpu_scaffold():
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
return tf.train.Scaffold()
scaffold_fn = tpu_scaffold
else:
tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
tf.logging.info("**** Trainable Variables ****")
for var in tvars:
init_string = ""
if var.name in initialized_variable_names:
init_string = ", *INIT_FROM_CKPT*"
tf.logging.info(" name = %s, shape = %s%s", var.name, var.shape,
init_string)
output_spec = None
if mode == tf.estimator.ModeKeys.TRAIN:
train_op = optimization.create_optimizer(
total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
train_op=train_op,
scaffold_fn=scaffold_fn)
elif mode == tf.estimator.ModeKeys.EVAL:
def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
masked_lm_weights):
"""Computes the loss and accuracy of the model."""
masked_lm_log_probs = tf.reshape(masked_lm_log_probs,
[-1, masked_lm_log_probs.shape[-1]])
masked_lm_predictions = tf.argmax(
masked_lm_log_probs, axis=-1, output_type=tf.int32)
masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1])
masked_lm_ids = tf.reshape(masked_lm_ids, [-1])
masked_lm_weights = tf.reshape(masked_lm_weights, [-1])
masked_lm_accuracy = tf.metrics.accuracy(
labels=masked_lm_ids,
predictions=masked_lm_predictions,
weights=masked_lm_weights)
masked_lm_mean_loss = tf.metrics.mean(
values=masked_lm_example_loss, weights=masked_lm_weights)
return {
"masked_lm_accuracy": masked_lm_accuracy,
"masked_lm_loss": masked_lm_mean_loss,
}
eval_metrics = (metric_fn, [
masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
masked_lm_weights
])
output_spec = tf.contrib.tpu.TPUEstimatorSpec(
mode=mode,
loss=total_loss,
eval_metrics=eval_metrics,
scaffold_fn=scaffold_fn)
else:
raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))
return output_spec
return model_fn
def get_masked_lm_output(bert_config, input_raw_tensor, output_weights, positions,
label_ids, class_label_ids, label_weights):
"""Get loss and log probs for the masked LM."""
# input_raw_tensor [B, L, H]
# input_tensor [B*75, H]
input_tensor = gather_indexes(input_raw_tensor, positions)
with tf.variable_scope("cls/predictions"):
# We apply one more non-linear transformation before the output layer.
# This matrix is not used after pre-training.
with tf.variable_scope("transform"):
input_tensor = tf.layers.dense(
input_tensor,
units=bert_config.hidden_size,
activation=modeling.get_activation(bert_config.hidden_act),
kernel_initializer=modeling.create_initializer(
bert_config.initializer_range))
input_tensor = modeling.layer_norm(input_tensor)
# The output weights are the same as the input embeddings, but there is
# an output-only bias for each token.
output_bias = tf.get_variable(
"output_bias",
shape=[bert_config.vocab_size],
initializer=tf.zeros_initializer())
logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
logits = tf.nn.bias_add(logits, output_bias)
log_probs = tf.nn.log_softmax(logits, axis=-1)
label_ids = tf.reshape(label_ids, [-1])
label_weights = tf.reshape(label_weights, [-1])
one_hot_labels = tf.one_hot(
label_ids, depth=bert_config.vocab_size, dtype=tf.float32)
# linguistic task head
def create_linguistic_layer_and_loss(input_tensor, class_label_ids, label_weights, class_num, name_of_layer):
class_logits = tf.layers.dense(
input_tensor,
units=class_num,
activation=None,
kernel_initializer=modeling.create_initializer(bert_config.initializer_range),
name=name_of_layer)
class_log_probs = tf.nn.log_softmax(class_logits, axis=-1)
class_label_ids = tf.reshape(class_label_ids, [-1])
class_one_hot_labels = tf.one_hot(class_label_ids, depth=class_num, dtype=tf.float32)
return get_loss(class_log_probs, class_one_hot_labels, label_weights)
def get_loss(log_probs, one_hot_labels, label_weights):
# The `positions` tensor might be zero-padded (if the sequence is too
# short to have the maximum number of predictions). The `label_weights`
# tensor has a value of 1.0 for every real prediction and 0.0 for the
# padding predictions.
per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
numerator = tf.reduce_sum(label_weights * per_example_loss)
denominator = tf.reduce_sum(label_weights) + 1e-5
loss = numerator / denominator
return per_example_loss, loss
per_mlm_loss, mlm_loss = get_loss(log_probs, one_hot_labels, label_weights)
per_pos_loss, pos_loss = create_linguistic_layer_and_loss(input_tensor, class_label_ids[0], label_weights, 28, "output_pos_layer")
per_ner_loss, ner_loss = create_linguistic_layer_and_loss(input_tensor, class_label_ids[1], label_weights, 13, "output_ner_layer")
per_dep_loss, dep_loss = create_linguistic_layer_and_loss(input_tensor, class_label_ids[2], label_weights, 14, "output_dep_layer")
# specify end steps for scaling here.
end_pos_steps = 333000
end_ner_steps = 666000
end_dep_steps = 1000000
global_steps = tf.cast(tf.train.get_or_create_global_step(), tf.float32)
pos_weight = tf.clip_by_value(global_steps / end_pos_steps, 0.0, 1.0)
ner_weight = tf.clip_by_value(global_steps / end_ner_steps, 0.0, 1.0)
dep_weight = tf.clip_by_value(global_steps / end_dep_steps, 0.0, 1.0)
loss = mlm_loss + pos_weight*pos_loss + ner_weight*ner_loss + dep_weight*dep_loss
per_example_loss = per_mlm_loss + pos_weight*per_pos_loss + ner_weight*per_ner_loss + dep_weight*per_dep_loss
return (loss, per_example_loss, log_probs)
def gather_indexes(sequence_tensor, positions):
"""Gathers the vectors at the specific positions over a minibatch."""
sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
batch_size = sequence_shape[0]
seq_length = sequence_shape[1]
width = sequence_shape[2]
flat_offsets = tf.reshape(
tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
flat_positions = tf.reshape(positions + flat_offsets, [-1])
flat_sequence_tensor = tf.reshape(sequence_tensor,
[batch_size * seq_length, width])
output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
return output_tensor
def input_fn_builder(input_files,
max_seq_length,
max_predictions_per_seq,
is_training,
num_cpu_threads=4):
"""Creates an `input_fn` closure to be passed to TPUEstimator."""
def input_fn(params):
"""The actual input function."""
batch_size = params["batch_size"]
name_to_features = {
"input_ids":
tf.FixedLenFeature([max_seq_length], tf.int64),
"input_mask":
tf.FixedLenFeature([max_seq_length], tf.int64),
"segment_ids":
tf.FixedLenFeature([max_seq_length], tf.int64),
"masked_lm_positions":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
"masked_lm_ids":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
"masked_lm_weights":
tf.FixedLenFeature([max_predictions_per_seq], tf.float32),
"pos_labels":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
"ner_labels":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
"dep_labels":
tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
}
# For training, we want a lot of parallel reading and shuffling.
# For eval, we want no shuffling and parallel reading doesn't matter.
if is_training:
d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files))
d = d.repeat()
d = d.shuffle(buffer_size=len(input_files))
# `cycle_length` is the number of parallel files that get read.
cycle_length = min(num_cpu_threads, len(input_files))
# `sloppy` mode means that the interleaving is not exact. This adds
# even more randomness to the training pipeline.
d = d.apply(
tf.contrib.data.parallel_interleave(
tf.data.TFRecordDataset,
sloppy=is_training,
cycle_length=cycle_length))
d = d.shuffle(buffer_size=100)
else:
d = tf.data.TFRecordDataset(input_files)
# Since we evaluate for a fixed number of steps we don't want to encounter
# out-of-range exceptions.
d = d.repeat()
# We must `drop_remainder` on training because the TPU requires fixed
# size dimensions. For eval, we assume we are evaluating on the CPU or GPU
# and we *don't* want to drop the remainder, otherwise we wont cover
# every sample.
d = d.apply(
tf.contrib.data.map_and_batch(
lambda record: _decode_record(record, name_to_features),
batch_size=batch_size,
num_parallel_batches=num_cpu_threads,
drop_remainder=True))
return d
return input_fn
def _decode_record(record, name_to_features):
"""Decodes a record to a TensorFlow example."""
example = tf.parse_single_example(record, name_to_features)
# tf.Example only supports tf.int64, but the TPU only supports tf.int32.
# So cast all int64 to int32.
for name in list(example.keys()):
t = example[name]
if t.dtype == tf.int64:
t = tf.to_int32(t)
example[name] = t
return example
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
if not FLAGS.do_train and not FLAGS.do_eval:
raise ValueError("At least one of `do_train` or `do_eval` must be True.")
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
tf.gfile.MakeDirs(FLAGS.output_dir)
input_files = []
for input_pattern in FLAGS.input_file.split(","):
input_files.extend(tf.gfile.Glob(input_pattern))
tf.logging.info("*** Input Files ***")
for input_file in input_files:
tf.logging.info(" %s" % input_file)
tpu_cluster_resolver = None
if FLAGS.use_tpu and FLAGS.tpu_name:
tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
run_config = tf.contrib.tpu.RunConfig(
cluster=tpu_cluster_resolver,
master=FLAGS.master,
model_dir=FLAGS.output_dir,
save_checkpoints_steps=FLAGS.save_checkpoints_steps,
keep_checkpoint_max=10,
tpu_config=tf.contrib.tpu.TPUConfig(
iterations_per_loop=FLAGS.iterations_per_loop,
num_shards=FLAGS.num_tpu_cores,
per_host_input_for_training=is_per_host))
model_fn = model_fn_builder(
bert_config=bert_config,
init_checkpoint=FLAGS.init_checkpoint,
learning_rate=FLAGS.learning_rate,
num_train_steps=FLAGS.num_train_steps,
num_warmup_steps=FLAGS.num_warmup_steps,
use_tpu=FLAGS.use_tpu,
use_one_hot_embeddings=FLAGS.use_tpu)
# If TPU is not available, this will fall back to normal Estimator on CPU
# or GPU.
estimator = tf.contrib.tpu.TPUEstimator(
use_tpu=FLAGS.use_tpu,
model_fn=model_fn,
config=run_config,
train_batch_size=FLAGS.train_batch_size,
eval_batch_size=FLAGS.eval_batch_size)
if FLAGS.do_train:
tf.logging.info("***** Running training *****")
tf.logging.info(" Batch size = %d", FLAGS.train_batch_size)
train_input_fn = input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=True)
estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps)
if FLAGS.do_eval:
tf.logging.info("***** Running evaluation *****")
tf.logging.info(" Batch size = %d", FLAGS.eval_batch_size)
eval_input_fn = input_fn_builder(
input_files=input_files,
max_seq_length=FLAGS.max_seq_length,
max_predictions_per_seq=FLAGS.max_predictions_per_seq,
is_training=False)
result = estimator.evaluate(
input_fn=eval_input_fn, steps=FLAGS.max_eval_steps)
output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
with tf.gfile.GFile(output_eval_file, "w") as writer:
tf.logging.info("***** Eval results *****")
for key in sorted(result.keys()):
tf.logging.info(" %s = %s", key, str(result[key]))
writer.write("%s = %s\n" % (key, str(result[key])))
if __name__ == "__main__":
flags.mark_flag_as_required("input_file")
flags.mark_flag_as_required("bert_config_file")
flags.mark_flag_as_required("output_dir")
tf.app.run()
| ymcui/LERT | 221 | LERT: A Linguistically-motivated Pre-trained Language Model（语言学信息增强的预训练模型LERT） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
src/tokenization.py | Python | # coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Tokenization classes."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import re
import unicodedata
import six
import tensorflow as tf
def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
"""Checks whether the casing config is consistent with the checkpoint name."""
# The casing has to be passed in by the user and there is no explicit check
# as to whether it matches the checkpoint. The casing information probably
# should have been stored in the bert_config.json file, but it's not, so
# we have to heuristically detect it to validate.
if not init_checkpoint:
return
m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
if m is None:
return
model_name = m.group(1)
lower_models = [
"uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
"multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
]
cased_models = [
"cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
"multi_cased_L-12_H-768_A-12"
]
is_bad_config = False
if model_name in lower_models and not do_lower_case:
is_bad_config = True
actual_flag = "False"
case_name = "lowercased"
opposite_flag = "True"
if model_name in cased_models and do_lower_case:
is_bad_config = True
actual_flag = "True"
case_name = "cased"
opposite_flag = "False"
if is_bad_config:
raise ValueError(
"You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
"However, `%s` seems to be a %s model, so you "
"should pass in `--do_lower_case=%s` so that the fine-tuning matches "
"how the model was pre-training. If this error is wrong, please "
"just comment out this check." % (actual_flag, init_checkpoint,
model_name, case_name, opposite_flag))
def convert_to_unicode(text):
"""Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text.decode("utf-8", "ignore")
elif isinstance(text, unicode):
return text
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def printable_text(text):
"""Returns text encoded in a way suitable for print or `tf.logging`."""
# These functions want `str` for both Python2 and Python3, but in one case
# it's a Unicode string and in the other it's a byte string.
if six.PY3:
if isinstance(text, str):
return text
elif isinstance(text, bytes):
return text.decode("utf-8", "ignore")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
elif six.PY2:
if isinstance(text, str):
return text
elif isinstance(text, unicode):
return text.encode("utf-8")
else:
raise ValueError("Unsupported string type: %s" % (type(text)))
else:
raise ValueError("Not running on Python2 or Python 3?")
def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
index = 0
with tf.gfile.GFile(vocab_file, "r") as reader:
while True:
token = convert_to_unicode(reader.readline())
if not token:
break
token = token.strip()
vocab[token] = index
index += 1
return vocab
def convert_by_vocab(vocab, items):
"""Converts a sequence of [tokens|ids] using the vocab."""
output = []
for item in items:
output.append(vocab[item])
return output
def convert_tokens_to_ids(vocab, tokens):
return convert_by_vocab(vocab, tokens)
def convert_ids_to_tokens(inv_vocab, ids):
return convert_by_vocab(inv_vocab, ids)
def whitespace_tokenize(text):
"""Runs basic whitespace cleaning and splitting on a piece of text."""
text = text.strip()
if not text:
return []
tokens = text.split()
return tokens
class FullTokenizer(object):
"""Runs end-to-end tokenziation."""
def __init__(self, vocab_file, do_lower_case=True):
self.vocab = load_vocab(vocab_file)
self.inv_vocab = {v: k for k, v in self.vocab.items()}
self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
def tokenize(self, text):
split_tokens = []
for token in self.basic_tokenizer.tokenize(text):
for sub_token in self.wordpiece_tokenizer.tokenize(token):
split_tokens.append(sub_token)
return split_tokens
def convert_tokens_to_ids(self, tokens):
return convert_by_vocab(self.vocab, tokens)
def convert_ids_to_tokens(self, ids):
return convert_by_vocab(self.inv_vocab, ids)
class BasicTokenizer(object):
"""Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
def __init__(self, do_lower_case=True):
"""Constructs a BasicTokenizer.
Args:
do_lower_case: Whether to lower case the input.
"""
self.do_lower_case = do_lower_case
def tokenize(self, text):
"""Tokenizes a piece of text."""
text = convert_to_unicode(text)
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not trained on any Chinese data
# and generally don't have any Chinese data in them (there are Chinese
# characters in the vocabulary because Wikipedia does have some Chinese
# words in the English Wikipedia.).
text = self._tokenize_chinese_chars(text)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for token in orig_tokens:
if self.do_lower_case:
token = token.lower()
token = self._run_strip_accents(token)
split_tokens.extend(self._run_split_on_punc(token))
output_tokens = whitespace_tokenize(" ".join(split_tokens))
return output_tokens
def _run_strip_accents(self, text):
"""Strips accents from a piece of text."""
text = unicodedata.normalize("NFD", text)
output = []
for char in text:
cat = unicodedata.category(char)
if cat == "Mn":
continue
output.append(char)
return "".join(output)
def _run_split_on_punc(self, text):
"""Splits punctuation on a piece of text."""
chars = list(text)
i = 0
start_new_word = True
output = []
while i < len(chars):
char = chars[i]
if _is_punctuation(char):
output.append([char])
start_new_word = True
else:
if start_new_word:
output.append([])
start_new_word = False
output[-1].append(char)
i += 1
return ["".join(x) for x in output]
def _tokenize_chinese_chars(self, text):
"""Adds whitespace around any CJK character."""
output = []
for char in text:
cp = ord(char)
if self._is_chinese_char(cp):
output.append(" ")
output.append(char)
output.append(" ")
else:
output.append(char)
return "".join(output)
def _is_chinese_char(self, cp):
"""Checks whether CP is the codepoint of a CJK character."""
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if ((cp >= 0x4E00 and cp <= 0x9FFF) or #
(cp >= 0x3400 and cp <= 0x4DBF) or #
(cp >= 0x20000 and cp <= 0x2A6DF) or #
(cp >= 0x2A700 and cp <= 0x2B73F) or #
(cp >= 0x2B740 and cp <= 0x2B81F) or #
(cp >= 0x2B820 and cp <= 0x2CEAF) or
(cp >= 0xF900 and cp <= 0xFAFF) or #
(cp >= 0x2F800 and cp <= 0x2FA1F)): #
return True
return False
def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp == 0 or cp == 0xfffd or _is_control(char):
continue
if _is_whitespace(char):
output.append(" ")
else:
output.append(char)
return "".join(output)
class WordpieceTokenizer(object):
"""Runs WordPiece tokenziation."""
def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):
self.vocab = vocab
self.unk_token = unk_token
self.max_input_chars_per_word = max_input_chars_per_word
def tokenize(self, text):
"""Tokenizes a piece of text into its word pieces.
This uses a greedy longest-match-first algorithm to perform tokenization
using the given vocabulary.
For example:
input = "unaffable"
output = ["un", "##aff", "##able"]
Args:
text: A single token or whitespace separated tokens. This should have
already been passed through `BasicTokenizer.
Returns:
A list of wordpiece tokens.
"""
text = convert_to_unicode(text)
output_tokens = []
for token in whitespace_tokenize(text):
chars = list(token)
if len(chars) > self.max_input_chars_per_word:
output_tokens.append(self.unk_token)
continue
is_bad = False
start = 0
sub_tokens = []
while start < len(chars):
end = len(chars)
cur_substr = None
while start < end:
substr = "".join(chars[start:end])
if start > 0:
substr = "##" + substr
if substr in self.vocab:
cur_substr = substr
break
end -= 1
if cur_substr is None:
is_bad = True
break
sub_tokens.append(cur_substr)
start = end
if is_bad:
output_tokens.append(self.unk_token)
else:
output_tokens.extend(sub_tokens)
return output_tokens
def _is_whitespace(char):
"""Checks whether `chars` is a whitespace character."""
# \t, \n, and \r are technically contorl characters but we treat them
# as whitespace since they are generally considered as such.
if char == " " or char == "\t" or char == "\n" or char == "\r":
return True
cat = unicodedata.category(char)
if cat == "Zs":
return True
return False
def _is_control(char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char == "\t" or char == "\n" or char == "\r":
return False
cat = unicodedata.category(char)
if cat.startswith("C"):
return True
return False
def _is_punctuation(char):
"""Checks whether `chars` is a punctuation character."""
cp = ord(char)
# We treat all non-letter/number ASCII as punctuation.
# Characters such as "^", "$", and "`" are not in the Unicode
# Punctuation class but we treat them as punctuation anyways, for
# consistency.
if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
(cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
return True
cat = unicodedata.category(char)
if cat.startswith("P"):
return True
return False | ymcui/LERT | 221 | LERT: A Linguistically-motivated Pre-trained Language Model（语言学信息增强的预训练模型LERT） | Python | ymcui | Yiming Cui | Joint Laboratory of HIT and iFLYTEK Research (HFL) |
gatsby-config.js | JavaScript | require('dotenv').config()
module.exports = {
plugins: [
`gatsby-plugin-sharp`,
{
resolve: `gatsby-source-graphcms`,
options: {
downloadLocalImages: true,
endpoint: process.env.GRAPHCMS_ENDPOINT,
token: process.env.GRAPHCMS_TOKEN,
},
},
`gatsby-transformer-sharp`,
],
}
| ynnoj/2020-07-17-gatsby-preview-graphcms | 1 | 📹 Preview GraphCMS content with Gatsby Cloud | JavaScript | ynnoj | Jonathan Steele | stripe |
src/pages/index.js | JavaScript | import React from 'react'
import { graphql } from 'gatsby'
import Img from 'gatsby-image'
function IndexPage({ data }) {
const { products } = data
return products.nodes.map((product) => (
<React.Fragment>
<h1 key={product.id}>{product.name}</h1>
{product.images.map((image) => (
<Img
key={image.id}
fixed={image.localFile.childImageSharp.fixed}
fadeIn={false}
/>
))}
</React.Fragment>
))
}
export const query = graphql`
query IndexPageQuery {
products: allGraphCmsProduct {
nodes {
id
name
slug
images {
id
localFile {
childImageSharp {
fixed(width: 500) {
...GatsbyImageSharpFixed
}
}
}
}
}
}
}
`
export default IndexPage
| ynnoj/2020-07-17-gatsby-preview-graphcms | 1 | 📹 Preview GraphCMS content with Gatsby Cloud | JavaScript | ynnoj | Jonathan Steele | stripe |
gatsby-browser.js | JavaScript | import React from 'react'
import { MDXProvider } from '@mdx-js/react'
const wrapRootElement = ({ element }) => {
return (
<MDXProvider
components={{
h2: (props) => <h2 style={{ color: 'blue' }} {...props} />,
p: (props) => <p style={{ color: 'red' }} {...props} />,
CTA: (props) => (
<div style={{ color: 'red' }}>
{props.sales
? 'Please contact us for pricing'
: 'Pricing is $255 per month'}
</div>
),
Test: () => (
<div style={{ backgroundColor: 'black', color: 'white' }}>
This is from MDX
</div>
),
}}
>
{element}
</MDXProvider>
)
}
export { wrapRootElement }
| ynnoj/2020-08-07-working-with-mdx-graphcms | 2 | 📹 Working with MDX and GraphCMS | JavaScript | ynnoj | Jonathan Steele | stripe |
gatsby-config.js | JavaScript | require('dotenv').config()
module.exports = {
plugins: [
'gatsby-plugin-mdx',
{
resolve: 'gatsby-source-graphcms',
options: {
endpoint: process.env.GRAPHCMS_ENDPOINT,
token: process.env.GRAPHCMS_TOKEN,
buildMarkdownNodes: true,
},
},
],
}
| ynnoj/2020-08-07-working-with-mdx-graphcms | 2 | 📹 Working with MDX and GraphCMS | JavaScript | ynnoj | Jonathan Steele | stripe |
src/pages/index.js | JavaScript | import React from 'react'
import { graphql } from 'gatsby'
import { MDXRenderer } from 'gatsby-plugin-mdx'
function IndexPage({ data }) {
const { posts } = data
return posts.nodes.map((post) => (
<div key={post.id}>
<h1>{post.title}</h1>
<MDXRenderer>{post.content.markdownNode.childMdx.body}</MDXRenderer>
</div>
))
}
export const pageQuery = graphql`
{
posts: allGraphCmsPost {
nodes {
id
title
content {
markdownNode {
childMdx {
body
}
}
}
}
}
}
`
export default IndexPage
| ynnoj/2020-08-07-working-with-mdx-graphcms | 2 | 📹 Working with MDX and GraphCMS | JavaScript | ynnoj | Jonathan Steele | stripe |
gatsby-browser.js | JavaScript | import React from 'react'
import {
ApolloClient,
ApolloProvider,
HttpLink,
InMemoryCache,
} from '@apollo/client'
import { MDXProvider } from '@mdx-js/react'
import fetch from 'isomorphic-fetch'
import './src/styles/index.css'
import Layout from './src/components/layout'
const httpLink = new HttpLink({
uri: process.env.GATSBY_GRAPHCMS_ENDPOINT,
headers: {
Authorization: `Bearer ${process.env.GATSBY_GRAPHCMS_TOKEN}`,
},
fetch,
})
const apolloClient = new ApolloClient({
link: httpLink,
cache: new InMemoryCache(),
})
const wrapPageElement = ({ element, props }) => (
<Layout {...props}>{element}</Layout>
)
const wrapRootElement = ({ element }) => (
<ApolloProvider client={apolloClient}>
<MDXProvider>{element}</MDXProvider>
</ApolloProvider>
)
export { wrapPageElement, wrapRootElement }
| ynnoj/2020-08-28-dynamic-content-in-gatsby | 2 | 📹 Dynamic content in Gatsby with Apollo Client | JavaScript | ynnoj | Jonathan Steele | stripe |
gatsby-config.js | JavaScript | require('dotenv').config()
module.exports = {
siteMetadata: {
title: 'GraphCMS Blog',
description:
'Gatsby blog starter for GraphCMS! Powered by `gatsby-source-graphcms`, featuring `gatsby-image` and MDX!',
keywords: 'Headless CMS, GraphCMS, GraphQL CMS, Gatsby',
},
plugins: [
'gatsby-plugin-mdx',
{
resolve: 'gatsby-plugin-react-svg',
ptions: {
rule: {
include: /svg/,
},
},
},
'gatsby-plugin-react-helmet',
'gatsby-plugin-sharp',
'gatsby-plugin-postcss',
{
resolve: 'gatsby-source-graphcms',
options: {
endpoint: process.env.GATSBY_GRAPHCMS_ENDPOINT,
token: process.env.GATSBY_GRAPHCMS_TOKEN,
buildMarkdownNodes: true,
downloadLocalImages: true,
},
},
'gatsby-transformer-sharp',
],
}
| ynnoj/2020-08-28-dynamic-content-in-gatsby | 2 | 📹 Dynamic content in Gatsby with Apollo Client | JavaScript | ynnoj | Jonathan Steele | stripe |
gatsby-node.js | JavaScript | const path = require('path')
exports.createPages = async ({ actions: { createPage }, graphql }) => {
const { data } = await graphql(
`
{
pages: allGraphCmsPage {
nodes {
id
content {
markdownNode {
childMdx {
body
}
}
}
seo {
description
image {
url
}
keywords
title
}
slug
subtitle
title
}
}
posts: allGraphCmsPost(sort: { fields: date, order: ASC }) {
edges {
nextPost: next {
slug
title
}
page: node {
id
author {
id
name
title
}
content {
markdownNode {
childMdx {
body
}
}
}
date: formattedDate
excerpt
remoteId
seo {
description
image {
url
}
keywords
title
}
slug
title
}
previousPost: previous {
slug
title
}
}
}
}
`
)
if (data.errors) throw data.errors
data.posts.edges.forEach(({ nextPost, page, previousPost }) => {
createPage({
component: path.resolve('./src/templates/blog-post.js'),
context: {
id: page.id,
page,
previousPost,
nextPost,
},
path: `/posts/${page.slug}`,
})
})
data.pages.nodes.forEach((page) => {
createPage({
component: path.resolve('./src/templates/default-page.js'),
context: {
page,
},
path: `/${page.slug}`,
})
})
}
exports.createResolvers = ({ createResolvers }) => {
const resolvers = {
GraphCMS_Post: {
formattedDate: {
type: 'String',
resolve: (source) => {
const date = new Date(source.date)
return new Intl.DateTimeFormat('en-US', {
weekday: 'long',
year: 'numeric',
month: 'long',
day: 'numeric',
}).format(date)
},
},
},
}
createResolvers(resolvers)
}
| ynnoj/2020-08-28-dynamic-content-in-gatsby | 2 | 📹 Dynamic content in Gatsby with Apollo Client | JavaScript | ynnoj | Jonathan Steele | stripe |
gatsby-ssr.js | JavaScript | export { wrapPageElement, wrapRootElement } from './gatsby-browser'
| ynnoj/2020-08-28-dynamic-content-in-gatsby | 2 | 📹 Dynamic content in Gatsby with Apollo Client | JavaScript | ynnoj | Jonathan Steele | stripe |
postcss.config.js | JavaScript | module.exports = {
plugins: [require('postcss-preset-env'), require('tailwindcss')],
}
| ynnoj/2020-08-28-dynamic-content-in-gatsby | 2 | 📹 Dynamic content in Gatsby with Apollo Client | JavaScript | ynnoj | Jonathan Steele | stripe |
src/components/footer.js | JavaScript | import React from 'react'
import GitHubSVG from '../svg/github.svg'
import LinkedInSVG from '../svg/linkedin.svg'
import SlackSVG from '../svg/slack.svg'
import TwitterSVG from '../svg/twitter.svg'
const socialLinks = [
{
Component: GitHubSVG,
href: 'https://github.com/graphcms/gatsby-graphcms-ecommerce-starter',
title: 'GitHub',
},
{
Component: SlackSVG,
href: 'http://slack.graphcms.com',
title: 'Slack',
},
{
Component: TwitterSVG,
href: 'https://twitter.com/graphcms',
title: 'Twitter',
},
{
Component: LinkedInSVG,
href: 'https://www.linkedin.com/company/graphcms',
title: 'LinkedIn',
},
]
function Footer() {
return (
<footer className="bg-gray-800">
<div className="flex flex-col md:flex-row items-center md:justify-between py-6 max-w-3xl mx-auto px-4 sm:px-6 lg:max-w-5xl space-y-6 md:space-y-0">
<p className="text-gray-300">Powered by GraphCMS & Gatsby</p>
<ul className="inline-flex space-x-6">
{socialLinks.map(({ Component, href, title }, index) => (
<li key={index}>
<a
href={href}
target="_blank"
className="block text-gray-300 hover:text-white p-1 text-sm"
rel="noopener noreferrer"
title={title}
>
<Component className="h-6 w-6" />
</a>
</li>
))}
</ul>
</div>
</footer>
)
}
export default Footer
| ynnoj/2020-08-28-dynamic-content-in-gatsby | 2 | 📹 Dynamic content in Gatsby with Apollo Client | JavaScript | ynnoj | Jonathan Steele | stripe |
src/components/header.js | JavaScript | import React, { useEffect, useState } from 'react'
import { graphql, Link, useStaticQuery } from 'gatsby'
import { globalHistory, useLocation } from '@reach/router'
import cx from 'classnames'
import GraphCMSLogo from '../svg/logo.svg'
import GraphCMSMark from '../svg/mark.svg'
import Transition from './transition'
function Header() {
const [mobileNavOpen, setMobileNavOpen] = useState(false)
const location = useLocation()
const { pages } = useStaticQuery(graphql`
{
pages: allGraphCmsPage {
nodes {
id
slug
title
}
}
}
`)
useEffect(
() =>
globalHistory.listen(({ action }) => {
if (action === 'PUSH') setMobileNavOpen(false)
}),
[setMobileNavOpen]
)
const toggleMobileNavOpen = () => setMobileNavOpen((open) => !open)
return (
<header className="py-10 relative">
<nav className="relative flex items-center justify-between sm:h-10 lg:justify-start">
<div className="flex items-center flex-grow flex-shrink-0 lg:flex-grow-0">
<div className="flex items-center justify-between w-full md:w-auto">
<Link to="/" aria-label="GraphCMS Gatsby Blog Starter">
<GraphCMSLogo className="hidden sm:block h-10" />
<GraphCMSMark className="h-10 sm:hidden" />
</Link>
<div className="-mr-2 flex items-center md:hidden">
<button
onClick={() => toggleMobileNavOpen()}
type="button"
className="inline-flex items-center justify-center p-2 rounded-md text-gray-400 hover:text-gray-500 hover:bg-gray-100 focus:outline-none focus:bg-gray-100 focus:text-gray-500 transition duration-150 ease-in-out"
id="main-menu"
aria-label="Main menu"
aria-haspopup="true"
>
<svg
className="h-6 w-6"
stroke="currentColor"
fill="none"
viewBox="0 0 24 24"
>
<path
strokeLinecap="round"
strokeLinejoin="round"
strokeWidth="2"
d="M4 6h16M4 12h16M4 18h16"
/>
</svg>
</button>
</div>
</div>
</div>
<div className="hidden md:flex md:ml-10 md:pr-4 space-x-8">
{pages.nodes.map((page) => {
const isActive = location.pathname.startsWith(`/${page.slug}`)
return (
<Link
key={page.id}
to={`/${page.slug}`}
className={cx(
'inline-flex items-center px-1 pt-1 border-b-2 text-lg font-medium leading-5 focus:outline-none transition duration-150 ease-in-out',
{
'border-purple-500 text-gray-900 focus:border-purple-600': isActive,
'border-transparent text-gray-500 hover:text-gray-600 hover:border-gray-300 focus:text-gray-600 focus:border-grey-600': !isActive,
}
)}
>
{page.title}
</Link>
)
})}
</div>
</nav>
<Transition
show={mobileNavOpen}
enter="duration-150 ease-out"
enterFrom="opacity-0 scale-95"
enterTo="opacity-100 scale-100"
leave="duration-100 ease-in"
leaveFrom="opacity-100 scale-100"
leaveTo="opacity-0 scale-95"
>
<div className="absolute top-0 inset-x-0 py-2 -mx-2 transition transform origin-top-right md:hidden">
<div className="rounded-lg shadow-md">
<div
className="rounded-lg bg-white shadow-xs overflow-hidden"
role="menu"
aria-orientation="vertical"
aria-labelledby="main-menu"
>
<div className="px-2 pt-8 flex items-center justify-between">
<div>
<GraphCMSMark className="h-10" />
</div>
<div className="-mr-2">
<button
onClick={() => toggleMobileNavOpen()}
type="button"
className="inline-flex items-center justify-center p-2 rounded-md text-gray-400 hover:text-gray-500 hover:bg-gray-100 focus:outline-none focus:bg-gray-100 focus:text-gray-500 transition duration-150 ease-in-out"
aria-label="Close menu"
>
<svg
className="h-6 w-6"
stroke="currentColor"
fill="none"
viewBox="0 0 24 24"
>
<path
strokeLinecap="round"
strokeLinejoin="round"
strokeWidth="2"
d="M6 18L18 6M6 6l12 12"
/>
</svg>
</button>
</div>
</div>
<div className="mt-1 px-2 pt-2 pb-3 space-y-1">
{pages.nodes.map((page) => {
const isActive = location.pathname.startsWith(`/${page.slug}`)
return (
<Link
key={page.id}
to={`/${page.slug}`}
className={cx(
'block pl-3 pr-4 py-2 border-l-4 font-medium focus:outline-none transition duration-150 ease-in-out',
{
'border-purple-500 text-purple-500 bg-purple-50 focus:text-purple-600 focus:bg-purple-100 focus:border-purple-600': isActive,
'border-transparent text-gray-500 hover:text-gray-600 hover:bg-gray-50 hover:border-gray-300 focus:text-gray-600 focus:bg-gray-50 focus:border-gray-300': !isActive,
}
)}
role="menuitem"
>
{page.title}
</Link>
)
})}
</div>
</div>
</div>
</div>
</Transition>
</header>
)
}
export default Header
| ynnoj/2020-08-28-dynamic-content-in-gatsby | 2 | 📹 Dynamic content in Gatsby with Apollo Client | JavaScript | ynnoj | Jonathan Steele | stripe |
src/components/layout.js | JavaScript | import React from 'react'
import Footer from './footer'
import Header from './header'
import SEO from './seo'
function Layout({ children, pageContext: { page } }) {
return (
<React.Fragment>
<SEO {...page} />
<div className="flex flex-col min-h-screen">
<div className="flex-grow max-w-3xl mx-auto px-4 sm:px-6 lg:max-w-5xl w-full">
<Header />
<main className="flex-grow mb-8">{children}</main>
</div>
<Footer />
</div>
</React.Fragment>
)
}
export default Layout
| ynnoj/2020-08-28-dynamic-content-in-gatsby | 2 | 📹 Dynamic content in Gatsby with Apollo Client | JavaScript | ynnoj | Jonathan Steele | stripe |
src/components/seo.js | JavaScript | import React from 'react'
import { graphql, useStaticQuery } from 'gatsby'
import { Helmet } from 'react-helmet'
function SEO({ title, seo }) {
const {
site: { siteMetadata },
} = useStaticQuery(graphql`
{
site {
siteMetadata {
description
keywords
title
}
}
}
`)
const defaultTitle = siteMetadata.title
const pageDescription = seo?.description || siteMetadata.description
const pageKeywords = seo?.keywords || siteMetadata.keywords
const pageTitle = seo?.title || title || 'Home'
return (
<Helmet
htmlAttributes={{ lang: 'en' }}
defaultTitle={defaultTitle}
titleTemplate={`%s | ${defaultTitle}`}
>
<title>{pageTitle}</title>
<meta name="description" content={pageDescription} />
<meta name="keywords" content={pageKeywords} />
{seo?.image && <meta property="image" content={seo.image.url} />}
<meta property="og:title" content={pageTitle} />
<meta property="og:description" content={pageDescription} />
<meta property="og:site_name" content={defaultTitle} />
{seo?.image && <meta property="og:image" content={seo.image.url} />}
<meta name="og:type" content="website" />
<meta name="twitter:site" content="@GraphCMS" />
<meta name="twitter:title" content={`${pageTitle} | ${defaultTitle}`} />
<meta name="twitter:card" content="summary_large_image" />
{seo?.image && <meta name="twitter:image:src" content={seo.image.url} />}
</Helmet>
)
}
export default SEO
| ynnoj/2020-08-28-dynamic-content-in-gatsby | 2 | 📹 Dynamic content in Gatsby with Apollo Client | JavaScript | ynnoj | Jonathan Steele | stripe |
src/components/transition.js | JavaScript | import React, { useRef, useEffect, useContext } from 'react'
import { CSSTransition as ReactCSSTransition } from 'react-transition-group'
const TransitionContext = React.createContext({
parent: {},
})
function useIsInitialRender() {
const isInitialRender = useRef(true)
useEffect(() => {
isInitialRender.current = false
}, [])
return isInitialRender.current
}
function CSSTransition({
show,
enter = '',
enterFrom = '',
enterTo = '',
leave = '',
leaveFrom = '',
leaveTo = '',
appear,
children,
}) {
const enterClasses = enter.split(' ').filter((s) => s.length)
const enterFromClasses = enterFrom.split(' ').filter((s) => s.length)
const enterToClasses = enterTo.split(' ').filter((s) => s.length)
const leaveClasses = leave.split(' ').filter((s) => s.length)
const leaveFromClasses = leaveFrom.split(' ').filter((s) => s.length)
const leaveToClasses = leaveTo.split(' ').filter((s) => s.length)
function addClasses(node, classes) {
classes.length && node.classList.add(...classes)
}
function removeClasses(node, classes) {
classes.length && node.classList.remove(...classes)
}
return (
<ReactCSSTransition
appear={appear}
unmountOnExit
in={show}
addEndListener={(node, done) => {
node.addEventListener('transitionend', done, false)
}}
onEnter={(node) => {
addClasses(node, [...enterClasses, ...enterFromClasses])
}}
onEntering={(node) => {
removeClasses(node, enterFromClasses)
addClasses(node, enterToClasses)
}}
onEntered={(node) => {
removeClasses(node, [...enterToClasses, ...enterClasses])
}}
onExit={(node) => {
addClasses(node, [...leaveClasses, ...leaveFromClasses])
}}
onExiting={(node) => {
removeClasses(node, leaveFromClasses)
addClasses(node, leaveToClasses)
}}
onExited={(node) => {
removeClasses(node, [...leaveToClasses, ...leaveClasses])
}}
>
{children}
</ReactCSSTransition>
)
}
function Transition({ show, appear, ...rest }) {
const { parent } = useContext(TransitionContext)
const isInitialRender = useIsInitialRender()
const isChild = show === undefined
if (isChild) {
return (
<CSSTransition
appear={parent.appear || !parent.isInitialRender}
show={parent.show}
{...rest}
/>
)
}
return (
<TransitionContext.Provider
value={{
parent: {
show,
isInitialRender,
appear,
},
}}
>
<CSSTransition appear={appear} show={show} {...rest} />
</TransitionContext.Provider>
)
}
export default Transition
| ynnoj/2020-08-28-dynamic-content-in-gatsby | 2 | 📹 Dynamic content in Gatsby with Apollo Client | JavaScript | ynnoj | Jonathan Steele | stripe |
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