update
This commit is contained in:
@@ -0,0 +1,111 @@
|
||||
本目录提供辅助模型训练的一些工具，文件结构如下所示：
|
||||
|
||||
```bash
|
||||
├── transformers # 适配hugging face的transformers的一些工具
|
||||
│ ├── configuration_internlm.py # config适配工具
|
||||
│ ├── modeling_internlm.py # model适配工具
|
||||
│ ├── tokenization_internlm.py # tokenizer适配工具
|
||||
│ └── convert2hf.py # 模型适配hugging face工具
|
||||
└── tokenizer.py # 将原始数据转换成bin和meta文件的工具
|
||||
```
|
||||
|
||||
# tokenizer.py
|
||||
|
||||
生成原始数据的`bin`和`meta`文件需要使用`tokenizer`，我们通过在`tools/tokenizer.py`中指定模型参数路径的方式来导入tokenizer模型。目前我们提供了`V7_sft.model`来生成tokens。若想使用不同的模型，可直接修改`tokernizer.py`中的模型参数路径。
|
||||
|
||||
可以运行以下命令生成原始数据对应的`bin`和`meta`文件，其中参数`text_input_path`表示原始文本数据路径，目前支持`txt`、`json`和`jsonl`三种输入格式，`bin_output_path`表示生成的`bin`文件的保存路径。
|
||||
|
||||
```bash
|
||||
$ python tools/tokenizer.py --text_input_path your_input_text_path --bin_output_path your_output_bin_path
|
||||
```
|
||||
|
||||
下面是一个数据处理的例子：
|
||||
|
||||
给定一个包含原始数据集的文件`raw_data.txt`，原始数据集如下所示：
|
||||
|
||||
```bash
|
||||
感恩生活中的每一个细节，才能真正体会到幸福的滋味。
|
||||
梦想是人生的动力源泉，努力追逐，才能实现自己的目标。
|
||||
学会宽容和理解，才能建立真正和谐的人际关系。
|
||||
```
|
||||
|
||||
可以通过运行以下命令来生成`bin`和`meta`文件：
|
||||
```bash
|
||||
$ python tools/tokenizer.py --text_input_path raw_data.txt --bin_output_path cn/output.bin
|
||||
```
|
||||
|
||||
需要注意的是，生成的`bin`文件需要保存在`cn`或者`en`或者`code`或者`ja`或者`ar`或者`kaoshi`这五个目录下，以区分数据集的类型。
|
||||
|
||||
其中，`cn`表示中文数据集；`en`表示英文数据集；`code`表示代码数据集；`ja`表示日语数据集；`ar`表示阿拉伯语数据集；`kaoshi`表示考试数据集。
|
||||
|
||||
生成的bin文件的格式如下：
|
||||
|
||||
```python
|
||||
{"tokens": [73075, 75302, 69522, 69022, 98899, 67713, 68015, 81269, 74637, 75445, 99157]}
|
||||
{"tokens": [69469, 60355, 73026, 68524, 60846, 61844, 98899, 67775, 79241, 98899, 67713, 67800, 67453, 67838, 99157]}
|
||||
{"tokens": [68057, 79017, 60378, 68014, 98899, 67713, 67990, 68015, 70381, 67428, 61003, 67622, 99157]}
|
||||
```
|
||||
|
||||
`bin`文件中的每一行均对应原始数据集中的每一个句子，表示每个句子的`token`（下文将用sequence指定）。
|
||||
|
||||
生成的`meta`文件的格式如下：
|
||||
|
||||
```bash
|
||||
(0, 11), (90, 15), (208, 13)
|
||||
```
|
||||
|
||||
在`meta`文件中，每个元组对应着`bin`文件中每一个`sequence`的元信息。其中，元组的第一个元素表示每个`sequence`在所有`sequence`中的`starting index`，第二个元素表示每个`sequence`中有多少个`tokens`。
|
||||
|
||||
例如，对于第一个`sequence`，`starting index`为 0，有 11 个`tokens`；对于第二个`sequence`，由于第一个`sequence`转换为`string`后的长度为`89`，因此它的`starting index`为 90，有 15 个`tokens`。
|
||||
|
||||
`json`和`jsonl`类型的文件的`bin`和`meta`文件格式和`txt`一致，此处不再赘叙。
|
||||
|
||||
# pal_inference.py
|
||||
|
||||
在 [GSM8K](https://huggingface.co/datasets/gsm8k) 数据集上使用 [PAL](https://github.com/reasoning-machines/pal) 范式推理，使模型编写代码并通过 Python 解释器执行来解决数学问题。其用法如下：
|
||||
|
||||
```python
|
||||
# 用法:
|
||||
python pal_inference.py <model> <out_dir> [--dataset <dataset>] [--max_length <length>] [--top_p <threshold>] [--eoh <end token>] [--eoa <end token>] [--eos <end token>] [--temperature <temp>] [--time_out <time>] [--verbose, -v] [--append, -a]
|
||||
|
||||
# 参数:
|
||||
# <model> 用于推理的模型的路径。
|
||||
# <out_dir> 生成代码将保存在指定的输出文件夹中。
|
||||
|
||||
# 可选参数:
|
||||
# --dataset <dataset> 用于代码生成的数据集名称（默认：gsm8k）。
|
||||
# --max_length <length> 模型最大输入 token 长度（默认：2048）。
|
||||
# --top_p <threshold> 候选 token 相加的概率阈值（默认：0.8）。
|
||||
# --eoh <end token> 用户输入结束标识符 (默认: "") 。
|
||||
# --eoa <end token> 模型输入结束标识符 (默认: "") 。
|
||||
# --eos <end token> 系统输入结束标识符. (默认: "") 。
|
||||
# --temperature， -t <temp> 生成过程中的采样温度（默认：1.0）。
|
||||
# --time_out <time> 执行生成的代码的最大时间（秒）（默认：100）。
|
||||
# --verbose, -v 打印代码错误信息（可选）。
|
||||
# --append, -a 将输出追加到历史结果中（可选）。
|
||||
```
|
||||
|
||||
以下是使用示例：
|
||||
|
||||
```bash
|
||||
python tools/pal_inference.py internlm/internlm-chat-7k ./output -v
|
||||
```
|
||||
|
||||
其输出文件每一行包括输入的问题，正确答案，执行答案，得分，以及模型生成的 Python 代码块：
|
||||
|
||||
````json
|
||||
{
|
||||
"question": "Janet\u2019s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?",
|
||||
"target": 18.0,
|
||||
"answer": 18.0,
|
||||
"score": 1,
|
||||
"generation": ["```python\ndef solution():\n eggs_per_day = 16\n eggs_per_breakfast = 3\n eggs_per_muffin = 4\n eggs_used = eggs_per_day - eggs_per_breakfast - eggs_per_muffin\n eggs_sold = eggs_used\n price_per_egg = 2\n eggs_made = eggs_sold * price_per_egg\n result = eggs_made\n return result\n```"]
|
||||
}
|
||||
````
|
||||
|
||||
InternLM 在 GSM8K 数据集中带工具和不带工具的性能表现：
|
||||
|
||||
| Method | **InternLM-Chat-7B** |
|
||||
| -------- | -------------------- |
|
||||
| w/o tool | 34.5 |
|
||||
| w tool | 39.2 |
|
||||
@@ -0,0 +1,109 @@
|
||||
This directory provide some tools for model training with the following file structure.
|
||||
|
||||
```bash
|
||||
├── transformers # tools for adapting Hugging Face's transformers
|
||||
│ ├── configuration_internlm.py # tools for adapting config
|
||||
│ ├── modeling_internlm.py # tools for adapting model
|
||||
│ └── tokenization_internlm.py # tools for adapting tokenizer
|
||||
│ └── convert2hf.py # tools for adapting models to Hugging Face's format
|
||||
└── tokenizer.py # tools for generating `bin` and `meta` file for raw data
|
||||
```
|
||||
|
||||
# tokenizer.py
|
||||
|
||||
We need to use a `tokenizer` to generate `bin` and `meta` files for raw data. We import the tokenizer model by specifying the model weight path in `tools/tokenizer.py`. Currently, we provide `V7.model` to generate tokens. If you want to use a different model, you can modify the model weight path in `tokenizer.py` directly.
|
||||
|
||||
We can run the following command to generate `bin` and `meta` files corresponding to the original data. The parameter `text_input_path` represents the path of the original text data, currently supporting `txt`, `json`, and `jsonl` formats, while `bin_output_path` represents the save path of the generated `bin` files.
|
||||
```bash
|
||||
$ python tools/tokenizer.py --text_input_path your_input_text_path --bin_output_path your_output_bin_path
|
||||
```
|
||||
|
||||
An example of data processing in `txt` format is given here:
|
||||
|
||||
Given a file `raw_data.txt` containg raw data with the following content.
|
||||
|
||||
```bash
|
||||
Appreciate every detail in life to truly taste the flavor of happiness.
|
||||
Dreams are the source of life’s motivation. Pursue them diligently to achieve your goals.
|
||||
Learn to be tolerant and understanding to establish truly harmonious interpersonal relationships.
|
||||
```
|
||||
|
||||
Next, we can run the following command to generate `bin` and `meta` files for raw data.
|
||||
|
||||
```bash
|
||||
$ python tools/tokenizer.py --text_input_path your_input_text_path --bin_output_path your_output_bin_path
|
||||
```
|
||||
|
||||
It should be noted that the generated `bin` files should be placed in one of the following directories to clarify the data type: `cn`(Chinese), `en`(English), `code`(code data), `ja`(Japanese), `ar`(Arabic) and `kaoshi`(kaoshi data).
|
||||
|
||||
The format of generated `bin` file is as follows.
|
||||
|
||||
```python
|
||||
{"tokens": [98655, 2317, 2922, 6649, 1595, 7856, 435, 2424, 442, 9556, 12807, 410, 17313, 446, 23331, 95746]}
|
||||
{"tokens": [98655, 302, 1383, 269, 657, 410, 2687, 446, 2424, 98667, 269, 25220, 281, 523, 1874, 492, 1248, 38127, 4563, 442, 11227, 829, 8980, 95746]}
|
||||
{"tokens": [98655, 24190, 442, 517, 15013, 649, 454, 8793, 442, 5849, 9556, 17917, 1369, 1084, 29890, 12021, 95746]}
|
||||
```
|
||||
|
||||
In the generated `bin` file, each line (`sequence`) corresponds to the `tokens` for each sentence in the raw data.
|
||||
|
||||
The format of generated `meta` file in as follows.
|
||||
|
||||
```bash
|
||||
(0, 16), (110, 24), (262, 17)
|
||||
```
|
||||
|
||||
Each tuple in the `meta` file represents the meta information of each `sequence` where the first element in the tuple indicates the `starting index` of each `sequence` among all `sequences` and the second element indicates the amount of `tokens` for each `sequence`.
|
||||
|
||||
For example, the `starting index` is 0 for the first `sequence` with 16 `tokens`. Since the length of `sequence` in `string` format is 109, the `starting index` is 110. And the number of `tokens` of the sencond `sequence` is 24.
|
||||
|
||||
The `bin` and `meta` file formats for `json` and `jsonl` type files are the same as for `txt`, so we won't go over them here.
|
||||
|
||||
# pal_inference.py
|
||||
|
||||
Perform reasoning using [PAL](https://github.com/reasoning-machines/pal) on the [GSM8K](https://huggingface.co/datasets/gsm8k) dataset, allowing the model to generate code and solve mathematical problems through Python interpretation. Here's how you can use it:
|
||||
|
||||
```bash
|
||||
# Usage:
|
||||
python pal_inference.py <model> <out_dir> [--dataset <dataset>] [--max_length <length>] [--top_p <threshold>] [--eoh <end token>] [--eoa <end token>] [--eos <end token>] [--temperature <temp>] [--time_out <time>] [--verbose, -v] [--append, -a]
|
||||
|
||||
# Parameters:
|
||||
# <model> Path to the model used for inference.
|
||||
# <out_dir> Generated code will be saved in the specified output folder.
|
||||
|
||||
# Optional arguments:
|
||||
# --dataset <dataset> Dataset name used for code generation (default: gsm8k).
|
||||
# --max_length <length> Model's maximum input token length (default: 2048).
|
||||
# --top_p <threshold> Probability threshold for candidate tokens (default: 0.8).
|
||||
# --eoh <end token> End of human (user) token. (default: "").
|
||||
# --eoa <end token> End of assistant (bot) token. (default: "").
|
||||
# --eos <end token> End of system token. (default: "").
|
||||
# --temperature, -t <temp> Sampling temperature during generation (default: 1.0).
|
||||
# --time_out <time> Maximum time (in seconds) for executing the generated code (default: 100).
|
||||
# --verbose, -v Print code error messages (optional).
|
||||
# --append, -a ppend the output to historical results (optional).
|
||||
```
|
||||
|
||||
Below is an example of usage:
|
||||
|
||||
```bash
|
||||
python tools/pal_inference.py internlm/internlm-chat-7k ./output -v
|
||||
```
|
||||
|
||||
The output file contains each line with the input question, the correct answer, the executed answer, the score, and the Python code block generated by the model:
|
||||
|
||||
````json
|
||||
{
|
||||
"question": "Janet\u2019s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?",
|
||||
"target": 18.0,
|
||||
"answer": 18.0,
|
||||
"score": 1,
|
||||
"generation": ["```python\ndef solution():\n eggs_per_day = 16\n eggs_per_breakfast = 3\n eggs_per_muffin = 4\n eggs_used = eggs_per_day - eggs_per_breakfast - eggs_per_muffin\n eggs_sold = eggs_used\n price_per_egg = 2\n eggs_made = eggs_sold * price_per_egg\n result = eggs_made\n return result\n```"]
|
||||
}
|
||||
````
|
||||
|
||||
InternLM performance in the GSM8K dataset with and without tools:
|
||||
|
||||
| Method | **InternLM-Chat-7B** |
|
||||
| -------- | -------------------- |
|
||||
| w/o tool | 34.5 |
|
||||
| w tool | 39.2 |
|
||||
Binary file not shown.
@@ -0,0 +1,164 @@
|
||||
import argparse
|
||||
import json
|
||||
import os.path as osp
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import sentencepiece as spm
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
def process(dataset_path, sp_model):
|
||||
"""Process data sample from input dataset
|
||||
|
||||
Args:
|
||||
dataset_path (str): Path of dataset json file.
|
||||
sp_model (str): Path of tokenizer.
|
||||
|
||||
Yields:
|
||||
tuple: dumped processed data sample and length of tokens.
|
||||
"""
|
||||
|
||||
dataset = json.load(open(dataset_path))
|
||||
|
||||
for data in dataset:
|
||||
yield tokenize(get_chat_format_data(data), sp_model)
|
||||
|
||||
|
||||
def get_chat_format_data(ori_data):
|
||||
"""Format original data
|
||||
|
||||
Args:
|
||||
ori_data (dict): input data sample.
|
||||
|
||||
Returns:
|
||||
dict: data sample with chat format.
|
||||
"""
|
||||
input_str = ori_data["input"]
|
||||
instruction_str = ori_data["instruction"]
|
||||
output_str = ori_data["output"]
|
||||
data = dict()
|
||||
if input_str != "":
|
||||
data["user"] = f"<|User|>:{instruction_str}\n{input_str}"
|
||||
else:
|
||||
data["user"] = f"<|User|>:{instruction_str}"
|
||||
data["bot"] = f"<|Bot|>:{output_str}"
|
||||
return data
|
||||
|
||||
|
||||
def tokenize(sample, sp_model):
|
||||
"""Tokenize input dataset
|
||||
|
||||
Args:
|
||||
sample (dict): Input data sample.
|
||||
sp_model (str): Path of tokenizer.
|
||||
|
||||
Returns:
|
||||
tuple: dumped processed data sample and length of tokens.
|
||||
"""
|
||||
special_tokens_map = {"<eoh>": 103167, "<eoa>": 103166, "nl_id": 13}
|
||||
token_ids = [sp_model.bos_id()]
|
||||
human_s = sample["user"]
|
||||
ass_s = sample["bot"]
|
||||
|
||||
human_ids = sp_model.encode(human_s) + [special_tokens_map["<eoh>"], special_tokens_map["nl_id"]]
|
||||
human_ids_ignore = [-token_id for token_id in human_ids]
|
||||
|
||||
ass_template_ids = sp_model.encode("<|Bot|>:")
|
||||
ass_template_ids_ignore = [-token_ids for token_ids in ass_template_ids]
|
||||
ass_ids = (
|
||||
ass_template_ids_ignore
|
||||
+ sp_model.encode(ass_s[8:])
|
||||
+ [special_tokens_map["<eoa>"], special_tokens_map["nl_id"]]
|
||||
)
|
||||
|
||||
token_ids += human_ids_ignore + ass_ids
|
||||
if len(token_ids) > 2047:
|
||||
token_ids = token_ids[:2047]
|
||||
token_ids += [sp_model.eos_id()]
|
||||
line = str.encode(json.dumps({"tokens": token_ids}) + "\n")
|
||||
return line, len(token_ids)
|
||||
|
||||
|
||||
def dump_bin_meta_bin(samples, path, split_ratio=0.1):
|
||||
"""Dump processed dataset
|
||||
|
||||
Args:
|
||||
samples (dict): Input data sample.
|
||||
path (str): Path for output dataset.
|
||||
split_ratio (float): Ratio for validation dataset splitting.
|
||||
Default to: 0.1.
|
||||
|
||||
Returns:
|
||||
tuple: number of train/valid tokens of processed dataset,
|
||||
number of train/valid samples of processed dataset.
|
||||
"""
|
||||
|
||||
train_path = osp.join(path, "train/en/")
|
||||
valid_path = osp.join(path, "valid/en/")
|
||||
train_dir = Path(train_path)
|
||||
valid_dir = Path(valid_path)
|
||||
train_dir.mkdir(exist_ok=True, parents=True)
|
||||
valid_dir.mkdir(exist_ok=True, parents=True)
|
||||
train_f = open(train_dir.joinpath("dataset.bin"), "wb")
|
||||
valid_f = open(valid_dir.joinpath("dataset.bin"), "wb")
|
||||
|
||||
train_tokens = 0
|
||||
valid_tokens = 0
|
||||
last_train_position = 0
|
||||
last_valid_position = 0
|
||||
train_samples = 0
|
||||
valid_samples = 0
|
||||
train_meta = []
|
||||
valid_meta = []
|
||||
|
||||
sample_length = len(samples)
|
||||
np.random.seed(0)
|
||||
valid_indices = np.random.choice(range(sample_length), int(sample_length * split_ratio)).tolist()
|
||||
|
||||
count = -1
|
||||
for line, token_num in samples:
|
||||
count += 1
|
||||
if count in valid_indices:
|
||||
valid_tokens += token_num
|
||||
valid_f.write(line)
|
||||
valid_meta.append((last_valid_position, token_num))
|
||||
last_valid_position += len(line)
|
||||
valid_samples += 1
|
||||
else:
|
||||
train_tokens += token_num
|
||||
train_f.write(line)
|
||||
train_meta.append((last_train_position, token_num))
|
||||
last_train_position += len(line)
|
||||
train_samples += 1
|
||||
|
||||
train_f.close()
|
||||
valid_f.close()
|
||||
np.save(open(train_dir.joinpath("dataset.bin.meta"), "wb"), train_meta)
|
||||
np.save(open(valid_dir.joinpath("dataset.bin.meta"), "wb"), valid_meta)
|
||||
|
||||
return train_tokens, valid_tokens, train_samples, valid_samples
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("dataset_path", type=str, help="path of dataset json file")
|
||||
parser.add_argument("output_path", type=str, help="path of processed dataset")
|
||||
parser.add_argument("tokenizer_path", type=str, help="path of tokenizer")
|
||||
parser.add_argument("--split_ratio", type=float, default=0.1, help="ratio for validation dataset splitting")
|
||||
|
||||
args = parser.parse_args()
|
||||
sp_model = spm.SentencePieceProcessor(model_file=args.tokenizer_path)
|
||||
split_ratio = args.split_ratio
|
||||
samples = []
|
||||
|
||||
dataset = process(args.dataset_path, sp_model)
|
||||
for sample in tqdm(dataset):
|
||||
samples.append(sample)
|
||||
|
||||
train_tokens, valid_tokens, train_samples, valid_samples = dump_bin_meta_bin(
|
||||
samples, args.output_path, args.split_ratio
|
||||
)
|
||||
print(f"number of train dataset: {train_samples}, number of train dataset token: {train_tokens}")
|
||||
print(f"number of validation dataset: {valid_samples}, number of validation dataset token: {valid_tokens}")
|
||||
@@ -0,0 +1,320 @@
|
||||
# This file is modified from:
|
||||
# hhttps://github.com/reasoning-machines/pal/blob/main/pal/core/interface.py
|
||||
#
|
||||
# Copyright 2022 PAL 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.
|
||||
|
||||
import argparse
|
||||
import copy
|
||||
import json
|
||||
import os
|
||||
from dataclasses import asdict
|
||||
from typing import Any, Dict, List
|
||||
|
||||
import torch
|
||||
import tqdm
|
||||
from datasets import load_dataset
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
|
||||
from tools.transformers.interface import GenerationConfig, generate_interactive
|
||||
from internlm.utils.timeout import Timeout
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(description="PAL Inference")
|
||||
parser.add_argument("model", type=str, help="Path to the pre-trained LLM used for inference.")
|
||||
parser.add_argument(
|
||||
"out_dir", type=str, help="Name of the output folder where generated code snippets will be saved."
|
||||
)
|
||||
parser.add_argument("--dataset", default="gsm8k", type=str, help="Name of the dataset used for code generation.")
|
||||
parser.add_argument(
|
||||
"--max_length",
|
||||
default=2048,
|
||||
type=int,
|
||||
help="Maximum input token length for the natural language description.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--top_p",
|
||||
default=0.8,
|
||||
type=float,
|
||||
help="Probability threshold to choose sample tokens during generation.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--eoh",
|
||||
default="",
|
||||
type=str,
|
||||
help="End of human (user) token.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--eoa",
|
||||
default="",
|
||||
type=str,
|
||||
help="End of assistant (bot) token.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--eos",
|
||||
default="",
|
||||
type=str,
|
||||
help="End of system token.",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--temperature", "-t", default=1.0, type=float, help="Temperature of token sampling during generation."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--time_out", default=100, type=float, help="Maximum time allowed for executing generated code."
|
||||
)
|
||||
parser.add_argument(
|
||||
"--verbose",
|
||||
"-v",
|
||||
action="store_true",
|
||||
help="Print code error information when executing generated code (optional).",
|
||||
)
|
||||
parser.add_argument("--append", "-a", action="store_true", help="Append output to the history results (optional).")
|
||||
args = parser.parse_args()
|
||||
return args
|
||||
|
||||
|
||||
class GenericRuntime:
|
||||
"""Adapted from https://github.com/reasoning-machines/pal"""
|
||||
|
||||
GLOBAL_DICT: dict = {}
|
||||
LOCAL_DICT = None
|
||||
HEADERS: List = []
|
||||
|
||||
def __init__(self):
|
||||
self._global_vars = copy.copy(self.GLOBAL_DICT)
|
||||
self._local_vars = copy.copy(self.LOCAL_DICT) if self.LOCAL_DICT else None
|
||||
|
||||
for c in self.HEADERS:
|
||||
self.exec_code(c)
|
||||
|
||||
def exec_code(self, code_piece: str) -> None:
|
||||
exec(code_piece, self._global_vars)
|
||||
|
||||
def eval_code(self, expr: str) -> Any:
|
||||
return eval(expr, self._global_vars)
|
||||
|
||||
def inject(self, var_dict: Dict[str, Any]) -> None:
|
||||
for k, v in var_dict.items():
|
||||
self._global_vars[k] = v
|
||||
|
||||
@property
|
||||
def answer(self):
|
||||
return self._global_vars["answer"]
|
||||
|
||||
|
||||
class PALInterface:
|
||||
"""PAL interface wrap fun:`generate_interactive` to extract and execute
|
||||
generated code.
|
||||
|
||||
Adapted from https://github.com/reasoning-machines/pal
|
||||
|
||||
Args:
|
||||
model (AutoModelForCausalLM)
|
||||
tokenizer (AutoTokenizer)
|
||||
generation_config (GenerationConfig): Decode strategies
|
||||
additional_eos_token_id (int): End of sentence token id, default: 103028
|
||||
get_answer_expr (str): The function name of generated code, default: "solution()"
|
||||
verbose (bool): Print error information
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
model: AutoModelForCausalLM,
|
||||
tokenizer: AutoTokenizer,
|
||||
generation_config: GenerationConfig,
|
||||
additional_eos_token_id: int = 103028,
|
||||
get_answer_expr: str = "solution()",
|
||||
verbose: bool = False,
|
||||
):
|
||||
self.runtime = GenericRuntime()
|
||||
self.history: List = []
|
||||
self.model = model
|
||||
self.tokenizer = tokenizer
|
||||
self.generation_config = generation_config
|
||||
self.additional_eos_token_id = additional_eos_token_id
|
||||
self.answer_expr = get_answer_expr
|
||||
self.verbose = verbose
|
||||
|
||||
def generate(self, prompt):
|
||||
# The api will generate response word by word
|
||||
# we only need the last generation as the final results
|
||||
for cur_gen in generate_interactive(
|
||||
model=self.model,
|
||||
tokenizer=self.tokenizer,
|
||||
prompt=prompt,
|
||||
additional_eos_token_id=self.additional_eos_token_id,
|
||||
**asdict(self.generation_config),
|
||||
):
|
||||
continue
|
||||
# Get final response
|
||||
self.history.append(cur_gen)
|
||||
# Extract code block
|
||||
code = self.process_generation_to_code(cur_gen)
|
||||
return code
|
||||
|
||||
def process_generation_to_code(self, gens: str):
|
||||
if "```python" in gens:
|
||||
gens = gens.split("```python")[1].split("```")[0]
|
||||
elif "```" in gens:
|
||||
gens = gens.split("```")[1].split("```")[0]
|
||||
code = gens.split("\n")
|
||||
return code
|
||||
|
||||
def run(self, prompt, time_out: float = 100):
|
||||
code = self.generate(prompt)
|
||||
with Timeout(time_out):
|
||||
try:
|
||||
exec_result = self.execute(code)
|
||||
except Exception as e:
|
||||
if self.verbose:
|
||||
print(e)
|
||||
return exec_result
|
||||
|
||||
def execute(self, code: List[str]):
|
||||
self.runtime.exec_code("\n".join(code))
|
||||
return self.runtime.eval_code(self.answer_expr)
|
||||
|
||||
def clear_history(self):
|
||||
self.history = []
|
||||
|
||||
|
||||
def load_model(args):
|
||||
model = AutoModelForCausalLM.from_pretrained(args.model, trust_remote_code=True).to(torch.bfloat16).cuda()
|
||||
tokenizer = AutoTokenizer.from_pretrained(args.model, trust_remote_code=True)
|
||||
return model, tokenizer
|
||||
|
||||
|
||||
def load_data(args):
|
||||
# Load data from huggingface dataset
|
||||
if args.dataset == "gsm8k":
|
||||
gsm8k = load_dataset(path=args.dataset, name="main")
|
||||
test_set = gsm8k["test"]
|
||||
input_data = []
|
||||
for data in test_set:
|
||||
question = data["question"]
|
||||
target = float(data["answer"].split("#")[-1].replace(",", ""))
|
||||
input_data.append({"question": question, "target": target})
|
||||
else:
|
||||
raise NotImplementedError
|
||||
return input_data
|
||||
|
||||
|
||||
PROMPT = """<|System|>:You are a helpful assistant which use tools to solve mathematical reasoning questions. The tools you can use are:
|
||||
PythonExecutor: It can execute Python code. The code must be a function, and the function name must be 'solution'. The example format is as follows:
|
||||
```python
|
||||
def solution():
|
||||
variable_names_with_real_meaning = func(variable)
|
||||
return variable_names_with_real_meaning
|
||||
```{eos}
|
||||
<|User|>:Olivia has $23. She bought five bagels for $3 each. How much money does she have left?{eoh}
|
||||
<|Bot|>:
|
||||
```python
|
||||
def solution():
|
||||
money_initial = 23
|
||||
bagels = 5
|
||||
bagel_cost = 3
|
||||
money_spent = bagels * bagel_cost
|
||||
money_left = money_initial - money_spent
|
||||
result = money_left
|
||||
return result
|
||||
```{eoa}
|
||||
<|User|>:Michael had 58 golf balls. On tuesday, he lost 23 golf balls. On wednesday, he lost 2 more. How many golf balls did he have at the end of wednesday?{eoh}
|
||||
<|Bot|>:
|
||||
```python
|
||||
def solution():
|
||||
golf_balls_initial = 58
|
||||
golf_balls_lost_tuesday = 23
|
||||
golf_balls_lost_wednesday = 2
|
||||
golf_balls_left = golf_balls_initial - golf_balls_lost_tuesday - golf_balls_lost_wednesday
|
||||
result = golf_balls_left
|
||||
return result
|
||||
```{eoa}
|
||||
<|User|>:There were nine computers in the server room. Five more computers were installed each day, from monday to thursday. How many computers are now in the server room?{eoh}
|
||||
<|Bot|>:
|
||||
```python
|
||||
def solution():
|
||||
computers_initial = 9
|
||||
computers_per_day = 5
|
||||
num_days = 4 # 4 days between monday and thursday
|
||||
computers_added = computers_per_day * num_days
|
||||
computers_total = computers_initial + computers_added
|
||||
result = computers_total
|
||||
return result
|
||||
```{eoa}
|
||||
<|System|>:How about this question?{eos}
|
||||
<|User|>:{question}{eoh}
|
||||
<|Bot|>:""".strip()
|
||||
|
||||
|
||||
def main():
|
||||
|
||||
args = parse_args()
|
||||
|
||||
print("load model begin.")
|
||||
model, tokenizer = load_model(args)
|
||||
print("load model end.")
|
||||
|
||||
generation_config = GenerationConfig(max_length=args.max_length, top_p=args.top_p, temperature=args.temperature)
|
||||
|
||||
verbose = args.verbose
|
||||
interface = PALInterface(model=model, tokenizer=tokenizer, generation_config=generation_config, verbose=verbose)
|
||||
|
||||
if not os.path.exists(args.out_dir):
|
||||
os.makedirs(args.out_dir)
|
||||
savepath = os.path.join(args.out_dir, args.dataset + ".json")
|
||||
|
||||
# Load from history results
|
||||
if args.append and os.path.exists(savepath):
|
||||
lines = open(savepath).readlines()
|
||||
num_skip_exps = len(lines)
|
||||
scores = [x["score"] for x in map(json.loads, lines)]
|
||||
else:
|
||||
num_skip_exps = 0
|
||||
scores = []
|
||||
|
||||
examples = load_data(args)
|
||||
with open(savepath, "a" if args.append else "w") as f:
|
||||
pbar = tqdm.tqdm(examples[num_skip_exps:], initial=num_skip_exps, total=len(examples))
|
||||
for x in pbar:
|
||||
question = x["question"]
|
||||
result = copy.copy(x)
|
||||
|
||||
try:
|
||||
answer = interface.run(
|
||||
prompt=PROMPT.format(question=question, eoh=args.eoh, eoa=args.eoa, eos=args.eos),
|
||||
time_out=args.time_out,
|
||||
)
|
||||
answer = float(answer)
|
||||
score = 1 if abs(answer - x["target"]) < 1e-3 else 0
|
||||
except Exception as e:
|
||||
if verbose:
|
||||
print(e)
|
||||
answer = ""
|
||||
score = 0
|
||||
scores.append(score)
|
||||
result["answer"] = answer
|
||||
result["score"] = score
|
||||
result["generation"] = interface.history
|
||||
f.write(json.dumps(result) + "\n")
|
||||
|
||||
interface.clear_history()
|
||||
f.flush()
|
||||
|
||||
print(f"{args.model}: Accuracy - {sum(scores) / len(scores)}")
|
||||
torch.cuda.empty_cache()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,142 @@
|
||||
import argparse
|
||||
import json
|
||||
import os
|
||||
import sys
|
||||
|
||||
import numpy as np
|
||||
|
||||
current_dir = os.path.dirname(os.path.abspath(__file__))
|
||||
model_path = os.path.join(current_dir, "V7_sft.model")
|
||||
sys.path.append(os.path.join(current_dir, "transformers"))
|
||||
from tokenization_internlm import InternLMTokenizer
|
||||
|
||||
tokenizer = InternLMTokenizer(vocab_file=model_path)
|
||||
|
||||
|
||||
def write_bin(context: str, bin_file) -> None:
|
||||
"""
|
||||
Write bin file based on the context.
|
||||
|
||||
Args:
|
||||
context (str): the context of raw file.
|
||||
bin_file (file handler): the opened bin file.
|
||||
|
||||
Example:
|
||||
>>> write_bin("今天天气晴朗适合出门散步", "out.bin") # the output file format is 'txt'
|
||||
>>> out.bin
|
||||
>>> {"tokens": [67577, 69095, 63010, 61770, 67783, 69301, 74732]}
|
||||
"""
|
||||
# encode the context into tokens, which is a list, eg. [67577, 69095, 63010, 61770, 67783, 69301, 74732]
|
||||
tokens = tokenizer.encode(context)
|
||||
# transfer the list into dic, key is str 'tokens', value is tokens.
|
||||
# eg. {"tokens": [67577, 69095, 63010, 61770, 67783, 69301, 74732]}
|
||||
data = dict(tokens=tokens)
|
||||
# encode the data into bytes to save
|
||||
saved_bin = str.encode(json.dumps(data) + "\n")
|
||||
|
||||
# write bytes into bin_file
|
||||
bin_file.write(saved_bin)
|
||||
|
||||
|
||||
def prepare_meta(bin_output_path: str):
|
||||
"""
|
||||
Prepare metadata for the given bin file.
|
||||
|
||||
Args:
|
||||
bin_output_path (str): Output bin file path.
|
||||
"""
|
||||
meta = []
|
||||
cur = 0
|
||||
with open(bin_output_path, "rb") as f:
|
||||
while True:
|
||||
# read lines
|
||||
line = f.readline()
|
||||
# if line is empty, then break
|
||||
if line == b"":
|
||||
break
|
||||
# obtain the token amount of each line
|
||||
length = len(json.loads(line)["tokens"])
|
||||
# meta is a list of tuple(cur, length)
|
||||
# cur: the start index of each line
|
||||
# length: the token amount of each line
|
||||
meta.append((cur, length))
|
||||
# update the cur to generate the meta information of next line
|
||||
cur += len(line)
|
||||
|
||||
# define path of the generated meta file
|
||||
meta_fp = bin_output_path + ".meta"
|
||||
# save the generated meta information
|
||||
with open(meta_fp, "wb") as f:
|
||||
meta = np.array(meta, dtype=np.int32)
|
||||
np.save(f, meta)
|
||||
|
||||
|
||||
def text2bin(text_input_path: str, bin_output_path: str):
|
||||
"""
|
||||
Read content from the input file and write to bin file.
|
||||
Currently support 3 input formats: 'txt', 'json' and 'jsonl'.
|
||||
|
||||
Args:
|
||||
text_input_path (str): txt file path.
|
||||
bin_output_path (str): output bin file path.
|
||||
"""
|
||||
# Check if the txt file exists
|
||||
if not os.path.isfile(text_input_path):
|
||||
raise FileNotFoundError(f"{text_input_path} does not exist.")
|
||||
|
||||
file_format = text_input_path.split(".")[-1]
|
||||
assert file_format in ["txt", "json", "jsonl"], print(
|
||||
"Invalid input file type. Currently support `txt`, `json` and `jsonl`."
|
||||
)
|
||||
|
||||
with open(text_input_path, "r") as text_file, open(bin_output_path, "ab") as bin_file:
|
||||
if file_format == "txt":
|
||||
for line in text_file:
|
||||
# Strip any leading/trailing whitespace
|
||||
stripped_line = line.strip()
|
||||
if stripped_line:
|
||||
# Pass each line to the write_bin function
|
||||
write_bin(stripped_line, bin_file)
|
||||
|
||||
elif file_format == "json":
|
||||
data = json.load(text_file)
|
||||
# assuming data is a list of dictionaries
|
||||
for record in data:
|
||||
# the type of record is dict, transfer the dict into str
|
||||
context = json.dumps(record)
|
||||
# encode the str and write into bin
|
||||
write_bin(context, bin_file)
|
||||
|
||||
elif file_format == "jsonl":
|
||||
for line in text_file:
|
||||
# encode the str and write into bin
|
||||
write_bin(line, bin_file)
|
||||
|
||||
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument(
|
||||
"--text_input_path",
|
||||
type=str,
|
||||
required=True,
|
||||
help="Path to the input text file.",
|
||||
)
|
||||
parser.add_argument("--bin_output_path", type=str, required=True, help="Path to the output bin file.")
|
||||
|
||||
return parser.parse_args()
|
||||
|
||||
|
||||
def main():
|
||||
# parse arguments
|
||||
args = parse_args()
|
||||
|
||||
text2bin(args.text_input_path, args.bin_output_path)
|
||||
print(f"Successfully converted {args.text_input_path} to {args.bin_output_path}")
|
||||
|
||||
# To avoid potential read/write errors, the metadata preparation follows after creating the .bin file.
|
||||
prepare_meta(args.bin_output_path)
|
||||
print(f"Successfully generated {args.bin_output_path}.meta")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,25 @@
|
||||
# InternLM Transformers
|
||||
|
||||
[English](./README.md) |
|
||||
[简体中文](./README-zh-Hans.md)
|
||||
|
||||
该文件夹下包含了 transformers 格式的 `InternLM` 模型。
|
||||
|
||||
|
||||
## 权重转换
|
||||
|
||||
`convert2hf.py` 可以将训练保存的权重一键转换为 transformers 格式。在仓库根目录运行以下命令：
|
||||
|
||||
```bash
|
||||
python tools/transformers/convert2hf.py --src_folder origin_ckpt/ --tgt_folder hf_ckpt/ --tokenizer ./tools/V7_sft.model
|
||||
```
|
||||
|
||||
然后可以使用 `from_pretrained` 接口加载：
|
||||
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, AutoModel
|
||||
>>> model = AutoModel.from_pretrained("hf_ckpt/", trust_remote_code=True).cuda()
|
||||
```
|
||||
|
||||
|
||||
`intern_moss_example.py` 展示了如何使用 LoRA 来在 `fnlp/moss-moon-002-sft` 数据集上进行微调的样例。
|
||||
@@ -0,0 +1,23 @@
|
||||
# InternLM Transformers
|
||||
|
||||
[English](./README.md) |
|
||||
[简体中文](./README-zh-Hans.md)
|
||||
|
||||
This folder contains the `InternLM` model in transformers format.
|
||||
|
||||
## Weight Conversion
|
||||
|
||||
`convert2hf.py` can convert saved training weights into the transformers format with a single command. Execute the command in the root directory of repository:
|
||||
|
||||
```bash
|
||||
python tools/transformers/convert2hf.py --src_folder origin_ckpt/ --tgt_folder hf_ckpt/ --tokenizer ./tools/V7_sft.model
|
||||
```
|
||||
|
||||
Then, you can load it using the `from_pretrained` interface:
|
||||
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, AutoModel
|
||||
>>> model = AutoModel.from_pretrained("hf_ckpt/", trust_remote_code=True).cuda()
|
||||
```
|
||||
|
||||
`intern_moss_example.py` demonstrates an example of how to use LoRA for fine-tuning on the `fnlp/moss-moon-002-sft` dataset.
|
||||
@@ -0,0 +1,120 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# 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.
|
||||
""" InternLM model configuration"""
|
||||
|
||||
from transformers.utils import logging
|
||||
from transformers.configuration_utils import PretrainedConfig
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
INTERNLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
|
||||
|
||||
|
||||
class InternLMConfig(PretrainedConfig):
|
||||
r"""
|
||||
This is the configuration class to store the configuration of a [`InternLMModel`]. It is used to instantiate an InternLM
|
||||
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
|
||||
defaults will yield a similar configuration to that of the InternLM-7B.
|
||||
|
||||
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
||||
documentation from [`PretrainedConfig`] for more information.
|
||||
|
||||
|
||||
Args:
|
||||
vocab_size (`int`, *optional*, defaults to 32000):
|
||||
Vocabulary size of the InternLM model. Defines the number of different tokens that can be represented by the
|
||||
`inputs_ids` passed when calling [`InternLMModel`]
|
||||
hidden_size (`int`, *optional*, defaults to 4096):
|
||||
Dimension of the hidden representations.
|
||||
intermediate_size (`int`, *optional*, defaults to 11008):
|
||||
Dimension of the MLP representations.
|
||||
num_hidden_layers (`int`, *optional*, defaults to 32):
|
||||
Number of hidden layers in the Transformer encoder.
|
||||
num_attention_heads (`int`, *optional*, defaults to 32):
|
||||
Number of attention heads for each attention layer in the Transformer encoder.
|
||||
hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
|
||||
The non-linear activation function (function or string) in the decoder.
|
||||
max_position_embeddings (`int`, *optional*, defaults to 2048):
|
||||
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).
|
||||
initializer_range (`float`, *optional*, defaults to 0.02):
|
||||
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
|
||||
rms_norm_eps (`float`, *optional*, defaults to 1e-12):
|
||||
The epsilon used by the rms normalization layers.
|
||||
use_cache (`bool`, *optional*, defaults to `True`):
|
||||
Whether or not the model should return the last key/values attentions (not used by all models). Only
|
||||
relevant if `config.is_decoder=True`.
|
||||
tie_word_embeddings(`bool`, *optional*, defaults to `False`):
|
||||
Whether to tie weight embeddings
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import InternLMModel, InternLMConfig
|
||||
|
||||
>>> # Initializing a InternLM internlm-7b style configuration
|
||||
>>> configuration = InternLMConfig()
|
||||
|
||||
>>> # Initializing a model from the internlm-7b style configuration
|
||||
>>> model = InternLMModel(configuration)
|
||||
|
||||
>>> # Accessing the model configuration
|
||||
>>> configuration = model.config
|
||||
```"""
|
||||
model_type = "internlm"
|
||||
_auto_class = "AutoConfig"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_size=103168,
|
||||
hidden_size=4096,
|
||||
intermediate_size=11008,
|
||||
num_hidden_layers=32,
|
||||
num_attention_heads=32,
|
||||
hidden_act="silu",
|
||||
max_position_embeddings=2048,
|
||||
initializer_range=0.02,
|
||||
rms_norm_eps=1e-6,
|
||||
use_cache=True,
|
||||
pad_token_id=0,
|
||||
bos_token_id=1,
|
||||
eos_token_id=2,
|
||||
tie_word_embeddings=False,
|
||||
bias=True,
|
||||
**kwargs,
|
||||
):
|
||||
self.vocab_size = vocab_size
|
||||
self.max_position_embeddings = max_position_embeddings
|
||||
self.hidden_size = hidden_size
|
||||
self.intermediate_size = intermediate_size
|
||||
self.num_hidden_layers = num_hidden_layers
|
||||
self.num_attention_heads = num_attention_heads
|
||||
self.hidden_act = hidden_act
|
||||
self.initializer_range = initializer_range
|
||||
self.rms_norm_eps = rms_norm_eps
|
||||
self.use_cache = use_cache
|
||||
self.bias = bias
|
||||
super().__init__(
|
||||
pad_token_id=pad_token_id,
|
||||
bos_token_id=bos_token_id,
|
||||
eos_token_id=eos_token_id,
|
||||
tie_word_embeddings=tie_word_embeddings,
|
||||
**kwargs,
|
||||
)
|
||||
@@ -0,0 +1,175 @@
|
||||
import argparse
|
||||
import math
|
||||
import json
|
||||
import os
|
||||
import re
|
||||
import tempfile
|
||||
|
||||
import torch
|
||||
from modeling_internlm import InternLMConfig, InternLMForCausalLM
|
||||
from tokenization_internlm import InternLMTokenizer
|
||||
|
||||
NUM_SHARDS = {
|
||||
"7B": 1,
|
||||
}
|
||||
|
||||
|
||||
def convert2hf(model_config, states_tp_pps):
|
||||
|
||||
with tempfile.TemporaryDirectory() as folder:
|
||||
states = merge_pp(states_tp_pps)[0]
|
||||
|
||||
if "embedding.word_embeddings.weight" in states:
|
||||
embedding_key = "embedding.word_embeddings.weight"
|
||||
elif "embedding.weight" in states:
|
||||
embedding_key = "embedding.weight"
|
||||
else:
|
||||
print("Check embedding states'names in below:", flush=True)
|
||||
print(list(states.keys()), flush=True)
|
||||
|
||||
dims_per_head = model_config["hidden_size"] // model_config["num_attention_heads"]
|
||||
base = 10000.0
|
||||
inv_freq = 1.0 / (base ** (torch.arange(0, dims_per_head, 2).float() / dims_per_head))
|
||||
|
||||
current_states = {}
|
||||
|
||||
current_states["model.embed_tokens.weight"] = states.pop(embedding_key)
|
||||
current_states["model.norm.weight"] = states.pop("norm.weight")
|
||||
current_states["lm_head.weight"] = states.pop("head.weight")
|
||||
|
||||
for i in range(model_config["num_layers"]):
|
||||
states.pop(f"blocks.{i}.mixer.rotary_emb.inv_freq", None)
|
||||
|
||||
wqkv = states.pop(f"blocks.{i}.mixer.Wqkv.weight").reshape(
|
||||
3, model_config["num_attention_heads"], -1, model_config["hidden_size"]
|
||||
)
|
||||
bqkv = states.pop(f"blocks.{i}.mixer.Wqkv.bias").reshape(3, model_config["num_attention_heads"], -1)
|
||||
|
||||
current_states[f"model.layers.{i}.self_attn.q_proj.weight"] = wqkv[0].reshape(
|
||||
-1, model_config["hidden_size"]
|
||||
)
|
||||
current_states[f"model.layers.{i}.self_attn.q_proj.bias"] = bqkv[0].reshape(-1)
|
||||
current_states[f"model.layers.{i}.self_attn.k_proj.weight"] = wqkv[1].reshape(
|
||||
-1, model_config["hidden_size"]
|
||||
)
|
||||
current_states[f"model.layers.{i}.self_attn.k_proj.bias"] = bqkv[1].reshape(-1)
|
||||
current_states[f"model.layers.{i}.self_attn.v_proj.weight"] = wqkv[2].reshape(
|
||||
-1, model_config["hidden_size"]
|
||||
)
|
||||
current_states[f"model.layers.{i}.self_attn.v_proj.bias"] = bqkv[2].reshape(-1)
|
||||
|
||||
current_states[f"model.layers.{i}.self_attn.o_proj.weight"] = states.pop(
|
||||
f"blocks.{i}.mixer.out_proj.weight"
|
||||
)
|
||||
current_states[f"model.layers.{i}.self_attn.o_proj.bias"] = states.pop(f"blocks.{i}.mixer.out_proj.bias")
|
||||
|
||||
current_states[f"model.layers.{i}.mlp.gate_proj.weight"] = states.pop(f"blocks.{i}.mlp.w1.weight")
|
||||
current_states[f"model.layers.{i}.mlp.down_proj.weight"] = states.pop(f"blocks.{i}.mlp.w3.weight")
|
||||
current_states[f"model.layers.{i}.mlp.up_proj.weight"] = states.pop(f"blocks.{i}.mlp.w2.weight")
|
||||
|
||||
current_states[f"model.layers.{i}.input_layernorm.weight"] = states.pop(f"blocks.{i}.norm1.weight")
|
||||
current_states[f"model.layers.{i}.post_attention_layernorm.weight"] = states.pop(f"blocks.{i}.norm2.weight")
|
||||
current_states[f"model.layers.{i}.self_attn.rotary_emb.inv_freq"] = inv_freq
|
||||
|
||||
config = InternLMConfig(
|
||||
hidden_size=model_config["hidden_size"],
|
||||
intermediate_size=compute_intermediate_size(model_config["hidden_size"]),
|
||||
num_attention_heads=model_config["num_attention_heads"],
|
||||
num_hidden_layers=model_config["num_layers"],
|
||||
rms_norm_eps=1e-06,
|
||||
bias=True,
|
||||
)
|
||||
|
||||
if model_config["vocab_size"] != -1:
|
||||
config.vocab_size = model_config["vocab_size"]
|
||||
|
||||
config.save_pretrained(folder)
|
||||
torch.save(current_states, os.path.join(folder, "pytorch_model.bin"))
|
||||
|
||||
model = InternLMForCausalLM.from_pretrained(folder, torch_dtype=torch.float16)
|
||||
del model.config._name_or_path
|
||||
|
||||
return config, model
|
||||
|
||||
|
||||
def compute_intermediate_size(n):
|
||||
return int(math.ceil(n * 8 / 3) + 255) // 256 * 256
|
||||
|
||||
|
||||
def merge_pp(states_tp_pp):
|
||||
max_tp = len(states_tp_pp)
|
||||
max_pp = len(states_tp_pp[0])
|
||||
|
||||
full_states = []
|
||||
for tp in range(max_tp):
|
||||
layer_shift = 0
|
||||
|
||||
tp_states = {}
|
||||
for pp in range(max_pp):
|
||||
_layer_shift = 0
|
||||
states = states_tp_pp[tp][pp]
|
||||
keys = list(states.keys())
|
||||
for key in keys:
|
||||
match = re.search("\.\d+\.", key)
|
||||
if match is not None:
|
||||
s, e = match.span()
|
||||
layer_idx = int(key[s + 1 : e - 1]) + layer_shift
|
||||
_layer_shift = max(_layer_shift, int(key[s + 1 : e - 1]))
|
||||
name = key[:s] + f".{layer_idx}." + key[e:]
|
||||
tp_states[name] = states[key]
|
||||
else:
|
||||
tp_states[key] = states[key]
|
||||
layer_shift += _layer_shift + 1
|
||||
full_states.append({(key[6:] if key.startswith("model.") else key): value for key, value in tp_states.items()})
|
||||
return full_states
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--src_folder', type=str, default='~/test/') # 需要转换为hf格式的checkpoint文件夹
|
||||
parser.add_argument('--tgt_folder', type=str, default='~/output/') # 存放转换后checkpoint的目标文件夹
|
||||
parser.add_argument('--tokenizer', type=str, default='~/test/tokenizer.model') # Tokenizer 文件的路径
|
||||
args = parser.parse_args()
|
||||
|
||||
def load(fp):
|
||||
with open(fp, "rb") as f:
|
||||
pt_data = torch.load(f, map_location="cpu")
|
||||
return pt_data
|
||||
|
||||
folder = args.src_folder
|
||||
target_folder = args.tgt_folder
|
||||
model_config = load(os.path.join(folder, "model_config.pt"))
|
||||
|
||||
fns = list(os.listdir(folder))
|
||||
|
||||
model_fns = []
|
||||
for fn in fns:
|
||||
if fn.startswith("model_t") and not fn.endswith("md5"):
|
||||
model_fns.append(fn)
|
||||
|
||||
max_tp, max_pp = -1, -1
|
||||
for fn in model_fns:
|
||||
_, tp, pp = os.path.splitext(fn)[0].split("_")
|
||||
max_pp = max(max_pp, int(pp[2:]) + 1)
|
||||
max_tp = max(max_tp, int(tp[2:]) + 1)
|
||||
|
||||
states_tp_pps = [[]]
|
||||
|
||||
for pp in range(max_pp):
|
||||
model_name = f"model_tp0_pp{pp}.pt"
|
||||
states = load(os.path.join(folder, model_name))
|
||||
states_tp_pps[0].append(states)
|
||||
|
||||
config, model = convert2hf(model_config, states_tp_pps)
|
||||
|
||||
os.makedirs(target_folder, exist_ok=True)
|
||||
model.save_pretrained(target_folder, max_shard_size="20GB")
|
||||
# TODO There should be a better way to add this.
|
||||
with open(os.path.join(target_folder, "config.json")) as fp:
|
||||
config_dict = json.load(fp)
|
||||
config_dict["auto_map"]["AutoModel"] = "modeling_internlm.InternLMForCausalLM"
|
||||
with open(os.path.join(target_folder, "config.json"), "w") as fp:
|
||||
json.dump(config_dict, fp, indent=2)
|
||||
|
||||
tokenizer = InternLMTokenizer(args.tokenizer)
|
||||
tokenizer.save_pretrained(target_folder)
|
||||
@@ -0,0 +1,137 @@
|
||||
import copy
|
||||
import warnings
|
||||
from dataclasses import dataclass
|
||||
from typing import Callable, List, Optional
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from transformers import AutoModel, AutoTokenizer
|
||||
from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList
|
||||
from transformers.utils import logging
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
|
||||
@dataclass
|
||||
class GenerationConfig:
|
||||
max_length: Optional[int] = None
|
||||
top_p: Optional[float] = None
|
||||
temperature: Optional[float] = None
|
||||
do_sample: Optional[bool] = True
|
||||
repetition_penalty: Optional[float] = 1.0
|
||||
|
||||
|
||||
@torch.inference_mode()
|
||||
def generate_interactive(
|
||||
model,
|
||||
tokenizer,
|
||||
prompt,
|
||||
generation_config: Optional[GenerationConfig] = None,
|
||||
logits_processor: Optional[LogitsProcessorList] = None,
|
||||
stopping_criteria: Optional[StoppingCriteriaList] = None,
|
||||
prefix_allowed_tokens_fn: Optional[Callable[[int, torch.Tensor], List[int]]] = None,
|
||||
additional_eos_token_id: Optional[int] = None,
|
||||
**kwargs,
|
||||
):
|
||||
inputs = tokenizer([prompt], padding=True, return_tensors="pt")
|
||||
input_length = len(inputs["input_ids"][0])
|
||||
for k, v in inputs.items():
|
||||
inputs[k] = v.cuda()
|
||||
input_ids = inputs["input_ids"]
|
||||
batch_size, input_ids_seq_length = input_ids.shape[0], input_ids.shape[-1]
|
||||
if generation_config is None:
|
||||
generation_config = model.generation_config
|
||||
generation_config = copy.deepcopy(generation_config)
|
||||
model_kwargs = generation_config.update(**kwargs)
|
||||
bos_token_id, eos_token_id = generation_config.bos_token_id, generation_config.eos_token_id
|
||||
if isinstance(eos_token_id, int):
|
||||
eos_token_id = [eos_token_id]
|
||||
if additional_eos_token_id is not None:
|
||||
eos_token_id.append(additional_eos_token_id)
|
||||
has_default_max_length = kwargs.get("max_length") is None and generation_config.max_length is not None
|
||||
if has_default_max_length and generation_config.max_new_tokens is None:
|
||||
warnings.warn(
|
||||
f"Using `max_length`'s default ({generation_config.max_length}) to control the generation length. "
|
||||
"This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we"
|
||||
" recommend using `max_new_tokens` to control the maximum length of the generation.",
|
||||
UserWarning,
|
||||
)
|
||||
elif generation_config.max_new_tokens is not None:
|
||||
generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
|
||||
if not has_default_max_length:
|
||||
logger.warn(
|
||||
f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
|
||||
f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
|
||||
"Please refer to the documentation for more information. "
|
||||
"(https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)",
|
||||
UserWarning,
|
||||
)
|
||||
|
||||
if input_ids_seq_length >= generation_config.max_length:
|
||||
input_ids_string = "input_ids"
|
||||
logger.warning(
|
||||
f"Input length of {input_ids_string} is {input_ids_seq_length}, but `max_length` is set to"
|
||||
f" {generation_config.max_length}. This can lead to unexpected behavior. You should consider"
|
||||
" increasing `max_new_tokens`."
|
||||
)
|
||||
|
||||
# 2. Set generation parameters if not already defined
|
||||
logits_processor = logits_processor if logits_processor is not None else LogitsProcessorList()
|
||||
stopping_criteria = stopping_criteria if stopping_criteria is not None else StoppingCriteriaList()
|
||||
|
||||
logits_processor = model._get_logits_processor(
|
||||
generation_config=generation_config,
|
||||
input_ids_seq_length=input_ids_seq_length,
|
||||
encoder_input_ids=input_ids,
|
||||
prefix_allowed_tokens_fn=prefix_allowed_tokens_fn,
|
||||
logits_processor=logits_processor,
|
||||
)
|
||||
|
||||
stopping_criteria = model._get_stopping_criteria(
|
||||
generation_config=generation_config, stopping_criteria=stopping_criteria
|
||||
)
|
||||
logits_warper = model._get_logits_warper(generation_config)
|
||||
|
||||
unfinished_sequences = input_ids.new(input_ids.shape[0]).fill_(1)
|
||||
scores = None
|
||||
while True:
|
||||
model_inputs = model.prepare_inputs_for_generation(input_ids, **model_kwargs)
|
||||
# forward pass to get next token
|
||||
outputs = model(
|
||||
**model_inputs,
|
||||
return_dict=True,
|
||||
output_attentions=False,
|
||||
output_hidden_states=False,
|
||||
)
|
||||
|
||||
next_token_logits = outputs.logits[:, -1, :]
|
||||
|
||||
# pre-process distribution
|
||||
next_token_scores = logits_processor(input_ids, next_token_logits)
|
||||
next_token_scores = logits_warper(input_ids, next_token_scores)
|
||||
|
||||
# sample
|
||||
probs = nn.functional.softmax(next_token_scores, dim=-1)
|
||||
if generation_config.do_sample:
|
||||
next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)
|
||||
else:
|
||||
next_tokens = torch.argmax(probs, dim=-1)
|
||||
|
||||
# update generated ids, model inputs, and length for next step
|
||||
input_ids = torch.cat([input_ids, next_tokens[:, None]], dim=-1)
|
||||
model_kwargs = model._update_model_kwargs_for_generation(
|
||||
outputs, model_kwargs, is_encoder_decoder=False
|
||||
)
|
||||
unfinished_sequences = unfinished_sequences.mul((min(next_tokens != i for i in eos_token_id)).long())
|
||||
|
||||
output_token_ids = input_ids[0].cpu().tolist()
|
||||
output_token_ids = output_token_ids[input_length:]
|
||||
for each_eos_token_id in eos_token_id:
|
||||
if output_token_ids[-1] == each_eos_token_id:
|
||||
output_token_ids = output_token_ids[:-1]
|
||||
response = tokenizer.decode(output_token_ids)
|
||||
|
||||
yield response
|
||||
# stop when each sentence is finished, or if we exceed the maximum length
|
||||
if unfinished_sequences.max() == 0 or stopping_criteria(input_ids, scores):
|
||||
break
|
||||
@@ -0,0 +1,69 @@
|
||||
import torch
|
||||
from torch.utils.data import DataLoader
|
||||
from peft import get_peft_model, LoraConfig, TaskType
|
||||
from transformers import get_linear_schedule_with_warmup
|
||||
from transformers import AutoModelForCausalLM, AutoTokenizer
|
||||
from tqdm import tqdm
|
||||
|
||||
from moss_002_sft import get_dataset, collate_fn
|
||||
|
||||
model_path = "model_path"
|
||||
data_dir = "moss_002_sft"
|
||||
data_num = -1
|
||||
test_size = 10
|
||||
train_batch_size = 1
|
||||
epochs = 5
|
||||
val_per_steps = 1000
|
||||
lr = 9e-6
|
||||
peft_config = LoraConfig(
|
||||
task_type=TaskType.CAUSAL_LM, r=32, lora_alpha=32, lora_dropout=0.1,
|
||||
target_modules=["gate_proj", "down_proj", "up_proj", "q_proj", "k_proj", "v_proj", "o_proj"]
|
||||
)
|
||||
|
||||
|
||||
# model
|
||||
model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)
|
||||
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
|
||||
model = get_peft_model(model, peft_config)
|
||||
model.cuda()
|
||||
|
||||
# dataset
|
||||
train_dataset, val_dataset = get_dataset(tokenizer, data_dir, num=data_num, test_size=test_size)
|
||||
train_dataloader = DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True, collate_fn=lambda x: collate_fn(x, tokenizer))
|
||||
|
||||
optimizer = torch.optim.AdamW(model.parameters(), lr)
|
||||
scheduler = get_linear_schedule_with_warmup(
|
||||
optimizer, 1000, epochs * len(train_dataloader)
|
||||
)
|
||||
|
||||
# train
|
||||
fp = open("output", "w")
|
||||
model.train()
|
||||
for epoch in tqdm(range(epochs), desc="Traning Epoch"):
|
||||
batch_bar = tqdm(train_dataloader, desc="Training Batch")
|
||||
for step, batch in enumerate(batch_bar):
|
||||
batch = {k:v.cuda() for k, v in batch.items()}
|
||||
with torch.amp.autocast(device_type="cuda", dtype=torch.bfloat16):
|
||||
output = model(**batch)
|
||||
|
||||
loss = output.loss
|
||||
loss.backward()
|
||||
optimizer.step()
|
||||
scheduler.step()
|
||||
optimizer.zero_grad()
|
||||
batch_bar.set_postfix({"loss": loss.item()})
|
||||
if (step + 1) % val_per_steps == 0:
|
||||
fp.write(f"Epoch {epoch} Batch {step}: Loss={loss.item()}\n")
|
||||
for i in tqdm(range(len(val_dataset)), desc="Generating"):
|
||||
data, label = val_dataset[i]
|
||||
prefix = tokenizer.decode(data.tolist(), skip_special_tokens=True)
|
||||
try:
|
||||
generate = model.generate(input_ids=data.unsqueeze(0).cuda(), temperature=0.7, top_k=50, do_sample=True, repetition_penalty=1.02, max_new_tokens=100, top_p=0.9)
|
||||
text = tokenizer.decode(generate[0].tolist(), skip_special_tokens=True)
|
||||
text = text.replace(prefix, "")
|
||||
fp.write(f"Prefix: {prefix}\nGenerated: {text}" + "\n---------------------------------\n")
|
||||
except Exception as e:
|
||||
fp.write(f"Prefix: {prefix}\nError: {e}" + "\n---------------------------------\n")
|
||||
fp.write("\n==============================\n")
|
||||
model.train()
|
||||
torch.cuda.empty_cache()
|
||||
@@ -0,0 +1,105 @@
|
||||
import os
|
||||
import copy
|
||||
|
||||
import torch
|
||||
from torch.utils.data import Dataset
|
||||
from datasets import load_dataset, Dataset as HFDataset
|
||||
|
||||
class SFTDataset(Dataset):
|
||||
# https://github.com/OpenLMLab/MOSS/blob/main/finetune_moss.py
|
||||
def __init__(self, dataset):
|
||||
super().__init__()
|
||||
self.dataset = dataset
|
||||
|
||||
def __len__(self):
|
||||
return len(self.dataset)
|
||||
|
||||
def __getitem__(self, index):
|
||||
data = copy.deepcopy(self.dataset[index]["input_ids"])
|
||||
no_loss_spans = copy.deepcopy(self.dataset[index]["no_loss_spans"])
|
||||
|
||||
data = torch.tensor(data, dtype=torch.long)
|
||||
label = copy.deepcopy(data)
|
||||
|
||||
for no_loss_span in no_loss_spans:
|
||||
label[no_loss_span[0] : no_loss_span[1]] = -100
|
||||
|
||||
return data, label
|
||||
|
||||
def collate_fn(batch, tokenizer):
|
||||
batch_input_ids, batch_labels = [], []
|
||||
for input_ids, label in batch:
|
||||
batch_input_ids.append(input_ids)
|
||||
batch_labels.append(label)
|
||||
|
||||
batch_input_ids = torch.nn.utils.rnn.pad_sequence(batch_input_ids, batch_first=True, padding_value=tokenizer.eos_token_id)
|
||||
batch_labels = torch.nn.utils.rnn.pad_sequence(batch_labels, batch_first=True, padding_value=-100)
|
||||
|
||||
return {
|
||||
"input_ids": batch_input_ids,
|
||||
"attention_mask": (batch_input_ids == tokenizer.eos_token_id).long(),
|
||||
"labels": batch_labels
|
||||
}
|
||||
|
||||
def process(sample, tokenizer, max_len):
|
||||
chat = sample["plain_text"].split("<eoa>")[:-1]
|
||||
num_turns = sample["num_turns"]
|
||||
meta_instruction = sample["prefix"]
|
||||
|
||||
# encode instruction
|
||||
instruction_ids = tokenizer.encode(meta_instruction)
|
||||
assert isinstance(instruction_ids, list), instruction_ids
|
||||
assert len(instruction_ids) > 0, len(instruction_ids)
|
||||
input_ids = copy.deepcopy(instruction_ids)
|
||||
# We do not calculate loss for instruction.
|
||||
no_loss_spans = [(0, len(instruction_ids))]
|
||||
|
||||
for i in range(num_turns):
|
||||
# Collect dialogues
|
||||
cur_turn_ids = []
|
||||
cur_no_loss_spans = []
|
||||
# Add to cur_turn_ids
|
||||
cur_turn_ids.extend(tokenizer.encode(chat[i] + "<eoa>"))
|
||||
# if key == 'Tool Responses':
|
||||
# # The format tokens (<|Results|>:...<eor>\n) should have losses.
|
||||
# cur_no_loss_spans.append((len(input_ids + cur_turn_ids) + 5, len(input_ids + cur_turn_ids + cur_ids) - 2))
|
||||
if len(input_ids + cur_turn_ids) > max_len:
|
||||
# Too long, break
|
||||
break
|
||||
# Extend input_ids
|
||||
input_ids.extend(cur_turn_ids)
|
||||
no_loss_spans.extend(cur_no_loss_spans)
|
||||
|
||||
if len(input_ids) == len(instruction_ids):
|
||||
# No dialogue, return
|
||||
return {"input_ids": [], "no_loss_spans": []}
|
||||
else:
|
||||
return {"input_ids": input_ids, "no_loss_spans": no_loss_spans}
|
||||
|
||||
|
||||
def load_data(save_dir, tokenizer, max_len, num=-1) -> HFDataset:
|
||||
if os.path.exists(save_dir):
|
||||
print(f"Loading moss-002-sft from {save_dir}")
|
||||
else:
|
||||
print(f"Loading moss-002-sft from datasets")
|
||||
moss_sft = load_dataset("fnlp/moss-002-sft-data", split="train")
|
||||
moss_sft = moss_sft.map(lambda x:process(x, tokenizer, max_len), num_proc=10)
|
||||
moss_sft = moss_sft.filter(lambda x:len(x["input_ids"]) != 0)
|
||||
moss_sft.save_to_disk(save_dir)
|
||||
|
||||
moss_sft = HFDataset.load_from_disk(save_dir)
|
||||
if num != -1:
|
||||
moss_sft = moss_sft.select(range(num))
|
||||
print(
|
||||
f"Load successfully, total {len(moss_sft)} samples.")
|
||||
|
||||
return moss_sft
|
||||
|
||||
def get_dataset(tokenizer, save_dir, max_len=1024, num=-1, test_size=0.1):
|
||||
moss_sft_data = load_data(save_dir, tokenizer, max_len, num)
|
||||
moss_sft_split = moss_sft_data.train_test_split(test_size=test_size)
|
||||
train_dataset = SFTDataset(moss_sft_split["train"])
|
||||
val_dataset = SFTDataset(moss_sft_split["test"])
|
||||
|
||||
return train_dataset, val_dataset
|
||||
|
||||
@@ -0,0 +1,998 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# 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.
|
||||
""" PyTorch InternLM model."""
|
||||
import math
|
||||
from typing import List, Optional, Tuple, Union
|
||||
import threading, queue
|
||||
|
||||
import torch
|
||||
import torch.utils.checkpoint
|
||||
from torch import nn
|
||||
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
|
||||
|
||||
from transformers.activations import ACT2FN
|
||||
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
|
||||
from transformers.modeling_utils import PreTrainedModel
|
||||
from transformers.generation.streamers import BaseStreamer
|
||||
from transformers.utils import add_start_docstrings, add_start_docstrings_to_model_forward, logging, replace_return_docstrings
|
||||
from configuration_internlm import InternLMConfig
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
_CONFIG_FOR_DOC = "InternLMConfig"
|
||||
|
||||
# Copied from transformers.models.bart.modeling_bart._make_causal_mask
|
||||
def _make_causal_mask(
|
||||
input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
|
||||
):
|
||||
"""
|
||||
Make causal mask used for bi-directional self-attention.
|
||||
"""
|
||||
bsz, tgt_len = input_ids_shape
|
||||
mask = torch.full((tgt_len, tgt_len), torch.tensor(torch.finfo(dtype).min, device=device), device=device)
|
||||
mask_cond = torch.arange(mask.size(-1), device=device)
|
||||
mask.masked_fill_(mask_cond < (mask_cond + 1).view(mask.size(-1), 1), 0)
|
||||
mask = mask.to(dtype)
|
||||
|
||||
if past_key_values_length > 0:
|
||||
mask = torch.cat([torch.zeros(tgt_len, past_key_values_length, dtype=dtype, device=device), mask], dim=-1)
|
||||
return mask[None, None, :, :].expand(bsz, 1, tgt_len, tgt_len + past_key_values_length)
|
||||
|
||||
|
||||
# Copied from transformers.models.bart.modeling_bart._expand_mask
|
||||
def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
|
||||
"""
|
||||
Expands attention_mask from `[bsz, seq_len]` to `[bsz, 1, tgt_seq_len, src_seq_len]`.
|
||||
"""
|
||||
bsz, src_len = mask.size()
|
||||
tgt_len = tgt_len if tgt_len is not None else src_len
|
||||
|
||||
expanded_mask = mask[:, None, None, :].expand(bsz, 1, tgt_len, src_len).to(dtype)
|
||||
|
||||
inverted_mask = 1.0 - expanded_mask
|
||||
|
||||
return inverted_mask.masked_fill(inverted_mask.to(torch.bool), torch.finfo(dtype).min)
|
||||
|
||||
|
||||
class InternLMRMSNorm(nn.Module):
|
||||
def __init__(self, hidden_size, eps=1e-6):
|
||||
"""
|
||||
InternLMRMSNorm is equivalent to T5LayerNorm
|
||||
"""
|
||||
super().__init__()
|
||||
self.weight = nn.Parameter(torch.ones(hidden_size))
|
||||
self.variance_epsilon = eps
|
||||
|
||||
def forward(self, hidden_states):
|
||||
variance = hidden_states.to(torch.float32).pow(2).mean(-1, keepdim=True)
|
||||
hidden_states = hidden_states * torch.rsqrt(variance + self.variance_epsilon)
|
||||
|
||||
# convert into half-precision if necessary
|
||||
if self.weight.dtype in [torch.float16, torch.bfloat16]:
|
||||
hidden_states = hidden_states.to(self.weight.dtype)
|
||||
|
||||
return self.weight * hidden_states
|
||||
|
||||
|
||||
class InternLMRotaryEmbedding(torch.nn.Module):
|
||||
def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
|
||||
super().__init__()
|
||||
inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float().to(device) / dim))
|
||||
self.register_buffer("inv_freq", inv_freq)
|
||||
|
||||
# Build here to make `torch.jit.trace` work.
|
||||
self.max_seq_len_cached = max_position_embeddings
|
||||
t = torch.arange(self.max_seq_len_cached, device=self.inv_freq.device, dtype=self.inv_freq.dtype)
|
||||
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
||||
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
||||
emb = torch.cat((freqs, freqs), dim=-1)
|
||||
self.register_buffer("cos_cached", emb.cos()[None, None, :, :], persistent=False)
|
||||
self.register_buffer("sin_cached", emb.sin()[None, None, :, :], persistent=False)
|
||||
|
||||
def forward(self, x, seq_len=None):
|
||||
# x: [bs, num_attention_heads, seq_len, head_size]
|
||||
# This `if` block is unlikely to be run after we build sin/cos in `__init__`. Keep the logic here just in case.
|
||||
if seq_len > self.max_seq_len_cached:
|
||||
self.max_seq_len_cached = seq_len
|
||||
t = torch.arange(self.max_seq_len_cached, device=x.device, dtype=self.inv_freq.dtype)
|
||||
freqs = torch.einsum("i,j->ij", t, self.inv_freq)
|
||||
# Different from paper, but it uses a different permutation in order to obtain the same calculation
|
||||
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),
|
||||
)
|
||||
|
||||
|
||||
def rotate_half(x):
|
||||
"""Rotates half the hidden dims of the input."""
|
||||
x1 = x[..., : x.shape[-1] // 2]
|
||||
x2 = x[..., x.shape[-1] // 2 :]
|
||||
return torch.cat((-x2, x1), dim=-1)
|
||||
|
||||
|
||||
def apply_rotary_pos_emb(q, k, 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)
|
||||
k_embed = (k * cos) + (rotate_half(k) * sin)
|
||||
return q_embed, k_embed
|
||||
|
||||
|
||||
class InternLMMLP(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size: int,
|
||||
intermediate_size: int,
|
||||
hidden_act: str,
|
||||
):
|
||||
super().__init__()
|
||||
self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
|
||||
self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
|
||||
self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
|
||||
self.act_fn = ACT2FN[hidden_act]
|
||||
|
||||
def forward(self, x):
|
||||
return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
|
||||
|
||||
|
||||
class InternLMAttention(nn.Module):
|
||||
"""Multi-headed attention from 'Attention Is All You Need' paper"""
|
||||
|
||||
def __init__(self, config: InternLMConfig):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.hidden_size = config.hidden_size
|
||||
self.num_heads = config.num_attention_heads
|
||||
self.head_dim = self.hidden_size // self.num_heads
|
||||
self.max_position_embeddings = config.max_position_embeddings
|
||||
|
||||
if (self.head_dim * self.num_heads) != self.hidden_size:
|
||||
raise ValueError(
|
||||
f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
|
||||
f" and `num_heads`: {self.num_heads})."
|
||||
)
|
||||
self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias)
|
||||
self.k_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias)
|
||||
self.v_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.bias)
|
||||
self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.bias)
|
||||
self.rotary_emb = InternLMRotaryEmbedding(self.head_dim, max_position_embeddings=self.max_position_embeddings)
|
||||
|
||||
def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int):
|
||||
return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
|
||||
|
||||
def 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]
|
||||
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
|
||||
|
||||
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))
|
||||
|
||||
# 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
|
||||
|
||||
|
||||
class InternLMDecoderLayer(nn.Module):
|
||||
def __init__(self, config: InternLMConfig):
|
||||
super().__init__()
|
||||
self.hidden_size = config.hidden_size
|
||||
self.self_attn = InternLMAttention(config=config)
|
||||
self.mlp = InternLMMLP(
|
||||
hidden_size=self.hidden_size,
|
||||
intermediate_size=config.intermediate_size,
|
||||
hidden_act=config.hidden_act,
|
||||
)
|
||||
self.input_layernorm = InternLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.post_attention_layernorm = InternLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
def 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: Optional[bool] = False,
|
||||
use_cache: Optional[bool] = False,
|
||||
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
|
||||
"""
|
||||
Args:
|
||||
hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
|
||||
attention_mask (`torch.FloatTensor`, *optional*): attention mask of size
|
||||
`(batch, 1, tgt_len, src_len)` where padding elements are indicated by very large negative values.
|
||||
output_attentions (`bool`, *optional*):
|
||||
Whether or not to return the attentions tensors of all attention layers. See `attentions` under
|
||||
returned tensors for more detail.
|
||||
use_cache (`bool`, *optional*):
|
||||
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding
|
||||
(see `past_key_values`).
|
||||
past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
|
||||
"""
|
||||
|
||||
residual = hidden_states
|
||||
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
|
||||
# Self Attention
|
||||
hidden_states, self_attn_weights, present_key_value = self.self_attn(
|
||||
hidden_states=hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
# Fully Connected
|
||||
residual = hidden_states
|
||||
hidden_states = self.post_attention_layernorm(hidden_states)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
hidden_states = residual + hidden_states
|
||||
|
||||
outputs = (hidden_states,)
|
||||
|
||||
if output_attentions:
|
||||
outputs += (self_attn_weights,)
|
||||
|
||||
if use_cache:
|
||||
outputs += (present_key_value,)
|
||||
|
||||
return outputs
|
||||
|
||||
|
||||
INTERNLM_START_DOCSTRING = r"""
|
||||
This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the
|
||||
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
|
||||
etc.)
|
||||
|
||||
This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass.
|
||||
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
|
||||
and behavior.
|
||||
|
||||
Parameters:
|
||||
config ([`InternLMConfig`]):
|
||||
Model configuration class with all the parameters of the model. Initializing with a config file does not
|
||||
load the weights associated with the model, only the configuration. Check out the
|
||||
[`~PreTrainedModel.from_pretrained`] method to load the model weights.
|
||||
"""
|
||||
|
||||
|
||||
@add_start_docstrings(
|
||||
"The bare InternLM Model outputting raw hidden-states without any specific head on top.",
|
||||
INTERNLM_START_DOCSTRING,
|
||||
)
|
||||
class InternLMPreTrainedModel(PreTrainedModel):
|
||||
config_class = InternLMConfig
|
||||
base_model_prefix = "model"
|
||||
supports_gradient_checkpointing = True
|
||||
_no_split_modules = ["InternLMDecoderLayer"]
|
||||
_keys_to_ignore_on_load_unexpected = [r"decoder\.version"]
|
||||
|
||||
def _init_weights(self, module):
|
||||
std = self.config.initializer_range
|
||||
if isinstance(module, nn.Linear):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.bias is not None:
|
||||
module.bias.data.zero_()
|
||||
elif isinstance(module, nn.Embedding):
|
||||
module.weight.data.normal_(mean=0.0, std=std)
|
||||
if module.padding_idx is not None:
|
||||
module.weight.data[module.padding_idx].zero_()
|
||||
|
||||
def _set_gradient_checkpointing(self, module, value=False):
|
||||
if isinstance(module, InternLMModel):
|
||||
module.gradient_checkpointing = value
|
||||
|
||||
|
||||
INTERNLM_INPUTS_DOCSTRING = r"""
|
||||
Args:
|
||||
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
|
||||
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
|
||||
it.
|
||||
|
||||
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
||||
[`PreTrainedTokenizer.__call__`] for details.
|
||||
|
||||
[What are input IDs?](../glossary#input-ids)
|
||||
attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||||
Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`:
|
||||
|
||||
- 1 for tokens that are **not masked**,
|
||||
- 0 for tokens that are **masked**.
|
||||
|
||||
[What are attention masks?](../glossary#attention-mask)
|
||||
|
||||
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
|
||||
[`PreTrainedTokenizer.__call__`] for details.
|
||||
|
||||
If `past_key_values` is used, optionally only the last `decoder_input_ids` have to be input (see
|
||||
`past_key_values`).
|
||||
|
||||
If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
|
||||
and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
|
||||
information on the default strategy.
|
||||
|
||||
- 1 indicates the head is **not masked**,
|
||||
- 0 indicates the head is **masked**.
|
||||
position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||||
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0,
|
||||
config.n_positions - 1]`.
|
||||
|
||||
[What are position IDs?](../glossary#position-ids)
|
||||
past_key_values (`tuple(tuple(torch.FloatTensor))`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`):
|
||||
Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape
|
||||
`(batch_size, num_heads, sequence_length, embed_size_per_head)`) and 2 additional tensors of shape
|
||||
`(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)`.
|
||||
|
||||
Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
|
||||
blocks) that can be used (see `past_key_values` input) to speed up sequential decoding.
|
||||
|
||||
If `past_key_values` are used, the user can optionally input only the last `decoder_input_ids` (those that
|
||||
don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all
|
||||
`decoder_input_ids` of shape `(batch_size, sequence_length)`.
|
||||
inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*):
|
||||
Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This
|
||||
is useful if you want more control over how to convert `input_ids` indices into associated vectors than the
|
||||
model's internal embedding lookup matrix.
|
||||
use_cache (`bool`, *optional*):
|
||||
If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see
|
||||
`past_key_values`).
|
||||
output_attentions (`bool`, *optional*):
|
||||
Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned
|
||||
tensors for more detail.
|
||||
output_hidden_states (`bool`, *optional*):
|
||||
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
|
||||
more detail.
|
||||
return_dict (`bool`, *optional*):
|
||||
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
|
||||
"""
|
||||
|
||||
|
||||
@add_start_docstrings(
|
||||
"The bare InternLM Model outputting raw hidden-states without any specific head on top.",
|
||||
INTERNLM_START_DOCSTRING,
|
||||
)
|
||||
class InternLMModel(InternLMPreTrainedModel):
|
||||
"""
|
||||
Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`InternLMDecoderLayer`]
|
||||
|
||||
Args:
|
||||
config: InternLMConfig
|
||||
"""
|
||||
_auto_class = "AutoModel"
|
||||
|
||||
def __init__(self, config: InternLMConfig):
|
||||
super().__init__(config)
|
||||
self.padding_idx = config.pad_token_id
|
||||
self.vocab_size = config.vocab_size
|
||||
|
||||
self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
||||
self.layers = nn.ModuleList([InternLMDecoderLayer(config) for _ in range(config.num_hidden_layers)])
|
||||
self.norm = InternLMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
self.gradient_checkpointing = False
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.embed_tokens
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.embed_tokens = value
|
||||
|
||||
# Copied from transformers.models.bart.modeling_bart.BartDecoder._prepare_decoder_attention_mask
|
||||
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
|
||||
# create causal mask
|
||||
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
||||
combined_attention_mask = None
|
||||
if input_shape[-1] > 1:
|
||||
combined_attention_mask = _make_causal_mask(
|
||||
input_shape,
|
||||
inputs_embeds.dtype,
|
||||
device=inputs_embeds.device,
|
||||
past_key_values_length=past_key_values_length,
|
||||
)
|
||||
|
||||
if attention_mask is not None:
|
||||
# [bsz, seq_len] -> [bsz, 1, tgt_seq_len, src_seq_len]
|
||||
expanded_attn_mask = _expand_mask(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(
|
||||
inputs_embeds.device
|
||||
)
|
||||
combined_attention_mask = (
|
||||
expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
||||
)
|
||||
|
||||
return combined_attention_mask
|
||||
|
||||
@add_start_docstrings_to_model_forward(INTERNLM_INPUTS_DOCSTRING)
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||||
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# retrieve input_ids and inputs_embeds
|
||||
if input_ids is not None and inputs_embeds is not None:
|
||||
raise ValueError("You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time")
|
||||
elif input_ids is not None:
|
||||
batch_size, seq_length = input_ids.shape
|
||||
elif inputs_embeds is not None:
|
||||
batch_size, seq_length, _ = inputs_embeds.shape
|
||||
else:
|
||||
raise ValueError("You have to specify either decoder_input_ids or decoder_inputs_embeds")
|
||||
|
||||
seq_length_with_past = seq_length
|
||||
past_key_values_length = 0
|
||||
|
||||
if past_key_values is not None:
|
||||
past_key_values_length = past_key_values[0][0].shape[2]
|
||||
seq_length_with_past = seq_length_with_past + past_key_values_length
|
||||
|
||||
if position_ids is None:
|
||||
device = input_ids.device if input_ids is not None else inputs_embeds.device
|
||||
position_ids = torch.arange(
|
||||
past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
|
||||
)
|
||||
position_ids = position_ids.unsqueeze(0).view(-1, seq_length)
|
||||
else:
|
||||
position_ids = position_ids.view(-1, seq_length).long()
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embed_tokens(input_ids)
|
||||
# embed positions
|
||||
if attention_mask is None:
|
||||
attention_mask = torch.ones(
|
||||
(batch_size, seq_length_with_past), dtype=torch.bool, device=inputs_embeds.device
|
||||
)
|
||||
attention_mask = self._prepare_decoder_attention_mask(
|
||||
attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
|
||||
)
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
if use_cache:
|
||||
logger.warning_once(
|
||||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..."
|
||||
)
|
||||
use_cache = False
|
||||
|
||||
# decoder layers
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
next_decoder_cache = () if use_cache else None
|
||||
|
||||
for idx, decoder_layer in enumerate(self.layers):
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
past_key_value = past_key_values[idx] if past_key_values is not None else None
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
|
||||
def create_custom_forward(module):
|
||||
def custom_forward(*inputs):
|
||||
# None for past_key_value
|
||||
return module(*inputs, output_attentions, None)
|
||||
|
||||
return custom_forward
|
||||
|
||||
layer_outputs = torch.utils.checkpoint.checkpoint(
|
||||
create_custom_forward(decoder_layer),
|
||||
hidden_states,
|
||||
attention_mask,
|
||||
position_ids,
|
||||
None,
|
||||
)
|
||||
else:
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if use_cache:
|
||||
next_decoder_cache += (layer_outputs[2 if output_attentions else 1],)
|
||||
|
||||
if output_attentions:
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
|
||||
hidden_states = self.norm(hidden_states)
|
||||
|
||||
# add hidden states from the last decoder layer
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
next_cache = next_decoder_cache if use_cache else None
|
||||
if not return_dict:
|
||||
return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None)
|
||||
return BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=next_cache,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attns,
|
||||
)
|
||||
|
||||
|
||||
class InternLMForCausalLM(InternLMPreTrainedModel):
|
||||
_auto_class = "AutoModelForCausalLM"
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.model = InternLMModel(config)
|
||||
|
||||
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.model.embed_tokens
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.model.embed_tokens = value
|
||||
|
||||
def get_output_embeddings(self):
|
||||
return self.lm_head
|
||||
|
||||
def set_output_embeddings(self, new_embeddings):
|
||||
self.lm_head = new_embeddings
|
||||
|
||||
def set_decoder(self, decoder):
|
||||
self.model = decoder
|
||||
|
||||
def get_decoder(self):
|
||||
return self.model
|
||||
|
||||
@add_start_docstrings_to_model_forward(INTERNLM_INPUTS_DOCSTRING)
|
||||
@replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
) -> Union[Tuple, CausalLMOutputWithPast]:
|
||||
r"""
|
||||
Args:
|
||||
labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
|
||||
Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
|
||||
config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
|
||||
(masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
|
||||
|
||||
Returns:
|
||||
|
||||
Example:
|
||||
|
||||
```python
|
||||
>>> from transformers import AutoTokenizer, InternLMForCausalLM
|
||||
|
||||
>>> model = InternLMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
|
||||
>>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
|
||||
|
||||
>>> prompt = "Hey, are you consciours? Can you talk to me?"
|
||||
>>> inputs = tokenizer(prompt, return_tensors="pt")
|
||||
|
||||
>>> # Generate
|
||||
>>> generate_ids = model.generate(inputs.input_ids, max_length=30)
|
||||
>>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
|
||||
"Hey, are you consciours? Can you talk to me?\nI'm not consciours, but I can talk to you."
|
||||
```"""
|
||||
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
|
||||
outputs = self.model(
|
||||
input_ids=input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
|
||||
hidden_states = outputs[0]
|
||||
logits = self.lm_head(hidden_states)
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
# Shift so that tokens < n predict n
|
||||
shift_logits = logits[..., :-1, :].contiguous()
|
||||
shift_labels = labels[..., 1:].contiguous()
|
||||
# Flatten the tokens
|
||||
loss_fct = CrossEntropyLoss()
|
||||
shift_logits = shift_logits.view(-1, self.config.vocab_size)
|
||||
shift_labels = shift_labels.view(-1)
|
||||
# Enable model parallelism
|
||||
shift_labels = shift_labels.to(shift_logits.device)
|
||||
loss = loss_fct(shift_logits, shift_labels)
|
||||
|
||||
if not return_dict:
|
||||
output = (logits,) + outputs[1:]
|
||||
return (loss,) + output if loss is not None else output
|
||||
|
||||
return CausalLMOutputWithPast(
|
||||
loss=loss,
|
||||
logits=logits,
|
||||
past_key_values=outputs.past_key_values,
|
||||
hidden_states=outputs.hidden_states,
|
||||
attentions=outputs.attentions,
|
||||
)
|
||||
|
||||
def prepare_inputs_for_generation(
|
||||
self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
|
||||
):
|
||||
if past_key_values:
|
||||
input_ids = input_ids[:, -1:]
|
||||
|
||||
position_ids = kwargs.get("position_ids", None)
|
||||
if attention_mask is not None and position_ids is None:
|
||||
# create position_ids on the fly for batch generation
|
||||
position_ids = attention_mask.long().cumsum(-1) - 1
|
||||
position_ids.masked_fill_(attention_mask == 0, 1)
|
||||
if past_key_values:
|
||||
position_ids = position_ids[:, -1].unsqueeze(-1)
|
||||
|
||||
# if `inputs_embeds` are passed, we only want to use them in the 1st generation step
|
||||
if inputs_embeds is not None and past_key_values is None:
|
||||
model_inputs = {"inputs_embeds": inputs_embeds}
|
||||
else:
|
||||
model_inputs = {"input_ids": input_ids}
|
||||
|
||||
model_inputs.update(
|
||||
{
|
||||
"position_ids": position_ids,
|
||||
"past_key_values": past_key_values,
|
||||
"use_cache": kwargs.get("use_cache"),
|
||||
"attention_mask": attention_mask,
|
||||
}
|
||||
)
|
||||
return model_inputs
|
||||
|
||||
@staticmethod
|
||||
def _reorder_cache(past_key_values, beam_idx):
|
||||
reordered_past = ()
|
||||
for layer_past in past_key_values:
|
||||
reordered_past += (tuple(past_state.index_select(0, beam_idx) for past_state in layer_past),)
|
||||
return reordered_past
|
||||
|
||||
def build_inputs(self, tokenizer, query: str, history: List[Tuple[str, str]] = []):
|
||||
prompt = ""
|
||||
for record in history:
|
||||
prompt += f"""<s><|User|>:{record[0]}<eoh>\n<|Bot|>:{record[1]}<eoa>\n"""
|
||||
if len(prompt) == 0:
|
||||
prompt += "<s>"
|
||||
prompt += f"""<|User|>:{query}<eoh>\n<|Bot|>:"""
|
||||
return tokenizer([prompt], return_tensors="pt")
|
||||
|
||||
@torch.no_grad()
|
||||
def chat(self,
|
||||
tokenizer,
|
||||
query: str,
|
||||
history: List[Tuple[str, str]] = [],
|
||||
streamer: Optional[BaseStreamer] = None,
|
||||
max_new_tokens: int = 1024,
|
||||
do_sample: bool = True,
|
||||
temperature: float = 0.8,
|
||||
top_p: float = 0.8,
|
||||
**kwargs):
|
||||
inputs = self.build_inputs(tokenizer, query, history)
|
||||
inputs = {k: v.to(self.device) for k, v in inputs.items() if torch.is_tensor(v)}
|
||||
outputs = self.generate(**inputs,
|
||||
streamer=streamer,
|
||||
max_new_tokens=max_new_tokens,
|
||||
do_sample=do_sample,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
**kwargs)
|
||||
outputs = outputs[0].cpu().tolist()[len(inputs["input_ids"][0]):]
|
||||
response = tokenizer.decode(outputs, skip_special_tokens=True)
|
||||
response = response.split("<eoa>")[0]
|
||||
history = history + [(query, response)]
|
||||
return response, history
|
||||
|
||||
@torch.no_grad()
|
||||
def stream_chat(self,
|
||||
tokenizer,
|
||||
query: str,
|
||||
history: List[Tuple[str, str]] = [],
|
||||
max_new_tokens: int = 1024,
|
||||
do_sample: bool = True,
|
||||
temperature: float = 0.8,
|
||||
top_p: float = 0.8,
|
||||
**kwargs):
|
||||
"""
|
||||
Return a generator in format: (response, history)
|
||||
Eg.
|
||||
('你好，有什么可以帮助您的吗', [('你好', '你好，有什么可以帮助您的吗')])
|
||||
('你好，有什么可以帮助您的吗？', [('你好', '你好，有什么可以帮助您的吗？')])
|
||||
"""
|
||||
|
||||
response_queue = queue.Queue(maxsize=20)
|
||||
|
||||
class ChatStreamer(BaseStreamer):
|
||||
def __init__(self, tokenizer) -> None:
|
||||
super().__init__()
|
||||
self.tokenizer = tokenizer
|
||||
self.queue = response_queue
|
||||
self.query = query
|
||||
self.history = history
|
||||
self.response = ""
|
||||
self.received_inputs = False
|
||||
self.queue.put((self.response, history + [(self.query, self.response)]))
|
||||
|
||||
def put(self, value):
|
||||
if len(value.shape) > 1 and value.shape[0] > 1:
|
||||
raise ValueError("ChatStreamer only supports batch size 1")
|
||||
elif len(value.shape) > 1:
|
||||
value = value[0]
|
||||
|
||||
if not self.received_inputs:
|
||||
# The first received value is input_ids, ignore here
|
||||
self.received_inputs = True
|
||||
return
|
||||
|
||||
token = self.tokenizer.decode([value[-1]], skip_special_tokens=True)
|
||||
if token.strip() != "<eoa>":
|
||||
self.response = self.response + token
|
||||
history = self.history + [(self.query, self.response)]
|
||||
self.queue.put((self.response, history))
|
||||
|
||||
def end(self):
|
||||
self.queue.put(None)
|
||||
|
||||
def stream_producer():
|
||||
return self.chat(
|
||||
tokenizer=tokenizer,
|
||||
query=query,
|
||||
streamer=ChatStreamer(tokenizer=tokenizer),
|
||||
history=history,
|
||||
max_new_tokens=max_new_tokens,
|
||||
do_sample=do_sample,
|
||||
temperature=temperature,
|
||||
top_p=top_p,
|
||||
**kwargs
|
||||
)
|
||||
|
||||
def consumer():
|
||||
producer = threading.Thread(target=stream_producer)
|
||||
producer.start()
|
||||
while True:
|
||||
res = response_queue.get()
|
||||
if res is not None:
|
||||
return
|
||||
yield res
|
||||
|
||||
return consumer()
|
||||
|
||||
|
||||
@add_start_docstrings(
|
||||
"""
|
||||
The InternLM Model transformer with a sequence classification head on top (linear layer).
|
||||
|
||||
[`InternLMForSequenceClassification`] uses the last token in order to do the classification, as other causal models
|
||||
(e.g. GPT-2) do.
|
||||
|
||||
Since it does classification on the last token, it requires to know the position of the last token. If a
|
||||
`pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each row. If
|
||||
no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
|
||||
padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take the last value in
|
||||
each row of the batch).
|
||||
""",
|
||||
INTERNLM_START_DOCSTRING,
|
||||
)
|
||||
class InternLMForSequenceClassification(InternLMPreTrainedModel):
|
||||
_keys_to_ignore_on_load_missing = [r"lm_head.weight"]
|
||||
|
||||
def __init__(self, config):
|
||||
super().__init__(config)
|
||||
self.num_labels = config.num_labels
|
||||
self.model = InternLMModel(config)
|
||||
self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def get_input_embeddings(self):
|
||||
return self.model.embed_tokens
|
||||
|
||||
def set_input_embeddings(self, value):
|
||||
self.model.embed_tokens = value
|
||||
|
||||
@add_start_docstrings_to_model_forward(INTERNLM_INPUTS_DOCSTRING)
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[List[torch.FloatTensor]] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
labels: Optional[torch.LongTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
) -> Union[Tuple, SequenceClassifierOutputWithPast]:
|
||||
r"""
|
||||
labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
|
||||
Labels for computing the sequence classification/regression loss. Indices should be in `[0, ...,
|
||||
config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
|
||||
`config.num_labels > 1` a classification loss is computed (Cross-Entropy).
|
||||
"""
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
transformer_outputs = self.model(
|
||||
input_ids,
|
||||
attention_mask=attention_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_values=past_key_values,
|
||||
inputs_embeds=inputs_embeds,
|
||||
use_cache=use_cache,
|
||||
output_attentions=output_attentions,
|
||||
output_hidden_states=output_hidden_states,
|
||||
return_dict=return_dict,
|
||||
)
|
||||
hidden_states = transformer_outputs[0]
|
||||
logits = self.score(hidden_states)
|
||||
|
||||
if input_ids is not None:
|
||||
batch_size = input_ids.shape[0]
|
||||
else:
|
||||
batch_size = inputs_embeds.shape[0]
|
||||
|
||||
if self.config.pad_token_id is None and batch_size != 1:
|
||||
raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.")
|
||||
if self.config.pad_token_id is None:
|
||||
sequence_lengths = -1
|
||||
else:
|
||||
if input_ids is not None:
|
||||
sequence_lengths = (torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1).to(logits.device)
|
||||
else:
|
||||
sequence_lengths = -1
|
||||
|
||||
pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths]
|
||||
|
||||
loss = None
|
||||
if labels is not None:
|
||||
labels = labels.to(logits.device)
|
||||
if self.config.problem_type is None:
|
||||
if self.num_labels == 1:
|
||||
self.config.problem_type = "regression"
|
||||
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
|
||||
self.config.problem_type = "single_label_classification"
|
||||
else:
|
||||
self.config.problem_type = "multi_label_classification"
|
||||
|
||||
if self.config.problem_type == "regression":
|
||||
loss_fct = MSELoss()
|
||||
if self.num_labels == 1:
|
||||
loss = loss_fct(pooled_logits.squeeze(), labels.squeeze())
|
||||
else:
|
||||
loss = loss_fct(pooled_logits, labels)
|
||||
elif self.config.problem_type == "single_label_classification":
|
||||
loss_fct = CrossEntropyLoss()
|
||||
loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1))
|
||||
elif self.config.problem_type == "multi_label_classification":
|
||||
loss_fct = BCEWithLogitsLoss()
|
||||
loss = loss_fct(pooled_logits, labels)
|
||||
if not return_dict:
|
||||
output = (pooled_logits,) + transformer_outputs[1:]
|
||||
return ((loss,) + output) if loss is not None else output
|
||||
|
||||
return SequenceClassifierOutputWithPast(
|
||||
loss=loss,
|
||||
logits=pooled_logits,
|
||||
past_key_values=transformer_outputs.past_key_values,
|
||||
hidden_states=transformer_outputs.hidden_states,
|
||||
attentions=transformer_outputs.attentions,
|
||||
)
|
||||
@@ -0,0 +1,242 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
|
||||
#
|
||||
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
|
||||
# and OPT implementations in this library. It has been modified from its
|
||||
# original forms to accommodate minor architectural differences compared
|
||||
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
|
||||
#
|
||||
# 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 for IntermLM."""
|
||||
import os
|
||||
from shutil import copyfile
|
||||
from typing import Any, Dict, List, Optional, Tuple
|
||||
|
||||
import sentencepiece as spm
|
||||
|
||||
from transformers.tokenization_utils import PreTrainedTokenizer
|
||||
from transformers.utils import logging
|
||||
|
||||
|
||||
logger = logging.get_logger(__name__)
|
||||
|
||||
VOCAB_FILES_NAMES = {"vocab_file": "./tokenizer.model"}
|
||||
|
||||
PRETRAINED_VOCAB_FILES_MAP = {}
|
||||
|
||||
|
||||
class InternLMTokenizer(PreTrainedTokenizer):
|
||||
"""
|
||||
Construct a InternLM tokenizer. Based on byte-level Byte-Pair-Encoding.
|
||||
|
||||
Args:
|
||||
vocab_file (`str`):
|
||||
Path to the vocabulary file.
|
||||
"""
|
||||
|
||||
vocab_files_names = VOCAB_FILES_NAMES
|
||||
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
|
||||
model_input_names = ["input_ids", "attention_mask"]
|
||||
_auto_class = "AutoTokenizer"
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
vocab_file,
|
||||
unk_token="<unk>",
|
||||
bos_token="<s>",
|
||||
eos_token="</s>",
|
||||
pad_token="</s>",
|
||||
sp_model_kwargs: Optional[Dict[str, Any]] = None,
|
||||
add_bos_token=True,
|
||||
add_eos_token=False,
|
||||
decode_with_prefix_space=False,
|
||||
clean_up_tokenization_spaces=False,
|
||||
**kwargs,
|
||||
):
|
||||
self.sp_model_kwargs = {} if sp_model_kwargs is None else sp_model_kwargs
|
||||
super().__init__(
|
||||
bos_token=bos_token,
|
||||
eos_token=eos_token,
|
||||
unk_token=unk_token,
|
||||
pad_token=pad_token,
|
||||
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
||||
**kwargs,
|
||||
)
|
||||
self.vocab_file = vocab_file
|
||||
self.add_bos_token = add_bos_token
|
||||
self.add_eos_token = add_eos_token
|
||||
self.decode_with_prefix_space = decode_with_prefix_space
|
||||
self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
|
||||
self.sp_model.Load(vocab_file)
|
||||
self._no_prefix_space_tokens = None
|
||||
|
||||
""" Initialisation"""
|
||||
|
||||
@property
|
||||
def no_prefix_space_tokens(self):
|
||||
if self._no_prefix_space_tokens is None:
|
||||
vocab = self.convert_ids_to_tokens(list(range(self.vocab_size)))
|
||||
self._no_prefix_space_tokens = {i for i, tok in enumerate(vocab) if not tok.startswith("▁")}
|
||||
return self._no_prefix_space_tokens
|
||||
|
||||
@property
|
||||
def vocab_size(self):
|
||||
"""Returns vocab size"""
|
||||
return self.sp_model.get_piece_size()
|
||||
|
||||
@property
|
||||
def bos_token_id(self) -> Optional[int]:
|
||||
return self.sp_model.bos_id()
|
||||
|
||||
@property
|
||||
def eos_token_id(self) -> Optional[int]:
|
||||
return self.sp_model.eos_id()
|
||||
|
||||
def get_vocab(self):
|
||||
"""Returns vocab as a dict"""
|
||||
vocab = {self.convert_ids_to_tokens(i): i for i in range(self.vocab_size)}
|
||||
vocab.update(self.added_tokens_encoder)
|
||||
return vocab
|
||||
|
||||
def _tokenize(self, text):
|
||||
"""Returns a tokenized string."""
|
||||
return self.sp_model.encode(text, out_type=str)
|
||||
|
||||
def _convert_token_to_id(self, token):
|
||||
"""Converts a token (str) in an id using the vocab."""
|
||||
return self.sp_model.piece_to_id(token)
|
||||
|
||||
def _convert_id_to_token(self, index):
|
||||
"""Converts an index (integer) in a token (str) using the vocab."""
|
||||
token = self.sp_model.IdToPiece(index)
|
||||
return token
|
||||
|
||||
def _maybe_add_prefix_space(self, tokens, decoded):
|
||||
if tokens and tokens[0] not in self.no_prefix_space_tokens:
|
||||
return " " + decoded
|
||||
else:
|
||||
return decoded
|
||||
|
||||
def convert_tokens_to_string(self, tokens):
|
||||
"""Converts a sequence of tokens (string) in a single string."""
|
||||
current_sub_tokens = []
|
||||
out_string = ""
|
||||
prev_is_special = False
|
||||
for token in tokens:
|
||||
# make sure that special tokens are not decoded using sentencepiece model
|
||||
if token in self.all_special_tokens:
|
||||
if not prev_is_special:
|
||||
out_string += " "
|
||||
out_string += self.sp_model.decode(current_sub_tokens) + token
|
||||
prev_is_special = True
|
||||
current_sub_tokens = []
|
||||
else:
|
||||
current_sub_tokens.append(token)
|
||||
prev_is_special = False
|
||||
out_string += self.sp_model.decode(current_sub_tokens)
|
||||
out_string = self.clean_up_tokenization(out_string)
|
||||
out_string = self._maybe_add_prefix_space(tokens=tokens, decoded=out_string)
|
||||
return out_string[1:]
|
||||
|
||||
def save_vocabulary(self, save_directory, filename_prefix: Optional[str] = None) -> Tuple[str]:
|
||||
"""
|
||||
Save the vocabulary and special tokens file to a directory.
|
||||
|
||||
Args:
|
||||
save_directory (`str`):
|
||||
The directory in which to save the vocabulary.
|
||||
|
||||
Returns:
|
||||
`Tuple(str)`: Paths to the files saved.
|
||||
"""
|
||||
if not os.path.isdir(save_directory):
|
||||
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
|
||||
return
|
||||
out_vocab_file = os.path.join(
|
||||
save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
|
||||
)
|
||||
|
||||
if os.path.abspath(self.vocab_file) != os.path.abspath(out_vocab_file) and os.path.isfile(self.vocab_file):
|
||||
copyfile(self.vocab_file, out_vocab_file)
|
||||
elif not os.path.isfile(self.vocab_file):
|
||||
with open(out_vocab_file, "wb") as fi:
|
||||
content_spiece_model = self.sp_model.serialized_model_proto()
|
||||
fi.write(content_spiece_model)
|
||||
|
||||
return (out_vocab_file,)
|
||||
|
||||
def build_inputs_with_special_tokens(self, token_ids_0, token_ids_1=None):
|
||||
if self.add_bos_token:
|
||||
bos_token_ids = [self.bos_token_id]
|
||||
else:
|
||||
bos_token_ids = []
|
||||
|
||||
output = bos_token_ids + token_ids_0
|
||||
|
||||
if token_ids_1 is not None:
|
||||
output = output + token_ids_1
|
||||
|
||||
if self.add_eos_token:
|
||||
output = output + [self.eos_token_id]
|
||||
|
||||
return output
|
||||
|
||||
def get_special_tokens_mask(
|
||||
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None, already_has_special_tokens: bool = False
|
||||
) -> List[int]:
|
||||
"""
|
||||
Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
|
||||
special tokens using the tokenizer `prepare_for_model` method.
|
||||
|
||||
Args:
|
||||
token_ids_0 (`List[int]`):
|
||||
List of IDs.
|
||||
token_ids_1 (`List[int]`, *optional*):
|
||||
Optional second list of IDs for sequence pairs.
|
||||
already_has_special_tokens (`bool`, *optional*, defaults to `False`):
|
||||
Whether or not the token list is already formatted with special tokens for the model.
|
||||
|
||||
Returns:
|
||||
`List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
|
||||
"""
|
||||
if already_has_special_tokens:
|
||||
return super().get_special_tokens_mask(
|
||||
token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
|
||||
)
|
||||
|
||||
if token_ids_1 is None:
|
||||
return [1] + ([0] * len(token_ids_0)) + [1]
|
||||
return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]
|
||||
|
||||
def create_token_type_ids_from_sequences(
|
||||
self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
|
||||
) -> List[int]:
|
||||
"""
|
||||
Create a mask from the two sequences passed to be used in a sequence-pair classification task. T5 does not make
|
||||
use of token type ids, therefore a list of zeros is returned.
|
||||
|
||||
Args:
|
||||
token_ids_0 (`List[int]`):
|
||||
List of IDs.
|
||||
token_ids_1 (`List[int]`, *optional*):
|
||||
Optional second list of IDs for sequence pairs.
|
||||
|
||||
Returns:
|
||||
`List[int]`: List of zeros.
|
||||
"""
|
||||
eos = [self.eos_token_id]
|
||||
|
||||
if token_ids_1 is None:
|
||||
return len(token_ids_0 + eos) * [0]
|
||||
return len(token_ids_0 + eos + token_ids_1 + eos) * [0]
|
||||
Reference in New Issue
Block a user