diff --git a/README.md b/README.md index 468cc88..53b5687 100644 --- a/README.md +++ b/README.md @@ -107,9 +107,8 @@ **MFTCoder** is an open-source project of CodeFuse for accurate and efficient Multi-task Fine-tuning(MFT) on Large Language Models(LLMs), especially on Code-LLMs(large language model for code tasks). Moreover, we open source Code LLM models and code-related datasets along with the MFTCoder framework. -In MFTCoder, we released two codebases for finetuning Large Language Models: +In MFTCoder, we maintained an open-source codebase for finetuning Large Language Models: - **```MFTCoder-accelerate```** is a framework with accelerate and DeepSpeed/FSDP. All tech-stacks are open-source and vibrant. We highly recommend you try this framework and make your fintuning accurate and efficient. -- ```MFTCoder-atorch``` is based on the [ATorch frameworks](https://github.com/intelligent-machine-learning/dlrover), which is a fast distributed training framework of LLM. The aim of this project is to foster collaboration and share advancements in large language models, particularly within the domain of code development. @@ -121,7 +120,7 @@ The aim of this project is to foster collaboration and share advancements in lar :white_check_mark: **Multi-model**: It integrates state-of-the-art open-source models such as gpt-neox, llama, llama-2, baichuan, Qwen, chatglm2, and more. (These finetuned models will be released in the near future.) -:white_check_mark: **Multi-framework**: It provides support for both Accelerate (with Deepspeed and FSDP) and ATorch +:white_check_mark: **Multi-framework**: It provides support for both Accelerate with Deepspeed and FSDP. :white_check_mark: **Efficient fine-tuning**: It supports LoRA, QLoRA as well as Full-parameters training, enabling fine-tuning of large models with minimal resources. The training speed meets the demands of almost all fine-tuning scenarios. @@ -145,17 +144,13 @@ We highly recommend training with flash attention(version >= 2.3.0), please refe ## Training -As mentioned above, we open source two training frameworks. You could refer to their own READMEs for more details as followed. +You could refer to their own READMEs for more details as followed. If you are familiar with open source ```transformers```, ```DeepSpeed``` or ```FSDP```, we highly recommend you try: 🚀🚀 [**MFTCoder-accelerate: Accelerate + Deepspeed/FSDP Codebase for MFT(Multi-task Finetuning)**](mftcoder_accelerate/README.md) -If you want to explore some new framework like atorch, you could check: - -🚀 [MFTCoder-atorch: Atorch Codebase for MFT(Multi-task Finetuning)](mftcoder_atorch/README.md) - ## Models diff --git a/README_cn.md b/README_cn.md index 3102d9f..8bf01cf 100644 --- a/README_cn.md +++ b/README_cn.md @@ -113,7 +113,7 @@ :white_check_mark: **多模型**：支持最新的多个开源模型，包括gpt-neox，llama，llama-2，baichuan，Qwen，chatglm2等； -:white_check_mark: **多框架**：既支持主流开源的Accelerate+DeepSpeed/FSDP，也支持新开源的[ATorch 框架](https://github.com/intelligent-machine-learning/dlrover)； +:white_check_mark: **多框架**：既支持主流开源的Accelerate+DeepSpeed/FSDP； :white_check_mark: **高效微调**：支持LoRA和QLoRA，可以用很少的资源去微调很大的模型，且训练速度能满足几乎所有微调场景； @@ -142,10 +142,6 @@ sh init_env.sh 🚀🚀 [MFTCoder-accelerate: Accelerate + DeepSpeed/FSDP Codebase for MFT(Multi-task Finetuning)](mftcoder_accelerate/README.md) -如果你想探索一些新兴的训练框架，可以尝试： - -🚀 [MFTCoder-atorch: Atorch Codebase for MFT(Multi-task Finetuning)](mftcoder_atorch/README.md) - ## 模型 diff --git a/init_env.sh b/init_env.sh index 834b38d..251ab97 100644 --- a/init_env.sh +++ b/init_env.sh @@ -1,4 +1,5 @@ pip install torch==2.1.0 && \ pip install tensorboard==2.11.0 && \ pip install packaging && \ +MAX_JOBS=4 pip install flash-attn==2.3.6 --no-build-isolation && \ pip install -r requirements.txt diff --git a/mftcoder_accelerate/README.md b/mftcoder_accelerate/README.md index 87b4b63..d720e66 100644 --- a/mftcoder_accelerate/README.md +++ b/mftcoder_accelerate/README.md @@ -161,12 +161,19 @@ Here are some excellent pre-trained models weights available on Huggingface that ``` mftcoder_accelerate | - src - configs - | - data + data + | + inference + | + model + | + tokenization + | + tokenizer + | + training | - model + configs | *pefts* | @@ -174,41 +181,22 @@ mftcoder_accelerate | *mpt* | - *offline_tokenization* - | - tokenizer - | - utils - | - evals -``` -我们将训练中使用的各种组件抽取出来，以便后续的扩展和优化， 详见```src```目录下的实现。 - -MFT训练入口文件是```mftcoder_accelerate/src/pefts/mft_accelerate.py``` + train_utils -DPO/ORPO训练入口文件是```mftcoder_accelerate/src/xxpo/xxpo_accelerate.py``` - -MPT(全量加训)训练入口文件是```mftcoder_accelerate/src/mpt/mpt_accelerate.py``` - -参数配置存储在```mftcoder_accelerate/src/configs```目录下，方便统一管理和更改。 - -**_所以，在你开启训练之前，请进入src目录_** -``` -cd mftcoder_accelerate/src ``` -You can find the implementations in the ```mftcoder_accelerate/src``` directory -The entry file for MFT training is ```mftcoder_accelerate/src/pefts/mft_accelerate.py```. +You can find the implementations in the ```mftcoder_accelerate``` directory +The entry file for MFT training is ```mftcoder_accelerate/training/mft_accelerate.py```. -The entry file for DPO/ORPO training is ```mftcoder_accelerate/src/xxpo/xxpo_accelerate.py```. +The entry file for DPO/ORPO training is ```mftcoder_accelerate/training/xxpo_accelerate.py```. -The entry file for MPT(Continue Training) is ```mftcoder_accelerate/src/mpt/mpt_accelerate.py```. You need finish offline tokenization of your data via ```mftcoder_accelerate/src/run_offline_tokenization.sh```, which is different from the online tokenizaion used in MFT/DPO. +The entry file for MPT(Continue Training) is ```mftcoder_accelerate/training/mpt_accelerate.py```. You need finish offline tokenization of your data via ```mftcoder_accelerate/run_offline_tokenization.sh```, which is different from the online tokenizaion used in MFT/DPO. -Configurations are stored in the ```mftcoder_accelerate/src/configs``` directory for easy management and modification. +Configurations are stored in the ```mftcoder_accelerate/training/configs``` directory for easy management and modification. **_As a result, before you start training, you should first change your dir by_** ``` -cd mftcoder_accelerate/src +cd mftcoder_accelerate ``` ### 3.1 MFT Tokenization @@ -217,7 +205,6 @@ During training, we concatenate multi-turn dialogues into the following format ( In default format, ```human\n``` starts the user's input (i.e., prompt),```bot\n``` starts the assistant's output (i.e., response) ```{EOS_TOKEN}``` represents the proper eos_token. -We have different eos_tokens in ```src/pefts/model_mapping.py``` which fits different base models. Here is a visionable example of the training data after formatting: ``` @@ -244,7 +231,7 @@ To perform LoRA/QLoRA fine-tuning, you can execute the following command: #### Launch via Deepspeed DeepSpeed config in accelerate_ds_config.yaml. ```bash -accelerate launch --config_file accelerate_ds_config.yaml pefts/mft_accelerate.py --train_config configs/xxx_train_config.json --distributed_type "DeepSpeed" +accelerate launch --config_file accelerate_ds_config.yaml training/mft_accelerate.py --train_config training/configs/xxx_train_config.json --distributed_type "DeepSpeed" ``` or DeepSpeed Zero2 config in command line arguments @@ -259,7 +246,7 @@ sh ds_zero3_single_launch.sh #### Launch via FSDP FSDP config in accelerate_fsdp_config.yaml. ```bash -accelerate launch --config_file accelerate_fsdp_config.yaml pefts/mft_accelerate.py --train_config configs/xxx_train_config.json --distributed_type "FSDP" +accelerate launch --config_file accelerate_fsdp_config.yaml training/mft_accelerate.py --train_config training/configs/xxx_train_config.json --distributed_type "FSDP" ``` or FSDP config in command line arguments @@ -276,7 +263,7 @@ sh ds_multinode_launch.sh #### Traing Arguments All arguments allowed in ***_train_config.josn are defined in ```arguments.py```. -Frequently used arguments are provided in ```configs/***_train_config``` and explained as follows. You can modify these parameters according to your needs: +Frequently used arguments are provided in ```training/configs/***_train_config``` and explained as follows. You can modify these parameters according to your needs: - **load_raw_dataset**: Need to be true at present. Only JSONL format is supported. diff --git a/mftcoder_accelerate/README_cn.md b/mftcoder_accelerate/README_cn.md index 39631c5..4cdcade 100644 --- a/mftcoder_accelerate/README_cn.md +++ b/mftcoder_accelerate/README_cn.md @@ -152,12 +152,19 @@ ``` mftcoder_accelerate | - src - configs - | - data + data + | + inference + | + model + | + tokenization + | + tokenizer + | + training | - model + configs | *pefts* | @@ -165,27 +172,22 @@ mftcoder_accelerate | *mpt* | - *offline_tokenization* - | - tokenizer - | - utils - | - evals + train_utils + ``` -我们将训练中使用的各种组件抽取出来，以便后续的扩展和优化， 详见```src```目录下的实现。 +我们将训练中使用的各种组件抽取出来，以便后续的扩展和优化， 详见```mftcoder_accelerate```目录下的实现。 -MFT训练入口文件是```mftcoder_accelerate/src/pefts/mft_accelerate.py``` +MFT训练入口文件是```mftcoder_accelerate/training/mft_accelerate.py``` -DPO/ORPO训练入口文件是```mftcoder_accelerate/src/xxpo/xxpo_accelerate.py``` +DPO/ORPO训练入口文件是```mftcoder_accelerate/training/xxpo_accelerate.py``` -MPT(全量加训)训练入口文件是```mftcoder_accelerate/src/mpt/mpt_accelerate.py```. MPT加训需要提前做好数据的tokenziation，通过```mftcoder_accelerate/src/run_offline_tokenization.sh```，你可以将数据通过cpu进行离线的tokenization。这和MFT/DPO中使用的在线tokenziation不同。 +MPT(全量加训)训练入口文件是```mftcoder_accelerate/training/mpt_accelerate.py```. MPT加训需要提前做好数据的tokenziation，通过```mftcoder_accelerate/run_offline_tokenization.sh```，你可以将数据通过cpu进行离线的tokenization。这和MFT/DPO中使用的在线tokenziation不同。 -参数配置存储在```mftcoder_accelerate/src/configs```目录下，方便统一管理和更改。 +参数配置存储在```mftcoder_accelerate/training/configs```目录下，方便统一管理和更改。 -**_所以，在你开启训练之前，请进入src目录_** +**你开启训练之前** ``` -cd mftcoder_accelerate/src +cd mftcoder_accelerate ``` @@ -216,7 +218,7 @@ QLoRA论文指出，该方法可以在一张V100上对33B的模型进行微调 #### Deepspeed 单机启动 DeepSpeed配置在accelerate_ds_config.yaml中。 ```bash -accelerate launch --config_file accelerate_ds_config.yaml pefts/mft_accelerate.py --train_config configs/xxx_train_config.json --distributed_type "DeepSpeed" +accelerate launch --config_file accelerate_ds_config.yaml training/mft_accelerate.py --train_config training/configs/xxx_train_config.json --distributed_type "DeepSpeed" ``` 或者 @@ -233,7 +235,7 @@ sh ds_zero3_single_launch.sh #### FSDP 单机启动 FSDP配置在accelerate_fsdp_config.yaml中。 ```bash -accelerate launch --config_file accelerate_fsdp_config.yaml pefts/mft_accelerate.py --train_config configs/xxx_train_config.json --distributed_type "FSDP" +accelerate launch --config_file accelerate_fsdp_config.yaml training/mft_accelerate.py --train_config training/configs/xxx_train_config.json --distributed_type "FSDP" ``` 或者 @@ -249,7 +251,7 @@ sh ds_multinode_launch.sh ``` #### 训练参数 -_**训练需要的参数配置在```configs/*_train_config```中，主要参数说明如下：**_ +_**训练需要的参数配置在```training/configs/*_train_config```中，主要参数说明如下：**_ - **load_raw_dataset**: 需要保持true，后续会支持其它模式数据，当前仅支持jsonl输入 - **data_paths**: "[path1,path2,path3]" 输入数据地址，字符串，开头结尾用[]，中间用```,```间隔不同path，每个path是一个目录，目录的最后一级名字作为任务名称，下面包含1到多个jsonl数据 @@ -295,7 +297,7 @@ _**训练需要的参数配置在```configs/*_train_config```中，主要参数 - **xxpo**: 偏好对齐方法, "dpo" 或者 "orpo"。 - **beta**: DPO beta, beta 越小，允许对齐后的dpo模型与ref模型的距离越远。 - **rpo_alpha**: 加到dop损失的```chosen``` NLL损失的系数，0的话就是原始DPO。 -- + ## 4. 模型使用 ### 4.1 权重合并 diff --git a/mftcoder_accelerate/src/accelerate_ds_config.yaml b/mftcoder_accelerate/accelerate_ds_config.yaml similarity index 100% rename from mftcoder_accelerate/src/accelerate_ds_config.yaml rename to mftcoder_accelerate/accelerate_ds_config.yaml diff --git a/mftcoder_accelerate/src/accelerate_fsdp_config.yaml b/mftcoder_accelerate/accelerate_fsdp_config.yaml similarity index 100% rename from mftcoder_accelerate/src/accelerate_fsdp_config.yaml rename to mftcoder_accelerate/accelerate_fsdp_config.yaml diff --git a/mftcoder_accelerate/src/data/Makefile b/mftcoder_accelerate/data/Makefile similarity index 100% rename from mftcoder_accelerate/src/data/Makefile rename to mftcoder_accelerate/data/Makefile diff --git a/mftcoder_accelerate/src/data/__init__.py b/mftcoder_accelerate/data/__init__.py similarity index 100% rename from mftcoder_accelerate/src/data/__init__.py rename to mftcoder_accelerate/data/__init__.py diff --git a/mftcoder_accelerate/src/data/blendable_dataset.py b/mftcoder_accelerate/data/blendable_dataset.py similarity index 98% rename from mftcoder_accelerate/src/data/blendable_dataset.py rename to mftcoder_accelerate/data/blendable_dataset.py index 84b9756..d810da8 100644 --- a/mftcoder_accelerate/src/data/blendable_dataset.py +++ b/mftcoder_accelerate/data/blendable_dataset.py @@ -22,8 +22,6 @@ import numpy as np import torch -from utils.common_utils import print_rank_0 - class BlendableDataset(torch.utils.data.Dataset): def __init__(self, datasets, weights): diff --git a/mftcoder_accelerate/src/data/data_utils.py b/mftcoder_accelerate/data/data_utils.py similarity index 99% rename from mftcoder_accelerate/src/data/data_utils.py rename to mftcoder_accelerate/data/data_utils.py index 8d168bd..1f8c30d 100644 --- a/mftcoder_accelerate/src/data/data_utils.py +++ b/mftcoder_accelerate/data/data_utils.py @@ -20,7 +20,7 @@ from typing import List, Tuple from functools import partial -from utils.common_utils import print_rank_0, TASK2ID, ID2TASK +from training.train_utils.common_utils import print_rank_0, TASK2ID, ID2TASK from data.indexed_dataset import make_dataset as make_indexed_dataset from data.blendable_dataset import BlendableDataset from data.gpt2_dataset import GPT2Dataset, GPT2PromptDataset diff --git a/mftcoder_accelerate/src/data/gpt2_dataset.py b/mftcoder_accelerate/data/gpt2_dataset.py similarity index 99% rename from mftcoder_accelerate/src/data/gpt2_dataset.py rename to mftcoder_accelerate/data/gpt2_dataset.py index 12eeb87..6b780c2 100644 --- a/mftcoder_accelerate/src/data/gpt2_dataset.py +++ b/mftcoder_accelerate/data/gpt2_dataset.py @@ -23,7 +23,7 @@ import numpy as np import torch -from utils.common_utils import print_rank_0, TASK2ID, ID2TASK +from training.train_utils.common_utils import print_rank_0, TASK2ID, ID2TASK class GPT2PromptDataset(torch.utils.data.Dataset): diff --git a/mftcoder_accelerate/src/data/helpers.cpp b/mftcoder_accelerate/data/helpers.cpp similarity index 100% rename from mftcoder_accelerate/src/data/helpers.cpp rename to mftcoder_accelerate/data/helpers.cpp diff --git a/mftcoder_accelerate/src/data/indexed_dataset.py b/mftcoder_accelerate/data/indexed_dataset.py similarity index 99% rename from mftcoder_accelerate/src/data/indexed_dataset.py rename to mftcoder_accelerate/data/indexed_dataset.py index 12ea9c2..40389f3 100644 --- a/mftcoder_accelerate/src/data/indexed_dataset.py +++ b/mftcoder_accelerate/data/indexed_dataset.py @@ -22,7 +22,7 @@ import numpy as np import torch -from utils.common_utils import print_rank_0 +from training.train_utils.common_utils import print_rank_0 def __best_fitting_dtype(vocab_size=None): diff --git a/mftcoder_accelerate/src/data/lm_dataformat.py b/mftcoder_accelerate/data/lm_dataformat.py similarity index 86% rename from mftcoder_accelerate/src/data/lm_dataformat.py rename to mftcoder_accelerate/data/lm_dataformat.py index 340377b..ed42d08 100644 --- a/mftcoder_accelerate/src/data/lm_dataformat.py +++ b/mftcoder_accelerate/data/lm_dataformat.py @@ -126,58 +126,32 @@ def handle_jsonl(jsonl_reader, get_meta, autojoin_paragraphs, para_joiner, key=" yield ob continue - if isinstance(key, str): - text = ob.get(key) - if autojoin_paragraphs and isinstance(text, list): - text = para_joiner.join(text) + if key is None: + yield ob + continue - if get_meta: - yield text, (ob["meta"] if "meta" in ob else {}) - else: - yield text - elif isinstance(key, list): - - task = ob.get(key[0], "") - src_language = ob.get(key[1], "") - src_code = ob.get(key[2], "") - tgt_language = ob.get(key[3], "") - tgt_code = ob.get(key[4], "") - sql = ob.get(key[5], "") - prompt_in = ob.get(key[6], "") - content_in = ob.get(key[7], "") - bad_content_in = ob.get(key[8], "") - - if task: - task = "task: " + task - if src_language: - src_language = "language: " + src_language - if sql: - sql = sql.strip() + "\n" - task = "task: text to sql\n" - src_language = "language: text\n" - tgt_language = "language: sql\n" - prompt_in = prompt_in.strip() + "\n" - elif tgt_language: - tgt_language = "language: " + tgt_language - - prompt = task + src_language + src_code + prompt_in + tgt_language - content = tgt_code + sql + content_in - bad_content = bad_content_in - - yield (prompt, content, bad_content) + text = ob[key] + + if autojoin_paragraphs and isinstance(text, list): + text = para_joiner.join(text) + + if get_meta: + yield text, (ob["meta"] if "meta" in ob else {}) + else: + yield text class Reader: def __init__(self, in_path): self.in_path = in_path - def stream_data(self, get_meta=False, threaded=False, key=["prompt", "content", "bad_content"]): + def stream_data(self, get_meta=False, threaded=False, key=None): if not threaded: - yield from self._stream_data(get_meta, jsonl_key=key) + yield from self._stream_data(get_meta, key=key) return q = mp.Queue(1000) - p = mp.Process(target=self._stream_data_threaded, args=(q, get_meta)) + p = mp.Process(target=self._stream_data_threaded, args=(q, get_meta), kwargs={"key": key}) p.start() while p.is_alive(): res = q.get() @@ -190,7 +164,7 @@ def _stream_data_threaded(self, q, get_meta=False): q.put(data) q.put(None) - def _stream_data(self, get_meta=False, jsonl_key="text"): + def _stream_data(self, get_meta=False, key="text"): self.f_name = "" files = listdir_or_file(self.in_path) if not files: @@ -210,11 +184,11 @@ def _stream_data(self, get_meta=False, jsonl_key="text"): yield from self.read_dat(f) elif f.endswith(".jsonl"): - yield from self.read_jsonl(f, get_meta, key=jsonl_key) + yield from self.read_jsonl(f, get_meta, key=key) elif f.endswith(".jsonl.zst"): - yield from self.read_jsonl_zst(f, get_meta, key=jsonl_key) + yield from self.read_jsonl_zst(f, get_meta, key=key) elif f.endswith(".jsonl.zst.tar"): - yield from self.read_jsonl_tar(f, get_meta, jsonl_key=key) + yield from self.read_jsonl_tar(f, get_meta, key=key) elif f.endswith(".json.zst"): assert not get_meta diff --git a/mftcoder_accelerate/src/data/multi_task_dataset.py b/mftcoder_accelerate/data/multi_task_dataset.py similarity index 99% rename from mftcoder_accelerate/src/data/multi_task_dataset.py rename to mftcoder_accelerate/data/multi_task_dataset.py index 63c4b27..6db0056 100644 --- a/mftcoder_accelerate/src/data/multi_task_dataset.py +++ b/mftcoder_accelerate/data/multi_task_dataset.py @@ -14,7 +14,7 @@ import torch from functools import partial from data.preprocess_data import UniformEncoder -from utils.common_utils import TASK2ID +from training.train_utils.common_utils import TASK2ID class GPT2FromRawDataset(torch.utils.data.Dataset): @@ -193,7 +193,7 @@ def load_dataset_from_jsonl(args, shard_data=False, world_size=1, global_rank=0, local_train_num = 0 local_valid_num = 0 - # 不同数据集在不同文件夹下 + # each path as a task for dataset_index in range(len(data_prefixes)): # files = os.listdir(data_prefixes[dataset_index]) # get all jsonl files and corresponding reading handler diff --git a/mftcoder_accelerate/src/data/preprocess_data.py b/mftcoder_accelerate/data/preprocess_data.py similarity index 99% rename from mftcoder_accelerate/src/data/preprocess_data.py rename to mftcoder_accelerate/data/preprocess_data.py index f7226bd..dfd1ceb 100644 --- a/mftcoder_accelerate/src/data/preprocess_data.py +++ b/mftcoder_accelerate/data/preprocess_data.py @@ -9,7 +9,7 @@ import ftfy import glob -# print("In preprocess_data_new.py, sys path:", sys.path) +# print("In preprocess_data.py, sys path:", sys.path) from tokenizer import build_tokenizer diff --git a/mftcoder_accelerate/src/ds_multinode_launch.sh b/mftcoder_accelerate/ds_multinode_launch.sh similarity index 89% rename from mftcoder_accelerate/src/ds_multinode_launch.sh rename to mftcoder_accelerate/ds_multinode_launch.sh index dca0670..88acd50 100755 --- a/mftcoder_accelerate/src/ds_multinode_launch.sh +++ b/mftcoder_accelerate/ds_multinode_launch.sh @@ -12,7 +12,7 @@ N_GPU_PER_NODE=8 # You need to export $MACHINE_RANK, $MASTER_ADDR, $MASTER_PORT automatically for each Node. # config path -CONFIG="configs/xxx_train_config.json" +CONFIG="training/configs/xxx_train_config.json" # envs used inside training export OMP_NUM_THREADS=4 @@ -41,4 +41,4 @@ accelerate launch \ --main_process_ip $MASTER_ADDR \ --main_process_port $MASTER_PORT \ --rdzv_backend 'static' \ - pefts/mft_accelerate.py --train_config "$CONFIG" --distributed_type "deepspeed" \ No newline at end of file + training/mft_accelerate.py --train_config "$CONFIG" --distributed_type "deepspeed" \ No newline at end of file diff --git a/mftcoder_accelerate/src/ds_single_launch.sh b/mftcoder_accelerate/ds_single_launch.sh similarity index 90% rename from mftcoder_accelerate/src/ds_single_launch.sh rename to mftcoder_accelerate/ds_single_launch.sh index d6c84bb..0f5edee 100755 --- a/mftcoder_accelerate/src/ds_single_launch.sh +++ b/mftcoder_accelerate/ds_single_launch.sh @@ -7,7 +7,7 @@ N_GPU_PER_NODE=8 # config path -CONFIG="configs/xxx_train_config.json" +CONFIG="training/configs/xxx_train_config.json" # envs used inside training export OMP_NUM_THREADS=4 @@ -33,6 +33,6 @@ accelerate launch \ --same_network \ --machine_rank 0 \ --rdzv_backend 'static' \ - pefts/mft_accelerate.py --train_config "$CONFIG" \ + training/mft_accelerate.py --train_config "$CONFIG" \ --distributed_type "deepspeed" \ > MFTCoder-training-"$TODAY".log 2>&1 & diff --git a/mftcoder_accelerate/src/ds_zero3_single_launch.sh b/mftcoder_accelerate/ds_zero3_single_launch.sh similarity index 90% rename from mftcoder_accelerate/src/ds_zero3_single_launch.sh rename to mftcoder_accelerate/ds_zero3_single_launch.sh index 5f581c9..838ca44 100755 --- a/mftcoder_accelerate/src/ds_zero3_single_launch.sh +++ b/mftcoder_accelerate/ds_zero3_single_launch.sh @@ -7,7 +7,7 @@ N_GPU_PER_NODE=8 # config path -CONFIG="configs/xxx_train_config.json" +CONFIG="training/configs/xxx_train_config.json" # envs used inside training export OMP_NUM_THREADS=4 @@ -33,6 +33,6 @@ accelerate launch \ --same_network \ --machine_rank 0 \ --rdzv_backend 'static' \ - pefts/mft_accelerate.py --train_config "$CONFIG" \ + training/mft_accelerate.py --train_config "$CONFIG" \ --distributed_type "deepspeed" \ > MFTCoder-training-"$TODAY".log 2>&1 & diff --git a/mftcoder_accelerate/src/fsdp_single_launch.sh b/mftcoder_accelerate/fsdp_single_launch.sh similarity index 91% rename from mftcoder_accelerate/src/fsdp_single_launch.sh rename to mftcoder_accelerate/fsdp_single_launch.sh index 2dc8f89..ce073e8 100755 --- a/mftcoder_accelerate/src/fsdp_single_launch.sh +++ b/mftcoder_accelerate/fsdp_single_launch.sh @@ -7,7 +7,7 @@ N_GPU_PER_NODE=8 # config path -CONFIG="configs/xxx_train_config.json" +CONFIG="training/configs/xxx_train_config.json" # fsdp_transformer_layer_cls_to_wrap, choose the DecoderLayer WRAP_MODULE="LlamaDecoderLayer" @@ -37,7 +37,7 @@ accelerate launch \ --same_network \ --machine_rank=0 \ --rdzv_backend=static \ - pefts/mft_accelerate.py --train_config "$CONFIG" \ + training/mft_accelerate.py --train_config "$CONFIG" \ --distributed_type "fsdp" \ > MFTCoder-training-"$TODAY".log 2>&1 & diff --git a/mftcoder_accelerate/src/model/__init__.py b/mftcoder_accelerate/inference/__init__.py old mode 100755 new mode 100644 similarity index 100% rename from mftcoder_accelerate/src/model/__init__.py rename to mftcoder_accelerate/inference/__init__.py diff --git a/mftcoder_accelerate/inference/acceelerate_infer_launch.sh b/mftcoder_accelerate/inference/acceelerate_infer_launch.sh new file mode 100644 index 0000000..7df51c1 --- /dev/null +++ b/mftcoder_accelerate/inference/acceelerate_infer_launch.sh @@ -0,0 +1,53 @@ +####################################################################################################################### +MODEL_PATH="path/to/hf/model" +DATA_FILE="path/to/input/file/jsonl" +BATCH_SIZE=2 + +N_NODE=1 +N_GPU_PER_NODE=2 +####################################################################################################################### +# you could add some specific commnads here if you need. +pip install transformers==4.44.2 +pip install -U accelerate + + +export OMP_NUM_THREADS=16 + + +# One Node inference +# accelerate launch \ +# --num_machines 1 \ +# --num_processes 2 \ +# --machine_rank 0 \ +# --dynamo_backend 'no' \ +# --same_network \ +# --rdzv_backend 'static' \ +# infer_accelerate.py \ +# --model_path $MODEL_PATH \ +# --data_file $DATA_FILE \ +# --batch_size $BATCH_SIZE \ +# --output_dir "." \ +# > "path/to/local/log" 2>&1 & + +# You need to export $MACHINE_RANK, $MASTER_ADDR, $MASTER_PORT automatically for each Node. + +echo "RANK: $RANK" +echo "MASTER_ADDR: $MASTER_ADDR" +echo "MASTER_PORT: $MASTER_PORT" + + +# launch by multi-node, multi-gpu +accelerate launch \ + --num_machines $N_NODE \ + --num_processes $(($N_NODE*$N_GPU_PER_NODE)) \ + --machine_rank $RANK \ + --main_process_ip $MASTER_ADDR \ + --main_process_port $MASTER_PORT \ + --dynamo_backend 'no' \ + --same_network \ + --rdzv_backend 'static' \ + infer_accelerate.py \ + --model_path $MODEL_PATH \ + --data_file $DATA_FILE \ + --batch_size $BATCH_SIZE \ + --output_dir "path/to/output/directory" \ No newline at end of file diff --git a/mftcoder_accelerate/inference/hf_inference.py b/mftcoder_accelerate/inference/demo_inference.py similarity index 98% rename from mftcoder_accelerate/inference/hf_inference.py rename to mftcoder_accelerate/inference/demo_inference.py index 67f9ba0..7af3a6a 100644 --- a/mftcoder_accelerate/inference/hf_inference.py +++ b/mftcoder_accelerate/inference/demo_inference.py @@ -2,15 +2,9 @@ # @author Chaoyu Chen # @date 2024/1/4 # @module hf_inference.py -""" -# @author qumu -# @date 2023/9/19 -# @module hf_inference.py -""" import os import sys import torch -import textwrap from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM, StoppingCriteria, StoppingCriteriaList from peft import PeftModel diff --git a/mftcoder_accelerate/inference/gen.py b/mftcoder_accelerate/inference/gen.py new file mode 100644 index 0000000..ae8184d --- /dev/null +++ b/mftcoder_accelerate/inference/gen.py @@ -0,0 +1,126 @@ +# @author Chaoyu Chen +# @date 2024/11/13 +# @module gen.py +"""Gen demo""" + +from typing import Iterable, Dict, List +import gzip +import json +import os +import argparse +import time +import sys +from tqdm import tqdm +import torch + +from transformers import ( + AutoConfig, + AutoTokenizer, + AutoModelForCausalLM, +) +from peft import PeftModel +from infer_base import HFInferenceBase +from tokenizer import MFTCoder_template + + +class HFGenerationInfer(HFInferenceBase): + def __init__(self): + super().__init__() + + def load_model_tokenizer(self, args): + """ + load generation model and tokenizer using self._load_model_tokenizer + """ + self._load_model_tokenizer(AutoModelForCausalLM, args.model_path, peft_path=args.peft_path) + + def handler(self, dataloader, args): + for batch in dataloader: + prompts = [ + self.tokenizer.apply_chat_template([{"role": "user", "content": sample["prompt"]}], tokenize=False) + for sample in batch + ] + inputs = self.tokenizer(prompts, return_tensors="pt", padding=True, add_special_tokens=False).to("cuda") + + # print(inputs) + print("=================Prompts and Generations===================") + + outputs = self.model.generate( + inputs=inputs["input_ids"], + attention_mask=inputs["attention_mask"], + max_new_tokens=args.max_new_tokens, + do_sample=args.do_sample, + top_p=args.top_p, + temperature=args.temperature, + num_beams=args.num_beams, + num_return_sequences=args.num_return_sequences, + eos_token_id=self.tokenizer.eos_token_id, + pad_token_id=self.tokenizer.pad_token_id, + ) + + gen_text = self.tokenizer.batch_decode( + outputs[:, inputs["input_ids"].shape[1] :], skip_special_tokens=True + ) + num_return_sequences = len(gen_text) // len(prompts) + for i in range(len(prompts)): + print("=========" * 10) + print(f"Prompt:\n{prompts[i]}") + batch[i]["generations"] = gen_text[ + i * num_return_sequences : i * num_return_sequences + num_return_sequences + ] + for j in range(num_return_sequences): + print(f"Generation {j+1}/{num_return_sequences}:\n{gen_text[i * num_return_sequences + j]}") + # print(f"Outputs ids:\n{outputs[i]}") + sys.stdout.flush() + yield batch + + def prepare_args(self, args): + name = args.data_file.split("/")[-1].replace(".jsonl", "") + "-GEN" + args.output_path = os.path.join(args.output_dir, f"{name}.jsonl") + + +def get_args(): + parser = argparse.ArgumentParser(description="Generation args.") + parser.add_argument( + "--model_path", + type=str, + help="huggingface model path", + ) + parser.add_argument( + "--data_file", + type=str, + help="data file path", + ) + parser.add_argument( + "--output_dir", + type=str, + help="output directory", + ) + parser.add_argument("--batch_size", type=int, default=1) + parser.add_argument("--num_return_sequences", type=int, default=20, help="pass1:20,pass10:20,pass100:100") + parser.add_argument("--num_beams", type=int, default=1, help="beam1, beam3, beam5, beam7") + parser.add_argument("--do_sample", action="store_true", default=False) + parser.add_argument("--temperature", type=float, default=0.2) + parser.add_argument("--top_p", type=float, default=0.95) + + parser.add_argument("--peft_path", type=str, default="", help="peft path：None") + parser.add_argument("--eos_token", type=str, default=None, help="eos token") + args = parser.parse_args() + return args + + +def main(args): + st = time.time() + runner = HFGenerationInfer() + runner.run(args) + + print("{} finish in: {:.4f} Minutes {}".format("+++" * 10, (time.time() - st) / 60, "+++" * 10)) + + +if __name__ == "__main__": + print("-----------" * 10) + print("-----------" * 10) + print("-----------" * 10) + main(get_args()) + print("-----------" * 10) + print("-----------" * 10) + print("-----------" * 10) diff --git a/mftcoder_accelerate/inference/infer_accelerate.py b/mftcoder_accelerate/inference/infer_accelerate.py new file mode 100644 index 0000000..ff5f602 --- /dev/null +++ b/mftcoder_accelerate/inference/infer_accelerate.py @@ -0,0 +1,170 @@ +# @author Chaoyu Chen +# @date 2024/11/19 +"""Distributed Inference by accelerate""" +import os +import json +import argparse +import queue +from concurrent.futures import ThreadPoolExecutor +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +from accelerate import PartialState +from accelerate.utils import gather_object + +from infer_utils import ( + print_args, + get_line_count, + stream_jsonl, + batch_stream_jsonl, + flatten_batch_stream, + write_jsonl, +) + + +def get_args(): + parser = argparse.ArgumentParser(description="Generation args.") + parser.add_argument( + "--model_path", + type=str, + help="huggingface model path", + ) + parser.add_argument( + "--data_file", + type=str, + help="data file path", + ) + parser.add_argument( + "--output_dir", + type=str, + help="output directory", + ) + parser.add_argument("--batch_size", type=int, default=1) + parser.add_argument("--num_return_sequences", type=int, default=20, help="pass1:20,pass10:20,pass100:100") + parser.add_argument("--num_beams", type=int, default=1, help="beam1, beam3, beam5, beam7") + parser.add_argument("--do_sample", action="store_true", default=False) + parser.add_argument("--temperature", type=float, default=0.2) + parser.add_argument("--top_p", type=float, default=0.95) + + parser.add_argument("--peft_path", type=str, default="", help="peft path：None") + parser.add_argument("--eos_token", type=str, default=None, help="eos token") + args = parser.parse_args() + + name = args.data_file.split("/")[-1].replace(".jsonl", "") + "-GEN" + args.output_path = os.path.join(args.output_dir, f"{name}.jsonl") + + return args + + +def main(): + args = get_args() + # Start up the distributed environment without needing the Accelerator. + distributed_state = PartialState() + + # You can change the model to any LLM + model = AutoModelForCausalLM.from_pretrained( + args.model_path, device_map=distributed_state.device, torch_dtype=torch.bfloat16 + ) + + tokenizer = AutoTokenizer.from_pretrained(args.model_path) + # Need to set the padding token to the eos token for generation + tokenizer.pad_token = tokenizer.eos_token + + # get dataloader + total_num = get_line_count(args.data_file) + # You can change the batch size depending on your GPU RAM + global_batch_size = args.batch_size * distributed_state.num_processes + stream = stream_jsonl(args.data_file) + dataloader = batch_stream_jsonl(stream, global_batch_size) + + # We set it to 8 since it is better for some hardware. More information here https://github.com/huggingface/tokenizers/issues/991 + pad_to_multiple_of = 8 + + def handler(): + # streaming process + for samples in dataloader: + prompts = [sample['prompt'] for sample in samples] + distributed_state.print(f"length of global batch: {len(prompts)}") + + # Split a global batch into batches with automatic padding so that the GPUs will have 1 batch with same size, and you can then gather the results. + if len(prompts) == global_batch_size: + formatted_prompts = [prompts[i: i + args.batch_size] for i in range(0, len(prompts), args.batch_size)] + else: + padded_prompts = prompts + [prompts[-1]] * (global_batch_size - len(prompts)) + formatted_prompts = [padded_prompts[i: i + args.batch_size] for i in + range(0, len(padded_prompts), args.batch_size)] + # distributed_state.print(f"formatted prompts {formatted_prompts}") + + # Apply padding on the left since we are doing generation + padding_side_default = tokenizer.padding_side + tokenizer.padding_side = "left" + # Tokenize each batch + tokenized_prompts = [ + tokenizer(formatted_prompt, padding=True, pad_to_multiple_of=pad_to_multiple_of, return_tensors="pt") + for formatted_prompt in formatted_prompts + ] + # Put back the original padding behavior + tokenizer.padding_side = padding_side_default + + completions_per_process = [] + with distributed_state.split_between_processes(tokenized_prompts) as batched_prompts: + + for batch in batched_prompts: + # Move the batch to the device + batch = batch.to(distributed_state.device) + + # YOU WILL FILL YOUR CODE HERE! + # We generate the text, decode it and add it to the list completions_per_process + outputs = model.generate( + inputs=batch["input_ids"], + attention_mask=batch["attention_mask"], + max_new_tokens=128 + ) + + # generated_text = tokenizer.batch_decode(outputs, skip_special_tokens=True) + generated_text = tokenizer.batch_decode(outputs[:, batch["input_ids"].shape[1]:], + skip_special_tokens=True) + # print(f"index: {distributed_state.process_index}, {generated_text}, {len(generated_text)}") + completions_per_process.extend(generated_text) + + # We are gathering string, so we need to use gather_object. + # If you need to gather tensors, you can use gather from accelerate.utils + completions_gather = gather_object(completions_per_process) + # Drop duplicates produced by apply_padding in split_between_processes + completions = completions_gather[: len(prompts)] + distributed_state.print(completions, len(completions)) + for sample, completion in zip(samples, completions): + sample['generation'] = completion + + yield samples + + def save_results(output_queue: queue.Queue, output_path): + with open(output_path, "w") as fp: + while True: + try: + item = output_queue.get(timeout=5) + if item is None: + break + fp.write(json.dumps(item, ensure_ascii=False) + "\n") + fp.flush() + except queue.Empty: + continue + + # handel batch dataloader + batch_res_stream = handler() + res_stream = flatten_batch_stream(batch_res_stream) + + if distributed_state.is_main_process: + output_queue = queue.Queue() + save_thread = ThreadPoolExecutor(max_workers=16) + save_future = save_thread.submit(save_results, output_queue, args.output_path) + for res in res_stream: + output_queue.put(res) + output_queue.put(None) + save_thread.shutdown(wait=True) + else: + list(res_stream) + + +if __name__ == "__main__": + main() diff --git a/mftcoder_accelerate/inference/infer_base.py b/mftcoder_accelerate/inference/infer_base.py new file mode 100644 index 0000000..cb46fc0 --- /dev/null +++ b/mftcoder_accelerate/inference/infer_base.py @@ -0,0 +1,175 @@ +# @author Chaoyu Chen +# @date 2024/11/12 +# @module infer_base.py +"""HF InferenceBase""" + +from typing import Iterable, Dict, List +import gzip +import json +import os +import argparse +import time +from tqdm import tqdm +import torch +from transformers import ( + AutoConfig, + AutoTokenizer, + AutoModel, + AutoModelForCausalLM, + AutoModelForSequenceClassification, +) +from peft import PeftModel + +from infer_utils import ( + print_args, + get_line_count, + stream_jsonl, + batch_stream_jsonl, + flatten_batch_stream, + write_jsonl, +) + + +class HFInferenceBase: + def __init__(self): + # load model and tokenizer + self.tokenizer = None + self.model = None + + def _load_model_tokenizer( + self, + model_cls, + model_path, + torch_dtype=torch.bfloat16, + peft_path=None, + quantization=None, + eos_token=None, + **kwargs, + ): + """ + load model and tokenizer by transfromers + """ + # pass if ckpt already loaded + if self.model and self.tokenizer: + print("CKPT: {} already loaded".format(model_path)) + return + + # load tokenizer first + print("LOAD CKPT and Tokenizer: {}".format(model_path)) + tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) + tokenizer.padding_side = "left" + + config, unused_kwargs = AutoConfig.from_pretrained( + model_path, trust_remote_code=True, return_unused_kwargs=True + ) + print("unused_kwargs:", unused_kwargs) + print("config input:\n", config) + + # eos token parsing + if eos_token: + eos_token = eos_token + eos_token_id = tokenizer.convert_tokens_to_ids(eos_token) + print(f"eos_token {eos_token} from user input") + elif hasattr(tokenizer, "eos_token_id") and tokenizer.eos_token_id: + print(f"Initial eos_token_id {tokenizer.eos_token_id} from tokenizer") + eos_token_id = tokenizer.eos_token_id + eos_token = tokenizer.convert_ids_to_tokens(eos_token_id) + elif hasattr(tokenizer, "eos_token") and tokenizer.eos_token: + print(f"Initial eos_token {tokenizer.eos_token} from tokenizer") + eos_token = tokenizer.eos_token + eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token) + elif hasattr(config, "eos_token_id") and config.eos_token_id: + print(f"Initial eos_token_id {config.eos_token_id} from config.json") + eos_token_id = config.eos_token_id + eos_token = tokenizer.convert_ids_to_tokens(config.eos_token_id) + elif hasattr(config, "eos_token") and config.eos_token: + print(f"Initial eos_token {config.eos_token} from config.json") + eos_token = config.eos_token + eos_token_id = tokenizer.convert_tokens_to_ids(config.eos_token) + else: + raise ValueError("No available eos_token or eos_token_id.") + + try: + tokenizer.eos_token = eos_token + tokenizer.eos_token_id = eos_token_id + # set pad_token to be same as eos_token, it is ok because is will be masked out. + tokenizer.pad_token = eos_token + tokenizer.pad_token_id = eos_token_id + except: + print(f"[WARNING]Cannot set tokenizer.eos_token") + + print(f"tokenizer's eos_token: {tokenizer.eos_token}, pad_token: {tokenizer.pad_token}") + print(f"tokenizer's eos_token_id: {tokenizer.eos_token_id}, pad_token_id: {tokenizer.pad_token_id}") + print(type(tokenizer)) + + base_model = model_cls.from_pretrained( + model_path, + config=config, + load_in_8bit=(quantization == "8bit"), + load_in_4bit=(quantization == "4bit"), + device_map="auto", + torch_dtype=torch_dtype, + attn_implementation="flash_attention_2", + trust_remote_code=True, + low_cpu_mem_usage=True, + **kwargs, + ) + + if peft_path: + print("Loading PEFT MODEL...") + model = PeftModel.from_pretrained(base_model, peft_path) + else: + print("Loading Original MODEL...") + model = base_model + + model.eval() + + print("===============================MODEL Configs=============================") + print(model.config) + print("=========================================================================") + print("==============================MODEL Archetecture=========================") + print(model) + print("=========================================================================") + + self.model = model + self.tokenizer = tokenizer + + def load_model_tokenizer(self, args): + raise NotImplementedError + + def handler(self, dataloader, args): + """consume batch dataloader, yield result batch""" + raise NotImplementedError + + @staticmethod + def check_args(args): + if not os.path.exists(args.model_path): + raise FileNotFoundError("Model path {} not exists!".format(args.model_path)) + + if not os.path.exists(args.data_file): + raise FileNotFoundError("Data file {} not exists!".format(args.data_file)) + + os.makedirs(args.output_dir, exist_ok=True) + + def prepare_args(self, args): + raise NotImplementedError("You need assign args.output_path for result writing") + + def run(self, args): + self.check_args(args) + self.prepare_args(args) + print_args(args) + + # load model if not yet loaded + self.load_model_tokenizer(args) + + # get dataloader + total_num = get_line_count(args.data_file) + stream = stream_jsonl(args.data_file) + dataloader = batch_stream_jsonl(stream, args.batch_size) + + # handel batch dataloader + batch_res_stream = self.handler(dataloader, args) + res_stream = flatten_batch_stream(batch_res_stream) + + # write results into output_path streamingly + write_jsonl(args.output_path, res_stream, total=total_num) diff --git a/mftcoder_accelerate/inference/infer_utils.py b/mftcoder_accelerate/inference/infer_utils.py new file mode 100644 index 0000000..6d5f626 --- /dev/null +++ b/mftcoder_accelerate/inference/infer_utils.py @@ -0,0 +1,122 @@ +# @author Chaoyu Chen +# @date 2024/11/12 +"""Some inference utils""" +import torch +import json +import os +import gzip +import re +import ast +from tqdm import tqdm +from typing import Iterable, Dict, List + + +def print_args(args): + message = "\n".join([f"{k:<20}: {v}" for k, v in vars(args).items()]) + print("====" * 30) + print(message) + print("====" * 30) + print("GPU: {}".format(torch.cuda.current_device())) + + +def get_line_count(file_path): + with open(file_path, "r", encoding="utf-8") as file: + return sum(1 for _ in file) + + +def stream_jsonl(filename: str) -> Iterable[Dict]: + """ + Parses each jsonl line and yields it as a dictionary + """ + if filename.endswith(".gz"): + with open(filename, "rb") as gzfp: + with gzip.open(gzfp, "rt") as fp: + for line in fp: + if any(not x.isspace() for x in line): + yield json.loads(line) + else: + with open(filename, "r") as fp: + for line in fp: + if any(not x.isspace() for x in line): + yield json.loads(line) + + +def batch_stream_jsonl(stream: Iterable[Dict], batch_size) -> Iterable[List]: + batch = list() + for item in stream: + batch.append(item) + if len(batch) == batch_size: + yield batch + batch = [] + if batch: + yield batch + + +def flatten_batch_stream(batch_stream): + for batch in batch_stream: + for item in batch: + yield item + + +def write_jsonl(filename: str, data: Iterable[Dict], total, append: bool = False): + """ + Writes an iterable of dictionaries to jsonl + """ + if append: + mode = "ab" + else: + mode = "wb" + filename = os.path.expanduser(filename) + if filename.endswith(".gz"): + with open(filename, mode) as fp: + with gzip.GzipFile(fileobj=fp, mode="wb") as gzfp: + for x in data: + gzfp.write((json.dumps(x) + "\n").encode("utf-8")) + else: + with open(filename, mode) as fp: + for x in tqdm(data, total=total): + fp.write((json.dumps(x) + "\n").encode("utf-8")) + + +def is_compilable(code): + try: + ast.parse(code) + return True + except SyntaxError: + return False + + +def is_tests(code): + return code.strip().startswith("assert") + + +def extract_python_code_block(response): + # pattern = r"^```[Pp]ython\s*\n(.*?)(?=^```)" + pattern = r"^```\s*(?:[Pp]ython)?\s*\n(.*?)(?=^```)" + result = re.findall(pattern, response, re.DOTALL | re.MULTILINE) + return "\n".join([x for x in result if is_compilable(x) and not is_tests(x)]) + + +def extract_python_test_block(response): + # pattern = r"^```[Pp]ython\s*\n(.*?)(?=^```)" + pattern = r"^```\s*(?:[Pp]ython)?\s*\n(.*?)(?=^```)" + result = re.findall(pattern, response, re.DOTALL | re.MULTILINE) + return "\n".join([x for x in result if is_compilable(x) and is_tests(x)]) + + +def extract_code_with_lang(text): + """ + 使用正则表达式从文本中提取任意语言的代码块。 + + 参数: + - text: 包含Markdown代码块的字符串。 + + 返回: + - 一个包含所有匹配的代码块及其语言的列表，每一个元组都是 (language, code_block)。 + """ + # 编译正则表达式模式，匹配任意语言的代码块 + pattern = re.compile(r"```([\w+#-]*)\s*\n(.*?)```", re.DOTALL | re.MULTILINE) + + # 使用findall方法找出所有匹配的代码块及其语言标识 + # 返回的是一个元组列表，每个元组包含 (language, code_block) + return pattern.findall(text) \ No newline at end of file diff --git a/mftcoder_accelerate/inference/ppl.py b/mftcoder_accelerate/inference/ppl.py new file mode 100644 index 0000000..8690000 --- /dev/null +++ b/mftcoder_accelerate/inference/ppl.py @@ -0,0 +1,122 @@ +# coding=utf-8 +# @author Chaoyu Chen +# @date 2024/11/12 +# @module ppl.py +"""ppl demo""" +from typing import Iterable, Dict, List +import gzip +import json +import os +import argparse +import time +from tqdm import tqdm +import torch + +from transformers import ( + AutoConfig, + AutoTokenizer, + AutoModelForCausalLM, +) +from peft import PeftModel +from infer_base import HFInferenceBase +from tokenizer import MFTCoder_template + + +class HFPerplexityPairInfer(HFInferenceBase): + def __init__(self): + super().__init__() + + def load_model_tokenizer(self, args): + """ + load ppl model and tokenizer using self._load_model_tokenizer + """ + self._load_model_tokenizer(AutoModelForCausalLM, args.model_path) + + def handler(self, dataloader, args): + for batch in dataloader: + for key in ["chosen", "rejected"]: + # apply chat template on chatml messages + input_text = [self.tokenizer.apply_chat_template(sample[key], tokenize=False) for sample in batch] + + # tokenization + inputs = self.tokenizer( + input_text, + return_tensors="pt", + padding=True, + truncation=True, + max_length=4096, + ).to("cuda") + # print(inputs) + + # ppl computing + input_ids = inputs["input_ids"] + attention_mask = inputs["attention_mask"] + labels = inputs["input_ids"] + print(input_ids.shape) + # forward outputs + with torch.no_grad(): + outputs = self.model( + input_ids=input_ids, + attention_mask=attention_mask, + labels=input_ids, + ) + logits = outputs.logits + + # loss fuction + loss_fct = torch.nn.CrossEntropyLoss(ignore_index=self.tokenizer.pad_token_id, reduction="none") + # shift logits and labels to make them aligned + shift_logits = logits[:, :-1, :].contiguous() + shift_labels = labels[:, 1:].contiguous() + + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + # get mean loss of all tokens of each sample + losses = loss.view(len(input_text), -1).mean(dim=1) + # save loss and ppl + for i, individual_loss in enumerate(losses): + batch[i][f"{key}_loss"] = individual_loss.item() + batch[i][f"{key}_ppl"] = torch.exp(individual_loss).item() + yield batch + + def prepare_args(self, args): + name = args.data_file.split("/")[-1].replace(".jsonl", "") + "-PPL" + args.output_path = os.path.join(args.output_dir, f"{name}.jsonl") + + +def get_args(): + parser = argparse.ArgumentParser(description="PPL args.") + parser.add_argument( + "--model_path", + type=str, + help="huggingface model path", + ) + parser.add_argument( + "--data_file", + type=str, + help="data file path", + ) + parser.add_argument( + "--output_dir", + type=str, + help="output directory", + ) + parser.add_argument("--batch_size", type=int, default=1) + args = parser.parse_args() + return args + + +def main(args): + st = time.time() + runner = HFPerplexityPairInfer() + runner.run(args) + + print("{} finish in: {:.4f} Minutes {}".format("+++" * 10, (time.time() - st) / 60, "+++" * 10)) + + +if __name__ == "__main__": + print("-----------" * 10) + print("-----------" * 10) + print("-----------" * 10) + main(get_args()) + print("-----------" * 10) + print("-----------" * 10) + print("-----------" * 10) diff --git a/mftcoder_accelerate/inference/reward.py b/mftcoder_accelerate/inference/reward.py new file mode 100644 index 0000000..fc35b06 --- /dev/null +++ b/mftcoder_accelerate/inference/reward.py @@ -0,0 +1,131 @@ +# @author Chaoyu Chen +# @date 2024/11/12 +# @module reward.py +"""Reward demo""" + +from typing import Iterable, Dict, List +import gzip +import json +import os +import argparse +import time +from tqdm import tqdm +import torch +from transformers import ( + AutoConfig, + AutoTokenizer, + AutoModelForCausalLM, + AutoModelForSequenceClassification, +) + +from infer_base import HFInferenceBase +from tokenizer import MFTCoder_template + + +class HFRewardPairInfer(HFInferenceBase): + def __init__(self): + super().__init__() + + def load_model_tokenizer(self, args): + """ + load ppl model and tokenizer using self._load_model_tokenizer + """ + self._load_model_tokenizer(AutoModelForCausalLM, args.model_path) + + def handler(self, dataloader, args): + correct_num = 0 + total_num = 0 + for batch in dataloader: + try: + for key in ["chosen", "rejected"]: + + input_text = [ + self.tokenizer.apply_chat_template(sample[key], tokenize=False, add_generation_prompt=False) + for sample in batch + ] + + # tokenization + inputs = self.tokenizer( + input_text, + return_tensors="pt", + padding=True, + truncation=True, + max_length=4096, + add_special_tokens=False, + ).to("cuda") + + # prepare input_ids and attention_mask + input_ids = inputs["input_ids"] + attention_mask = inputs["attention_mask"] + + # generate score + response_token_ids = self.model.generate( + input_ids, + attention_mask=attention_mask, + max_new_tokens=1, + return_dict_in_generate=True, + output_scores=True, + ) + + # print(response_token_ids["scores"], response_token_ids["scores"][0].shape) + rewards = response_token_ids["scores"][0][:, 0].reshape(-1) + for i, reward in enumerate(rewards): + batch[i][f"{key}_reward"] = reward.item() + # print(reward.item()) + for sample in batch: + total_num += 1 + # print(sample["chosen_reward"], sample["rejected_reward"]) + if sample["chosen_reward"] > sample["rejected_reward"]: + correct_num += 1 + print(f"correct {correct_num} of total {total_num}") + torch.cuda.empty_cache() + yield batch + except Exception as e: + print(f"[ERROR] {e}") + continue + # break + print(f"correct {correct_num} of total {total_num}") + + def prepare_args(self, args): + name = args.data_file.split("/")[-1].replace(".jsonl", "") + "-REWARD" + args.output_path = os.path.join(args.output_dir, f"{name}.jsonl") + + +def get_args(): + parser = argparse.ArgumentParser(description="REWARD args.") + parser.add_argument( + "--model_path", + type=str, + help="huggingface model path", + ) + parser.add_argument( + "--data_file", + type=str, + help="data file path", + ) + parser.add_argument( + "--output_dir", + type=str, + help="output directory", + ) + parser.add_argument("--batch_size", type=int, default=1) + + return parser.parse_args() + + +def main(args): + st = time.time() + runner = HFRewardPairInfer() + runner.run(args) + + print("{} finish in: {:.4f} Minutes {}".format("+++" * 10, (time.time() - st) / 60, "+++" * 10)) + + +if __name__ == "__main__": + print("-----------" * 10) + print("-----------" * 10) + print("-----------" * 10) + main(get_args()) + print("-----------" * 10) + print("-----------" * 10) + print("-----------" * 10) diff --git a/mftcoder_accelerate/inference/split.sh b/mftcoder_accelerate/inference/split.sh new file mode 100644 index 0000000..665ad83 --- /dev/null +++ b/mftcoder_accelerate/inference/split.sh @@ -0,0 +1,23 @@ +# 输入文件的完整路径 +input_file_path="$1" + +# 获取输入文件的目录 +input_directory=$(dirname "$input_file_path") + +# 获取文件名，不带扩展名 +input_filename=$(basename "$input_file_path" .jsonl) + +# 计算原始jsonl文件的总行数 +total_lines=$(wc -l < "$input_file_path") +echo "total lines: "$total_lines +# 要分成的文件数K，例如我们假设为4 +K=$2 + +# 计算每个文件应该有多少行（向上取整） +lines_per_file=$(( (total_lines + K - 1) / K )) +echo "lines_per_file: "$lines_per_file +# 检查输出目录是否存在，如果不存在则创建它 +mkdir -p "$input_directory" + +# 使用split命令等分文件，并将结果文件放在输入文件的目录中，前缀为输入文件的名字 +split -l $lines_per_file -d --additional-suffix=.jsonl "$input_file_path" "${input_directory}/${input_filename}-part-" \ No newline at end of file diff --git a/mftcoder_accelerate/model/__init__.py b/mftcoder_accelerate/model/__init__.py new file mode 100755 index 0000000..e69de29 diff --git a/mftcoder_accelerate/model/aquila2/__init__.py b/mftcoder_accelerate/model/aquila2/__init__.py new file mode 100644 index 0000000..ab80b6d --- /dev/null +++ b/mftcoder_accelerate/model/aquila2/__init__.py @@ -0,0 +1,5 @@ +# coding=utf-8 +# @author Chaoyu Chen +# @date 2024/11/18 +# @module __init__.py +"""Introduce the module""" diff --git a/mftcoder_accelerate/src/model/aquila2/configuration_aquila.py b/mftcoder_accelerate/model/aquila2/configuration_aquila.py similarity index 100% rename from mftcoder_accelerate/src/model/aquila2/configuration_aquila.py rename to mftcoder_accelerate/model/aquila2/configuration_aquila.py diff --git a/mftcoder_accelerate/src/model/aquila2/modeling_aquila.py b/mftcoder_accelerate/model/aquila2/modeling_aquila.py similarity index 100% rename from mftcoder_accelerate/src/model/aquila2/modeling_aquila.py rename to mftcoder_accelerate/model/aquila2/modeling_aquila.py diff --git a/mftcoder_accelerate/model/baichuan2/__init__.py b/mftcoder_accelerate/model/baichuan2/__init__.py new file mode 100644 index 0000000..ab80b6d --- /dev/null +++ b/mftcoder_accelerate/model/baichuan2/__init__.py @@ -0,0 +1,5 @@ +# coding=utf-8 +# @author Chaoyu Chen +# @date 2024/11/18 +# @module __init__.py +"""Introduce the module""" diff --git a/mftcoder_accelerate/src/model/baichuan2/configuration_baichuan.py b/mftcoder_accelerate/model/baichuan2/configuration_baichuan.py similarity index 100% rename from mftcoder_accelerate/src/model/baichuan2/configuration_baichuan.py rename to mftcoder_accelerate/model/baichuan2/configuration_baichuan.py diff --git a/mftcoder_accelerate/src/model/baichuan2/generation_utils.py b/mftcoder_accelerate/model/baichuan2/generation_utils.py similarity index 100% rename from mftcoder_accelerate/src/model/baichuan2/generation_utils.py rename to mftcoder_accelerate/model/baichuan2/generation_utils.py diff --git a/mftcoder_accelerate/src/model/baichuan2/modeling_baichuan.py b/mftcoder_accelerate/model/baichuan2/modeling_baichuan.py similarity index 100% rename from mftcoder_accelerate/src/model/baichuan2/modeling_baichuan.py rename to mftcoder_accelerate/model/baichuan2/modeling_baichuan.py diff --git a/mftcoder_accelerate/src/model/baichuan2/quantizer.py b/mftcoder_accelerate/model/baichuan2/quantizer.py similarity index 100% rename from mftcoder_accelerate/src/model/baichuan2/quantizer.py rename to mftcoder_accelerate/model/baichuan2/quantizer.py diff --git a/mftcoder_accelerate/src/model/baichuan2/tokenization_baichuan.py b/mftcoder_accelerate/model/baichuan2/tokenization_baichuan.py similarity index 100% rename from mftcoder_accelerate/src/model/baichuan2/tokenization_baichuan.py rename to mftcoder_accelerate/model/baichuan2/tokenization_baichuan.py diff --git a/mftcoder_accelerate/model/chatglm2/__init__.py b/mftcoder_accelerate/model/chatglm2/__init__.py new file mode 100644 index 0000000..ab80b6d --- /dev/null +++ b/mftcoder_accelerate/model/chatglm2/__init__.py @@ -0,0 +1,5 @@ +# coding=utf-8 +# @author Chaoyu Chen +# @date 2024/11/18 +# @module __init__.py +"""Introduce the module""" diff --git a/mftcoder_accelerate/src/model/chatglm2/config.json b/mftcoder_accelerate/model/chatglm2/config.json similarity index 100% rename from mftcoder_accelerate/src/model/chatglm2/config.json rename to mftcoder_accelerate/model/chatglm2/config.json diff --git a/mftcoder_accelerate/src/model/chatglm2/configuration_chatglm.py b/mftcoder_accelerate/model/chatglm2/configuration_chatglm.py similarity index 100% rename from mftcoder_accelerate/src/model/chatglm2/configuration_chatglm.py rename to mftcoder_accelerate/model/chatglm2/configuration_chatglm.py diff --git a/mftcoder_accelerate/src/model/chatglm2/modeling_chatglm.py b/mftcoder_accelerate/model/chatglm2/modeling_chatglm.py similarity index 100% rename from mftcoder_accelerate/src/model/chatglm2/modeling_chatglm.py rename to mftcoder_accelerate/model/chatglm2/modeling_chatglm.py diff --git a/mftcoder_accelerate/src/model/chatglm2/quantization.py b/mftcoder_accelerate/model/chatglm2/quantization.py similarity index 100% rename from mftcoder_accelerate/src/model/chatglm2/quantization.py rename to mftcoder_accelerate/model/chatglm2/quantization.py diff --git a/mftcoder_accelerate/src/model/chatglm2/tokenization_chatglm.py b/mftcoder_accelerate/model/chatglm2/tokenization_chatglm.py similarity index 100% rename from mftcoder_accelerate/src/model/chatglm2/tokenization_chatglm.py rename to mftcoder_accelerate/model/chatglm2/tokenization_chatglm.py diff --git a/mftcoder_accelerate/src/model/chatglm2/tokenizer_config.json b/mftcoder_accelerate/model/chatglm2/tokenizer_config.json similarity index 100% rename from mftcoder_accelerate/src/model/chatglm2/tokenizer_config.json rename to mftcoder_accelerate/model/chatglm2/tokenizer_config.json diff --git a/mftcoder_accelerate/model/chatglm3/__init__.py b/mftcoder_accelerate/model/chatglm3/__init__.py new file mode 100644 index 0000000..ab80b6d --- /dev/null +++ b/mftcoder_accelerate/model/chatglm3/__init__.py @@ -0,0 +1,5 @@ +# coding=utf-8 +# @author Chaoyu Chen +# @date 2024/11/18 +# @module __init__.py +"""Introduce the module""" diff --git a/mftcoder_accelerate/src/model/chatglm3/config.json b/mftcoder_accelerate/model/chatglm3/config.json similarity index 100% rename from mftcoder_accelerate/src/model/chatglm3/config.json rename to mftcoder_accelerate/model/chatglm3/config.json diff --git a/mftcoder_accelerate/src/model/chatglm3/configuration_chatglm.py b/mftcoder_accelerate/model/chatglm3/configuration_chatglm.py similarity index 100% rename from mftcoder_accelerate/src/model/chatglm3/configuration_chatglm.py rename to mftcoder_accelerate/model/chatglm3/configuration_chatglm.py diff --git a/mftcoder_accelerate/src/model/chatglm3/modeling_chatglm.py b/mftcoder_accelerate/model/chatglm3/modeling_chatglm.py similarity index 100% rename from mftcoder_accelerate/src/model/chatglm3/modeling_chatglm.py rename to mftcoder_accelerate/model/chatglm3/modeling_chatglm.py diff --git a/mftcoder_accelerate/src/model/chatglm3/quantization.py b/mftcoder_accelerate/model/chatglm3/quantization.py similarity index 100% rename from mftcoder_accelerate/src/model/chatglm3/quantization.py rename to mftcoder_accelerate/model/chatglm3/quantization.py diff --git a/mftcoder_accelerate/src/model/chatglm3/tokenization_chatglm.py b/mftcoder_accelerate/model/chatglm3/tokenization_chatglm.py similarity index 100% rename from mftcoder_accelerate/src/model/chatglm3/tokenization_chatglm.py rename to mftcoder_accelerate/model/chatglm3/tokenization_chatglm.py diff --git a/mftcoder_accelerate/src/model/code_llama/__init__.py b/mftcoder_accelerate/model/code_llama/__init__.py similarity index 100% rename from mftcoder_accelerate/src/model/code_llama/__init__.py rename to mftcoder_accelerate/model/code_llama/__init__.py diff --git a/mftcoder_accelerate/src/model/code_llama/configuration_llama.py b/mftcoder_accelerate/model/code_llama/configuration_llama.py similarity index 100% rename from mftcoder_accelerate/src/model/code_llama/configuration_llama.py rename to mftcoder_accelerate/model/code_llama/configuration_llama.py diff --git a/mftcoder_accelerate/src/model/code_llama/convert_llama_weights_to_hf.py b/mftcoder_accelerate/model/code_llama/convert_llama_weights_to_hf.py similarity index 100% rename from mftcoder_accelerate/src/model/code_llama/convert_llama_weights_to_hf.py rename to mftcoder_accelerate/model/code_llama/convert_llama_weights_to_hf.py diff --git a/mftcoder_accelerate/src/model/code_llama/modeling_llama.py b/mftcoder_accelerate/model/code_llama/modeling_llama.py similarity index 100% rename from mftcoder_accelerate/src/model/code_llama/modeling_llama.py rename to mftcoder_accelerate/model/code_llama/modeling_llama.py diff --git a/mftcoder_accelerate/src/model/code_llama/tokenization_code_llama.py b/mftcoder_accelerate/model/code_llama/tokenization_code_llama.py similarity index 100% rename from mftcoder_accelerate/src/model/code_llama/tokenization_code_llama.py rename to mftcoder_accelerate/model/code_llama/tokenization_code_llama.py diff --git a/mftcoder_accelerate/src/model/code_llama/tokenization_code_llama_fast.py b/mftcoder_accelerate/model/code_llama/tokenization_code_llama_fast.py similarity index 100% rename from mftcoder_accelerate/src/model/code_llama/tokenization_code_llama_fast.py rename to mftcoder_accelerate/model/code_llama/tokenization_code_llama_fast.py diff --git a/mftcoder_accelerate/src/model/code_llama/tokenization_llama.py b/mftcoder_accelerate/model/code_llama/tokenization_llama.py similarity index 100% rename from mftcoder_accelerate/src/model/code_llama/tokenization_llama.py rename to mftcoder_accelerate/model/code_llama/tokenization_llama.py diff --git a/mftcoder_accelerate/src/model/code_llama/tokenization_llama_fast.py b/mftcoder_accelerate/model/code_llama/tokenization_llama_fast.py similarity index 100% rename from mftcoder_accelerate/src/model/code_llama/tokenization_llama_fast.py rename to mftcoder_accelerate/model/code_llama/tokenization_llama_fast.py diff --git a/mftcoder_accelerate/model/deepseek_v2/__init__.py b/mftcoder_accelerate/model/deepseek_v2/__init__.py new file mode 100644 index 0000000..ab80b6d --- /dev/null +++ b/mftcoder_accelerate/model/deepseek_v2/__init__.py @@ -0,0 +1,5 @@ +# coding=utf-8 +# @author Chaoyu Chen +# @date 2024/11/18 +# @module __init__.py +"""Introduce the module""" diff --git a/mftcoder_accelerate/src/model/deepseek_v2/configuration_deepseek.py b/mftcoder_accelerate/model/deepseek_v2/configuration_deepseek.py similarity index 100% rename from mftcoder_accelerate/src/model/deepseek_v2/configuration_deepseek.py rename to mftcoder_accelerate/model/deepseek_v2/configuration_deepseek.py diff --git a/mftcoder_accelerate/src/model/deepseek_v2/modeling_deepseek.py b/mftcoder_accelerate/model/deepseek_v2/modeling_deepseek.py similarity index 100% rename from mftcoder_accelerate/src/model/deepseek_v2/modeling_deepseek.py rename to mftcoder_accelerate/model/deepseek_v2/modeling_deepseek.py diff --git a/mftcoder_accelerate/src/model/deepseek_v2/tokenization_deepseek_fast.py b/mftcoder_accelerate/model/deepseek_v2/tokenization_deepseek_fast.py similarity index 100% rename from mftcoder_accelerate/src/model/deepseek_v2/tokenization_deepseek_fast.py rename to mftcoder_accelerate/model/deepseek_v2/tokenization_deepseek_fast.py diff --git a/mftcoder_accelerate/src/model/gpt_bigcode/__init__.py b/mftcoder_accelerate/model/gpt_bigcode/__init__.py similarity index 100% rename from mftcoder_accelerate/src/model/gpt_bigcode/__init__.py rename to mftcoder_accelerate/model/gpt_bigcode/__init__.py diff --git a/mftcoder_accelerate/src/model/gpt_bigcode/configuration_gpt_bigcode.py b/mftcoder_accelerate/model/gpt_bigcode/configuration_gpt_bigcode.py similarity index 100% rename from mftcoder_accelerate/src/model/gpt_bigcode/configuration_gpt_bigcode.py rename to mftcoder_accelerate/model/gpt_bigcode/configuration_gpt_bigcode.py diff --git a/mftcoder_accelerate/src/model/gpt_bigcode/modeling_gpt_bigcode.py b/mftcoder_accelerate/model/gpt_bigcode/modeling_gpt_bigcode.py similarity index 100% rename from mftcoder_accelerate/src/model/gpt_bigcode/modeling_gpt_bigcode.py rename to mftcoder_accelerate/model/gpt_bigcode/modeling_gpt_bigcode.py diff --git a/mftcoder_accelerate/src/model/gpt_neox/__init__.py b/mftcoder_accelerate/model/gpt_neox/__init__.py similarity index 100% rename from mftcoder_accelerate/src/model/gpt_neox/__init__.py rename to mftcoder_accelerate/model/gpt_neox/__init__.py diff --git a/mftcoder_accelerate/src/model/gpt_neox/config.json b/mftcoder_accelerate/model/gpt_neox/config.json similarity index 100% rename from mftcoder_accelerate/src/model/gpt_neox/config.json rename to mftcoder_accelerate/model/gpt_neox/config.json diff --git a/mftcoder_accelerate/src/model/gpt_neox/configuration_gpt_neox.py b/mftcoder_accelerate/model/gpt_neox/configuration_gpt_neox.py similarity index 100% rename from mftcoder_accelerate/src/model/gpt_neox/configuration_gpt_neox.py rename to mftcoder_accelerate/model/gpt_neox/configuration_gpt_neox.py diff --git a/mftcoder_accelerate/src/model/gpt_neox/generation_config.json b/mftcoder_accelerate/model/gpt_neox/generation_config.json similarity index 100% rename from mftcoder_accelerate/src/model/gpt_neox/generation_config.json rename to mftcoder_accelerate/model/gpt_neox/generation_config.json diff --git a/mftcoder_accelerate/src/model/gpt_neox/modeling_gpt_neox.py b/mftcoder_accelerate/model/gpt_neox/modeling_gpt_neox.py similarity index 100% rename from mftcoder_accelerate/src/model/gpt_neox/modeling_gpt_neox.py rename to mftcoder_accelerate/model/gpt_neox/modeling_gpt_neox.py diff --git a/mftcoder_accelerate/src/model/gpt_neox/tokenization_gpt_neox_fast.py b/mftcoder_accelerate/model/gpt_neox/tokenization_gpt_neox_fast.py similarity index 100% rename from mftcoder_accelerate/src/model/gpt_neox/tokenization_gpt_neox_fast.py rename to mftcoder_accelerate/model/gpt_neox/tokenization_gpt_neox_fast.py diff --git a/mftcoder_accelerate/model/phi/__init__.py b/mftcoder_accelerate/model/phi/__init__.py new file mode 100644 index 0000000..ab80b6d --- /dev/null +++ b/mftcoder_accelerate/model/phi/__init__.py @@ -0,0 +1,5 @@ +# coding=utf-8 +# @author Chaoyu Chen +# @date 2024/11/18 +# @module __init__.py +"""Introduce the module""" diff --git a/mftcoder_accelerate/src/model/phi/configuration_mixformer_sequential.py b/mftcoder_accelerate/model/phi/configuration_mixformer_sequential.py similarity index 100% rename from mftcoder_accelerate/src/model/phi/configuration_mixformer_sequential.py rename to mftcoder_accelerate/model/phi/configuration_mixformer_sequential.py diff --git a/mftcoder_accelerate/src/model/phi/modeling_mixformer_sequential.py b/mftcoder_accelerate/model/phi/modeling_mixformer_sequential.py similarity index 100% rename from mftcoder_accelerate/src/model/phi/modeling_mixformer_sequential.py rename to mftcoder_accelerate/model/phi/modeling_mixformer_sequential.py diff --git a/mftcoder_accelerate/model/qwen/__init__.py b/mftcoder_accelerate/model/qwen/__init__.py new file mode 100644 index 0000000..ab80b6d --- /dev/null +++ b/mftcoder_accelerate/model/qwen/__init__.py @@ -0,0 +1,5 @@ +# coding=utf-8 +# @author Chaoyu Chen +# @date 2024/11/18 +# @module __init__.py +"""Introduce the module""" diff --git a/mftcoder_accelerate/src/model/qwen/cache_autogptq_cuda_256.cpp b/mftcoder_accelerate/model/qwen/cache_autogptq_cuda_256.cpp similarity index 100% rename from mftcoder_accelerate/src/model/qwen/cache_autogptq_cuda_256.cpp rename to mftcoder_accelerate/model/qwen/cache_autogptq_cuda_256.cpp diff --git a/mftcoder_accelerate/src/model/qwen/cache_autogptq_cuda_kernel_256.cu b/mftcoder_accelerate/model/qwen/cache_autogptq_cuda_kernel_256.cu similarity index 100% rename from mftcoder_accelerate/src/model/qwen/cache_autogptq_cuda_kernel_256.cu rename to mftcoder_accelerate/model/qwen/cache_autogptq_cuda_kernel_256.cu diff --git a/mftcoder_accelerate/src/model/qwen/configuration_qwen.py b/mftcoder_accelerate/model/qwen/configuration_qwen.py similarity index 100% rename from mftcoder_accelerate/src/model/qwen/configuration_qwen.py rename to mftcoder_accelerate/model/qwen/configuration_qwen.py diff --git a/mftcoder_accelerate/src/model/qwen/cpp_kernels.py b/mftcoder_accelerate/model/qwen/cpp_kernels.py similarity index 100% rename from mftcoder_accelerate/src/model/qwen/cpp_kernels.py rename to mftcoder_accelerate/model/qwen/cpp_kernels.py diff --git a/mftcoder_accelerate/src/model/qwen/modeling_qwen.py b/mftcoder_accelerate/model/qwen/modeling_qwen.py similarity index 100% rename from mftcoder_accelerate/src/model/qwen/modeling_qwen.py rename to mftcoder_accelerate/model/qwen/modeling_qwen.py diff --git a/mftcoder_accelerate/src/model/qwen/qwen_generation_utils.py b/mftcoder_accelerate/model/qwen/qwen_generation_utils.py similarity index 100% rename from mftcoder_accelerate/src/model/qwen/qwen_generation_utils.py rename to mftcoder_accelerate/model/qwen/qwen_generation_utils.py diff --git a/mftcoder_accelerate/src/model/qwen/tokenization_qwen.py b/mftcoder_accelerate/model/qwen/tokenization_qwen.py similarity index 100% rename from mftcoder_accelerate/src/model/qwen/tokenization_qwen.py rename to mftcoder_accelerate/model/qwen/tokenization_qwen.py diff --git a/mftcoder_accelerate/src/model/qwen/tokenizer_config.json b/mftcoder_accelerate/model/qwen/tokenizer_config.json similarity index 100% rename from mftcoder_accelerate/src/model/qwen/tokenizer_config.json rename to mftcoder_accelerate/model/qwen/tokenizer_config.json diff --git a/mftcoder_accelerate/src/run_offline_tokenization.sh b/mftcoder_accelerate/run_offline_tokenization.sh similarity index 78% rename from mftcoder_accelerate/src/run_offline_tokenization.sh rename to mftcoder_accelerate/run_offline_tokenization.sh index ed916da..e1fb273 100644 --- a/mftcoder_accelerate/src/run_offline_tokenization.sh +++ b/mftcoder_accelerate/run_offline_tokenization.sh @@ -3,7 +3,7 @@ DATA_PATH= DATASET_NAME= OUTPUT_PATH= -python offline_tokenization/concat_sst_bin_tokenization.py \ +python tokenization/concat_sst_bin_tokenization.py \ --model-path ${MODEL_PATH} \ --data-path ${DATA_PATH} \ --dataset-name ${DATASET_NAME} \ diff --git a/mftcoder_accelerate/src/offline_tokenization/lm_fmt.py b/mftcoder_accelerate/src/offline_tokenization/lm_fmt.py deleted file mode 100644 index c922859..0000000 --- a/mftcoder_accelerate/src/offline_tokenization/lm_fmt.py +++ /dev/null @@ -1,360 +0,0 @@ -import os -import zstandard -import ujson as json -import time -import tarfile -import codecs -from functools import reduce -import jsonlines -import io -from zipfile import ZipFile -import gzip -from math import ceil -import mmap -import multiprocessing as mp -from pathlib import Path - -VALID_EXTENSIONS = ['openwebtext.tar.xz', '_data.xz', '.dat.zst', '.jsonl', '.jsonl.zst', '.jsonl.zst.tar', '.json.zst', '.txt', '.zip', '.tar.gz', '.json.gz', '.gz'] - -def has_valid_extension(file): - return any([file.endswith(ext) for ext in VALID_EXTENSIONS]) - -def _listdir_or_file(x): - if isinstance(x, list): - return reduce(lambda x, y: x + y, map(listdir_or_file, sorted(x))) - if os.path.isfile(x): - return [x] - elif os.path.isdir(x): - return [str(Path(x) / fn) for fn in sorted(os.listdir(x))] - else: - raise FileNotFoundError(f"{x} not found") - -def listdir_or_file(x): - return list(filter(has_valid_extension, _listdir_or_file(x))) - -def tarfile_reader(file, streaming=False): - # we need our own tarfile parser because `tarfile` doesn't work well for - # big tarfiles; it seems to be reading the entire file to get a list of - # where all the files are - but we don't need that because we just need - # to see each file once. surprisingly, `tarfile` doesn't expose any - # facilities for this. the only options are 1. load the entire tarfile - # and then query by filename or 2. extract to disk - and neither of - # these is what we want. - - offset = 0 - paxfilesize = None - while True: - hdr = file.read(512) - offset += 512 - - # https://www.gnu.org/software/tar/manual/html_node/Standard.html - # end at 135 not 136 because of \0 terminator - if hdr[124:135] == b'\0'*11: - # end of record - break - - fname = hdr[:100].split(b'\0')[0] - - # if the file is too big to fit in the size field, tarfiles will actually - # include a PaxHeader with the size in it, applicable to the immediate next file. - if paxfilesize is not None: - size = paxfilesize - paxfilesize = None - else: - size = int(hdr[124:135], 8) - - padded_size = ceil(size / 512) * 512 - - # for handling PaxHeader files (which contain extra metadata about file size) and directories - # https://pubs.opengroup.org/onlinepubs/9699919799/utilities/pax.html#tag_20_92_13_03 - type = chr(hdr[156]) - - if type == 'x': - meta = file.read(padded_size)[:size] - def kv(x): - return x.decode('utf-8').split(' ')[1].split('=') - paxfileattrs = { - kv(x)[0]: kv(x)[1] - for x in meta.split(b'\n') if x - } - paxfilesize = int(paxfileattrs['size']) - - offset += padded_size - continue - elif type != '0' and type != '\0': - if streaming: - file.seek(padded_size, os.SEEK_CUR) - else: - file.read(padded_size) - offset += padded_size - continue - - if streaming: - # skip directory entries - if size != 0: - mmo = mmap.mmap(file.fileno(), length=offset + size, access=mmap.ACCESS_READ) - mmo.seek(offset) - yield mmo - - file.seek(padded_size, os.SEEK_CUR) - else: - yield file.read(padded_size)[:size] - offset += padded_size - -def handle_jsonl(jsonl_reader, get_meta, autojoin_paragraphs, para_joiner, key='text'): - for ob in jsonl_reader: - # naive jsonl where each object is just the string itself, with no meta. For legacy compatibility. - if isinstance(ob, str): - assert not get_meta - yield ob - continue - - if key is None: - yield ob - continue - - text = ob[key] - - if autojoin_paragraphs and isinstance(text, list): - text = para_joiner.join(text) - - if get_meta: - yield text, (ob['meta'] if 'meta' in ob else {}) - else: - yield text - - -class Reader: - def __init__(self, in_path): - self.in_path = in_path - - def stream_data(self, get_meta=False, threaded=False, key=None): - if not threaded: - yield from self._stream_data(get_meta, key=key) - return - - q = mp.Queue(1000) - p = mp.Process(target=self._stream_data_threaded, args=(q, get_meta), kwargs={"key": key}) - p.start() - while p.is_alive(): - res = q.get() - if res is None: break - yield res - - def _stream_data_threaded(self, q, get_meta=False): - for data in self._stream_data(get_meta): - q.put(data) - q.put(None) - - def _stream_data(self, get_meta=False, key="text"): - self.f_name = "" - files = listdir_or_file(self.in_path) - if not files: - raise FileNotFoundError(f"No valid file(s) found in {self.in_path}") - for f in files: - self.f_name = f - if f == 'openwebtext.tar.xz': - assert not get_meta - - yield from self.read_owt(f) - elif 'urlsf_subset' in f and f.endswith('_data.xz'): - assert not get_meta - - yield from self.read_owt_subset(f) - elif f.endswith('.dat.zst'): - assert not get_meta - - yield from self.read_dat(f) - elif f.endswith('.jsonl'): - yield from self.read_jsonl(f, get_meta, key=key) - elif f.endswith('.jsonl.zst'): - yield from self.read_jsonl_zst(f, get_meta, key=key) - elif f.endswith('.jsonl.zst.tar'): - yield from self.read_jsonl_tar(f, get_meta, key=key) - elif f.endswith('.json.zst'): - assert not get_meta - - yield from self.read_json(f) - elif f.endswith('.txt'): - assert not get_meta - - yield from self.read_txt(f) - elif f.endswith('.zip'): - assert not get_meta - - yield from self.read_zip(f) - elif f.endswith('.tar.gz'): - assert not get_meta - - yield from self.read_tgz(f) - elif f.endswith('.json.gz'): - assert not get_meta - - yield from self.read_jsongz(f) - elif f.endswith('.gz'): - assert not get_meta - - yield from self.read_gz(f) - else: - # shouldn't be reached - print(f'Skipping {f} as streaming for that filetype is not implemented') - - def read_txt(self, file): - with open(file, 'r') as fh: - yield fh.read() - - def read_zip(self, file): - archive = ZipFile(file, 'r') - for f in archive.namelist(): - yield archive.read(f).decode('UTF-8') - - def read_tgz(self, file): - gz = gzip.open(file) - yield from (x.decode('utf-8') for x in tarfile_reader(gz, streaming=False)) - - def read_gz(self, file): - with gzip.open(file, 'rb') as f: - for line in f: - yield line.decode('utf-8') - - def read_jsongz(self, file): - for line in self.read_gz(file): - yield json.loads(line) - - def read_json(self, file): - with open(file, 'rb') as fh: - cctx = zstandard.ZstdDecompressor() - reader = cctx.stream_reader(fh) - ob = json.load(reader) - yield from ob - - def read_dat(self, file): - with open(file, 'rb') as fh: - cctx = zstandard.ZstdDecompressor() - reader = cctx.stream_reader(fh) - while True: - ln = reader.read(16).decode('UTF-8') - if not ln: - break - - ln = int(ln) - - yield reader.read(ln).decode('UTF-8') - - def read_jsonl(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n', key='text'): - with jsonlines.open(file) as rdr: - yield from handle_jsonl(rdr, get_meta, autojoin_paragraphs, para_joiner, key) - - def read_jsonl_zst(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n', key='text'): - with open(file, 'rb') as fh: - cctx = zstandard.ZstdDecompressor() - reader = io.BufferedReader(cctx.stream_reader(fh)) - rdr = jsonlines.Reader(reader) - yield from handle_jsonl(rdr, get_meta, autojoin_paragraphs, para_joiner, key) - - def read_jsonl_tar(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n', key='text'): - with open(file, 'rb') as fh: - for f in tarfile_reader(fh, streaming=True): - cctx = zstandard.ZstdDecompressor() - reader = io.BufferedReader(cctx.stream_reader(f)) - rdr = jsonlines.Reader(reader) - yield from handle_jsonl(rdr, get_meta, autojoin_paragraphs, para_joiner, key) - f.close() - - def read_owt(self, file): - tar = tarfile.open(file, encoding='utf-8') - utf8reader = codecs.getreader('utf-8') - - for name in tar.getmembers(): - fp = tar.extractfile(name) - inner_tar = tarfile.open(fileobj=fp, encoding='utf-8') - for inner_name in inner_tar.getmembers(): - inner_fp = utf8reader(inner_tar.extractfile(inner_name)) - contents = inner_fp.read() - yield contents - - def read_owt_subset(self, file): - utf8reader = codecs.getreader('utf-8') - tar = tarfile.open(file, encoding='utf-8') - for name in tar.getmembers(): - fp = utf8reader(tar.extractfile(name)) - contents = fp.read() - yield contents - - -class Archive: - def __init__(self, out_dir, compression_level=3, threads=8): - self.out_dir = out_dir - os.makedirs(out_dir, exist_ok=True) - self.i = 0 - - self.fh = open(self.out_dir + '/current_chunk_incomplete', 'wb') - self.cctx = zstandard.ZstdCompressor(level=compression_level, threads=threads) - self.compressor = self.cctx.stream_writer(self.fh) - - - def add_data(self, data, meta={}): - self.compressor.write(json.dumps({'text': data, 'meta': meta}).encode('UTF-8') + b'\n') - - def commit(self, archive_name='default'): - fname = self.out_dir + '/data_' + str(self.i) + '_time' + str(int(time.time())) + '_' + archive_name + '.jsonl.zst' - self.compressor.flush(zstandard.FLUSH_FRAME) - - self.fh.flush() - self.fh.close() - os.rename(self.out_dir + '/current_chunk_incomplete', fname) - self.fh = open(self.out_dir + '/current_chunk_incomplete', 'wb') - self.compressor = self.cctx.stream_writer(self.fh) - - self.i += 1 - - -class DatArchive: - def __init__(self, out_dir): - self.out_dir = out_dir - os.makedirs(out_dir, exist_ok=True) - self.data = [] - self.i = 0 - if os.path.exists(out_dir) and len(os.listdir(out_dir)) > 0: - self.i = max(map(lambda x: int(x.split('_')[1].split('.')[0]), os.listdir(out_dir))) + 1 - - def add_data(self, data): - self.data.append(data) - - def commit(self, archive_name=None): - # TODO: streaming - cctx = zstandard.ZstdCompressor(level=3) - - if archive_name is None: - archive_name = str(int(time.time())) - - res = b''.join(map(lambda x: ("%016d" % len(x)).encode('UTF-8') + x, map(lambda x: x.encode('UTF-8'), self.data))) - cdata = cctx.compress(res) - - with open(self.out_dir + '/data_' + str(self.i) + '_' + archive_name + '.dat.zst', 'wb') as fh: - fh.write(cdata) - - self.i += 1 - self.data = [] - -class JSONArchive: - def __init__(self, out_dir): - self.out_dir = out_dir - os.makedirs(out_dir, exist_ok=True) - self.data = [] - self.i = 0 - if os.path.exists(out_dir) and len(os.listdir(out_dir)) > 0: - self.i = max(map(lambda x: int(x.split('_')[1].split('.')[0]), os.listdir(out_dir))) + 1 - - def add_data(self, data): - self.data.append(data) - - def commit(self): - cctx = zstandard.ZstdCompressor(level=3) - - cdata = cctx.compress(json.dumps(self.data).encode('UTF-8')) - with open(self.out_dir + '/data_' + str(self.i) + '_' + str(int(time.time())) + '.json.zst', 'wb') as fh: - fh.write(cdata) - - self.i += 1 - self.data = [] diff --git a/mftcoder_accelerate/src/offline_tokenization/pack_encoder.py b/mftcoder_accelerate/src/offline_tokenization/pack_encoder.py deleted file mode 100644 index 0678e27..0000000 --- a/mftcoder_accelerate/src/offline_tokenization/pack_encoder.py +++ /dev/null @@ -1,335 +0,0 @@ -from transformers import AutoTokenizer -from tokenizer import init_tokenizer - - -def load_tokenizer(model_path, tokenizer_type=None): - """ - Load tokenizer from the given - """ - - def load_tokenizer_manual(model_path, tokenizer_type): - """ - Load tokenizer by the concrete Tokenizer class instead of AutoTokenizer - """ - try: - if tokenizer_type.lower() == "LlamaTokenizer".lower(): - return LlamaTokenizer.from_pretrained(model_path) - - raise Exception(f"Unsupported tokenizer type {tokenizer_type}") - except: - raise Exception(f"Unable to load tokenizer {tokenizer_type} from the given path: {model_path}") - - def load_tokenizer_auto(model_path): - """ - Load tokenizer from the given path by HuggingFace AutoTokenizer - """ - try: - # tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False, trust_remote_code=True) # support CodeLlama - tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) - return tokenizer - except: - raise Exception( - f'Unable to load tokenizer from the given path: {model_path} using auto mode.\nPlease specify the tokenizer type with the command argument "--tokenizer-type" and retry.' - ) - - # First, try to load tokenizer by huggingface AutoTokenizer, If fail, try another manual way - try: - return load_tokenizer_auto(model_path) - except Exception as e: - print(str(e)) - if tokenizer_type is not None: - try: - tokenizer = load_tokenizer_manual(model_path, tokenizer_type) - return tokenizer - except Exception as ee: - raise ee - - -class PackPFTEncoder: - """ - A sample of this format will be: - <|role_start|>system<|role_end|> content of system_1 - <|role_start|>human<|role_end|> content of human_1 - <|role_start|>bot<|role_end|> content of bot_1 - <|endoftext|> - <|role_start|>system<|role_end|> content of system_2 - <|role_start|>human<|role_end|> content of human_2 - <|role_start|>bot<|role_end|> content of bot_2 - <|endoftext|> - <|role_start|>human<|role_end|> content of human_3 - <|role_start|>bot<|role_end|> content of bot_3 - <|endoftext|> - .... - <|role_start|>human<|role_end|> content of human_n - <|role_start|>bot<|role_end|> content of bot_n - <|endoftext|> - - <|pad|><|pad|>...<|pad|> - - system part is optional, i.e. '<|role_start|>system<|role_end|> content of system_i' - """ - - def __init__(self, seq_length, eod_token_id, pad_token_id, role_start_tag, role_end_tag, mode="pft"): - self.mode = mode - self.seq_length = seq_length - self.eod_token_id = eod_token_id - self.pad_token_id = pad_token_id - self.role_start_tag = role_start_tag - self.role_end_tag = role_end_tag - - def initializer(self, model_path, tokenizer_type=None): - # Use Encoder class as a container for global data - assert model_path is not None - self.tokenizer = load_tokenizer(model_path, tokenizer_type) - - def encode(self, item): - encode_res = { - "input_ids": [], - } - - item_len = sum([len(x["content"]) for x in item["chat_rounds"]]) - for token_res in self.tokenize_chat_prompt(item): - for k, v in token_res.items(): - encode_res[k].append(v) - return encode_res, item_len - - def tokenize_chat_prompt(self, item): - # role_start_marker = self.tokenizer.encode(self.role_start_tag, add_special_tokens=False) - # role_end_marker = self.tokenizer.encode(self.role_end_tag, add_special_tokens=False) - end_marker = [self.eod_token_id] - - input_ids = [] - raw_input = "" - # loss_mask = [] - for chat_round in item["chat_rounds"]: - role = chat_round["role"].strip() - # skip system prompt - # if role == 'system': - # continue - - content = chat_round["content"] - content = content if content.endswith("\n") else f"{content}\n" - text = f"{self.role_start_tag}{role}{self.role_end_tag}{content}" - chat_input_ids = self.tokenizer.encode(text, add_special_tokens=False) - - if role != "bot": - chat_input_ids = chat_input_ids - else: - chat_input_ids = chat_input_ids + end_marker - - input_ids += chat_input_ids - - # if this sample's length is more than the specified max length, drop it - # here, we don't add padding tokens for a single sample, however, we will append padding tokens for a combinated samaple - if len(input_ids) > self.seq_length: - yield {} - else: - yield {"input_ids": input_ids} - - def padding(self, key, data): - assert len(data) <= self.seq_length, f"padding sequence: {len(data)} > {self.seq_length}" - if key == "input_ids": - return data + [self.pad_token_id] * (self.seq_length - len(data)) - - if key == "loss_mask": - return data + [0] * (self.seq_length - len(data)) - - raise Exception("Should not reach here. There must be something wrong.") - - -class PackSFTEncoder: - """ - A sample of this format will be: - <|role_start|>system<|role_end|> content of system_1 - <|role_start|>human<|role_end|> content of human_1 - <|role_start|>bot<|role_end|> content of bot_1 - <|endoftext|> - <|role_start|>system<|role_end|> content of system_2 - <|role_start|>human<|role_end|> content of human_2 - <|role_start|>bot<|role_end|> content of bot_2 - <|endoftext|> - <|role_start|>human<|role_end|> content of human_3 - <|role_start|>bot<|role_end|> content of bot_3 - <|endoftext|> - .... - <|role_start|>human<|role_end|> content of human_n - <|role_start|>bot<|role_end|> content of bot_n - <|endoftext|> - - <|pad|><|pad|>...<|pad|> - - system part is optional, i.e. '<|role_start|>system<|role_end|> content of system_i' - """ - - def __init__(self, seq_length, eod_token, role_start_tag, role_end_tag, mode="sft"): - self.mode = mode - self.seq_length = seq_length - self.eod_token = eod_token - self.role_start_tag = role_start_tag - self.role_end_tag = role_end_tag - - def initializer(self, model_path, tokenizer_type=None): - # Use Encoder class as a container for global data - assert model_path is not None - self.tokenizer = load_tokenizer( - model_path, tokenizer_type - ) # AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) - - def encode(self, item): - encode_res = {"input_ids": [], "raw_input": []} - - item_len = sum([len(x["content"]) for x in item["chat_rounds"]]) - for token_res in self.tokenize_chat_prompt(item): - for k, v in token_res.items(): - encode_res[k].append(v) - return encode_res, item_len - - def tokenize_chat_prompt(self, item): - role_start_marker = self.tokenizer.encode(self.role_start_tag, add_special_tokens=False) - role_end_marker = self.tokenizer.encode(self.role_end_tag, add_special_tokens=False) - end_marker = [self.tokenizer.convert_tokens_to_ids(self.eod_token)] - - input_ids = [] - raw_input = "" - # loss_mask = [] - for chat_round in item["chat_rounds"]: - role = chat_round["role"] - content = chat_round["content"] - content = content if content.endswith("\n") else f"{content}\n" - chat_input_ids = self.tokenizer.encode(content, add_special_tokens=False) - role_input_ids = self.tokenizer.encode(role, add_special_tokens=False) - role_raw_input = "" - - if role != "bot": - # chat_loss_mask = [0] * len(role_start_marker) + [0] * len(role_input_ids) + [0] * len(role_end_marker) + [0] * len(chat_input_ids) - chat_input_ids = role_start_marker + role_input_ids + role_end_marker + chat_input_ids - role_raw_input = ROLE_START_MARKER + role + ROLE_END_MARKER + content - elif role == "human": - # chat_loss_mask = [0] * len(role_start_marker) + [0] * len(role_input_ids) + [0] * len(role_end_marker) + [1] * len(chat_input_ids) + [1] * len(end_marker) - chat_input_ids = role_start_marker + role_input_ids + role_end_marker + chat_input_ids + end_marker - role_raw_input = ROLE_START_MARKER + role + ROLE_END_MARKER + content + self.eod_token - - input_ids += chat_input_ids - raw_input += role_raw_input - # loss_mask += chat_loss_mask - - # assert len(input_ids) == len(loss_mask) - - # if this sample's length is more than the specified max length, drop it - # here, we don't add padding tokens for a single sample, however, we will append padding tokens for a combinated samaple - if len(input_ids) > self.seq_length: - yield {} - else: - yield { - "input_ids": input_ids, - "raw_input": raw_input, - # "loss_mask": loss_mask - } - - def padding(self, key, data, pad_token_id): - assert len(data) <= self.seq_length, f"padding sequence: {len(data)} > {self.seq_length}" - if key == "input_ids": - return data + [pad_token_id] * (self.seq_length - len(data)) - - if key == "loss_mask": - return data + [0] * (self.seq_length - len(data)) - - raise Exception("Should not reach here. There must be something wrong.") - - -class PackSSTBinEncoder: - """ - A sample of this format will be: - content of sample_1 - content of sample_2 - ... - content of sample_n - <|pad|><|pad|>...<|pad|> - """ - - def __init__(self, seq_length, model_path): - self.seq_length = seq_length - self.model_path = model_path - - def initializer(self): - # Use Encoder class as a container for global data - assert self.model_path is not None - # self.tokenizer = load_tokenizer(model_path, tokenizer_type) #AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) - # PackSSTBinEncoder.tokenizer = load_tokenizer(self.model_path, self.tokenizer_type) - PackSSTBinEncoder.tokenizer = init_tokenizer(self.model_path) - - def _encode_content(self, item, encode_res): - if "content" in item: - content = item["content"] - else: - content = item["text"] - - item_len = len(content) - - input_ids = self.tokenize_string(content) - encode_res["input_ids"].append(input_ids) - - return encode_res, item_len - - def _encode_chatml(self, item, encode_res): - input_ids = [] - item_len = 0 - one_round_content = "" - for i in range(len(item["chat_rounds"])): - chat_round = item["chat_rounds"][i] - role = chat_round["role"] - content = chat_round["content"] - content = content if content.endswith("\n") else f"{content}\n" - if role.lower() == "system": - continue - if role.lower() == "human": - one_round_content = content - else: - one_round_content += content - input_ids += self.tokenize_string(one_round_content) - item_len += len(one_round_content) - - encode_res["input_ids"].append(input_ids) - - return encode_res, item_len - - def encode(self, item): - encode_res = { - "input_ids": [], - } - - try: - if item is None: - encode_res["input_ids"].append([]) - return encode_res, 0 - - if "content" in item or "text" in item: - return self._encode_content(item, encode_res) - - if "chat_rounds" in item: - return self._encode_chatml(item, encode_res) - except Exception as e: - print("####JSON Exception", e, str(item)) - encode_res["input_ids"].append([]) - return encode_res, 0 - - raise Exception("Unsupported Format!") - - def tokenize_string(self, text): - end_marker = [PackSSTBinEncoder.tokenizer.eos_token_id] - - input_ids = [] - try: - input_ids = PackSSTBinEncoder.tokenizer.encode(text, add_special_tokens=False) - input_ids = input_ids + end_marker - return input_ids - except Exception as e: - print("####Tokenization Exception:", e, text) - return [] - except BaseException as e: - print("####Tokenization BaseException:", e, "Length of text", len(text)) - return [] - - def padding(self, data, pad_token_id): - assert len(data) <= self.seq_length, f"padding sequence: {len(data)} > {self.seq_length}" - return data + [pad_token_id] * (self.seq_length - len(data)) diff --git a/mftcoder_accelerate/src/utils/agd.py b/mftcoder_accelerate/src/utils/agd.py deleted file mode 100644 index 11929e3..0000000 --- a/mftcoder_accelerate/src/utils/agd.py +++ /dev/null @@ -1,138 +0,0 @@ -from typing import Any, Callable, Dict, Iterable, Optional, Tuple, Union - -import numpy as np -import torch -from torch import Tensor - -Params = Union[Iterable[Tensor], Iterable[Dict[str, Any]]] - -LossClosure = Callable[[], float] -OptLossClosure = Optional[LossClosure] -Betas2 = Tuple[float, float] -State = Dict[str, Any] -OptFloat = Optional[float] -Nus2 = Tuple[float, float] - -__all__ = ("AGD",) - - -class AGD(torch.optim.Optimizer): - r"""AGD: an Auto-switchable Optimizer using Stepwise Gradient Difference as Preconditioning Matrix. - Arguments: - params (Params): Collection of parameters to be optimized, or an iterable of dictionaries specifying separate groups. - lr (float, optional): The learning rate. Default is 1e-3. - betas (tuple of 2 floats, optional): Coefficients used for computing running averages of gradient and its square. Default is (0.9, 0.999). - delta (float, optional): Small constant for numerical stability to prevent division by zero. Default is 1e-5. - weight_decay (float, optional): Weight decay coefficient. Default is 0.0. - amsgrad (bool, optional): If set to True, applies the AMSGrad variant of the optimizer. Default is False. - win (bool, optional): If set to True, applies the Win variant of the optimizer. Default is False. - clip (bool, optional): Total update clip to prevent abnormal updates. Default is None. - """ - - def __init__( - self, - params: Params, - lr: float = 1e-3, - betas: Betas2 = (0.9, 0.999), - delta: float = 1e-5, - weight_decay: float = 0.0, - amsgrad: bool = False, - win: bool = False, - clip: float = None, - ) -> None: - if lr <= 0.0: - raise ValueError("Invalid learning rate: {}".format(lr)) - if delta < 0.0: - raise ValueError("Invalid delta value: {}".format(delta)) - if not 0.0 <= betas[0] < 1.0: - raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) - if not 0.0 <= betas[1] < 1.0: - raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) - if weight_decay < 0.0: - raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) - - defaults = dict( - lr=lr, - betas=betas, - delta=delta, - weight_decay=weight_decay, - amsgrad=amsgrad, - win=win, - clip=clip, - ) - super(AGD, self).__init__(params, defaults) - - def step(self, closure: OptLossClosure = None) -> OptFloat: - loss = None - if closure is not None: - loss = closure() - - for group in self.param_groups: - beta1, beta2 = group["betas"] - - for p in group["params"]: - if p.grad is None: - continue - grad = p.grad.data - if grad.is_sparse: - msg = "AGD does not support sparse gradients." - raise RuntimeError(msg) - - state = self.state[p] - # Lazy state initialization - if len(state) == 0: - state["step"] = 0 - # Exponential moving average of gradient values - state["exp_avg"] = torch.zeros_like(p, memory_format=torch.preserve_format) - # Exponential moving average of squared gradient values - state["exp_avg_sq"] = torch.zeros_like(p, memory_format=torch.preserve_format) - if group["amsgrad"]: - # Maintains max of all exp. moving avg. of sq. grad. values - state["max_exp_avg_sq"] = torch.zeros_like(p, memory_format=torch.preserve_format) - if group["win"]: - state["z"] = torch.zeros_like(p, memory_format=torch.preserve_format) - - exp_avg, exp_avg_sq = ( - state["exp_avg"], - state["exp_avg_sq"], - ) - - state["step"] += 1 - exp_avg_old = exp_avg.detach().clone() - exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) - bias_correction1_old = 1 - beta1 ** (state["step"] - 1) - bias_correction1, bias_correction2 = ( - 1 - beta1 ** state["step"], - 1 - beta2 ** state["step"], - ) - update = ( - exp_avg * (1 / bias_correction1) - if state["step"] == 1 - else exp_avg * (1 / bias_correction1) - exp_avg_old * (1 / bias_correction1_old) - ) - exp_avg_sq.mul_(beta2).addcmul_(update, update, value=1 - beta2) - - if group["amsgrad"]: - max_exp_avg_sq = state["max_exp_avg_sq"] - torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq) - update = max_exp_avg_sq.sqrt() - else: - update = exp_avg_sq.sqrt() - - delta_adjust = group["delta"] * np.sqrt(bias_correction2) - update.clamp_(min=delta_adjust) - - lr_adjust = group["lr"] * np.sqrt(bias_correction2) / bias_correction1 - update = exp_avg / update - if group["clip"] is not None: - update.clamp_(min=-group["clip"], max=group["clip"]) - weight_decay = group["weight_decay"] - if not group["win"]: - p.data.mul_(1 - group["lr"] * weight_decay).add_(update, alpha=-lr_adjust) - else: - z = state["z"] - z.data.add_(update, alpha=-lr_adjust).mul_(1.0 / (1.0 + weight_decay * lr_adjust)) - lr_adjust2 = 2 * lr_adjust - tao = 1.0 / (3.0 + lr_adjust2 * weight_decay) - p.data.mul_(tao).add_(update, alpha=-tao * lr_adjust2).add_(z, alpha=2 * tao) - return loss diff --git a/mftcoder_accelerate/tokenization/bin_encoder.py b/mftcoder_accelerate/tokenization/bin_encoder.py new file mode 100644 index 0000000..45d39bf --- /dev/null +++ b/mftcoder_accelerate/tokenization/bin_encoder.py @@ -0,0 +1,99 @@ +from transformers import AutoTokenizer +from tokenizer import init_tokenizer + + +class SSTBinEncoder: + """ + A sample of this format will be: + content of sample_1 + content of sample_2 + ... + content of sample_n + <|pad|><|pad|>...<|pad|> + """ + tokenizer = None + + def __init__(self, seq_length, model_path): + self.seq_length = seq_length + self.model_path = model_path + + def initializer(self): + # Use Encoder class as a container for global data + assert self.model_path is not None + SSTBinEncoder.tokenizer = init_tokenizer(self.model_path) + + def _encode_content(self, item, encode_res): + if "content" in item: + content = item["content"] + else: + content = item["text"] + + item_len = len(content) + + input_ids = self.tokenize_string(content) + encode_res["input_ids"].append(input_ids) + + return encode_res, item_len + + def _encode_chatml(self, item, encode_res): + input_ids = [] + item_len = 0 + one_round_content = "" + for i in range(len(item["chat_rounds"])): + chat_round = item["chat_rounds"][i] + role = chat_round["role"] + content = chat_round["content"] + content = content if content.endswith("\n") else f"{content}\n" + if role.lower() == "system": + continue + if role.lower() == "human": + one_round_content = content + else: + one_round_content += content + input_ids += self.tokenize_string(one_round_content) + item_len += len(one_round_content) + + encode_res["input_ids"].append(input_ids) + + return encode_res, item_len + + def encode(self, item): + encode_res = { + "input_ids": [], + } + + try: + if item is None: + encode_res["input_ids"].append([]) + return encode_res, 0 + + if "content" in item or "text" in item: + return self._encode_content(item, encode_res) + + if "chat_rounds" in item: + return self._encode_chatml(item, encode_res) + except Exception as e: + print("####JSON Exception", e, str(item)) + encode_res["input_ids"].append([]) + return encode_res, 0 + + raise Exception("Unsupported Format!") + + def tokenize_string(self, text): + end_marker = [SSTBinEncoder.tokenizer.eos_token_id] + + input_ids = [] + try: + input_ids = SSTBinEncoder.tokenizer.encode(text, add_special_tokens=False) + input_ids = input_ids + end_marker + return input_ids + except Exception as e: + print("####Tokenization Exception:", e, text) + return [] + except BaseException as e: + print("####Tokenization BaseException:", e, "Length of text", len(text)) + return [] + + def padding(self, data, pad_token_id): + assert len(data) <= self.seq_length, f"padding sequence: {len(data)} > {self.seq_length}" + return data + [pad_token_id] * (self.seq_length - len(data)) diff --git a/mftcoder_accelerate/src/offline_tokenization/concat_sst_bin_tokenization.py b/mftcoder_accelerate/tokenization/concat_sst_bin_tokenization.py similarity index 64% rename from mftcoder_accelerate/src/offline_tokenization/concat_sst_bin_tokenization.py rename to mftcoder_accelerate/tokenization/concat_sst_bin_tokenization.py index ca4347e..03ae2b0 100644 --- a/mftcoder_accelerate/src/offline_tokenization/concat_sst_bin_tokenization.py +++ b/mftcoder_accelerate/tokenization/concat_sst_bin_tokenization.py @@ -1,4 +1,8 @@ -# -*- coding: utf-8 -*- +""" +# @author Chaoyu Chen +# @date 2023/11/05 +Do tokenization for pretraining data and write binary files. +""" import argparse import multiprocessing @@ -10,31 +14,29 @@ import glob import json import numpy as np +from threading import Semaphore +from colorama import Fore -# 将父目录的父目录加入path +# add root to path current_path = os.path.abspath(__file__) parent_dir = os.path.dirname(os.path.dirname(current_path)) -grandparent_dir = os.path.dirname(parent_dir) -sys.path.append(grandparent_dir) +sys.path.append(parent_dir) from tokenizer import init_tokenizer -from pack_encoder import PackSSTBinEncoder, load_tokenizer from data import indexed_dataset +import data.lm_dataformat as lmd -from threading import Semaphore -from colorama import Fore -import lm_fmt as lmd +from bin_encoder import SSTBinEncoder def yield_from_files(files: list, semaphore): """ - Iterator over input documents - - :param fnames: list of filenames + Iterator over input documents """ + def yielder(fname, semaphore): - with open(fname, 'r') as f: + with open(fname, "r") as f: for line in f: semaphore.acquire() yield json.loads(line) @@ -43,29 +45,29 @@ def yielder(fname, semaphore): semaphore.acquire() yield from yielder(fname, semaphore) + def yield_from_files2(fnames: list, semaphore, sample_percent): """ Iterator over input documents using lm_dataformat. Should be able to handle jsons / texts / other compressed formats. Also filters out empty documents. - - :param fnames: list of filenames """ + def yielder(fname, semaphore): try: - sample_interval = int(1/sample_percent) + sample_interval = int(1 / sample_percent) for f in filter(lambda x: x, lmd.Reader(fname).stream_data(key=None)): - rand_value = random.randint(1, sample_interval*100) + rand_value = random.randint(1, sample_interval * 100) if rand_value % sample_interval != 0: continue semaphore.acquire() - - #rand_value = random.randint(1, sample_interval*100) - #if rand_value % sample_interval != 0: + + # rand_value = random.randint(1, sample_interval*100) + # if rand_value % sample_interval != 0: # yield None yield f except Exception as e: - print('####Exception:', e.args) + print("####Exception:", e.args) yield None for fname in fnames: @@ -75,17 +77,17 @@ def yielder(fname, semaphore): def print_example_doc(input_ids, tokenizer): - print(Fore.YELLOW + f'INPUT IDS len: {len(input_ids)}') - print(Fore.BLUE + f'INPUT IDS:\n {input_ids}\n\n') + print(Fore.YELLOW + f"INPUT IDS len: {len(input_ids)}") + print(Fore.BLUE + f"INPUT IDS:\n {input_ids}\n\n") - print(Fore.RED + f'DETOKENIZED INPUT:\n{tokenizer.decode(input_ids)}') + print(Fore.RED + f"DETOKENIZED INPUT:\n{tokenizer.decode(input_ids)}") def core_process(encoded_docs, semaphore, seq_length, tokenizer, encoder, builder, output_idx_file): """ core of Data Pack SFT processing """ - input_ids_key = 'input_ids' + input_ids_key = "input_ids" proc_start = time.time() total_bytes_processed = 0 @@ -95,7 +97,7 @@ def core_process(encoded_docs, semaphore, seq_length, tokenizer, encoder, builde print("PRINT BEFORE STREAM PROCESS DATA") - print_example_count = 0 + print_example_count = 0 for i, (doc, bytes_processed) in enumerate(encoded_docs, start=1): total_bytes_processed += bytes_processed @@ -111,8 +113,8 @@ def core_process(encoded_docs, semaphore, seq_length, tokenizer, encoder, builde builder.add_item(np.array(input_ids_sentence, dtype=builder.dtype)) builder.end_document() - #builder.finalize_without_close(output_idx_file) - #builder.add_item_and_end_document_and_finalize(np.array(input_ids_sentence, dtype=builder.dtype), output_idx_file) + # builder.finalize_without_close(output_idx_file) + # builder.add_item_and_end_document_and_finalize(np.array(input_ids_sentence, dtype=builder.dtype), output_idx_file) # print the first packed sample as example if print_example_count < 1: @@ -124,9 +126,7 @@ def core_process(encoded_docs, semaphore, seq_length, tokenizer, encoder, builde current = time.time() elapsed = current - proc_start mbs = total_bytes_processed / elapsed / 1024 / 1024 - pbar.set_description( - f"Processed {i} documents ({i / elapsed} docs/s, {mbs} MB/s)." - ) + pbar.set_description(f"Processed {i} documents ({i / elapsed} docs/s, {mbs} MB/s).") if i != 0: pbar.update(100) @@ -142,35 +142,31 @@ def process_dataset(dataset_path, output_path, model_path, parallel_num, seq_len """ # get all jsonl files and corresponding reading handler - files = glob.glob(os.path.join(dataset_path, '**/*.jsonl'), recursive=True) + files = glob.glob(os.path.join(dataset_path, "**/*.jsonl"), recursive=True) - # build a semaphore object to stop `yield_from_files` from getting ahead + # build a semaphore object to stop `yield_from_files` from getting ahead # of encoder.encode and hence building up memory semaphore = Semaphore(1000 + parallel_num) # build sample iterator - sample_iterator = yield_from_files2(files, semaphore, sample_percent) + sample_iterator = yield_from_files2(files, semaphore, sample_percent) # load tokenizer # tokenizer = load_tokenizer(model_path, tokenizer_type) tokenizer = init_tokenizer(model_path) - print('TOKEN of id=2:', tokenizer.convert_ids_to_tokens(2)) - print('ID of :', tokenizer.convert_tokens_to_ids('')) - print('TOKEN of id=0:', tokenizer.convert_ids_to_tokens(0)) - print('ID of :', tokenizer.convert_tokens_to_ids('')) + print("TOKEN of id=2:", tokenizer.convert_ids_to_tokens(2)) + print("ID of :", tokenizer.convert_tokens_to_ids("")) + print("TOKEN of id=0:", tokenizer.convert_ids_to_tokens(0)) + print("ID of :", tokenizer.convert_tokens_to_ids("")) # init encoder - encoder = PackSSTBinEncoder(seq_length, model_path) + encoder = SSTBinEncoder(seq_length, model_path) # create writer builder key = "input_ids" output_prefix = os.path.join(output_path, dataset_name) - output_bin_file = "{}_{}.bin".format( - output_prefix, key - ) - output_idx_file = "{}_{}.idx".format( - output_prefix, key - ) + output_bin_file = "{}_{}.bin".format(output_prefix, key) + output_idx_file = "{}_{}.idx".format(output_prefix, key) builder = indexed_dataset.make_builder( output_bin_file, impl="mmap", @@ -201,20 +197,37 @@ def main(data_path, output_path, model_path, parallel_num, seq_length, dataset_n if __name__ == "__main__": parser = argparse.ArgumentParser(description="Generate a packed jsonl file in the Data Pack SFT way.") - parser.add_argument('--model-path', type=str, help='Path of a pretrained model which contains tokenizer-related files.') - parser.add_argument('--parallel', type=int, default=1, help='The num of parallel processing.') - parser.add_argument('--output-path', type=str, help='Path to store the genered result file.') - parser.add_argument('--data-path', type=str, default=None, help='Path of files to be processed') - parser.add_argument('--seq-length', type=int, default=4096, help='The max input length (i.e. the max number of tokens in a sample)') + parser.add_argument( + "--model-path", type=str, help="Path of a pretrained model which contains tokenizer-related files." + ) + parser.add_argument("--parallel", type=int, default=1, help="The num of parallel processing.") + parser.add_argument("--output-path", type=str, help="Path to store the genered result file.") + parser.add_argument("--data-path", type=str, default=None, help="Path of files to be processed") + parser.add_argument( + "--seq-length", type=int, default=4096, help="The max input length (i.e. the max number of tokens in a sample)" + ) # parser.add_argument('--eod-token-id', type=int, default=2, help='EOD token id') # parser.add_argument('--pad-token-id', type=int, default=0, help='PAD token id') # parser.add_argument('--tokenizer-type', type=str, choices=["LLAMATokenizer", None], default=None, help="What type of tokenizer to use. Default is None.") - parser.add_argument('--dataset-name', type=str, default=None, help='The generated result dataset name. The folder name will be token by default.') - parser.add_argument('--sample-percent', type=float, default=1.0, help='Sample percentage') + parser.add_argument( + "--dataset-name", + type=str, + default=None, + help="The generated result dataset name. The folder name will be token by default.", + ) + parser.add_argument("--sample-percent", type=float, default=1.0, help="Sample percentage") args = parser.parse_args() - print('ARGS\n', '\n'.join([str(key) + ':' + str(value) for key,value in vars(args).items()])) + print("ARGS\n", "\n".join([str(key) + ":" + str(value) for key, value in vars(args).items()])) random.seed(9999) - main(args.data_path, args.output_path, args.model_path, args.parallel, args.seq_length, args.dataset_name, args.sample_percent) + main( + args.data_path, + args.output_path, + args.model_path, + args.parallel, + args.seq_length, + args.dataset_name, + args.sample_percent, + ) diff --git a/mftcoder_accelerate/src/offline_tokenization/writer.py b/mftcoder_accelerate/tokenization/writer.py similarity index 67% rename from mftcoder_accelerate/src/offline_tokenization/writer.py rename to mftcoder_accelerate/tokenization/writer.py index ab526a7..96b823c 100644 --- a/mftcoder_accelerate/src/offline_tokenization/writer.py +++ b/mftcoder_accelerate/tokenization/writer.py @@ -1,42 +1,36 @@ - -import threading import fcntl import json -class JSONLWriter(): + +class JSONLWriter: """ A writer used to save jsonl lines into a file. """ + def __init__(self, output_path, dataset_name): self.output_path = output_path - self.out_file = open(output_path, 'w') + self.out_file = open(output_path, "w") self.cache = [] self.cache_size = 4096 self.dataset_name = dataset_name self.index = 0 def pack_into_jsonl(self, line_text): - new_item = { - "data_name": self.dataset_name, - "id": self.index, - "content": line_text - } + new_item = {"data_name": self.dataset_name, "id": self.index, "content": line_text} return new_item - def add_item(self, line_text): if len(self.cache) >= self.cache_size: self.flush() - + item = self.pack_into_jsonl(line_text) self.cache.append(json.dumps(item)) self.index += 1 - def flush(self): - content = '\n'.join(self.cache) + content = "\n".join(self.cache) fcntl.flock(self.out_file, fcntl.LOCK_EX) - self.out_file.write(f'{content}\n') + self.out_file.write(f"{content}\n") fcntl.flock(self.out_file, fcntl.LOCK_UN) - self.cache = [] + self.cache = [] diff --git a/mftcoder_accelerate/src/tokenizer/__init__.py b/mftcoder_accelerate/tokenizer/__init__.py similarity index 100% rename from mftcoder_accelerate/src/tokenizer/__init__.py rename to mftcoder_accelerate/tokenizer/__init__.py diff --git a/mftcoder_accelerate/src/tokenizer/chat_template.py b/mftcoder_accelerate/tokenizer/chat_template.py similarity index 97% rename from mftcoder_accelerate/src/tokenizer/chat_template.py rename to mftcoder_accelerate/tokenizer/chat_template.py index 3d2ad03..e1af06e 100644 --- a/mftcoder_accelerate/src/tokenizer/chat_template.py +++ b/mftcoder_accelerate/tokenizer/chat_template.py @@ -11,8 +11,7 @@ {"role": "system", "content": "Be smart"}, {"role": "human", "content": "Hello, how are you?"}, {"role": "bot", "content": "I'm doing great. How can I help you today?"}, - {"role": "human", "content": "I'd like to show off how chat templating works!"}, -] + {"role": "human", "content": "I'd like to show off how chat templating works!"}] prompts = tokenizer.apply_chat_template(message, chat_template=MFTCoder_template, tokenize=False, add_generation_prompt=True) """ @@ -45,6 +44,3 @@ "{{ 'bot\n' }}" "{% endif %}" ) - -if __name__ == "__main__": - pass diff --git a/mftcoder_accelerate/src/tokenizer/tokenizer.py b/mftcoder_accelerate/tokenizer/tokenizer.py similarity index 96% rename from mftcoder_accelerate/src/tokenizer/tokenizer.py rename to mftcoder_accelerate/tokenizer/tokenizer.py index bc3ab56..9f67e2e 100644 --- a/mftcoder_accelerate/src/tokenizer/tokenizer.py +++ b/mftcoder_accelerate/tokenizer/tokenizer.py @@ -3,11 +3,9 @@ # @date 2023/6/19 """ -import numpy as np -from typing import List, Union -from utils.common_utils import print_rank_0 +from training.train_utils.common_utils import print_rank_0 from transformers import AutoTokenizer, AutoConfig -from tokenizer.chat_template import MFTCoder_template +from .chat_template import MFTCoder_template def init_tokenizer(path): diff --git a/mftcoder_accelerate/src/configs/coba_train_config.json b/mftcoder_accelerate/training/configs/coba_train_config.json similarity index 100% rename from mftcoder_accelerate/src/configs/coba_train_config.json rename to mftcoder_accelerate/training/configs/coba_train_config.json diff --git a/mftcoder_accelerate/src/configs/dpo_train_config.json b/mftcoder_accelerate/training/configs/dpo_train_config.json similarity index 100% rename from mftcoder_accelerate/src/configs/dpo_train_config.json rename to mftcoder_accelerate/training/configs/dpo_train_config.json diff --git a/mftcoder_accelerate/src/configs/full_train_config.json b/mftcoder_accelerate/training/configs/full_train_config.json similarity index 100% rename from mftcoder_accelerate/src/configs/full_train_config.json rename to mftcoder_accelerate/training/configs/full_train_config.json diff --git a/mftcoder_accelerate/src/configs/lora_train_config.json b/mftcoder_accelerate/training/configs/lora_train_config.json similarity index 100% rename from mftcoder_accelerate/src/configs/lora_train_config.json rename to mftcoder_accelerate/training/configs/lora_train_config.json diff --git a/mftcoder_accelerate/src/configs/qlora_train_config.json b/mftcoder_accelerate/training/configs/qlora_train_config.json similarity index 100% rename from mftcoder_accelerate/src/configs/qlora_train_config.json rename to mftcoder_accelerate/training/configs/qlora_train_config.json diff --git a/mftcoder_accelerate/src/pefts/merge_base_and_lora_to_hf.py b/mftcoder_accelerate/training/merge_base_and_lora_to_hf.py similarity index 98% rename from mftcoder_accelerate/src/pefts/merge_base_and_lora_to_hf.py rename to mftcoder_accelerate/training/merge_base_and_lora_to_hf.py index 26f8ec1..9cf7351 100644 --- a/mftcoder_accelerate/src/pefts/merge_base_and_lora_to_hf.py +++ b/mftcoder_accelerate/training/merge_base_and_lora_to_hf.py @@ -17,7 +17,7 @@ from peft import LoraConfig, get_peft_model from peft import PeftModel -# insert src as import path +# insert project root as import path current_path = os.path.abspath(__file__) parent_dir = os.path.dirname(os.path.dirname(current_path)) sys.path.insert(0, parent_dir) diff --git a/mftcoder_accelerate/src/pefts/mft_accelerate.py b/mftcoder_accelerate/training/mft_accelerate.py similarity index 98% rename from mftcoder_accelerate/src/pefts/mft_accelerate.py rename to mftcoder_accelerate/training/mft_accelerate.py index 0a0d42a..4cdf7ea 100644 --- a/mftcoder_accelerate/src/pefts/mft_accelerate.py +++ b/mftcoder_accelerate/training/mft_accelerate.py @@ -1,6 +1,6 @@ """ # @author Chaoyu Chen -# @date 2024/10/24 +# @date 2023/11/05 # @module mft_accelerate.py Accelerate + DeepSpeed/FSDP + QLoRA/LoRA/Full + Multi-task Finetuning @@ -56,11 +56,11 @@ from tokenizer import build_tokenizer from data.multi_task_dataset import load_dataset_from_jsonl, compile_helper from data.data_utils import load_dataset_from_bin -from utils.common_utils import print_rank_0, generate_task_id, TASK2ID, ID2TASK +from training.train_utils.common_utils import print_rank_0, generate_task_id, TASK2ID, ID2TASK +from training.train_utils.model_mapping import MODEL_TYPES, SUPPORT_IN_TRANSFORMERS -from pefts.mft_trainer import MftTrainer -from pefts.mft_arguments import MftTrainArgs -from utils.model_mapping import MODEL_TYPES, SUPPORT_IN_TRANSFORMERS +from training.pefts import MftTrainer +from training.pefts import MftTrainArgs logger = get_logger(__name__) diff --git a/mftcoder_accelerate/training/mpt/__init__.py b/mftcoder_accelerate/training/mpt/__init__.py new file mode 100644 index 0000000..35cbc21 --- /dev/null +++ b/mftcoder_accelerate/training/mpt/__init__.py @@ -0,0 +1,4 @@ +# @author Chaoyu Chen +# @date 2024/11/18 +from .mpt_arguments import MptTrainArgs +from .mpt_trainer import MptTrainer diff --git a/mftcoder_accelerate/src/mpt/mpt_arguments.py b/mftcoder_accelerate/training/mpt/mpt_arguments.py similarity index 100% rename from mftcoder_accelerate/src/mpt/mpt_arguments.py rename to mftcoder_accelerate/training/mpt/mpt_arguments.py diff --git a/mftcoder_accelerate/src/mpt/mpt_trainer.py b/mftcoder_accelerate/training/mpt/mpt_trainer.py similarity index 99% rename from mftcoder_accelerate/src/mpt/mpt_trainer.py rename to mftcoder_accelerate/training/mpt/mpt_trainer.py index b5e2da8..48f0079 100644 --- a/mftcoder_accelerate/src/mpt/mpt_trainer.py +++ b/mftcoder_accelerate/training/mpt/mpt_trainer.py @@ -29,8 +29,8 @@ from transformers import set_seed # sys.path.append("..") -from utils.common_utils import generate_task_id, TASK2ID, ID2TASK -from utils.loss_utils import loss_func_mft, CoBaStatus, load_balancing_loss_func +from training.train_utils.common_utils import generate_task_id, TASK2ID, ID2TASK +from training.train_utils.loss_utils import loss_func_mft, CoBaStatus, load_balancing_loss_func logger = get_logger(__name__) diff --git a/mftcoder_accelerate/src/mpt/mpt_accelerate.py b/mftcoder_accelerate/training/mpt_accelerate.py similarity index 98% rename from mftcoder_accelerate/src/mpt/mpt_accelerate.py rename to mftcoder_accelerate/training/mpt_accelerate.py index 5d187c9..1bc4c6c 100644 --- a/mftcoder_accelerate/src/mpt/mpt_accelerate.py +++ b/mftcoder_accelerate/training/mpt_accelerate.py @@ -1,6 +1,6 @@ """ # @author Chaoyu Chen -# @date 2024/6/1 +# @date 2023/11/05 # @module mpt_accelerate.py Accelerate + DeepSpeed + Full-parameter + Multi-task + Pre-training/Continue Training/Finetuning @@ -49,10 +49,10 @@ from tokenizer import build_tokenizer from data.multi_task_dataset import load_dataset_from_jsonl, compile_helper from data.data_utils import load_dataset_from_bin -from utils.common_utils import print_rank_0, generate_task_id, TASK2ID, ID2TASK -from mpt.mpt_trainer import MptTrainer -from mpt.mpt_arguments import MptTrainArgs -from utils.model_mapping import MODEL_TYPES, SUPPORT_IN_TRANSFORMERS +from training.train_utils.common_utils import print_rank_0, generate_task_id, TASK2ID, ID2TASK +from training.train_utils.model_mapping import MODEL_TYPES, SUPPORT_IN_TRANSFORMERS +from training.mpt import MptTrainer +from training.mpt import MptTrainArgs logger = get_logger(__name__) diff --git a/mftcoder_accelerate/training/pefts/__init__.py b/mftcoder_accelerate/training/pefts/__init__.py new file mode 100644 index 0000000..163341d --- /dev/null +++ b/mftcoder_accelerate/training/pefts/__init__.py @@ -0,0 +1,4 @@ +# @author Chaoyu Chen +# @date 2024/11/18 +from .mft_arguments import MftTrainArgs +from .mft_trainer import MftTrainer \ No newline at end of file diff --git a/mftcoder_accelerate/src/pefts/mft_arguments.py b/mftcoder_accelerate/training/pefts/mft_arguments.py similarity index 100% rename from mftcoder_accelerate/src/pefts/mft_arguments.py rename to mftcoder_accelerate/training/pefts/mft_arguments.py diff --git a/mftcoder_accelerate/src/pefts/mft_trainer.py b/mftcoder_accelerate/training/pefts/mft_trainer.py similarity index 99% rename from mftcoder_accelerate/src/pefts/mft_trainer.py rename to mftcoder_accelerate/training/pefts/mft_trainer.py index a2b00fb..033396b 100644 --- a/mftcoder_accelerate/src/pefts/mft_trainer.py +++ b/mftcoder_accelerate/training/pefts/mft_trainer.py @@ -32,8 +32,8 @@ from transformers import set_seed # sys.path.append("..") -from utils.common_utils import generate_task_id, TASK2ID, ID2TASK -from utils.loss_utils import loss_func_mft, CoBaStatus, load_balancing_loss_func +from training.train_utils.common_utils import generate_task_id, TASK2ID, ID2TASK +from training.train_utils.loss_utils import loss_func_mft, CoBaStatus, load_balancing_loss_func logger = get_logger(__name__) diff --git a/mftcoder_accelerate/src/utils/__init__.py b/mftcoder_accelerate/training/train_utils/__init__.py similarity index 100% rename from mftcoder_accelerate/src/utils/__init__.py rename to mftcoder_accelerate/training/train_utils/__init__.py diff --git a/mftcoder_accelerate/src/utils/common_utils.py b/mftcoder_accelerate/training/train_utils/common_utils.py similarity index 100% rename from mftcoder_accelerate/src/utils/common_utils.py rename to mftcoder_accelerate/training/train_utils/common_utils.py diff --git a/mftcoder_accelerate/src/utils/loss_utils.py b/mftcoder_accelerate/training/train_utils/loss_utils.py similarity index 99% rename from mftcoder_accelerate/src/utils/loss_utils.py rename to mftcoder_accelerate/training/train_utils/loss_utils.py index 5ca7c73..89333f4 100644 --- a/mftcoder_accelerate/src/utils/loss_utils.py +++ b/mftcoder_accelerate/training/train_utils/loss_utils.py @@ -1,6 +1,6 @@ import sys import torch -from utils.common_utils import print_rank_0, TASK2ID, ID2TASK +from training.train_utils.common_utils import print_rank_0, TASK2ID, ID2TASK from torch.nn import CrossEntropyLoss import torch.nn.functional as F from dataclasses import dataclass diff --git a/mftcoder_accelerate/src/utils/model_mapping.py b/mftcoder_accelerate/training/train_utils/model_mapping.py similarity index 93% rename from mftcoder_accelerate/src/utils/model_mapping.py rename to mftcoder_accelerate/training/train_utils/model_mapping.py index 8592e86..067caea 100644 --- a/mftcoder_accelerate/src/utils/model_mapping.py +++ b/mftcoder_accelerate/training/train_utils/model_mapping.py @@ -1,6 +1,6 @@ """ - @author qumu - transformers==4.40 is stable now + @author Chaoyu Chen + transformers==4.44.2 is stable now """ # Models that Transformers support Code and FA2 when flash_attn>=2.1.0 @@ -27,7 +27,6 @@ from model.chatglm2.modeling_chatglm import ChatGLMForConditionalGeneration as ChatGLMForConditionalGeneration2 from model.chatglm3.modeling_chatglm import ChatGLMForConditionalGeneration as ChatGLMForConditionalGeneration3 -# from model.phi.modeling_mixformer_sequential import MixFormerSequentialForCausalLM MODEL_TYPES = { "aquila2": AquilaForCausalLM, diff --git a/mftcoder_accelerate/training/train_utils/watchdog.py b/mftcoder_accelerate/training/train_utils/watchdog.py new file mode 100644 index 0000000..1e576b2 --- /dev/null +++ b/mftcoder_accelerate/training/train_utils/watchdog.py @@ -0,0 +1,120 @@ +""" + @author Chaoyu Chen + watchdog which supports callback functions for checkpointing +""" +import time +import os +import argparse +import json +import logging +import sys +import pprint + +# 配置logger +# 设置日志级别为DEBUG，这意味着DEBUG及以上级别的所有日志都会被捕获 +# 默认的日志格式包含了时间戳、日志级别、消息和日志发生地点的相关信息 +logging.basicConfig(level=logging.DEBUG, format="%(asctime)s - %(levelname)s - %(message)s") + +# 获取一个logger实例 +logger = logging.getLogger(__name__) + + +def func(ckpt_path, ckpt_name, base_model_path, model_type): + pass + + +def ckpt_callback(callback, ckpt_path, ckpt_name, base_model_path, model_type): + lora = os.path.isfile(os.path.join(ckpt_path, "adapter_config.json")) + logger.info(f"use lora inference: {lora}") + logger.info(f"submit: {ckpt_name}") + callback(ckpt_path, ckpt_name, base_model_path, model_type) + + +def main(ckpt_dir: str, base_model_path: str, model_type): + # 单ckpt评测 + if ckpt_dir.split("/")[-1].startswith(("epoch_", "step_", "checkpoint-")): + ckpt_path = ckpt_dir + ckpt_name = ckpt_dir.split("/")[-2] + "-" + ckpt_dir.split("/")[-1] + ckpt_callback(func, ckpt_path, ckpt_name, base_model_path, model_type) + return + + # 进入监控循环 + stop_limit = 100 + stop_num = 0 + # 读取history + history_path = os.path.join(ckpt_dir, "watchdog") + if os.path.exists(history_path): + with open(history_path, "r") as f: + history = json.load(f) + known_dirs = set(history["submitted"]) + logger.info(f"submitted: {known_dirs}") + else: + logger.info(f"history: {history_path} 不存在") + # 初始化已知目录状态 + # known_dirs = {name for name in os.listdir(ckpt_dir) if os.path.isdir(os.path.join(ckpt_dir, name)) and name.startswith(('epoch_', 'step_', "checkpoint-"))} + known_dirs = set() + logger.info(f"initialized: {known_dirs}") + + # 主监控循环 + try: + while True: + # 兼容ckpt_dir没有被创建 + if os.path.exists(ckpt_dir): + current_dirs = { + name + for name in os.listdir(ckpt_dir) + if + os.path.isdir(os.path.join(ckpt_dir, name)) and name.startswith(("epoch_", "step_", "checkpoint-")) + } + # ckpt_dir存在，但还没有任何submits的时候，打印一次训练的args + if not os.path.exists(history_path) and os.path.exists(os.path.join(ckpt_dir, "args.json")): + with open(os.path.join(ckpt_dir, "args.json"), "r") as f: + pprint.pprint(json.load(f)) + sys.stdout.flush() + else: + logger.info("ckpt dir not created, training may not strart") + time.sleep(300) # 定时间隔 5min + continue + # 检测新添加的文件夹 + new_dirs = current_dirs - known_dirs + logger.info(f"new_dirs: {new_dirs}") + if not new_dirs: + stop_num += 1 + else: + stop_num = 0 + time.sleep(60) + if stop_num > stop_limit: + break + for new_dir in new_dirs: + # 检查是否以 'epoch_' 或 'step_' 开头 + if new_dir.startswith(("epoch_", "step_", "checkpoint-")): + ckpt_path = os.path.join(ckpt_dir, new_dir) + ckpt_name = ckpt_dir.split("/")[-1] + "-" + new_dir + ckpt_callback(func, ckpt_path, ckpt_name, base_model_path, model_type) + + # 更新已知目录列表 + known_dirs = current_dirs + history = {"submitted": list(known_dirs)} + with open(history_path, "w") as f: + json.dump(history, f, indent=2) + time.sleep(300) # 定时间隔 5min + except KeyboardInterrupt: + print("监控停止") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="watchdog参数.") + parser.add_argument( + "--base_model_path", + type=str, + ) + parser.add_argument("--ckpt_dir", type=str) + + parser.add_argument("--model_type", type=str) + + args = parser.parse_args() + main( + ckpt_dir=args.ckpt_dir, + base_model_path=args.base_model_path, + model_type=args.model_type, + ) diff --git a/mftcoder_accelerate/training/xxpo/__init__.py b/mftcoder_accelerate/training/xxpo/__init__.py new file mode 100644 index 0000000..5301cfc --- /dev/null +++ b/mftcoder_accelerate/training/xxpo/__init__.py @@ -0,0 +1,4 @@ +# @author Chaoyu Chen +# @date 2024/11/18 +from .custom_callbacks import CustomProgressCallback, LogCallback +from .xxpo_arguments import XXPOTrainArgs diff --git a/mftcoder_accelerate/src/xxpo/custom_callbacks.py b/mftcoder_accelerate/training/xxpo/custom_callbacks.py similarity index 100% rename from mftcoder_accelerate/src/xxpo/custom_callbacks.py rename to mftcoder_accelerate/training/xxpo/custom_callbacks.py diff --git a/mftcoder_accelerate/src/xxpo/xxpo_arguments.py b/mftcoder_accelerate/training/xxpo/xxpo_arguments.py similarity index 100% rename from mftcoder_accelerate/src/xxpo/xxpo_arguments.py rename to mftcoder_accelerate/training/xxpo/xxpo_arguments.py diff --git a/mftcoder_accelerate/src/xxpo/xxpo_accelerate.py b/mftcoder_accelerate/training/xxpo_accelerate.py similarity index 97% rename from mftcoder_accelerate/src/xxpo/xxpo_accelerate.py rename to mftcoder_accelerate/training/xxpo_accelerate.py index 4c93520..e0a48f1 100644 --- a/mftcoder_accelerate/src/xxpo/xxpo_accelerate.py +++ b/mftcoder_accelerate/training/xxpo_accelerate.py @@ -1,7 +1,7 @@ """ -# @author qumu -# @date 2023/12/11 -# @module mft_accelerate.py +# @author Chaoyu Chen +# @date 2023/11/05 +# @module xxpo_accelerate.py Accelerate + DeepSpeed/FSDP + QLoRA/LoRA/Full + DPO/RPO/ORPO @@ -54,8 +54,8 @@ from tokenizer import build_tokenizer -from utils.common_utils import print_rank_0, generate_task_id, TASK2ID, ID2TASK -from utils.model_mapping import MODEL_TYPES, SUPPORT_IN_TRANSFORMERS +from training.train_utils.common_utils import print_rank_0, generate_task_id, TASK2ID, ID2TASK +from training.train_utils.model_mapping import MODEL_TYPES, SUPPORT_IN_TRANSFORMERS logger = get_logger(__name__) @@ -326,10 +326,11 @@ def main(): all_datasets.append(ds) all_dataset = concatenate_datasets(all_datasets) - # all_dataset = all_dataset.filter( - # lambda x: len(x["prompt"]) + len(x["chosen"]) <= args.max_length - # and len(x["prompt"]) + len(x["rejected"]) <= args.max_length - # ) + # drop samples which exceed max_length + all_dataset = all_dataset.filter( + lambda x: len(x["prompt"]) + len(x["chosen"]) <= args.max_length + and len(x["prompt"]) + len(x["rejected"]) <= args.max_length + ) accelerator.print(f"Length of all_dataset: {len(all_dataset)}") # split train/eval dataset diff --git a/mftcoder_atorch/.gitignore b/mftcoder_atorch/.gitignore deleted file mode 100644 index ecb01a4..0000000 --- a/mftcoder_atorch/.gitignore +++ /dev/null @@ -1,7 +0,0 @@ -*.log -*/__pycache__/ -*.pyc -*.ipynb -.DS_Store -.idea/ -evals/ \ No newline at end of file diff --git a/mftcoder_atorch/README.md b/mftcoder_atorch/README.md deleted file mode 100644 index 8ba6844..0000000 --- a/mftcoder_atorch/README.md +++ /dev/null @@ -1,231 +0,0 @@ -# MFTCoder Training: Atorch Framework -[![Generic badge](https://img.shields.io/badge/🤗-Huggingface%20Repo-green.svg)](https://huggingface.co/codefuse-ai) - - GitHub - - -[[中文]](README_cn.md) [**English**] - -## 1. Updates - -🔥 MFTCoder supports fine-tuning of the GPTNeoX model under the Atorch framework. - -🔥 MFTCoder supports both fully supervised fine-tuning. - -🔥 MFTCoder supports LoRA using the Atorch Framework. - -## 2. Data Format -### 2.1 Training Data Format -The training data is in a uniformed JSONL format, in which each line of data has the following JSON format. The "chat_rounds" field is required, and other fields can be added or removed based on the specific need. - -```json -{ - "id":0, - "data_name":"code-helper", - "chat_rounds":[ - { - "role": "system", - "content": "You are a expert in coding and help answer code questions", - "chat_round_id": 0 - }, - { - "role": "human", - "content": "Write a python function of quick sort", - "chat_round_id": 1 - }, - { - "role": "bot", - "content": "Below is the function of quick sort: ...", - "chat_round_id": 1 - }, - { - "role": "human", - "content": "Explain the code", - "chat_round_id": 2 - }, - { - "role": "bot", - "content": "OK, this code ...", - "chat_round_id": 2 - } - ] -} -``` - -### 2.2 Inference Data Format -The inference data contains strings concatenated by conversation data(system, human and bot contents) in the training data format. -It is used as the data "seen"(before tokenization) by the model in training process. -It is used as input during the inference process as well. -Here is an example format of the concatenated string: - -```python -""" -<|role_start|>system<|role_end|>System instruction -<|role_start|>human<|role_end|>Human 1st round input -<|role_start|>bot<|role_end|>Bot 1st round output -<|role_start|>human<|role_end|>Human 2nd round input -<|role_start|>bot<|role_end|>Bot 2nd round output -... -... -... -<|role_start|>human<|role_end|>Human nth round input -<|role_start|>bot<|role_end|>{Bot output to be genreated} -""" -``` -When applying inference, you always make your input string end with "<|role_start|>bot<|role_end|>" to request the model generating answers. - -## 3. Model Training -Currently, the "MFTCoder/mft_atorch" code repository supports fully instruction fine-tuning, and LoRA instruction fine-tuning. Only the training of the GPTNeoX model is supported. In theory, the pretrained weights of the GPTNeoX model available on HuggingFace can be used for training within this project. - -We have extracted various components used in training to facilitate future extension and optimization. Please refer to the implementation in the main directory for more details. The entry directory for fine-tuning training is ```train/```, and the entry file for training is ```train/run_train.py```. The parameter configurations are stored in the launch scripts such as ```train/run_gpt_*.sh```, making it easier to manage and modify them uniformly. - -### 3.1 Tokenization -During training, we concatenate multi-turn dialogues into the following format (also known as the inference data format mentioned earlier) and then tokenize it. In this format, <|role_start|>human<|role_end|> represents the human input (i.e., prompt), <|role_start|>bot<|role_end|> represents the bot output, and represents the eos_token. -You can modify and replace the eos_token based on different models' requirements. - -Here is an example of the concatenated format with prompts: -``` -"<|role_start|>human<|role_end|>input1target1input2target2... -``` -During the calculation of loss, we use a ```loss mask``` to ensure that the loss from the input part does not contribute to the parameter updates. Only the loss from the ```target``` part is used for updating parameters. -This approach takes full advantage of the benefits of model parallelism, making training more efficient. It also leverages the characteristic of decoder-only models with left-to-right attention. -By including all target parts from multiple turns in a single training iteration, the training process becomes more efficient. - -### 3.2 Fully Supervised Fine-Tuning (SFT) -To perform fully SFT, you can execute the following command: -```bash -sh run_gpt_mft.sh 10 1 8 5 -``` -Please note that the four parameters after the launch script have the following meanings: -- The first parameter is the per GPU batch size. -- The second parameter is the number of tensor parallelism (currently only supports 1). -- The third parameter is the number of data parallelism, which should match the number of GPUs used. -- The fourth parameter is the number of training epochs. - -For other training modes, the same four parameters need to be configured in the launch script. - -### 3.3 LoRA Supervised Fine-Tuning -To perform LoRA SFT, you can execute the following command: -```bash -sh run_gpt_mft_peft.sh 10 1 8 5 -``` - -### 3.4 Parameter Explanations -The main parameter explanations for the ```train/run_gpt_*.sh``` are as follows. You can modify these parameters according to your needs: - -- **tokenize_mode**: Need to be 'sft' at present. - -- **train_mode**: Need to be 'sft' at present. - -- **load_raw_dataset**: Need to be 'True' at present. Only JSONL format is supported. - -- **data_paths**: "[path1,path2,path3]" Input data addresses, a string enclosed in [], with different paths separated by commas (,). Each path is a directory where the last level of the directory name is considered as the task name. Each task directory contains 1 to multiple jsonl data files. - -- **output_dir**: Training output directory to store checkpoints, lora_adaptor checkpoints, etc. - -- **tensorboard_dir**: Can be temporarily ignored, as the actual tensorboard is stored in the runs directory under output_dir. - -- **model_type**: Currently only supports gpt_neox. - -- **peft_type**: Currently only supports lora. - -- **pretrained_model_path**: Local directory of the pre-trained model. - -- **total_train_batch_size**: The total batch size for training across all GPUs, calculated automatically based on per gpu batch size entered in the script. - -- **per_device_valid_batch_size**: The batch size for evaluation on each GPU, calculated automatically based on per gpu batch size entered in the script. - -- **gradient_accumulation_steps**: Number of gradient accumulation steps. Global batch size = num_gpus * per_device_train_batch_size * gradient_accumulation_steps. - -- **checkpoint_activations**: Enable if running out of GPU memory. Trades time for space by not caching activation states, resulting in two forward passes to save memory. - -- **learning_rate**: Learning rate. When fine-tuning the entire model, it is recommended to use a smaller value, such as 1e-5 or 5e-6. For lora, a larger learning rate is generally used, such as 1e-4 or 2e-4. - -- **min_lr**: Minimum learning rate, usually one-tenth of the learning_rate. - -- **seq_length**: Maximum length during training. Set according to your device, longer lengths require more memory. - -- **log_interval**: Frequency of logging training loss. - -- **checkpointing_steps**: Frequency of saving a model checkpoint. - -- **evalation_steps**: Frequency of evaluating on the validation set. - -- **early_stopping_patience**: Number of consecutive eval points without further convergence to stop training. - -- **lr_scheduler_type**: Learning rate changing strategy. - -- **num_warmup_steps**: Number of warm-up steps for the learning rate to increase to the specified value. - -- **seed**: Random seed used for reproducibility of experimental results. - -- **train_iters**: Can be temporarily set to a small value, such as 10, which does not affect the actual number of training steps, kept for future expansion to support reading datasets in other formats. - -- **valid_iters**: Can be temporarily set to a small value, such as 10, which does not affect the actual number of training steps, kept for future expansion to support reading datasets in other formats. - -- **evaluation_strategy**: Evaluation strategy during training. "steps" means to evaluate every "valid_interval" steps, "epoch" means to evaluate every epoch. Both can be enabled simultaneously. - -- **save_strategy**: Strategy for saving model weights during training. "steps" means to save every "checkpointing_steps" steps. -- **extra_save_by_epoch**: Whether to save an epoch-level checkpoint every epoch. - -- **save_total_limit**: Maximum number of model checkpoints to keep. Generally set to 2, retaining the checkpoint with the lowest valid loss and the latest checkpoint. Note that epoch-level checkpoints will always be retained and are not subject to this limit. - -- **weighted_loss_mode**: Loss weighting method for multi-task training. - -## 4. Model Usage - -### 4.1 Merge Adaptor weights -Using LoRA or QLoRA for training, this project only saves the weights and configuration files of the adapters. -To merge the adapter weights with the base model, see ```src/pefts/merge_base_and_lora_to_hf.py``` - -### 4.2 Inference demo -Here is the script for inference on our trained models, which is compatible with most Hugging Face models: -```python -from transformers import ( - AutoTokenizer, - AutoModelForCausalLM, -) -tokenizer = AutoTokenizer.from_pretrained(mode_name_or_path, trust_remote_code=True, use_fast=False, legacy=False) -tokenizer.padding_side = "left" -tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids("") -tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids("") -model = AutoModelForCausalLM.from_pretrained(mode_name_or_path, trust_remote_code=True) - -HUMAN_ROLE_START_TAG = "<|role_start|>human<|role_end|>" -BOT_ROLE_START_TAG = "<|role_start|>bot<|role_end|>" -texts = ["write a python function of quick sort."] -texts = [f"{HUMAN_ROLE_START_TAG}{text}{BOT_ROLE_START_TAG}" for text in texts] - -inputs = tokenizer(texts, return_tensors='pt', padding=True, add_special_tokens=False).to("cuda") -outputs = model.generate( - inputs=inputs["input_ids"], - attention_mask=inputs["attention_mask"], - max_new_tokens=512, - top_p=0.95, - temperature=0.1, - do_sample=True, - eos_token_id=tokenizer.eos_token_id, - pad_token_id=tokenizer.pad_token_id - ) -gen_text = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True) -print(gen_text) -``` - -Indeed, the parameters top_p, temperature, repetition_penalty, do_sample, etc., have a significant impact on the model's generation output. -You can modify these parameters based on your specific use case. - -In code generation scenarios, if you are using the sampling mode (do_sample=True), the following parameter settings can yield good results for the Pass@1 metric: - -top_p: Set a higher value, such as 0.95, to retain highly probable generated words. This helps ensure more accurate and fluent generation results. - -temperature: Set a lower value, such as 0.1, to reduce randomness. Lower temperature values make the generation output more deterministic. - -These parameter combinations can control the diversity of the generated outputs while maintaining naturalness. Additionally, you can adjust other related parameters, such as repetition_penalty, to reduce repetition in the generated results. - -If you choose the non-sampling mode (do_sample=False), you can consider the following parameter settings: - -beam_num: Set a smaller value such as 1 or 3. ```beam_num=1``` represents greedy decoding, which selects the most probable single generated word. ```beam_num=3``` represents beam search mode, which considers multiple potential generation paths and chooses the best path among them. - -## 5. FAQ -### Q1：What should I do when cuda OOM happens？ -If OOM (Out of Memory) occurs, you can mitigate it by reducing parameters such as per GPU batch size (the first argument when starting the training script) and seq_length. You can also set gradient_checkpointing=true, which significantly reduces memory usage but may slow down the training speed. diff --git a/mftcoder_atorch/README_cn.md b/mftcoder_atorch/README_cn.md deleted file mode 100644 index 12a4cde..0000000 --- a/mftcoder_atorch/README_cn.md +++ /dev/null @@ -1,218 +0,0 @@ -# MFTCoder训练: Atorch框架篇 -[![Generic badge](https://img.shields.io/badge/🤗-Huggingface%20Repo-green.svg)](https://huggingface.co/codefuse-ai) - - GitHub - - -[**中文**] [[English]](README.md) - -## 1. 更新 - -🔥 MFTCoder在Atorch框架下支持GPTNeoX模型的微调； - -🔥 MFTCoder支持全量的有监督微调； - -🔥 MFTCoder支持LoRA微调； - -## 2. 数据格式 - -### 2.1 训练数据格式 -训练数据为jsonl格式，每一行的数据格式如下，其中chat_rounds字段是必需的，可以根据实际需求添加或删除其他字段。 -可以参考项目中的xxx.jsonl文件。 -```json -{ - "id":0, - "data_name":"code-helper", - "chat_rounds":[ - { - "role": "system", - "content": "你是一个智能代码助手，可以回复用户与代码相关的问题", - "chat_round_id": 0 - }, - { - "role": "human", - "content": "写一个快速排序", - "chat_round_id": 1 - }, - { - "role": "bot", - "content": "以下是一个快速排序算法xxxxxx", - "chat_round_id": 1 - }, - { - "role": "human", - "content": "解释一下这段代码", - "chat_round_id": 2 - }, - { - "role": "bot", - "content": "好的，这段代码xxx", - "chat_round_id": 2 - } - ] -} -``` - -### 2.2 推理数据格式 -推理数据格式为模型在训练数据格式下拼接的字符串形式，它也是推理时输入prompt拼接的方式： -```python -""" -<|role_start|>system<|role_end|>这是System指令 -<|role_start|>human<|role_end|>这是第1轮用户输入的问题 -<|role_start|>bot<|role_end|>这是第1轮模型生成的内容 -<|role_start|>human<|role_end|>这是第2轮用户输入的问题 -<|role_start|>bot<|role_end|>这是第2轮模型生成的内容 -... -... -... -<|role_start|>human<|role_end|>这是第n轮用户输入的问题 -<|role_start|>bot<|role_end|>{模型现在要生成的内容} -""" -``` - - -## 3. 模型训练 -目前 "MFTCoder/mft_atorch" 代码库支持全量参数指令微调和LoRA指令微调。 -目前仅支持GPTNeoX模型的训练，理论上，HuggingFace上开源的GPTNeoX模型权重，均可使用本项目进行训练。 - -我们将训练中使用的各种组件抽取出来，以便后续的扩展和优化，详见主目录下的实现。微调训练的入口目录是```train/```, 训练入口文件是```train/run_train.py```, 参数配置存储在启动脚本```train/run_gpt_*.sh```等文件中，方便统一管理和更改。 - -### 3.1 数据格式 -训练时，我们将多轮对话拼接成如下格式，然后进行tokenize。其中<|role_start|>human<|role_end|>表示human输入提示符，<|role_start|>bot<|role_end|>表示bot输出提示符，`````````` 表示eos_token。 -``` -"<|role_start|>human<|role_end|>input1target1input2target2... -``` -在计算loss时，我们通过mask的方式，input部分的loss不参与参数更新，只有“target”部分的loss参与参数更新。 -这种方式充分利用了模型并行计算的优势，训练更加高效，且多轮对话中的每个target部分都参与了训练，训练更充分。 -否则，就需要把一个n轮对话，拆分成n条数据，且只计算最后一个target的loss，大大降低了训练效率。 - -### 3.2 全量SFT - -执行如下命令即可进行全量SFT： -```bash -sh run_gpt_mft.sh 10 1 8 5 -``` - -需注意，启动脚本后的四个参数，分别是： -- 第一个参数是总的per gpu batch size -- 第二个参数是tensor parallel数（暂时只支持1） -- 第三个参数是data parallel数，与所用GPU数保持一致 -- 第四个参数是训练epoch数 - -后面其他的训练方式启动脚本，也同样需要配置这四个参数 - -### 3.3 LoRA微调 - -执行如下命令即可进行Lora微调： -```bash -sh run_gpt_mft_peft.sh 10 1 8 5 -``` - -### 3.4 启动脚本中主要参数说明 -```train/run_gpt_*.sh```中的主要参数说明如下，以下参数可以根据需求进行修改，其他参数建议不做修改： -- tokenize_mode: 目前仅支持"sft"。 - -- train_mode: 目前仅支持"sft"。 - -- load_raw_dataset: 需要保持"True"，后续会支持其它模式数据，当前仅支持jsonl输入 - -- data_paths: "[path1,path2,path3]" 输入数据地址，字符串，开头结尾用[]，中间用```,```间隔不同path，每个path是一个目录，目录的最后一级名字作为任务名称，下面包含1到多个jsonl数据。 - -- output_dir: 训练输出目录，存储checkpoint、lora_adaptor checkpoint等。 - -- tensorboard_dir: 可以暂时忽略，实际tensorboard存储在output_dir的runs目录下。 - -- model_type: 目前仅支持 gpt_neox。 - -- peft_type: 目前仅支持 lora。 - -- pretrained_model_path: 预训练模型的本地目录。 - -- total_train_batch_size: 所有显卡train的batch size的总和，会根据启动脚本时输入的per gpu batch size自动计算。 - -- per_device_valid_batch_size: 每张显卡eval的batch size，会根据启动脚本时输入的per gpu batch size自动计算。 - -- gradient_accumulation_steps: 梯度累计步数。global batch=num_gpus * per_device_train_batch_size * gradient_accumulation_steps。 - -- checkpoint_activations: 如果显存捉襟见肘，可以开启。以时间换空间，模型不缓存激活状态，会进行两次forward计算，以节省显存。 - -- learning_rate: 学习率。全量参数微调的时候，建议小一些，1e-5或5e-6。qlora中的学习率设置更大一些，一般为1e-4、2e-4。 - -- min_lr: 最低学习率， 一般是learning_rate的十分之一。 - -- seq_length: 训练时的最大长度。按照自己的设备进行设置，越长需要占用越多显存。 - -- log_interval: 每隔多少步统计一次train loss。 - -- checkpointing_steps: 每隔多少步保存一个模型。 - -- evalation_steps: 每隔多少步在验证集上evaluate一次。 - -- early_stopping_patience: 多少个eval point不继续收敛，则停止训练。 - -- lr_scheduler_type: 学习率变化策略。 - -- num_warmup_steps: warm up步数，学习率经过多少步，增长到指定的数值。 - -- seed: 随机种子，用于复现实验结果。 - -- train_iters: 可以暂时设为比较小的数，如10，实际上不会影响训练步数，留作后面拓展读取其他形式数据集的功能。 - -- valid_iters: 可以暂时设为比较小的数，如10，实际上不会影响训练步数，留作后面拓展读取其他形式数据集的功能。 - -- evaluation_strategy: 训练期间evaluate的策略，"steps"表示每隔"valid_interval"步做一次evaluate，"epoch"表示每隔一个epoch做一次evaluate，支持同时开启。 - -- save_strategy: 训练期间保存模型权重的策略，"steps"表示每隔"checkpointing_steps"步保存一次。 - -- extra_save_by_epoch: 每过一个epoch是否要保存一个epoch级别的checkpoint。 - -- save_total_limit: 最多保留的模型checkpoint个数，一般设置为2，会保留valid loss最低，以及最新的checkpoint，注意epoch级别的checkpoint会一直保留，且不受限制。 - -- weighted_loss_mode: 多任务训练的loss加权方式。 - - -## 4. 模型使用 - -### 4.1 权重合并 -如果使用LoRA进行训练，本项目仅保存adapter的权重和配置文件，需要将adapter权重与base model进行合并。脚本见```utils/merge_base_and_lora_to_hf.py``` - -### 4.2 模型推理 -我们提供了单轮对话和多轮对话的如下脚本，该脚本可同时兼容大部分huggingface格式的模型。 -```python -from transformers import ( - AutoTokenizer, - AutoModelForCausalLM, -) -tokenizer = AutoTokenizer.from_pretrained(mode_name_or_path, trust_remote_code=True, use_fast=False, legacy=False) -tokenizer.padding_side = "left" -tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids("") -tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids("") -model = AutoModelForCausalLM.from_pretrained(mode_name_or_path, trust_remote_code=True) - -HUMAN_ROLE_START_TAG = "<|role_start|>human<|role_end|>" -BOT_ROLE_START_TAG = "<|role_start|>bot<|role_end|>" -texts = ["write a python function of quick sort."] -texts = [f"{HUMAN_ROLE_START_TAG}{text}{BOT_ROLE_START_TAG}" for text in texts] - -inputs = tokenizer(texts, return_tensors='pt', padding=True, add_special_tokens=False).to("cuda") -outputs = model.generate( - inputs=inputs["input_ids"], - attention_mask=inputs["attention_mask"], - max_new_tokens=512, - top_p=0.95, - temperature=0.1, - do_sample=True, - eos_token_id=tokenizer.eos_token_id, - pad_token_id=tokenizer.pad_token_id - ) -gen_text = tokenizer.batch_decode(outputs[:, inputs["input_ids"].shape[1]:], skip_special_tokens=True) -print(gen_text) -``` - -生成脚本中的top_p、temperature、repetition_penalty、do_sample等参数对模型的生成效果影响较大，可按照自己的使用场景进行调试修改。 -实践中，在代码生成场景中，如果采样模式，do_sample=True, top_p=0.95, temperature=0.1是pass@1指标的不错选择； -如果非采样模式， do_sample=False, beam_num=1或者3是不错的选择，其中beam_num=1即为greedy decoding。 - -## 5. FAQ -#### 问题1：OOM如何解决？ -如果发生OOM，可以缩小per GPU batch size (启动训练脚本时的第一个参数)、seq_length等参数来缓解。也可以设gradient_checkpointing=true，可以大幅降低显存占用，但训练速度会变慢一些。 diff --git a/mftcoder_atorch/arguments/get_arguments.py b/mftcoder_atorch/arguments/get_arguments.py deleted file mode 100644 index 52e52e4..0000000 --- a/mftcoder_atorch/arguments/get_arguments.py +++ /dev/null @@ -1,557 +0,0 @@ -import os -import argparse -from transformers import MODEL_MAPPING, SchedulerType - -MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys()) -MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) - - -def parse_args(): - parser = argparse.ArgumentParser(description="Finetune a transformers model on a causal language modeling task") - parser.add_argument( - "--load_raw_dataset", - action="store_true", - help="If passed, will load raw dataset.", - ) - parser.add_argument( - "--data_paths", - type=str, - default=None, - help="Data path list.", - ) - parser.add_argument( - "--data_weights", - type=str, - default=None, - help="Data weights.", - ) - parser.add_argument( - "--data_split", - type=str, - default=None, - help="Data split.", - ) - parser.add_argument( - "--padding", - action="store_true", - help="use padding in preprocess.", - ) - parser.add_argument( - "--tokenize_mode", - type=str, - default='sft', - choices=['pretrain', 'sft', 'coh'], - help="training mode" - ) - parser.add_argument( - "--train_mode", - type=str, - default='sft', - choices=['sst', 'sft'], - help="training mode" - ) - parser.add_argument( - "--weighted_loss_mode", - type=str, - default=None, - help="weighted loss mode.", - ) - parser.add_argument( - "--padding_mode", - type=str, - default='padding', - choices=['padding', 'concat', 'pack'], - help="padding mode" - ) - parser.add_argument( - "--shuffle_before_split", - action="store_true", - help="shuffle before split.", - ) - parser.add_argument( - "--use_random_sampler", - action="store_true", - help="use random sampler.", - ) - parser.add_argument( - "--use_weighted_loss", - action="store_true", - help="use weighted loss.", - ) - parser.add_argument( - "--early_stopping_patience", - type=int, - default=-1, - help="patience of early stopping.", - ) - parser.add_argument( - "--weight_by_num_documents", - action="store_true", - help="weight by num documents.", - ) - parser.add_argument( - "--make_vocab_size_divisible_by", - type=int, - default=128, - help="Pad the vocab size to be divisible by this value. This is added for computational efficiency reasons.", - ) - parser.add_argument( - "--validation_split_percentage", - default=5, - help="The percentage of the train set used as validation set in case there's no validation split", - ) - parser.add_argument( - "--pretrained_model_path", - type=str, - help="Path to pretrained model or model identifier from huggingface.co/models.", - required=False, - ) - parser.add_argument( - "--config_path", - type=str, - default=None, - help="Pretrained config name or path if not the same as model_name", - ) - parser.add_argument( - "--model_type", - type=str, - default='gpt_neox', - help="model type", - ) - parser.add_argument( - "--peft_type", - type=str, - default=None, - help="peft type", - ) - parser.add_argument( - "--bits", - type=int, - default=4, - help="How many bits to use when using qlora. Should be 4 or 8.", - ) - parser.add_argument( - "--use_xformers", - action="store_true", - help="use xformers in llama", - ) - parser.add_argument( - "--use_dynamic_padding", - action="store_true", - help="use xformers in llama", - ) - parser.add_argument( - "--tokenizer_path", - type=str, - default=None, - help="Pretrained tokenizer name or path if not the same as model_name", - ) - parser.add_argument( - "--vocab_file", - type=str, - default=None, - help="Vocab path", - ) - parser.add_argument( - "--tokenizer_type", - type=str, - default=None, - help="Pretrained tokenizer type", - ) - parser.add_argument( - "--use_slow_tokenizer", - action="store_true", - help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).", - ) - parser.add_argument( - "--trust_remote_code", - action="store_true", - help="If passed, will set trust_remote_code=True when calling from_pretrained.", - ) - parser.add_argument( - "--ignore_mismatched_sizes", - action="store_true", - help="If passed, will set ignore_mismatched_sizes=True when calling from_pretrained.", - ) - parser.add_argument( - "--per_device_train_batch_size", - type=int, - default=8, - help="Batch size (per device) for the training dataloader.", - ) - parser.add_argument( - "--per_device_valid_batch_size", - type=int, - default=8, - help="Batch size (per device) for the evaluation dataloader.", - ) - parser.add_argument( - "--total_train_batch_size", - type=int, - default=8, - help="All batch size for the training dataloader. Equals to per_device_train_batch_size * world_size", - ) - parser.add_argument( - "--world_size", - type=int, - default=8, - help="Total world size (i.e number of gpus in cluster). Configured post-launch using distributed launcher", - ) - parser.add_argument( - "--local_rank", - type=int, - default=8, - help="local rank passed from distributed launcher", - ) - parser.add_argument( - "--learning_rate", - type=float, - default=1e-5, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument( - "--min_lr", - type=float, - default=1e-7, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.") - parser.add_argument("--adam_beta1", type=float, default=0.9) - parser.add_argument("--adam_beta2", type=float, default=0.999) - parser.add_argument("--adam_epsilon", type=float, default=1e-8) - parser.add_argument( - "--num_train_epochs", - type=int, - default=3, - help="Total number of training epochs to perform.", - ) - parser.add_argument( - "--max_steps", - type=int, - default=-1, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - parser.add_argument( - "--lr_scheduler_type", - type=SchedulerType, - default="linear", - help="The scheduler type to use.", - choices=[ - "linear", - "cosine", - "cosine_with_restarts", - "polynomial", - "constant", - "constant_with_warmup", - ], - ) - parser.add_argument( - "--custom_lr_scheduler_type", - type=str, - default=None, - help="The scheduler type to use.", - ) - parser.add_argument( - "--num_warmup_steps", - type=int, - default=100, - help="Number of steps for the warmup in the lr scheduler.", - ) - parser.add_argument( - "--num_update_steps_per_epoch", - type=int, - default=500, - help="Number of update steps per epoch.", - ) - parser.add_argument( - "--resume_step", - type=int, - default=0, - help="resume step in checkpoint.", - ) - parser.add_argument( - "--fp16_lm_cross_entropy", - action="store_true", - help="Move the cross entropy unreduced loss calculation for lm head to fp16.", - ) - parser.add_argument( - "--bf16", - action="store_true", - help="use bf16.", - ) - parser.add_argument( - "--fp16", - action="store_true", - help="use fp16.", - ) - parser.add_argument( - "--output_dir", - type=str, - default=None, - help="Where to store the final model.") - parser.add_argument( - "--tensorboard_dir", - type=str, - default=None, - help="Where to store the tensorboard.") - parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") - parser.add_argument( - "--model_types", - type=str, - default=None, - help="Model type to use if training from scratch.", - choices=MODEL_TYPES, - ) - parser.add_argument( - "--seq_length", - type=int, - default=None, - help=( - "Optional input sequence length after tokenization. The training dataset will be truncated in block of" - " this size for training. Default to the model max input length for single sentence inputs (take into" - " account special tokens)." - ), - ) - parser.add_argument( - "--hidden_size", - type=int, - default=None, - help=( - "hidden states size" - ), - ) - parser.add_argument( - "--num_hidden_layers", - type=int, - default=None, - help=( - "hidden states layers number" - ), - ) - parser.add_argument( - "--vocab_size", - type=int, - default=None, - help=( - "vocab size" - ), - ) - parser.add_argument( - "--total_model_param", - type=int, - default=None, - help=( - "total model parameters" - ), - ) - parser.add_argument( - "--preprocessing_num_workers", - type=int, - default=None, - help="The number of processes to use for the preprocessing.", - ) - parser.add_argument( - "--num_workers", - type=int, - default=0, - help="The number of sub-processes to use for the dataloader.", - ) - parser.add_argument( - "--iteration", - type=int, - default=0, - help="Iteration to load checkpoint from in evaluate.py / generate.py. If None is provided, uses the latest iteration.", - ) - parser.add_argument( - "--overwrite_cache", - action="store_true", - help="Overwrite the cached training and evaluation sets", - ) - parser.add_argument( - "--no_keep_linebreaks", - action="store_true", - help="Do not keep line breaks when using TXT files.", - ) - parser.add_argument( - "--push_to_hub", - action="store_true", - help="Whether or not to push the model to the Hub.", - ) - parser.add_argument( - "--hub_model_id", - type=str, - help="The name of the repository to keep in sync with the local `output_dir`.", - ) - parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.") - parser.add_argument( - "--checkpointing_steps", - type=int, - default=None, - help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.", - ) - parser.add_argument( - "--resume_from_checkpoint", - type=str, - default=None, - help="If the training should continue from a checkpoint folder, including model checkpoint, optimizer and lr scheduler. path", - ) - parser.add_argument( - "--flash_attention", - action="store_true", - ) - parser.add_argument( - "--with_tracking", - action="store_true", - help="Whether to enable experiment trackers for logging.", - ) - parser.add_argument( - "--skip_atorch_autoacc_dryrun", - action="store_true", - ) - parser.add_argument( - "--zero_opt_level", - type=str, - default="zero2", - help="Model type to use if training from scratch.", - choices=["zero2", "zero3", "fsdp"], - ) - parser.add_argument( - "--report_to", - type=str, - default="all", - help=( - 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,' - ' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations.' - "Only applicable when `--with_tracking` is passed." - ), - ) - parser.add_argument( - "--real_dataloader", - action="store_true", - help="Whether to use real full dataset.", - ) - parser.add_argument( - "--split_before_read", - action="store_true", - help="Whether to use real full dataset.", - ) - parser.add_argument( - "--tp", - type=int, - help="tp size", - required=True, - ) - parser.add_argument( - "--dp", - type=int, - help="dp size", - required=True, - ) - parser.add_argument( - "--pipe_parallel_size", - type=int, - default=0, - help="Number of pipeline parallel stages. Disable with 0.", - ) - parser.add_argument( - "--model_parallel_size", - type=int, - default=1, - help="model parallelism. size", - ) - parser.add_argument( - "--train_iters", - type=int, - default=20000, - help="Number of iterations to run for training.", - ) - parser.add_argument( - "--valid_iters", - type=int, - default=100, - help="Number of iterations to run for evaluation validation/test for.", - ) - parser.add_argument( - "--valid_interval", - type=int, - default=1000, - help="Interval between running evaluation on validation set.", - ) - parser.add_argument( - '--is_pipe_parallel', - action='store_true', - help="") - parser.add_argument( - "--log_interval", - type=int, - help="interval of logger", - required=True, - ) - parser.add_argument( - '--checkpoint_activations', - action='store_true', - help="whether to use gradient checkpointing") - parser.add_argument( - "--max_grad_norm", - type=int, - help="max_grad_norm", - required=True, - ) - parser.add_argument( - "--evaluation_strategy", - type=str, - default="steps", - help="evaluation strategy", - choices=["steps", "epoch", "steps,epoch"], - ) - parser.add_argument( - "--save_strategy", - type=str, - default="steps", - help="save strategy", - choices=["steps", "epoch", "steps,epoch"], - ) - parser.add_argument( - '--extra_save_by_epoch', - action='store_true', - help="whether to save extra checkpoint for per epoch" - ) - parser.add_argument( - "--save_total_limit", - type=int, - help="num limitation of step strategy checkpoint file", - required=True, - ) - parser.add_argument( - "--metric_for_best_model", - type=str, - default="loss", - help="metric for best model checkpoint", - ) - parser.add_argument( - "--greater_is_better", - type=str, - default="false", - help="whether the metric greater is better", - ) - args = parser.parse_args() - - if os.getenv("OMPI_COMM_WORLD_LOCAL_RANK"): - args.local_rank = int(os.getenv("OMPI_COMM_WORLD_LOCAL_RANK")) - print(args.local_rank) - # Sanity checks - if ( - args.data_paths is None - ): - raise ValueError("Need either a dataset name or a training/validation file.") - - if args.push_to_hub: - assert args.output_dir is not None, "Need an `output_dir` to create a repo when `--push_to_hub` is passed." - - return args \ No newline at end of file diff --git a/mftcoder_atorch/data/Makefile b/mftcoder_atorch/data/Makefile deleted file mode 100644 index 8f9db76..0000000 --- a/mftcoder_atorch/data/Makefile +++ /dev/null @@ -1,9 +0,0 @@ -CXXFLAGS += -O3 -Wall -shared -std=c++11 -fPIC -fdiagnostics-color -CPPFLAGS += $(shell python3 -m pybind11 --includes) -LIBNAME = helpers -LIBEXT = $(shell python3-config --extension-suffix) - -default: $(LIBNAME)$(LIBEXT) - -%$(LIBEXT): %.cpp - $(CXX) $(CXXFLAGS) $(CPPFLAGS) $< -o $@ diff --git a/mftcoder_atorch/data/__init__.py b/mftcoder_atorch/data/__init__.py deleted file mode 100644 index b974282..0000000 --- a/mftcoder_atorch/data/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from . import * \ No newline at end of file diff --git a/mftcoder_atorch/data/gpt2_multi_task_dataset.py b/mftcoder_atorch/data/gpt2_multi_task_dataset.py deleted file mode 100644 index e97a6e9..0000000 --- a/mftcoder_atorch/data/gpt2_multi_task_dataset.py +++ /dev/null @@ -1,349 +0,0 @@ -import os -import json -import math -import time -import numpy as np -import torch -from functools import partial -from data.tokenization.preprocess_data import UniformEncoder -from utils.common_utils import get_local_rank, print_rank_0, TASK2ID, ID2TASK - - -class GPT2FromRawDataset(torch.utils.data.Dataset): - def __init__( - self, - name, - data_prefix, - input_dataset, - seq_length, - weighted_loss_mode=None, - ds_weight=1.0, - ): - - self.name = name - self.input_dataset = input_dataset - self.num_samples = len(self.input_dataset['input_ids']) - self.seq_length = seq_length - - self.weighted_loss_mode = weighted_loss_mode - self.ds_weight = ds_weight - self.task_name = data_prefix.split('/')[-1] - self.task_id = TASK2ID[self.task_name] - - def update_ds_weight(self, weight): - self.ds_weight = weight - - def __len__(self): - return self.num_samples - - def __getitem__(self, idx): - try: - # Get the shuffled index. - idx = idx % self.num_samples - idx_data = {key: self.input_dataset[key][idx] - for key in self.input_dataset} - - if self.weighted_loss_mode: - idx_data["weight"] = np.array([self.ds_weight], dtype=np.float32) - idx_data["task_id"] = np.array([self.task_id], dtype=np.int) - return idx_data - else: - idx_data["task_id"] = np.array([self.task_id], dtype=np.int) - return idx_data - except IndexError: - new_idx = idx % len(self) - print( - f"WARNING in GPT2FromRawDataset: Got index out of bounds error with index {idx} - taking modulo of index instead ({new_idx})" - ) - return self[new_idx] - - - -def ds_weights_by_num_docs_sft(l, alpha=0.3): - # ignore alpha - weights = [1 / i for i in l] - weights_sum = sum(weights) - weights = [weight / weights_sum for weight in weights] - return weights - - -class GPT2BlendableDataset(torch.utils.data.Dataset): - def __init__(self, datasets, weights, global_num_samples, local_num_samples): - self.datasets = datasets - num_datasets = len(datasets) - assert num_datasets == len(weights) - - self.size = 0 - for dataset in self.datasets: - self.size += len(dataset) - - assert local_num_samples == self.size - # Normalize weights. - weights = np.array(weights, dtype=np.float64) - sum_weights = np.sum(weights) - assert sum_weights > 0.0 - weights /= sum_weights - - # recompute weights - weights = self.calc_weights() - - # Build indices. - start_time = time.time() - assert num_datasets < 255 - self.dataset_index = np.zeros(self.size, dtype=np.uint8) - self.dataset_sample_index = np.zeros(self.size, dtype=np.int64) - - self.global_num_samples = global_num_samples - self.local_num_samples = local_num_samples - - from data import helpers - - helpers.build_blending_indices( - self.dataset_index, - self.dataset_sample_index, - weights, - num_datasets, - self.size, - torch.distributed.get_rank() == 0, - ) - - print( - "> RANK {} elapsed time for building blendable dataset indices: " - "{:.2f} (sec)".format( - torch.distributed.get_rank(), time.time() - start_time - ) - ) - - def calc_weights(self): - dataset_sample_cnt = [len(ds) for ds in self.datasets] - total_cnt = sum(dataset_sample_cnt) - weights = np.array([(cnt + 0.0) / total_cnt for cnt in dataset_sample_cnt], dtype=np.float64) - return weights - - def __len__(self): - return self.global_num_samples - - def __getitem__(self, idx): - try: - idx = idx % self.local_num_samples - dataset_idx = self.dataset_index[idx] - sample_idx = self.dataset_sample_index[idx] - return self.datasets[dataset_idx][sample_idx] - except IndexError: - # new_idx = idx % len(self) - new_idx = idx % self.local_num_samples - print(self.local_num_samples) - print( - f"WARNING in GPT2MultiTaskDataset: Got index out of bounds error with index {idx} - taking modulo of index instead ({new_idx})" - ) - return self[new_idx] - - -def shuffle_arrays(arrays, set_seed=-1): - """Shuffles arrays in-place, in the same order, along axis=0 - - Parameters: - ----------- - arrays : List of NumPy arrays. - set_seed : Seed value if int >= 0, else seed is random. - """ - assert all(len(arr) == len(arrays[0]) for arr in arrays) - seed = np.random.randint(0, 2**(32 - 1) - 1) if set_seed < 0 else set_seed - - for arr in arrays: - rstate = np.random.RandomState(seed) - rstate.shuffle(arr) - - -def load_dataset_from_jsonl(args, tokenizer=None, shard_data=False, world_size=1, global_rank=0): - - # tokenization encoder - encoder = UniformEncoder(args, args.tokenize_mode, tokenizer) - encoder.initializer() - - data_prefixes = list(args.data_paths[1:-1].split(',')) - - splits = [] - splits_string = args.data_split - if splits_string.find(",") != -1: - splits = [float(s) for s in splits_string.split(",")] - elif splits_string.find("/") != -1: - splits = [float(s) for s in splits_string.split("/")] - else: - splits = [float(splits_string)] - while len(splits) < 3: - splits.append(0.0) - splits = splits[:3] - print(f'data splits: {splits}') - - all_train_datasets = [] - all_valid_datasets = [] - all_train_datasets_length = [] - all_valid_datasets_length = [] - # valid token count in every dataset - num_tokens = [] - effective_token_rate = [] - total_sample_cnt = [] - - local_train_num = 0 - local_valid_num = 0 - - # multiple dataset paths - for dataset_index in range(len(data_prefixes)): - files = os.listdir(data_prefixes[dataset_index]) - cur_dataset_input_ids = [] - cur_dataset_loss_mask = [] - cur_dataset_global_num_samples = 0 - cur_dataset_num_tokens = 0 - # multiple jsonl files - for file in files: - file_name = data_prefixes[dataset_index] + '/' + file - if os.path.isdir(file_name): - continue - fin = open(file_name, 'r') - print(f'[Global Rank {global_rank}] open file {file_name}') - - if args.padding_mode == 'padding' or args.padding_mode == 'pack': - for i, line in enumerate(fin): - # pre-sharding - if shard_data and i % world_size != global_rank: - continue - data = json.loads(line.rstrip('\n\r')) - features, length = encoder.encode(data) - # multiple samples per document - for idx in range(len(features['input_ids'])): - cur_dataset_input_ids.append(features['input_ids'][idx]) - cur_dataset_loss_mask.append(features['loss_mask'][idx]) - - fin.close() - else: - i = 0 - for line in fin: - data = json.loads(line.rstrip('\n\r')) - features, length = encoder.encode(data) - - for idx in range(len(features['input_ids'])): - # post-sharding - if shard_data and i % world_size != global_rank: - i += 1 - continue - i += 1 - cur_dataset_input_ids.append(features['input_ids'][idx]) - cur_dataset_loss_mask.append(features['loss_mask'][idx]) - - fin.close() - - cur_dataset_input_ids = np.array(cur_dataset_input_ids, dtype=np.float32) - cur_dataset_loss_mask = np.array(cur_dataset_loss_mask, dtype=np.float32) - cur_dataset_num_tokens = np.sum(cur_dataset_loss_mask, dtype=np.int32) - cur_dataset_sample_num = len(cur_dataset_input_ids) - num_tokens.append(cur_dataset_num_tokens) - total_sample_cnt.append(cur_dataset_sample_num) - effective_token_rate.append(cur_dataset_num_tokens / (cur_dataset_sample_num * args.seq_length)) - - # shuffle before split - shuffle_arrays([cur_dataset_input_ids, cur_dataset_loss_mask], args.seed) - train_ratio = splits[0] / 100.0 - train_num = int(math.ceil(train_ratio * cur_dataset_sample_num)) - # split train/valid - cur_train_input_ids, cur_valid_input_ids = cur_dataset_input_ids[: train_num], cur_dataset_input_ids[train_num: ] - cur_train_loss_mask, cur_valid_loss_mask = cur_dataset_loss_mask[: train_num], cur_dataset_loss_mask[train_num: ] - local_train_num += train_num - local_valid_num += (cur_dataset_sample_num - train_num) - - cur_train_dataset = {'input_ids': cur_train_input_ids, - 'loss_mask': cur_train_loss_mask - } - cur_valid_dataset = {'input_ids': cur_valid_input_ids, - 'loss_mask': cur_valid_loss_mask - } - print(f"[Global Rank {global_rank}]shape of cur train dataset: {cur_train_dataset['input_ids'].shape}") - print(f"[Global Rank {global_rank}]shape of cur valid dataset: {cur_valid_dataset['input_ids'].shape}") - - cur_train_ds = GPT2FromRawDataset( - 'train', - data_prefixes[dataset_index], - cur_train_dataset, - args.seq_length, - weighted_loss_mode=args.weighted_loss_mode, - ds_weight=splits[0] - ) - cur_valid_ds = GPT2FromRawDataset( - 'valid', - data_prefixes[dataset_index], - cur_valid_dataset, - args.seq_length, - weighted_loss_mode=args.weighted_loss_mode, - ds_weight=splits[1] - ) - - all_train_datasets.append(cur_train_ds) - all_valid_datasets.append(cur_valid_ds) - all_train_datasets_length.append(len(cur_train_ds)) - all_valid_datasets_length.append(len(cur_valid_ds)) - - print(f'[Global Rank {global_rank}]num tokens: {num_tokens}') - print(f'[Global Rank {global_rank}]effective token rate: {effective_token_rate}') - - num_tokens = [] - ds_fn = partial(ds_weights_by_num_docs_sft) - train_loss_weights, valid_loss_weights = ( - ds_fn(all_train_datasets_length), - ds_fn(all_valid_datasets_length), - ) - - print(f"> train loss weights in rank {global_rank}: {train_loss_weights}") - print(f"> valid loss weights in rank {global_rank}: {valid_loss_weights}") - - factor = 1 - # calcualte common factor based on token cnt and total sample cnt - if num_tokens: - factor = sum(num_tokens) / (sum(total_sample_cnt) * args.seq_length) - factor /= sum([1.0 / w for w in train_loss_weights]) / len(train_loss_weights) - print(f"> common denomination factor for CE loss in rank {global_rank}: {factor}") - - train_sample_weights = [x / sum(all_train_datasets_length) for x in all_train_datasets_length] - valid_sample_weights = [x / sum(all_valid_datasets_length) for x in all_valid_datasets_length] - print(f"> train sample weights in rank {global_rank}: {train_sample_weights}") - print(f"> valid sample weights in rank {global_rank}: {valid_sample_weights}") - - # re-compute global_train_num and global_valid_num - - torch.distributed.barrier() - device = f"cuda:{get_local_rank()}" - - global_train_num_samples_tensor = torch.tensor(local_train_num, dtype=torch.int32) - global_train_num_samples_tensor = global_train_num_samples_tensor.to(device) - torch.distributed.all_reduce(global_train_num_samples_tensor, op=torch.distributed.ReduceOp.SUM) - global_train_num = global_train_num_samples_tensor.item() - - global_valid_num_samples_tensor = torch.tensor(local_valid_num, dtype=torch.int32) - global_valid_num_samples_tensor = global_valid_num_samples_tensor.to(device) - torch.distributed.all_reduce(global_valid_num_samples_tensor, op=torch.distributed.ReduceOp.SUM) - global_valid_num = global_valid_num_samples_tensor.item() - print(f"> global train num in rank {global_rank}: {global_train_num}") - print(f"> global valid num in rank {global_rank}: {global_valid_num}") - - torch.distributed.barrier() - - for i in range(len(all_train_datasets)): - print(f'loss weight of train dataset {i} before update in rank {global_rank}: {all_train_datasets[i].ds_weight}') - blending_train_dataset = None - if all_train_datasets: - args.do_train = True - for i in range(len(all_train_datasets)): - all_train_datasets[i].update_ds_weight(train_loss_weights[i] / factor) - print(f'loss weight of train dataset {i} after update in rank {global_rank}: {all_train_datasets[i].ds_weight}') - blending_train_dataset = GPT2BlendableDataset(all_train_datasets, train_sample_weights, global_train_num, local_train_num) - - for i in range(len(all_train_datasets)): - print(f'loss weight of valid dataset {i} before update in rank {global_rank}: {all_train_datasets[i].ds_weight}') - blending_valid_dataset = None - if all_valid_datasets: - args.do_valid = True - for i in range(len(all_valid_datasets)): - all_valid_datasets[i].update_ds_weight(valid_loss_weights[i] / factor) - print(f'loss weight of valid dataset {i} after update in rank {global_rank}: {all_train_datasets[i].ds_weight}') - blending_valid_dataset = GPT2BlendableDataset(all_valid_datasets, valid_sample_weights, global_valid_num, local_valid_num) - - return blending_train_dataset, blending_valid_dataset diff --git a/mftcoder_atorch/data/helpers.cpp b/mftcoder_atorch/data/helpers.cpp deleted file mode 100644 index a688d54..0000000 --- a/mftcoder_atorch/data/helpers.cpp +++ /dev/null @@ -1,758 +0,0 @@ -/* - coding=utf-8 - Copyright (c) 2020, NVIDIA CORPORATION. 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. - */ - -/* Helper methods for fast index mapping builds */ - -#include -#include -#include -#include -#include -#include -#include -#include - -namespace py = pybind11; -using namespace std; - -const int32_t LONG_SENTENCE_LEN = 512; - -void build_blending_indices(py::array_t& dataset_index, - py::array_t& dataset_sample_index, - const py::array_t& weights, - const int32_t num_datasets, - const int64_t size, - const bool verbose) -{ - /* Given multiple datasets and a weighting array, build samples - such that it follows those weights.*/ - - if (verbose) { std::cout << "> building indices for blendable datasets ..." << std::endl; } - - // Get the pointer access without the checks. - auto dataset_index_ptr = dataset_index.mutable_unchecked<1>(); - auto dataset_sample_index_ptr = dataset_sample_index.mutable_unchecked<1>(); - auto weights_ptr = weights.unchecked<1>(); - - // Initialize buffer for number of samples used for each dataset. - int64_t current_samples[num_datasets]; - for (int64_t i = 0; i < num_datasets; ++i) { current_samples[i] = 0; } - - // For each sample: - for (int64_t sample_idx = 0; sample_idx < size; ++sample_idx) { - // Determine where the max error in sampling is happening. - double sample_idx_double = std::max(static_cast(sample_idx), 1.0); - int64_t max_error_index = 0; - double max_error = - weights_ptr[0] * sample_idx_double - static_cast(current_samples[0]); - for (int64_t dataset_idx = 1; dataset_idx < num_datasets; ++dataset_idx) { - double error = weights_ptr[dataset_idx] * sample_idx_double - - static_cast(current_samples[dataset_idx]); - if (error > max_error) { - max_error = error; - max_error_index = dataset_idx; - } - } - - // Populate the indices. - dataset_index_ptr[sample_idx] = static_cast(max_error_index); - dataset_sample_index_ptr[sample_idx] = current_samples[max_error_index]; - - // Update the total samples. - current_samples[max_error_index] += 1; - } - - // print info - if (verbose) { - std::cout << " > sample ratios:" << std::endl; - for (int64_t dataset_idx = 0; dataset_idx < num_datasets; ++dataset_idx) { - auto ratio = - static_cast(current_samples[dataset_idx]) / static_cast(size); - std::cout << " dataset " << dataset_idx << ", input: " << weights_ptr[dataset_idx] - << ", achieved: " << ratio << std::endl; - } - } -} - -py::array build_sample_idx_int32(const py::array_t& sizes_, - const py::array_t& doc_idx_, - const int32_t seq_length, - const int32_t num_epochs, - const int64_t tokens_per_epoch) -{ - /* Sample index (sample_idx) is used for gpt2 like dataset for which - the documents are flattened and the samples are built based on this - 1-D flatten array. It is a 2D array with sizes [number-of-samples + 1, 2] - where [..., 0] contains the index into `doc_idx` and [..., 1] is the - starting offset in that document.*/ - - // Consistency checks. - assert(seq_length > 1); - assert(num_epochs > 0); - assert(tokens_per_epoch > 1); - - // Remove bound checks. - auto sizes = sizes_.unchecked<1>(); - auto doc_idx = doc_idx_.unchecked<1>(); - - // Mapping and it's length (1D). - // 这里的num_samples并不是实际的num_samples，因为计算Epoch时会向上取整 - int64_t num_samples = (num_epochs * tokens_per_epoch - 1) / seq_length; - int32_t* sample_idx = new int32_t[2 * (num_samples + 1)]; - - cout << " using:" << endl << std::flush; - cout << " number of documents: " << doc_idx_.shape(0) / num_epochs << endl - << std::flush; - cout << " number of epochs: " << num_epochs << endl << std::flush; - cout << " sequence length: " << seq_length << endl << std::flush; - cout << " total number of samples: " << num_samples << endl << std::flush; - - // Index into sample_idx. - int64_t sample_index = 0; - // Index into doc_idx. - int64_t doc_idx_index = 0; - // Beginning offset for each document. - int32_t doc_offset = 0; - // Start with first document and no offset. - sample_idx[2 * sample_index] = doc_idx_index; - sample_idx[2 * sample_index + 1] = doc_offset; - ++sample_index; - - while (sample_index <= num_samples) { - // Start with a fresh sequence. - int32_t remaining_seq_length = seq_length + 1; - while (remaining_seq_length != 0) { - // Get the document length. - auto doc_id = doc_idx[doc_idx_index]; - auto doc_length = sizes[doc_id] - doc_offset; - // And add it to the current sequence. - remaining_seq_length -= doc_length; - // If we have more than a full sequence, adjust offset and set - // remaining length to zero so we return from the while loop. - // Note that -1 here is for the same reason we have -1 in - // `_num_epochs` calculations. - if (remaining_seq_length <= 0) { - doc_offset += (remaining_seq_length + doc_length - 1); - remaining_seq_length = 0; - } else { - // Otherwise, start from the beginning of the next document. - ++doc_idx_index; - doc_offset = 0; - } - } - // Record the sequence. - sample_idx[2 * sample_index] = doc_idx_index; - sample_idx[2 * sample_index + 1] = doc_offset; - ++sample_index; - } - - // Method to deallocate memory. - py::capsule free_when_done(sample_idx, [](void* mem_) { - int32_t* mem = reinterpret_cast(mem_); - delete[] mem; - }); - - // Return the numpy array. - const auto byte_size = sizeof(int32_t); - return py::array(std::vector{num_samples + 1, 2}, // shape - {2 * byte_size, byte_size}, // C-style contiguous strides - sample_idx, // the data pointer - free_when_done); // numpy array references -} - - -py::array build_sample_idx_int64(const py::array_t& sizes_, - const py::array_t& doc_idx_, - const int32_t seq_length, - const int32_t num_epochs, - const int64_t tokens_per_epoch) -{ - /* Sample index (sample_idx) is used for gpt2 like dataset for which - the documents are flattened and the samples are built based on this - 1-D flatten array. It is a 2D array with sizes [number-of-samples + 1, 2] - where [..., 0] contains the index into `doc_idx` and [..., 1] is the - starting offset in that document.*/ - - // Consistency checks. - assert(seq_length > 1); - assert(num_epochs > 0); - assert(tokens_per_epoch > 1); - - // Remove bound checks. - auto sizes = sizes_.unchecked<1>(); - auto doc_idx = doc_idx_.unchecked<1>(); - - // Mapping and it's length (1D). - int64_t num_samples = (num_epochs * tokens_per_epoch - 1) / seq_length; - int64_t* sample_idx = new int64_t[2 * (num_samples + 1)]; - - cout << " using:" << endl << std::flush; - cout << " number of documents: " << doc_idx_.shape(0) / num_epochs << endl - << std::flush; - cout << " number of epochs: " << num_epochs << endl << std::flush; - cout << " sequence length: " << seq_length << endl << std::flush; - cout << " total number of samples: " << num_samples << endl << std::flush; - - // Index into sample_idx. - int64_t sample_index = 0; - // Index into doc_idx. - int64_t doc_idx_index = 0; - // Beginning offset for each document. - int32_t doc_offset = 0; - // Start with first document and no offset. - sample_idx[2 * sample_index] = doc_idx_index; - sample_idx[2 * sample_index + 1] = doc_offset; - ++sample_index; - - while (sample_index <= num_samples) { - // Start with a fresh sequence. - int32_t remaining_seq_length = seq_length + 1; - while (remaining_seq_length != 0) { - // Get the document length. - auto doc_id = doc_idx[doc_idx_index]; - auto doc_length = sizes[doc_id] - doc_offset; - // And add it to the current sequence. - remaining_seq_length -= doc_length; - // If we have more than a full sequence, adjust offset and set - // remaining length to zero so we return from the while loop. - // Note that -1 here is for the same reason we have -1 in - // `_num_epochs` calculations. - if (remaining_seq_length <= 0) { - doc_offset += (remaining_seq_length + doc_length - 1); - remaining_seq_length = 0; - } else { - // Otherwise, start from the beginning of the next document. - ++doc_idx_index; - doc_offset = 0; - } - } - // Record the sequence. - sample_idx[2 * sample_index] = doc_idx_index; - sample_idx[2 * sample_index + 1] = doc_offset; - ++sample_index; - } - - // Method to deallocate memory. - py::capsule free_when_done(sample_idx, [](void* mem_) { - int64_t* mem = reinterpret_cast(mem_); - delete[] mem; - }); - - // Return the numpy array. - const auto byte_size = sizeof(int64_t); - return py::array(std::vector{num_samples + 1, 2}, // shape - {2 * byte_size, byte_size}, // C-style contiguous strides - sample_idx, // the data pointer - free_when_done); // numpy array references -} - -inline int32_t get_target_sample_len(const int32_t short_seq_ratio, - const int32_t max_length, - std::mt19937& rand32_gen) -{ - /* Training sample length. */ - const auto random_number = rand32_gen(); - if ((random_number % short_seq_ratio) == 0) { return 2 + random_number % (max_length - 1); } - return max_length; -} - -template -py::array build_mapping_impl(const py::array_t& docs_, - const py::array_t& sizes_, - const int32_t num_epochs, - const uint64_t max_num_samples, - const int32_t max_seq_length, - const double short_seq_prob, - const int32_t seed, - const bool verbose) -{ - /* Build a mapping of (start-index, end-index, sequence-length) where - start and end index are the indices of the sentences in the sample - and sequence-length is the target sequence length. - */ - - // Consistency checks. - assert(num_epochs > 0); - assert(max_seq_length > 1); - assert(short_seq_prob > 0.0); - assert(short_seq_prob <= 1.0); - assert(seed > 0); - - // Remove bound checks. - auto docs = docs_.unchecked<1>(); - auto sizes = sizes_.unchecked<1>(); - - // For efficiency, convert probability to ratio. Note: rand() generates int. - const auto short_seq_ratio = static_cast(round(1.0 / short_seq_prob)); - - if (verbose) { - const auto sent_start_index = docs[0]; - const auto sent_end_index = docs[docs_.shape(0) - 1]; - const auto num_sentences = sent_end_index - sent_start_index; - cout << " using:" << endl << std::flush; - cout << " number of documents: " << docs_.shape(0) - 1 << endl << std::flush; - cout << " sentences range: [" << sent_start_index << ", " - << sent_end_index << ")" << endl - << std::flush; - cout << " total number of sentences: " << num_sentences << endl << std::flush; - cout << " number of epochs: " << num_epochs << endl << std::flush; - cout << " maximum number of samples: " << max_num_samples << endl << std::flush; - cout << " maximum sequence length: " << max_seq_length << endl << std::flush; - cout << " short sequence probability: " << short_seq_prob << endl << std::flush; - cout << " short sequence ration (1/prob): " << short_seq_ratio << endl << std::flush; - cout << " seed: " << seed << endl << std::flush; - } - - // Mapping and it's length (1D). - int64_t num_samples = -1; - DocIdx* maps = NULL; - - // Perform two iterations, in the first iteration get the size - // and allocate memory and in the second iteration populate the map. - bool second = false; - for (int32_t iteration = 0; iteration < 2; ++iteration) { - // Set the seed so both iterations produce the same results. - std::mt19937 rand32_gen(seed); - - // Set the flag on second iteration. - second = (iteration == 1); - - // Counters: - uint64_t empty_docs = 0; - uint64_t one_sent_docs = 0; - uint64_t long_sent_docs = 0; - - // Current map index. - uint64_t map_index = 0; - - // For each epoch: - for (int32_t epoch = 0; epoch < num_epochs; ++epoch) { - if (map_index >= max_num_samples) { - if (verbose && (!second)) { - cout << " reached " << max_num_samples << " samples after " << epoch - << " epochs ..." << endl - << std::flush; - } - break; - } - // For each document: - for (int32_t doc = 0; doc < (docs.shape(0) - 1); ++doc) { - // Document sentences are in [sent_index_first, sent_index_last) - const auto sent_index_first = docs[doc]; - const auto sent_index_last = docs[doc + 1]; - - // At the beginning of the document previous index is the - // start index. - auto prev_start_index = sent_index_first; - - // Remaining documents. - auto num_remain_sent = sent_index_last - sent_index_first; - - // Some bookkeeping - if ((epoch == 0) && (!second)) { - if (num_remain_sent == 0) { ++empty_docs; } - if (num_remain_sent == 1) { ++one_sent_docs; } - } - - // Detect documents with long sentences. - bool contains_long_sentence = false; - if (num_remain_sent > 1) { - for (auto sent_index = sent_index_first; sent_index < sent_index_last; - ++sent_index) { - if (sizes[sent_index] > LONG_SENTENCE_LEN) { - if ((epoch == 0) && (!second)) { ++long_sent_docs; } - contains_long_sentence = true; - break; - } - } - } - - // If we have more than two sentences. - if ((num_remain_sent > 1) && (!contains_long_sentence)) { - // Set values. - auto seq_len = int32_t{0}; - auto num_sent = int32_t{0}; - auto target_seq_len = - get_target_sample_len(short_seq_ratio, max_seq_length, rand32_gen); - - // Loop through sentences. - for (auto sent_index = sent_index_first; sent_index < sent_index_last; - ++sent_index) { - // Add the size and number of sentences. - seq_len += sizes[sent_index]; - ++num_sent; - --num_remain_sent; - - // If we have reached the target length. - // and if not only one sentence is left in the document. - // and if we have at least two sentneces. - // and if we have reached end of the document. - if (((seq_len >= target_seq_len) && (num_remain_sent > 1) && - (num_sent > 1)) || - (num_remain_sent == 0)) { - // Check for overflow. - if ((3 * map_index + 2) > std::numeric_limits::max()) { - cout << "number of samples exceeded maximum " - << "allowed by type int64: " - << std::numeric_limits::max() << endl; - throw std::overflow_error("Number of samples"); - } - - // Populate the map. - if (second) { - const auto map_index_0 = 3 * map_index; - maps[map_index_0] = static_cast(prev_start_index); - maps[map_index_0 + 1] = static_cast(sent_index + 1); - maps[map_index_0 + 2] = static_cast(target_seq_len); - } - - // Update indices / counters. - ++map_index; - prev_start_index = sent_index + 1; - target_seq_len = - get_target_sample_len(short_seq_ratio, max_seq_length, rand32_gen); - seq_len = 0; - num_sent = 0; - } - - } // for (auto sent_index=sent_index_first; ... - } // if (num_remain_sent > 1) { - } // for (int doc=0; doc < num_docs; ++doc) { - } // for (int epoch=0; epoch < num_epochs; ++epoch) { - - if (!second) { - if (verbose) { - cout << " number of empty documents: " << empty_docs << endl << std::flush; - cout << " number of documents with one sentence: " << one_sent_docs << endl - << std::flush; - cout << " number of documents with long sentences: " << long_sent_docs << endl - << std::flush; - cout << " will create mapping for " << map_index << " samples" << endl - << std::flush; - } - assert(maps == NULL); - assert(num_samples < 0); - maps = new DocIdx[3 * map_index]; - num_samples = static_cast(map_index); - } - - } // for (int iteration=0; iteration < 2; ++iteration) { - - // Shuffle. - // We need a 64 bit random number generator as we might have more - // than 2 billion samples. - std::mt19937_64 rand64_gen(seed + 1); - for (auto i = (num_samples - 1); i > 0; --i) { - const auto j = static_cast(rand64_gen() % (i + 1)); - const auto i0 = 3 * i; - const auto j0 = 3 * j; - // Swap values. - swap(maps[i0], maps[j0]); - swap(maps[i0 + 1], maps[j0 + 1]); - swap(maps[i0 + 2], maps[j0 + 2]); - } - - // Method to deallocate memory. - py::capsule free_when_done(maps, [](void* mem_) { - DocIdx* mem = reinterpret_cast(mem_); - delete[] mem; - }); - - // Return the numpy array. - const auto byte_size = sizeof(DocIdx); - return py::array(std::vector{num_samples, 3}, // shape - {3 * byte_size, byte_size}, // C-style contiguous strides - maps, // the data pointer - free_when_done); // numpy array references -} - -py::array build_mapping(const py::array_t& docs_, - const py::array_t& sizes_, - const int num_epochs, - const uint64_t max_num_samples, - const int max_seq_length, - const double short_seq_prob, - const int seed, - const bool verbose) -{ - if (sizes_.size() > std::numeric_limits::max()) { - if (verbose) { cout << " using uint64 for data mapping..." << endl << std::flush; } - return build_mapping_impl(docs_, - sizes_, - num_epochs, - max_num_samples, - max_seq_length, - short_seq_prob, - seed, - verbose); - } else { - if (verbose) { cout << " using uint32 for data mapping..." << endl << std::flush; } - return build_mapping_impl(docs_, - sizes_, - num_epochs, - max_num_samples, - max_seq_length, - short_seq_prob, - seed, - verbose); - } -} - -template -py::array build_blocks_mapping_impl(const py::array_t& docs_, - const py::array_t& sizes_, - const py::array_t& titles_sizes_, - const int32_t num_epochs, - const uint64_t max_num_samples, - const int32_t max_seq_length, - const int32_t seed, - const bool verbose, - const bool use_one_sent_blocks) -{ - /* Build a mapping of (start-index, end-index, sequence-length) where - start and end index are the indices of the sentences in the sample - and sequence-length is the target sequence length. - */ - - // Consistency checks. - assert(num_epochs > 0); - assert(max_seq_length > 1); - assert(seed > 0); - - // Remove bound checks. - auto docs = docs_.unchecked<1>(); - auto sizes = sizes_.unchecked<1>(); - auto titles_sizes = titles_sizes_.unchecked<1>(); - - if (verbose) { - const auto sent_start_index = docs[0]; - const auto sent_end_index = docs[docs_.shape(0) - 1]; - const auto num_sentences = sent_end_index - sent_start_index; - cout << " using:" << endl << std::flush; - cout << " number of documents: " << docs_.shape(0) - 1 << endl << std::flush; - cout << " sentences range: [" << sent_start_index << ", " - << sent_end_index << ")" << endl - << std::flush; - cout << " total number of sentences: " << num_sentences << endl << std::flush; - cout << " number of epochs: " << num_epochs << endl << std::flush; - cout << " maximum number of samples: " << max_num_samples << endl << std::flush; - cout << " maximum sequence length: " << max_seq_length << endl << std::flush; - cout << " seed: " << seed << endl << std::flush; - } - - // Mapping and its length (1D). - int64_t num_samples = -1; - DocIdx* maps = NULL; - - // Acceptable number of sentences per block. - int min_num_sent = 2; - if (use_one_sent_blocks) { min_num_sent = 1; } - - // Perform two iterations, in the first iteration get the size - // and allocate memory and in the second iteration populate the map. - bool second = false; - for (int32_t iteration = 0; iteration < 2; ++iteration) { - // Set the flag on second iteration. - second = (iteration == 1); - - // Current map index. - uint64_t map_index = 0; - - uint64_t empty_docs = 0; - uint64_t one_sent_docs = 0; - uint64_t long_sent_docs = 0; - // For each epoch: - for (int32_t epoch = 0; epoch < num_epochs; ++epoch) { - // assign every block a unique id - int32_t block_id = 0; - - if (map_index >= max_num_samples) { - if (verbose && (!second)) { - cout << " reached " << max_num_samples << " samples after " << epoch - << " epochs ..." << endl - << std::flush; - } - break; - } - // For each document: - for (int32_t doc = 0; doc < (docs.shape(0) - 1); ++doc) { - // Document sentences are in [sent_index_first, sent_index_last) - const auto sent_index_first = docs[doc]; - const auto sent_index_last = docs[doc + 1]; - const auto target_seq_len = max_seq_length - titles_sizes[doc]; - - // At the beginning of the document previous index is the - // start index. - auto prev_start_index = sent_index_first; - - // Remaining documents. - auto num_remain_sent = sent_index_last - sent_index_first; - - // Some bookkeeping - if ((epoch == 0) && (!second)) { - if (num_remain_sent == 0) { ++empty_docs; } - if (num_remain_sent == 1) { ++one_sent_docs; } - } - // Detect documents with long sentences. - bool contains_long_sentence = false; - if (num_remain_sent >= min_num_sent) { - for (auto sent_index = sent_index_first; sent_index < sent_index_last; - ++sent_index) { - if (sizes[sent_index] > LONG_SENTENCE_LEN) { - if ((epoch == 0) && (!second)) { ++long_sent_docs; } - contains_long_sentence = true; - break; - } - } - } - // If we have enough sentences and no long sentences. - if ((num_remain_sent >= min_num_sent) && (!contains_long_sentence)) { - // Set values. - auto seq_len = int32_t{0}; - auto num_sent = int32_t{0}; - - // Loop through sentences. - for (auto sent_index = sent_index_first; sent_index < sent_index_last; - ++sent_index) { - // Add the size and number of sentences. - seq_len += sizes[sent_index]; - ++num_sent; - --num_remain_sent; - - // If we have reached the target length. - // and there are an acceptable number of sentences left - // and if we have at least the minimum number of sentences. - // or if we have reached end of the document. - if (((seq_len >= target_seq_len) && (num_remain_sent >= min_num_sent) && - (num_sent >= min_num_sent)) || - (num_remain_sent == 0)) { - // Populate the map. - if (second) { - const auto map_index_0 = 4 * map_index; - // Each sample has 4 items: the starting sentence index, ending - // sentence index, the index of the document from which the block - // comes (used for fetching titles) and the unique id of the block - // (used for creating block indexes) - - maps[map_index_0] = static_cast(prev_start_index); - maps[map_index_0 + 1] = static_cast(sent_index + 1); - maps[map_index_0 + 2] = static_cast(doc); - maps[map_index_0 + 3] = static_cast(block_id); - } - - // Update indices / counters. - ++map_index; - ++block_id; - prev_start_index = sent_index + 1; - seq_len = 0; - num_sent = 0; - } - } // for (auto sent_index=sent_index_first; ... - } // if (num_remain_sent > 1) { - } // for (int doc=0; doc < num_docs; ++doc) { - } // for (int epoch=0; epoch < num_epochs; ++epoch) { - - if (!second) { - if (verbose) { - cout << " number of empty documents: " << empty_docs << endl << std::flush; - cout << " number of documents with one sentence: " << one_sent_docs << endl - << std::flush; - cout << " number of documents with long sentences: " << long_sent_docs << endl - << std::flush; - cout << " will create mapping for " << map_index << " samples" << endl - << std::flush; - } - assert(maps == NULL); - assert(num_samples < 0); - maps = new DocIdx[4 * map_index]; - num_samples = static_cast(map_index); - } - - } // for (int iteration=0; iteration < 2; ++iteration) { - - // Shuffle. - // We need a 64 bit random number generator as we might have more - // than 2 billion samples. - std::mt19937_64 rand64_gen(seed + 1); - for (auto i = (num_samples - 1); i > 0; --i) { - const auto j = static_cast(rand64_gen() % (i + 1)); - const auto i0 = 4 * i; - const auto j0 = 4 * j; - // Swap values. - swap(maps[i0], maps[j0]); - swap(maps[i0 + 1], maps[j0 + 1]); - swap(maps[i0 + 2], maps[j0 + 2]); - swap(maps[i0 + 3], maps[j0 + 3]); - } - - // Method to deallocate memory. - py::capsule free_when_done(maps, [](void* mem_) { - DocIdx* mem = reinterpret_cast(mem_); - delete[] mem; - }); - - // Return the numpy array. - const auto byte_size = sizeof(DocIdx); - return py::array(std::vector{num_samples, 4}, // shape - {4 * byte_size, byte_size}, // C-style contiguous strides - maps, // the data pointer - free_when_done); // numpy array references -} - -py::array build_blocks_mapping(const py::array_t& docs_, - const py::array_t& sizes_, - const py::array_t& titles_sizes_, - const int num_epochs, - const uint64_t max_num_samples, - const int max_seq_length, - const int seed, - const bool verbose, - const bool use_one_sent_blocks) -{ - if (sizes_.size() > std::numeric_limits::max()) { - if (verbose) { cout << " using uint64 for data mapping..." << endl << std::flush; } - return build_blocks_mapping_impl(docs_, - sizes_, - titles_sizes_, - num_epochs, - max_num_samples, - max_seq_length, - seed, - verbose, - use_one_sent_blocks); - } else { - if (verbose) { cout << " using uint32 for data mapping..." << endl << std::flush; } - return build_blocks_mapping_impl(docs_, - sizes_, - titles_sizes_, - num_epochs, - max_num_samples, - max_seq_length, - seed, - verbose, - use_one_sent_blocks); - } -} - -PYBIND11_MODULE(helpers, m) -{ - m.def("build_mapping", &build_mapping); - m.def("build_blocks_mapping", &build_blocks_mapping); - m.def("build_sample_idx_int32", &build_sample_idx_int32); - m.def("build_sample_idx_int64", &build_sample_idx_int64); - m.def("build_blending_indices", &build_blending_indices); -} diff --git a/mftcoder_atorch/data/helpers.cpython-38-x86_64-linux-gnu.so b/mftcoder_atorch/data/helpers.cpython-38-x86_64-linux-gnu.so deleted file mode 100755 index 6fbc1b7..0000000 Binary files a/mftcoder_atorch/data/helpers.cpython-38-x86_64-linux-gnu.so and /dev/null differ diff --git a/mftcoder_atorch/data/preprocess_data.py b/mftcoder_atorch/data/preprocess_data.py deleted file mode 100644 index 4ccdec2..0000000 --- a/mftcoder_atorch/data/preprocess_data.py +++ /dev/null @@ -1,313 +0,0 @@ -"""Processing data for pretraining.""" - -import argparse -import multiprocessing -import os -import sys -import numpy as np -import random - -# add src root path -current_path = os.path.abspath(__file__) -parent_dir = os.path.dirname(os.path.dirname(current_path)) -grandparent_dir = os.path.dirname(parent_dir) -sys.path.append(grandparent_dir) -# print(grandparent_dir) - -import data.tokenization.lm_dataformat as lmd - -import time -import tqdm -import torch -import ftfy -import glob - -from tokenizer import build_tokenizer -from threading import Semaphore - - -table = {ord(f):ord(t) for f,t in zip( - u'，。！？：【】（）％＃＠＆１２３４５６７８９０', - u',.!?:[]()%#@&1234567890')} - - -def punctuation_format(text: str): - # Replace non-breaking space with space - # text = text.strip() + '\n' - text = text.replace('\u202f', ' ').replace('\xa0', ' ') - # change chinese punctuation to english ones - text = text.translate(table) - return text - -def is_prompt_answer_format(data): - - if "prompt" in data and "answer" in data: - return True - else: - return False - - -def is_chatml_format(data): - if "chat_rounds" in data and len(data["chat_rounds"]) > 0: - return True - else: - return False - - -def is_text_format(data): - if "text" in data: - return True - else: - return False - -class Encoder(object): - def __init__(self, args, tokenizer=None): - self.args = args - self.tokenizer = tokenizer - - def initializer(self): - # Use Encoder class as a container for global data - if self.tokenizer is None: - self.tokenizer = build_tokenizer(self.args) - else: - self.tokenizer = self.tokenizer - - def encode(self, text): - if self.args.ftfy: - text = ftfy.fix_text(text) - ids = {} - for key in self.args.jsonl_keys: - doc_ids = [] - text_ids = self.tokenizer.encode(text, add_special_tokens=False) - if len(text_ids) > 0: - doc_ids.append(text_ids) - if self.args.append_eod: - doc_ids[-1].append(self.tokenizer.eod_id) - ids[key] = doc_ids - return ids, len(text) - - -class UniformEncoder(Encoder): - def __init__(self, args, mode='sft', tokenizer=None): - super().__init__(args, tokenizer=tokenizer) - self.mode = mode - # seq_length + 1 for shifting - if args.load_raw_dataset: - self.seq_length = args.seq_length + 1 - self.stride = args.seq_length - else: - self.seq_length = args.seq_length - - self.remain_input_ids = [] - self.remain_loss_mask = [] - - def encode(self, data): - - encode_res = { - "input_ids":[], - "loss_mask":[] - } - - if is_prompt_answer_format(data): - data_type = 'prompt_answer' - elif is_chatml_format(data): - data_type = 'chatML' - elif is_text_format(data): - data_type = 'text' - else: - raise ValueError("data format not supported, please use prompt/answer, or chatML or pretrain text") - - for token_res in self._tokenize_fields(data, data_type=data_type): - for k, v in token_res.items(): - encode_res[k].append(v) - - length = 0 - if data_type == 'prompt_answer': - length = len(data['prompt']) + len(data['answer']) - elif data_type == 'chatML': - for chat in data['chat_rounds']: - length += len(chat['content']) - elif data_type == 'text': - length += len(data['text']) - - return encode_res, length - - - def _tokenize_fields(self, data, data_type): - - CHAT_COL = 'chat_rounds' - ROLE_COL = 'role' - CONTENT_COL = 'content' - - PROMPT_COL = 'prompt' - ANSWER_COL = 'answer' - SYSTEM_COL = 'system' - - TEXT_COL = 'text' - - if self.mode == 'sft': - HUMAN = 'human' - BOT = 'bot' - SYSTEM = 'system' - ROLE_START_MARKER = '<|role_start|>' - ROLE_END_MARKER = '<|role_end|>' - elif self.mode == 'pretrain' or data_type == 'text': - HUMAN = '' - BOT = '' - SYSTEM = '' - ROLE_START_MARKER = '' - ROLE_END_MARKER = '' - else: - raise ValueError(f"tokenize_mode does not support {self.mode}, please use sft or pretrain") - - - human_marker_ids = self.tokenizer.encode(f"{ROLE_START_MARKER}{HUMAN}{ROLE_END_MARKER}", add_special_tokens=False) - bot_marker_ids = self.tokenizer.encode(f"{ROLE_START_MARKER}{BOT}{ROLE_END_MARKER}", add_special_tokens=False) - system_marker_ids = self.tokenizer.encode(f"{ROLE_START_MARKER}{SYSTEM}{ROLE_END_MARKER}", add_special_tokens=False) - sft_end_marker_ids = [self.tokenizer.eod_id] - - # uniform SST,SFT,MFT - - input_ids = [] - loss_mask = [] - - if data_type == "prompt_answer": - system = data.get(SYSTEM_COL, '') - prompt = data[PROMPT_COL] - answer = data[ANSWER_COL] - system = punctuation_format(system) - prompt = punctuation_format(prompt) - answer = punctuation_format(answer) - system_ids = system_marker_ids + self.tokenizer.encode(system, add_special_tokens=False) if system else [] - prompt_ids = self.tokenizer.encode(prompt, add_special_tokens=False) - answer_ids = self.tokenizer.encode(answer, add_special_tokens=False) + sft_end_marker_ids - input_ids += system_ids + human_marker_ids + prompt_ids + bot_marker_ids + answer_ids - loss_mask += [0] * len(system_ids) + [0] * len(human_marker_ids) + [0] * len(prompt_ids) + \ - [0] * len(bot_marker_ids) + [1] * len(answer_ids) - elif data_type == 'chatML': - chat = data[CHAT_COL] - for r in chat: - role = r[ROLE_COL] - content = r[CONTENT_COL] - content = punctuation_format(content) - if role == HUMAN: - role_marker_ids = human_marker_ids - content_ids = self.tokenizer.encode(content, add_special_tokens=False) - elif role == BOT: - # compute loss for eos token after bot's content - role_marker_ids = bot_marker_ids - content_ids = self.tokenizer.encode(content, add_special_tokens=False) + sft_end_marker_ids - elif role == SYSTEM: - role_marker_ids = system_marker_ids - content_ids = self.tokenizer.encode(content, add_special_tokens=False) - else: - raise ValueError(f"Role {role} not supported.") - - input_ids += role_marker_ids + content_ids - masklet = [1] if role == BOT else [0] - loss_mask += [0] * len(role_marker_ids) + masklet * len(content_ids) - elif data_type == "text": - text = data[TEXT_COL] - text = punctuation_format(text) - text_ids = self.tokenizer.encode(text, add_special_tokens=False) + sft_end_marker_ids - input_ids += text_ids - loss_mask += [1] * len(text_ids) - else: - raise ValueError( - f"data_type does not support {self.args.data_type}, please use chatML or prompt_answer or text(for pretrain)") - - # print(self.mode) - if self.mode == 'pretrain': - # change loss mask to all 1s - input_ids = input_ids - loss_mask = [1] * len(loss_mask) - elif self.mode == 'sft': - # do nothing - input_ids = input_ids - loss_mask = loss_mask - - assert len(input_ids) == len(loss_mask) - if self.args.padding_mode == 'padding': - if len(input_ids) <= self.seq_length: - yield self.padding(input_ids, loss_mask) - - # drop if too long - else: - yield {} - elif self.args.padding_mode == 'concat': - input_ids = self.remain_input_ids + input_ids - loss_mask = self.remain_loss_mask + loss_mask - if len(input_ids) < self.seq_length: - self.remain_input_ids = input_ids - self.remain_loss_mask = loss_mask - assert len(self.remain_input_ids) == len(self.remain_loss_mask) - yield {} - else: - cursor = 0 - while cursor + self.seq_length <= len(input_ids): - yield { - "input_ids": input_ids[cursor: cursor + self.seq_length], - "loss_mask": loss_mask[cursor: cursor + self.seq_length] - } - cursor = cursor + self.stride - self.remain_input_ids = input_ids[cursor:] - self.remain_loss_mask = loss_mask[cursor:] - assert len(self.remain_input_ids) == len(self.remain_loss_mask) - yield {} - elif self.args.padding_mode == 'pack': - if len(input_ids) > self.seq_length: - yield {} - elif len(self.remain_input_ids) + len(input_ids) > self.seq_length: - input_ids, self.remain_input_ids = self.remain_input_ids, input_ids - loss_mask, self.remain_loss_mask = self.remain_loss_mask, loss_mask - assert len(input_ids) == len(loss_mask) - yield self.padding(input_ids, loss_mask) - else: - self.remain_input_ids = self.remain_input_ids + input_ids - self.remain_loss_mask = self.remain_loss_mask + loss_mask - assert len(self.remain_input_ids) == len(self.remain_loss_mask) - yield {} - - - def padding(self, input_ids, loss_mask): - pad_id = self.tokenizer.pad_id - assert len(input_ids) <= self.seq_length, f"padding sequence: {len(input_ids)} > {self.seq_length}" - input_ids += [pad_id] * (self.seq_length - len(input_ids)) - loss_mask += [0] * (self.seq_length - len(loss_mask)) - return { - "input_ids": input_ids, - "loss_mask": loss_mask - } - -def find_jsonl_fnames(inputs): - fnames = [] - for p in inputs.split(","): - if not os.path.isdir(p): - if p.endswith(".jsonl"): - print(f"loading from {p}") - fnames.append(p) - else: - p_list = glob.glob(p + "/*") - for p_ in p_list: - if p_.endswith(".jsonl"): - print(f"loading from {p_}") - fnames.append(p_) - return fnames - -def yield_from_files(fnames: list, semaphore): - """ - Iterator over input documents using lm_dataformat. Should be able to handle jsons / texts / - other compressed formats. Also filters out empty documents. - - :param fnames: list of filenames - """ - - def yielder(fname, semaphore): - for f in filter(lambda x: x, lmd.Reader(fname).stream_data(key=['task', 'src_language', 'src_code', 'tgt_language', 'tgt_code', 'sql', 'prompt', 'answer', 'bad_answer'])): - semaphore.acquire() - yield f - - for fname in fnames: - semaphore.acquire() - - yield from yielder(fname, semaphore) \ No newline at end of file diff --git a/mftcoder_atorch/data/tokenization/lm_dataformat.py b/mftcoder_atorch/data/tokenization/lm_dataformat.py deleted file mode 100644 index f0e121b..0000000 --- a/mftcoder_atorch/data/tokenization/lm_dataformat.py +++ /dev/null @@ -1,386 +0,0 @@ -import os -import zstandard -import ujson as json -import time -import tarfile -import codecs -from functools import reduce -import jsonlines -import io -from zipfile import ZipFile -import gzip -from math import ceil -import mmap -import multiprocessing as mp -from pathlib import Path - -VALID_EXTENSIONS = ['openwebtext.tar.xz', '_data.xz', '.dat.zst', '.jsonl', '.jsonl.zst', '.jsonl.zst.tar', '.json.zst', '.txt', '.zip', '.tar.gz', '.json.gz', '.gz'] - -def has_valid_extension(file): - return any([file.endswith(ext) for ext in VALID_EXTENSIONS]) - -def _listdir_or_file(x): - if isinstance(x, list): - return reduce(lambda x, y: x + y, map(listdir_or_file, sorted(x))) - if os.path.isfile(x): - return [x] - elif os.path.isdir(x): - return [str(Path(x) / fn) for fn in sorted(os.listdir(x))] - else: - raise FileNotFoundError(f"{x} not found") - -def listdir_or_file(x): - return list(filter(has_valid_extension, _listdir_or_file(x))) - -def tarfile_reader(file, streaming=False): - # we need our own tarfile parser because `tarfile` doesn't work well for - # big tarfiles; it seems to be reading the entire file to get a list of - # where all the files are - but we don't need that because we just need - # to see each file once. surprisingly, `tarfile` doesn't expose any - # facilities for this. the only options are 1. load the entire tarfile - # and then query by filename or 2. extract to disk - and neither of - # these is what we want. - - offset = 0 - paxfilesize = None - while True: - hdr = file.read(512) - offset += 512 - - # https://www.gnu.org/software/tar/manual/html_node/Standard.html - # end at 135 not 136 because of \0 terminator - if hdr[124:135] == b'\0'*11: - # end of record - break - - fname = hdr[:100].split(b'\0')[0] - - # if the file is too big to fit in the size field, tarfiles will actually - # include a PaxHeader with the size in it, applicable to the immediate next file. - if paxfilesize is not None: - size = paxfilesize - paxfilesize = None - else: - size = int(hdr[124:135], 8) - - padded_size = ceil(size / 512) * 512 - - # for handling PaxHeader files (which contain extra metadata about file size) and directories - # https://pubs.opengroup.org/onlinepubs/9699919799/utilities/pax.html#tag_20_92_13_03 - type = chr(hdr[156]) - - if type == 'x': - meta = file.read(padded_size)[:size] - def kv(x): - return x.decode('utf-8').split(' ')[1].split('=') - paxfileattrs = { - kv(x)[0]: kv(x)[1] - for x in meta.split(b'\n') if x - } - paxfilesize = int(paxfileattrs['size']) - - offset += padded_size - continue - elif type != '0' and type != '\0': - if streaming: - file.seek(padded_size, os.SEEK_CUR) - else: - file.read(padded_size) - offset += padded_size - continue - - if streaming: - # skip directory entries - if size != 0: - mmo = mmap.mmap(file.fileno(), length=offset + size, access=mmap.ACCESS_READ) - mmo.seek(offset) - yield mmo - - file.seek(padded_size, os.SEEK_CUR) - else: - yield file.read(padded_size)[:size] - offset += padded_size - -def handle_jsonl(jsonl_reader, get_meta, autojoin_paragraphs, para_joiner, key='text'): - for ob in jsonl_reader: - # naive jsonl where each object is just the string itself, with no meta. For legacy compatibility. - if isinstance(ob, str): - assert not get_meta - yield ob - continue - - if isinstance(key, str): - text = ob.get(key) - if autojoin_paragraphs and isinstance(text, list): - text = para_joiner.join(text) - - if get_meta: - yield text, (ob['meta'] if 'meta' in ob else {}) - else: - yield text - elif isinstance(key, list): - - task = ob.get(key[0], '') - src_language = ob.get(key[1], '') - src_code = ob.get(key[2], '') - tgt_language = ob.get(key[3], '') - tgt_code = ob.get(key[4], '') - sql = ob.get(key[5], '') - prompt_in = ob.get(key[6], '') - content_in = ob.get(key[7], '') - bad_content_in = ob.get(key[8], '') - - if task: - task = "task: " + task - if src_language: - src_language = "language: " + src_language - if sql: - sql = sql.strip() + '\n' - task = "task: text to sql\n" - src_language = 'language: text\n' - tgt_language = "language: sql\n" - prompt_in = prompt_in.strip() + '\n' - elif tgt_language: - tgt_language = "language: " + tgt_language - - prompt = task + src_language + src_code + prompt_in + tgt_language - content = tgt_code + sql + content_in - bad_content = bad_content_in - - yield (prompt, content, bad_content) - - -class Reader: - def __init__(self, in_path): - self.in_path = in_path - - def stream_data(self, get_meta=False, threaded=False, key=['prompt', 'content', 'bad_content']): - if not threaded: - yield from self._stream_data(get_meta, jsonl_key=key) - return - - q = mp.Queue(1000) - p = mp.Process(target=self._stream_data_threaded, args=(q, get_meta)) - p.start() - while p.is_alive(): - res = q.get() - if res is None: break - yield res - - def _stream_data_threaded(self, q, get_meta=False): - for data in self._stream_data(get_meta): - q.put(data) - q.put(None) - - def _stream_data(self, get_meta=False, jsonl_key="text"): - self.f_name = "" - files = listdir_or_file(self.in_path) - if not files: - raise FileNotFoundError(f"No valid file(s) found in {self.in_path}") - for f in files: - self.f_name = f - if f == 'openwebtext.tar.xz': - assert not get_meta - - yield from self.read_owt(f) - elif 'urlsf_subset' in f and f.endswith('_data.xz'): - assert not get_meta - - yield from self.read_owt_subset(f) - elif f.endswith('.dat.zst'): - assert not get_meta - - yield from self.read_dat(f) - elif f.endswith('.jsonl'): - yield from self.read_jsonl(f, get_meta, key=jsonl_key) - elif f.endswith('.jsonl.zst'): - yield from self.read_jsonl_zst(f, get_meta, key=jsonl_key) - elif f.endswith('.jsonl.zst.tar'): - yield from self.read_jsonl_tar(f, get_meta, jsonl_key=key) - elif f.endswith('.json.zst'): - assert not get_meta - - yield from self.read_json(f) - elif f.endswith('.txt'): - assert not get_meta - - yield from self.read_txt(f) - elif f.endswith('.zip'): - assert not get_meta - - yield from self.read_zip(f) - elif f.endswith('.tar.gz'): - assert not get_meta - - yield from self.read_tgz(f) - elif f.endswith('.json.gz'): - assert not get_meta - - yield from self.read_jsongz(f) - elif f.endswith('.gz'): - assert not get_meta - - yield from self.read_gz(f) - else: - # shouldn't be reached - print(f'Skipping {f} as streaming for that filetype is not implemented') - - def read_txt(self, file): - with open(file, 'r') as fh: - yield fh.read() - - def read_zip(self, file): - archive = ZipFile(file, 'r') - for f in archive.namelist(): - yield archive.read(f).decode('UTF-8') - - def read_tgz(self, file): - gz = gzip.open(file) - yield from (x.decode('utf-8') for x in tarfile_reader(gz, streaming=False)) - - def read_gz(self, file): - with gzip.open(file, 'rb') as f: - for line in f: - yield line.decode('utf-8') - - def read_jsongz(self, file): - for line in self.read_gz(file): - yield json.loads(line) - - def read_json(self, file): - with open(file, 'rb') as fh: - cctx = zstandard.ZstdDecompressor() - reader = cctx.stream_reader(fh) - ob = json.load(reader) - yield from ob - - def read_dat(self, file): - with open(file, 'rb') as fh: - cctx = zstandard.ZstdDecompressor() - reader = cctx.stream_reader(fh) - while True: - ln = reader.read(16).decode('UTF-8') - if not ln: - break - - ln = int(ln) - - yield reader.read(ln).decode('UTF-8') - - def read_jsonl(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n', key='text'): - with jsonlines.open(file) as rdr: - yield from handle_jsonl(rdr, get_meta, autojoin_paragraphs, para_joiner, key) - - def read_jsonl_zst(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n', key='text'): - with open(file, 'rb') as fh: - cctx = zstandard.ZstdDecompressor() - reader = io.BufferedReader(cctx.stream_reader(fh)) - rdr = jsonlines.Reader(reader) - yield from handle_jsonl(rdr, get_meta, autojoin_paragraphs, para_joiner, key) - - def read_jsonl_tar(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n', key='text'): - with open(file, 'rb') as fh: - for f in tarfile_reader(fh, streaming=True): - cctx = zstandard.ZstdDecompressor() - reader = io.BufferedReader(cctx.stream_reader(f)) - rdr = jsonlines.Reader(reader) - yield from handle_jsonl(rdr, get_meta, autojoin_paragraphs, para_joiner, key) - f.close() - - def read_owt(self, file): - tar = tarfile.open(file, encoding='utf-8') - utf8reader = codecs.getreader('utf-8') - - for name in tar.getmembers(): - fp = tar.extractfile(name) - inner_tar = tarfile.open(fileobj=fp, encoding='utf-8') - for inner_name in inner_tar.getmembers(): - inner_fp = utf8reader(inner_tar.extractfile(inner_name)) - contents = inner_fp.read() - yield contents - - def read_owt_subset(self, file): - utf8reader = codecs.getreader('utf-8') - tar = tarfile.open(file, encoding='utf-8') - for name in tar.getmembers(): - fp = utf8reader(tar.extractfile(name)) - contents = fp.read() - yield contents - - -class Archive: - def __init__(self, out_dir, compression_level=3, threads=8): - self.out_dir = out_dir - os.makedirs(out_dir, exist_ok=True) - self.i = 0 - - self.fh = open(self.out_dir + '/current_chunk_incomplete', 'wb') - self.cctx = zstandard.ZstdCompressor(level=compression_level, threads=threads) - self.compressor = self.cctx.stream_writer(self.fh) - - - def add_data(self, data, meta={}): - self.compressor.write(json.dumps({'text': data, 'meta': meta}).encode('UTF-8') + b'\n') - - def commit(self, archive_name='default'): - fname = self.out_dir + '/data_' + str(self.i) + '_time' + str(int(time.time())) + '_' + archive_name + '.jsonl.zst' - self.compressor.flush(zstandard.FLUSH_FRAME) - - self.fh.flush() - self.fh.close() - os.rename(self.out_dir + '/current_chunk_incomplete', fname) - self.fh = open(self.out_dir + '/current_chunk_incomplete', 'wb') - self.compressor = self.cctx.stream_writer(self.fh) - - self.i += 1 - - -class DatArchive: - def __init__(self, out_dir): - self.out_dir = out_dir - os.makedirs(out_dir, exist_ok=True) - self.data = [] - self.i = 0 - if os.path.exists(out_dir) and len(os.listdir(out_dir)) > 0: - self.i = max(map(lambda x: int(x.split('_')[1].split('.')[0]), os.listdir(out_dir))) + 1 - - def add_data(self, data): - self.data.append(data) - - def commit(self, archive_name=None): - # TODO: streaming - cctx = zstandard.ZstdCompressor(level=3) - - if archive_name is None: - archive_name = str(int(time.time())) - - res = b''.join(map(lambda x: ("%016d" % len(x)).encode('UTF-8') + x, map(lambda x: x.encode('UTF-8'), self.data))) - cdata = cctx.compress(res) - - with open(self.out_dir + '/data_' + str(self.i) + '_' + archive_name + '.dat.zst', 'wb') as fh: - fh.write(cdata) - - self.i += 1 - self.data = [] - -class JSONArchive: - def __init__(self, out_dir): - self.out_dir = out_dir - os.makedirs(out_dir, exist_ok=True) - self.data = [] - self.i = 0 - if os.path.exists(out_dir) and len(os.listdir(out_dir)) > 0: - self.i = max(map(lambda x: int(x.split('_')[1].split('.')[0]), os.listdir(out_dir))) + 1 - - def add_data(self, data): - self.data.append(data) - - def commit(self): - cctx = zstandard.ZstdCompressor(level=3) - - cdata = cctx.compress(json.dumps(self.data).encode('UTF-8')) - with open(self.out_dir + '/data_' + str(self.i) + '_' + str(int(time.time())) + '.json.zst', 'wb') as fh: - fh.write(cdata) - - self.i += 1 - self.data = [] \ No newline at end of file diff --git a/mftcoder_atorch/data/tokenization/preprocess_data.py b/mftcoder_atorch/data/tokenization/preprocess_data.py deleted file mode 100644 index 79a9dd7..0000000 --- a/mftcoder_atorch/data/tokenization/preprocess_data.py +++ /dev/null @@ -1,327 +0,0 @@ -"""Processing data for pretraining.""" - -import argparse -import multiprocessing -import os -import sys -import numpy as np -import random -# sys.path.append( -# os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir)) -# ) - -# 将父目录的父目录加入path -current_path = os.path.abspath(__file__) -parent_dir = os.path.dirname(os.path.dirname(current_path)) -grandparent_dir = os.path.dirname(parent_dir) -sys.path.append(grandparent_dir) -# print(grandparent_dir) - -import data.tokenization.lm_dataformat as lmd - -import time -import tqdm -import torch -import ftfy -import glob - -from tokenizer import build_tokenizer -from threading import Semaphore - - - - -table = {ord(f):ord(t) for f,t in zip( - u'，。！？：【】（）％＃＠＆１２３４５６７８９０', - u',.!?:[]()%#@&1234567890')} - - -def punctuation_format(text: str): - # Replace non-breaking space with space - # text = text.strip() + '\n' - text = text.replace('\u202f', ' ').replace('\xa0', ' ') - # change chinese punctuation to english ones - text = text.translate(table) - return text - -def is_prompt_answer_format(data): - - if "prompt" in data and "answer" in data: - return True - else: - return False - - -def is_chatml_format(data): - if "chat_rounds" in data and len(data["chat_rounds"]) > 0: - return True - else: - return False - - -def is_text_format(data): - if "text" in data: - return True - else: - return False - -class Encoder(object): - def __init__(self, args, tokenizer=None): - self.args = args - self.tokenizer = tokenizer - - def initializer(self): - # Use Encoder class as a container for global data - if self.tokenizer is None: - self.tokenizer = build_tokenizer(self.args) - else: - self.tokenizer = self.tokenizer - - def encode(self, text): - if self.args.ftfy: - text = ftfy.fix_text(text) - ids = {} - for key in self.args.jsonl_keys: - doc_ids = [] - text_ids = self.tokenizer.encode(text, add_special_tokens=False) - if len(text_ids) > 0: - doc_ids.append(text_ids) - if self.args.append_eod: - doc_ids[-1].append(self.tokenizer.eod_id) - ids[key] = doc_ids - return ids, len(text) - - -class UniformEncoder(Encoder): - def __init__(self, args, mode='sft', tokenizer=None): - super().__init__(args, tokenizer=tokenizer) - self.mode = mode - # 实际计算时会Shift一位，因此这里seq_length + 1 - if args.load_raw_dataset: - self.seq_length = args.seq_length + 1 - self.stride = args.seq_length - else: - self.seq_length = args.seq_length - - self.remain_input_ids = [] - self.remain_loss_mask = [] - - def encode(self, data): - - encode_res = { - "input_ids":[], - "loss_mask":[] - } - - if is_prompt_answer_format(data): - data_type = 'prompt_answer' - elif is_chatml_format(data): - data_type = 'chatML' - elif is_text_format(data): - data_type = 'text' - else: - raise ValueError("data format not supported, please use prompt/answer, or chatML or pretrain text") - - for token_res in self._tokenize_fields(data, data_type=data_type): - for k, v in token_res.items(): - encode_res[k].append(v) - - length = 0 - if data_type == 'prompt_answer': - length = len(data['prompt']) + len(data['answer']) - elif data_type == 'chatML': - for chat in data['chat_rounds']: - length += len(chat['content']) - elif data_type == 'text': - length += len(data['text']) - - return encode_res, length - - - def _tokenize_fields(self, data, data_type): - - CHAT_COL = 'chat_rounds' - ROLE_COL = 'role' - CONTENT_COL = 'content' - - PROMPT_COL = 'prompt' - ANSWER_COL = 'answer' - SYSTEM_COL = 'system' - - TEXT_COL = 'text' - - if self.mode == 'sft': - HUMAN = 'human' - BOT = 'bot' - SYSTEM = 'system' - ROLE_START_MARKER = '<|role_start|>' - ROLE_END_MARKER = '<|role_end|>' - elif self.mode == 'pretrain' or data_type == 'text': - HUMAN = '' - BOT = '' - SYSTEM = '' - ROLE_START_MARKER = '' - ROLE_END_MARKER = '' - else: - raise ValueError(f"tokenize_mode does not support {self.mode}, please use sft or pretrain") - - - human_marker_ids = self.tokenizer.encode(f"{ROLE_START_MARKER}{HUMAN}{ROLE_END_MARKER}", add_special_tokens=False) - bot_marker_ids = self.tokenizer.encode(f"{ROLE_START_MARKER}{BOT}{ROLE_END_MARKER}", add_special_tokens=False) - system_marker_ids = self.tokenizer.encode(f"{ROLE_START_MARKER}{SYSTEM}{ROLE_END_MARKER}", add_special_tokens=False) - sft_end_marker_ids = [self.tokenizer.eod_id] - - # 处理逻辑： - # 统一处理SST,单轮、多轮sft的需求 - - input_ids = [] - loss_mask = [] - - if data_type == "prompt_answer": - system = data.get(SYSTEM_COL, '') - prompt = data[PROMPT_COL] - answer = data[ANSWER_COL] - system = punctuation_format(system) - prompt = punctuation_format(prompt) - answer = punctuation_format(answer) - system_ids = system_marker_ids + self.tokenizer.encode(system, add_special_tokens=False) if system else [] - prompt_ids = self.tokenizer.encode(prompt, add_special_tokens=False) - answer_ids = self.tokenizer.encode(answer, add_special_tokens=False) + sft_end_marker_ids - input_ids += system_ids + human_marker_ids + prompt_ids + bot_marker_ids + answer_ids - loss_mask += [0] * len(system_ids) + [0] * len(human_marker_ids) + [0] * len(prompt_ids) + \ - [0] * len(bot_marker_ids) + [1] * len(answer_ids) - elif data_type == 'chatML': - chat = data[CHAT_COL] - for r in chat: - role = r[ROLE_COL] - content = r[CONTENT_COL] - content = punctuation_format(content) - # if not content.endswith('\n'): # chatML格式 - # content = content + '\n' - if role == HUMAN: - role_marker_ids = human_marker_ids - content_ids = self.tokenizer.encode(content, add_special_tokens=False) - elif role == BOT: - # 每一个bot输出结尾的eod,计算loss, 学会在哪里停， human和system的eod不需要计算loss - role_marker_ids = bot_marker_ids - content_ids = self.tokenizer.encode(content, add_special_tokens=False) + sft_end_marker_ids - elif role == SYSTEM: - role_marker_ids = system_marker_ids - content_ids = self.tokenizer.encode(content, add_special_tokens=False) - else: - raise ValueError(f"Role {role} not supported.") - - input_ids += role_marker_ids + content_ids - masklet = [1] if role == BOT else [0] - loss_mask += [0] * len(role_marker_ids) + masklet * len(content_ids) - elif data_type == "text": - text = data[TEXT_COL] - text = punctuation_format(text) - text_ids = self.tokenizer.encode(text, add_special_tokens=False) + sft_end_marker_ids - input_ids += text_ids - loss_mask += [1] * len(text_ids) - else: - raise ValueError( - f"data_type does not support {self.args.data_type}, please use chatML or prompt_answer or text(for pretrain)") - - # print(self.mode) - if self.mode == 'pretrain': - # change loss mask to all 1s - input_ids = input_ids - loss_mask = [1] * len(loss_mask) - elif self.mode == 'sft': - # do nothing - input_ids = input_ids - loss_mask = loss_mask - - assert len(input_ids) == len(loss_mask) - if self.args.padding_mode == 'padding': - if len(input_ids) <= self.seq_length: - yield self.padding(input_ids, loss_mask) - - # 如果超长，直接抛弃 or 使用seq_length窗口滑动采样 - else: - # cursor = 0 - # while cursor < len(input_ids): - # end_idx = min(cursor + self.seq_length, len(input_ids)) - # yield self.padding(input_ids[cursor: end_idx], loss_mask[cursor: end_idx]) - # cursor = end_idx - yield {} - elif self.args.padding_mode == 'concat': - input_ids = self.remain_input_ids + input_ids - loss_mask = self.remain_loss_mask + loss_mask - if len(input_ids) < self.seq_length: - self.remain_input_ids = input_ids - self.remain_loss_mask = loss_mask - assert len(self.remain_input_ids) == len(self.remain_loss_mask) - yield {} - else: - cursor = 0 - while cursor + self.seq_length <= len(input_ids): - yield { - "input_ids": input_ids[cursor: cursor + self.seq_length], - "loss_mask": loss_mask[cursor: cursor + self.seq_length] - } - cursor = cursor + self.stride - self.remain_input_ids = input_ids[cursor:] - self.remain_loss_mask = loss_mask[cursor:] - assert len(self.remain_input_ids) == len(self.remain_loss_mask) - yield {} - elif self.args.padding_mode == 'pack': - if len(input_ids) > self.seq_length: - yield {} - elif len(self.remain_input_ids) + len(input_ids) > self.seq_length: - input_ids, self.remain_input_ids = self.remain_input_ids, input_ids - loss_mask, self.remain_loss_mask = self.remain_loss_mask, loss_mask - assert len(input_ids) == len(loss_mask) - yield self.padding(input_ids, loss_mask) - else: - self.remain_input_ids = self.remain_input_ids + input_ids - self.remain_loss_mask = self.remain_loss_mask + loss_mask - assert len(self.remain_input_ids) == len(self.remain_loss_mask) - yield {} - - - def padding(self, input_ids, loss_mask): - pad_id = self.tokenizer.pad_id - assert len(input_ids) <= self.seq_length, f"padding sequence: {len(input_ids)} > {self.seq_length}" - input_ids += [pad_id] * (self.seq_length - len(input_ids)) - loss_mask += [0] * (self.seq_length - len(loss_mask)) - return { - "input_ids": input_ids, - "loss_mask": loss_mask - } - -# 输入为args.input, 使用","进行分割，每一个itme可以是jsonl或文件夹 -def find_jsonl_fnames(inputs): - fnames = [] - for p in inputs.split(","): - if not os.path.isdir(p): - if p.endswith(".jsonl"): - print(f"loading from {p}") - fnames.append(p) - else: - p_list = glob.glob(p + "/*") - for p_ in p_list: - if p_.endswith(".jsonl"): - print(f"loading from {p_}") - fnames.append(p_) - return fnames - -def yield_from_files(fnames: list, semaphore): - """ - Iterator over input documents using lm_dataformat. Should be able to handle jsons / texts / - other compressed formats. Also filters out empty documents. - - :param fnames: list of filenames - """ - - def yielder(fname, semaphore): - for f in filter(lambda x: x, lmd.Reader(fname).stream_data(key=['task', 'src_language', 'src_code', 'tgt_language', 'tgt_code', 'sql', 'prompt', 'answer', 'bad_answer'])): - semaphore.acquire() - yield f - - for fname in fnames: - semaphore.acquire() - - yield from yielder(fname, semaphore) \ No newline at end of file diff --git a/mftcoder_atorch/model/__init__.py b/mftcoder_atorch/model/__init__.py deleted file mode 100755 index acaafbd..0000000 --- a/mftcoder_atorch/model/__init__.py +++ /dev/null @@ -1,20 +0,0 @@ -# -# Copyright 2021 Biderman et al. This file is based on code by the authors denoted below and has been modified from its original version. -# -# Copyright (c) 2020, NVIDIA CORPORATION. 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. - -# from .gpt2_model import GPT2ModelPipe -# from .utils import get_params_for_weight_decay_optimization -# from .word_embeddings import SoftEmbedding diff --git a/mftcoder_atorch/model/build_model.py b/mftcoder_atorch/model/build_model.py deleted file mode 100644 index 067d306..0000000 --- a/mftcoder_atorch/model/build_model.py +++ /dev/null @@ -1,153 +0,0 @@ -import os -import torch -import sys -sys.path.append("..") -from utils.common_utils import get_model_params_num -from transformers import ( # noqa: E402 - CONFIG_MAPPING, - AutoConfig, - AutoModelForCausalLM, - AutoTokenizer, - PreTrainedTokenizerFast -) -from .gpt_neox.configuration_gpt_neox import GPTNeoXConfig -from .gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM -from .gpt_neox.tokenization_gpt_neox_fast import GPTNeoXTokenizerFast - -from torch.distributed.fsdp import ( - FullyShardedDataParallel as FSDP, - StateDictType, -) -from utils.common_utils import print_rank_0, is_old_version -from tokenizer import build_tokenizer -from tokenizer.tokenizer import HFTokenizer - -import peft -from peft.tuners.lora import LoraLayer -from model.peft.utils import prepare_model_for_kbit_training -from peft import ( # noqa - LoraConfig, - PrefixTuningConfig, - PromptEncoderConfig, - PromptEncoderReparameterizationType, - PromptTuningConfig, - PromptTuningInit, - TaskType, - get_peft_model -) -import model.peft.modeling_peft # noqa -from model.peft.tuner import AdaLoraConfig - -try: - from transformers import BitsAndBytesConfig -except ImportError: - BitsAndBytesConfig = None -try: - import bitsandbytes as bnb # noqa -except ImportError: - bnb = None -from packaging import version - - -def find_all_linear_names(args, model): - cls = bnb.nn.Linear4bit if args.bits == 4 else (bnb.nn.Linear8bitLt if args.bits == 8 else torch.nn.Linear) - lora_module_names = set() - for name, module in model.named_modules(): - if isinstance(module, cls): - names = name.split('.') - lora_module_names.add(names[0] if len(names) == 1 else names[-1]) - if 'lm_head' in lora_module_names: # needed for 16-bit - lora_module_names.remove('lm_head') - return list(lora_module_names) - - -def setup_model(args, logger, use_cache=False): - # Load pretrained model and tokenizer - - if args.pretrained_model_path: - if args.model_type == 'gpt_neox': - tokenizer = GPTNeoXTokenizerFast.from_pretrained(args.pretrained_model_path) - tokenizer.eod_token = "<|endoftext|>" - tokenizer.pad_token = "<|pad|>" - tokenizer.sop_token = "<|endoftext|>" - tokenizer.eop_token = "<|endoftext|>" - tokenizer.eod_id = tokenizer.convert_tokens_to_ids(tokenizer.eod_token) - tokenizer.pad_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token) - - print_rank_0(f'tokenizer {tokenizer.eod_token} id: {tokenizer.eod_id}') - print_rank_0(f'tokenizer {tokenizer.pad_token} id: {tokenizer.pad_id}') - else: - raise ValueError( - "You are instantiating a new tokenizer from scratch. This is not supported by this script." - "You can do it from another script, save it, and load it from here, using --tokenizer_path." - ) - - if args.model_type == 'gpt_neox': - auto_config = GPTNeoXConfig - auto_model_class = GPTNeoXForCausalLM - else: - auto_config = AutoConfig - auto_model_class = AutoModelForCausalLM - - # with init_empty_weights_with_disk_offload(ignore_tie_weights=False): - if args.pretrained_model_path: - logger.info("Training model from checkpoint") - config = auto_config.from_pretrained(args.pretrained_model_path) - if args.peft_type != "qlora": - model = auto_model_class.from_pretrained(args.pretrained_model_path, trust_remote_code=True).cuda() - # TODO: qlora - else: - logger.info("Training model from scratch") - if args.model_type == 'gpt_neox': - config = GPTNeoXConfig.from_json_file(args.config_path + '/config.json') - model = GPTNeoXForCausalLM._from_config(config) - else: - config = AutoConfig.from_json_file(args.config_path + '/config.json') - model = AutoModelForCausalLM.from_config(config, trust_remote_code=args.trust_remote_code) - - # We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch - # on a small vocab and want a smaller embedding size, remove this test. - embedding_size = model.get_input_embeddings().weight.shape[0] - print_rank_0('embedding size: ' + str(embedding_size)) - print_rank_0('vocab size: ' + str(tokenizer.vocab_size)) - if tokenizer.vocab_size > embedding_size: - model.resize_token_embeddings(tokenizer.vocab_size) - print_rank_0('resize embedding size: ' + str(model.get_input_embeddings().weight.shape[0])) - - print_rank_0(config) - num_params = get_model_params_num(model) - print_rank_0("num_params of this model:", num_params) - args.total_model_param = num_params - args.hidden_size = config.hidden_size - args.num_hidden_layers = config.num_hidden_layers - args.vocab_size = tokenizer.vocab_size - print_rank_0(f'hidden size: {args.hidden_size}') - print_rank_0(f'num hidden layers: {args.num_hidden_layers}') - print_rank_0(f'vocab size: {args.vocab_size}') - - if args.peft_type: - if args.peft_type in ['lora', 'qlora']: - target_modules = None - # TODO: qlora - target_modules = ["query_key_value", "dense", "dense_h_to_4h", "dense_4h_to_h"] - print_rank_0(f'target modules: {target_modules}') - peft_config = LoraConfig( - task_type=TaskType.ANT_CAUSAL_LM, - inference_mode=False, - r=96, - lora_alpha=32, - lora_dropout=0.05, - target_modules=target_modules, - ) - logger.info( - f"Load Peft {args.peft_type} model ......") - if args.checkpoint_activations and args.peft_type in ["lora", "qlora"]: - # Make Lora and gradient checkpointing compatible - # https://github.com/huggingface/peft/issues/137 - model.enable_input_require_grads() - model = get_peft_model(model, peft_config) - logger.info( - f"Reduce trainalbe params:\n") - model.print_trainable_parameters() - - return model, config, tokenizer diff --git a/mftcoder_atorch/model/gpt_neox/__init__.py b/mftcoder_atorch/model/gpt_neox/__init__.py deleted file mode 100644 index 12bf883..0000000 --- a/mftcoder_atorch/model/gpt_neox/__init__.py +++ /dev/null @@ -1,76 +0,0 @@ -# Copyright 2022 The HuggingFace Team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -from typing import TYPE_CHECKING - -from transformers.file_utils import _LazyModule, is_tokenizers_available, is_torch_available -from transformers.utils import OptionalDependencyNotAvailable -# from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available -# from ...utils import OptionalDependencyNotAvailable - - -_import_structure = {"configuration_gpt_neox": ["GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoXConfig"]} - -try: - if not is_tokenizers_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["tokenization_gpt_neox_fast"] = ["GPTNeoXTokenizerFast"] - -try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() -except OptionalDependencyNotAvailable: - pass -else: - _import_structure["modeling_gpt_neox"] = [ - "GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST", - "GPTNeoXForCausalLM", - "GPTNeoXLayer", - "GPTNeoXModel", - "GPTNeoXPreTrainedModel", - ] - - -if TYPE_CHECKING: - from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig - - try: - if not is_tokenizers_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast - - try: - if not is_torch_available(): - raise OptionalDependencyNotAvailable() - except OptionalDependencyNotAvailable: - pass - else: - from .modeling_gpt_neox import ( - GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST, - GPTNeoXForCausalLM, - GPTNeoXLayer, - GPTNeoXModel, - GPTNeoXPreTrainedModel, - ) - - -else: - import sys - - sys.modules[__name__] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) \ No newline at end of file diff --git a/mftcoder_atorch/model/gpt_neox/config.json b/mftcoder_atorch/model/gpt_neox/config.json deleted file mode 100644 index b691b2c..0000000 --- a/mftcoder_atorch/model/gpt_neox/config.json +++ /dev/null @@ -1,24 +0,0 @@ -{ - "architectures": [ - "GPTNeoXForCausalLM" - ], - "bos_token_id": 100256, - "eos_token_id": 100256, - "hidden_act": "gelu", - "hidden_size": 768, - "initializer_range": 0.02, - "intermediate_size": 3072, - "layer_norm_eps": 1e-05, - "max_position_embeddings": 4096, - "model_type": "gpt_neox", - "num_attention_heads": 12, - "num_hidden_layers": 12, - "rotary_emb_base": 10000, - "rotary_pct": 1.0, - "tie_word_embeddings": false, - "torch_dtype": "float16", - "transformers_version": "4.26.1", - "use_cache": true, - "use_parallel_residual": true, - "vocab_size": 100864 -} diff --git a/mftcoder_atorch/model/gpt_neox/configuration_gpt_neox.py b/mftcoder_atorch/model/gpt_neox/configuration_gpt_neox.py deleted file mode 100644 index 60f8525..0000000 --- a/mftcoder_atorch/model/gpt_neox/configuration_gpt_neox.py +++ /dev/null @@ -1,122 +0,0 @@ -# coding=utf-8 -# Copyright 2022 EleutherAI and The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" GPTNeoX model configuration""" - -from transformers.configuration_utils import PretrainedConfig -from transformers.utils import logging - - -logger = logging.get_logger(__name__) - -GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP = { - "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json", - # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox -} - - -class GPTNeoXConfig(PretrainedConfig): - r""" - This is the configuration class to store the configuration of a [`GPTNeoXModel`]. It is used to instantiate an - GPTNeoX 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 GPTNeoX - [EleutherAI/gpt-neox-20b](https://huggingface.co/EleutherAI/gpt-neox-20b) architecture. - - 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 50432): - Vocabulary size of the GPTNeoX model. Defines the number of different tokens that can be represented by the - `inputs_ids` passed when calling [`GPTNeoXModel`]. - hidden_size (`int`, *optional*, defaults to 6144): - Dimension of the encoder layers and the pooler layer. - num_hidden_layers (`int`, *optional*, defaults to 44): - Number of hidden layers in the Transformer encoder. - num_attention_heads (`int`, *optional*, defaults to 64): - Number of attention heads for each attention layer in the Transformer encoder. - intermediate_size (`int`, *optional*, defaults to 24576): - Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder. - hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`): - The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, - `"relu"`, `"selu"` and `"gelu_new"` are supported. - rotary_pct (`float`, *optional*, defaults to 0.25): - percentage of hidden dimensions to allocate to rotary embeddings - rotary_emb_base (`int`, *optional*, defaults to 10000) - base for computing rotary embeddings frequency - 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 1e-5): - The standard deviation of the truncated_normal_initializer for initializing all weight matrices. - layer_norm_eps (`float`, *optional*, defaults to 1e-12): - The epsilon used by the layer 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`. - use_parallel_residual (`bool`, *optional*, defaults to `True`): - Whether to use a "parallel" formulation in each Transformer layer, which can provide a slight training - speedup at large scales (e.g. 20B). - Example: - - ```python - >>> from transformers import GPTNeoXConfig, GPTNeoXModel - - >>> # Initializing a GPTNeoX gpt-neox-20b style configuration - >>> configuration = GPTNeoXConfig() - - >>> # Initializing a model (with random weights) from the gpt-neox-20b style configuration - >>> model = GPTNeoXModel(configuration) # doctest: +SKIP - - >>> # Accessing the model configuration - >>> configuration = model.config # doctest: +SKIP - ```""" - model_type = "gpt_neox" - - def __init__( - self, - vocab_size=50432, - hidden_size=6144, - num_hidden_layers=44, - num_attention_heads=64, - intermediate_size=24576, - hidden_act="gelu", - rotary_pct=0.25, - rotary_emb_base=10000, - max_position_embeddings=2048, - initializer_range=0.02, - layer_norm_eps=1e-5, - use_cache=True, - bos_token_id=0, - eos_token_id=2, - tie_word_embeddings=False, - use_parallel_residual=True, - **kwargs, - ): - super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) - self.vocab_size = vocab_size - self.max_position_embeddings = max_position_embeddings - self.hidden_size = hidden_size - self.num_hidden_layers = num_hidden_layers - self.num_attention_heads = num_attention_heads - self.intermediate_size = intermediate_size - self.hidden_act = hidden_act - self.rotary_pct = rotary_pct - self.rotary_emb_base = rotary_emb_base - self.initializer_range = initializer_range - self.layer_norm_eps = layer_norm_eps - self.use_cache = use_cache - self.tie_word_embeddings = tie_word_embeddings - self.use_parallel_residual = use_parallel_residual \ No newline at end of file diff --git a/mftcoder_atorch/model/gpt_neox/generation_config.json b/mftcoder_atorch/model/gpt_neox/generation_config.json deleted file mode 100644 index 3dc481e..0000000 --- a/mftcoder_atorch/model/gpt_neox/generation_config.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "bos_token_id": 50256, - "eos_token_id": 50256, - "transformers_version": "4.26.0.dev0", - "_from_model_config": true -} diff --git a/mftcoder_atorch/model/gpt_neox/modeling_gpt_neox.py b/mftcoder_atorch/model/gpt_neox/modeling_gpt_neox.py deleted file mode 100644 index 78ca1d7..0000000 --- a/mftcoder_atorch/model/gpt_neox/modeling_gpt_neox.py +++ /dev/null @@ -1,753 +0,0 @@ -# coding=utf-8 -# Copyright 2022 EleutherAI The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -""" PyTorch GPTNeoX model.""" - -from typing import Optional, Tuple, Union - -import torch -import torch.utils.checkpoint -from torch import nn -from torch.nn import CrossEntropyLoss -import torch.nn.functional as F - -from transformers.activations import ACT2FN -from transformers.file_utils import ( - add_code_sample_docstrings, - add_start_docstrings, - add_start_docstrings_to_model_forward, - replace_return_docstrings, -) -from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast -from transformers.modeling_utils import PreTrainedModel -from transformers.utils import logging -from .configuration_gpt_neox import GPTNeoXConfig - -from utils.common_utils import print_rank_0 - -# import xformers.ops - -def check_fa2(): - import flash_attn - return hasattr(flash_attn, '__version__') - - -logger = logging.get_logger(__name__) - -_CHECKPOINT_FOR_DOC = "trl-internal-testing/tiny-random-GPTNeoXForCausalLM" -_REAL_CHECKPOINT_FOR_DOC = "EleutherAI/gpt-neox-20b" -_CONFIG_FOR_DOC = "GPTNeoXConfig" - -GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST = [ - "EleutherAI/gpt-neox-20b", - # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox -] - - -class GPTNeoXPreTrainedModel(PreTrainedModel): - """ - An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained - models. - """ - - config_class = GPTNeoXConfig - base_model_prefix = "gpt_neox" - supports_gradient_checkpointing = True - _no_split_modules = ["GPTNeoXLayer"] - - def _init_weights(self, module): - """Initialize the weights""" - if isinstance(module, nn.Linear): - module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) - if module.bias is not None: - module.bias.data.zero_() - elif isinstance(module, nn.Embedding): - module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) - if module.padding_idx is not None: - module.weight.data[module.padding_idx].zero_() - elif isinstance(module, nn.LayerNorm): - module.bias.data.zero_() - module.weight.data.fill_(1.0) - - def _set_gradient_checkpointing(self, module, value=False): - if isinstance(module, GPTNeoXModel): - module.gradient_checkpointing = value - - -class GPTNeoXAttention(nn.Module): - def __init__(self, config): - super().__init__() - self.num_attention_heads = config.num_attention_heads - self.hidden_size = config.hidden_size - self.head_size = self.hidden_size // self.num_attention_heads - self.rotary_ndims = int(self.head_size * config.rotary_pct) - max_positions = config.max_position_embeddings - self.register_buffer( - "bias", - torch.tril(torch.ones((max_positions, max_positions), dtype=torch.bool)).view( - 1, 1, max_positions, max_positions - ), - ) - self.register_buffer("masked_bias", torch.tensor(-1e9)) - self.rotary_emb = RotaryEmbedding( - self.rotary_ndims, config.max_position_embeddings, base=config.rotary_emb_base - ) - self.norm_factor = torch.sqrt(torch.tensor(self.head_size, dtype=torch.float32)).to(torch.get_default_dtype()) - self.query_key_value = nn.Linear(config.hidden_size, 3 * config.hidden_size) - self.dense = nn.Linear(config.hidden_size, config.hidden_size) - - - # new flash attention - def forward( - self, - hidden_states, - attention_mask, - position_ids=None, - head_mask=None, - layer_past=None, - use_cache=False, - output_attentions=False, - ): - has_layer_past = layer_past is not None - - # Compute QKV - # Attention heads [batch, seq_len, hidden_size] - # --> [batch, seq_len, (np * 3 * head_size)] - qkv = self.query_key_value(hidden_states) - - # [batch, seq_len, (num_heads * 3 * head_size)] - # --> [batch, seq_len, num_heads, 3 * head_size] - new_qkv_shape = qkv.size()[:-1] + (self.num_attention_heads, 3 * self.head_size) - qkv = qkv.view(*new_qkv_shape) - - # [batch, seq_len, num_attention_heads, 3 * head_size] --> 3 [batch, num_attention_heads, seq_len, head_size] - query = qkv[..., : self.head_size].permute(0, 2, 1, 3) - key = qkv[..., self.head_size : 2 * self.head_size].permute(0, 2, 1, 3) - value = qkv[..., 2 * self.head_size :].permute(0, 2, 1, 3) - - # Compute rotary embeddings on rotary_ndims - query_rot = query[..., : self.rotary_ndims] - query_pass = query[..., self.rotary_ndims :] - key_rot = key[..., : self.rotary_ndims] - key_pass = key[..., self.rotary_ndims :] - - # Compute token offset for rotary embeddings (when decoding) - seq_len = key.shape[-2] - offset = 0 - if has_layer_past: - offset = layer_past[0].shape[-2] - seq_len += offset - cos, sin = self.rotary_emb(value, seq_len=seq_len) - query, key = apply_rotary_pos_emb(query_rot, key_rot, cos, sin, offset=offset) - query = torch.cat((query, query_pass), dim=-1) - key = torch.cat((key, key_pass), dim=-1) - - # Cache QKV values - if has_layer_past: - past_key = layer_past[0] - past_value = layer_past[1] - key = torch.cat((past_key, key), dim=-2) - value = torch.cat((past_value, value), dim=-2) - present = (key, value) if use_cache else None - if query.dtype != value.dtype: - query = query.to(value.dtype) - key = key.to(value.dtype) - - query = query.permute(0, 2, 1, 3) - key = key.permute(0, 2, 1, 3) - value = value.permute(0, 2, 1, 3) - dropout_p = 0.0 - softmax_scale = 1.0 / (value.size(-1) ** 0.5) - if check_fa2(): - from flash_attn import flash_attn_func, flash_attn_qkvpacked_func - attn_output = flash_attn_func(query, key, value, dropout_p=dropout_p, softmax_scale=softmax_scale, causal=True) - # qkv = torch.cat([query.unsqueeze(2), key.unsqueeze(2), value.unsqueeze(2)], 2) - # attn_output = flash_attn_qkvpacked_func(qkv, dropout_p=dropout_p, softmax_scale=softmax_scale, causal=True) - - else: - from atorch.modules.transformer.layers import flash_attn_with_mask_bias - attn_output = flash_attn_with_mask_bias( - query, - key, - value, - mask=None, - dropout_p=dropout_p, - softmax_scale=softmax_scale, - causal=True, - ) - - # attn_output = xformers.ops.memory_efficient_attention(query, key, value, attn_bias=xformers.ops.LowerTriangularMask(), - # op=xformers.ops.MemoryEfficientAttentionFlashAttentionOp) - - attn_output = attn_output.contiguous().view( - attn_output.size(0), attn_output.size(1), self.num_attention_heads * self.head_size - ) - - # Compute attention - #attn_output, attn_weights = self._attn(query, key, value, attention_mask, head_mask) - - # Reshape outputs - #attn_output = self._merge_heads(attn_output, self.num_attention_heads, self.head_size) - attn_output = self.dense(attn_output) - - outputs = (attn_output, present) - if output_attentions: - outputs += (attn_weights,) - - return outputs - - @classmethod - def _split_heads(cls, tensor, num_attention_heads, attn_head_size): - """ - Splits hidden dim into attn_head_size and num_attention_heads - """ - # tensor: [bs, seq_len, hidden_size] - new_shape = tensor.size()[:-1] + (num_attention_heads, attn_head_size) - # -> [bs, seq_len, num_attention_heads, attn_head_size] - tensor = tensor.view(new_shape) - # -> [bs, num_attention_heads, seq_len, attn_head_size] - tensor = tensor.permute(0, 2, 1, 3) - return tensor - - @classmethod - def _merge_heads(cls, tensor, num_attention_heads, attn_head_size): - """ - Merges attn_head_size dim and num_attn_heads dim into hidden dim - """ - # tensor [bs, num_attention_heads, seq_len, attn_head_size] - tensor = tensor.permute(0, 2, 1, 3).contiguous() - # -> [bs, seq_len, num_attention_heads, attn_head_size] - tensor = tensor.view(tensor.size(0), tensor.size(1), num_attention_heads * attn_head_size) - # -> [bs, seq_len, hidden_size] - return tensor - - def _attn(self, query, key, value, attention_mask=None, head_mask=None): - # q, k, v: [bs, num_attention_heads, seq_len, attn_head_size] - # compute causal mask from causal mask buffer - batch_size, num_attention_heads, query_length, attn_head_size = query.size() - key_length = key.size(-2) - - causal_mask = self.bias[:, :, key_length - query_length : key_length, :key_length] - - query = query.view(batch_size * num_attention_heads, query_length, attn_head_size) - key = key.view(batch_size * num_attention_heads, key_length, attn_head_size) - attn_scores = torch.zeros( - batch_size * num_attention_heads, - query_length, - key_length, - dtype=query.dtype, - device=key.device, - ) - attn_scores = torch.baddbmm( - attn_scores, - query, - key.transpose(1, 2), - beta=1.0, - alpha=(torch.tensor(1.0, dtype=self.norm_factor.dtype, device=self.norm_factor.device) / self.norm_factor), - ) - attn_scores = attn_scores.view(batch_size, num_attention_heads, query_length, key_length) - - mask_value = torch.finfo(attn_scores.dtype).min - # Need to be a tensor, otherwise we get error: `RuntimeError: expected scalar type float but found double`. - # Need to be on the same device, otherwise `RuntimeError: ..., x and y to be on the same device` - mask_value = torch.tensor(mask_value, dtype=attn_scores.dtype).to(attn_scores.device) - attn_scores = torch.where(causal_mask, attn_scores, mask_value) - - if attention_mask is not None: - # Apply the attention mask - attn_scores = attn_scores + attention_mask - - attn_weights = nn.functional.softmax(attn_scores, dim=-1) - attn_weights = attn_weights.to(value.dtype) - - # Mask heads if we want to - if head_mask is not None: - attn_weights = attn_weights * head_mask - - attn_output = torch.matmul(attn_weights, value) - return attn_output, attn_weights - - -def attention_mask_func(attention_scores, ltor_mask): - attention_scores.masked_fill_(~ltor_mask, torch.finfo(attention_scores.dtype).min) - return attention_scores - - -class RotaryEmbedding(torch.nn.Module): - def __init__(self, dim, max_position_embeddings, 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.cos_cached = emb.cos()[None, None, :, :] - self.sin_cached = emb.sin()[None, None, :, :] - - 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.cos_cached = emb.cos()[None, None, :, :] - self.sin_cached = emb.sin()[None, None, :, :] - return self.cos_cached[:seq_len, ...].to(x.device), self.sin_cached[:seq_len, ...].to(x.device) - - -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, offset: int = 0): - cos = cos[..., offset : q.shape[-2] + offset, :] - sin = sin[..., offset : q.shape[-2] + offset, :] - q_embed = (q * cos) + (rotate_half(q) * sin) - k_embed = (k * cos) + (rotate_half(k) * sin) - return q_embed, k_embed - - -class GPTNeoXMLP(nn.Module): - def __init__(self, config): - super().__init__() - self.dense_h_to_4h = nn.Linear(config.hidden_size, config.intermediate_size) - self.dense_4h_to_h = nn.Linear(config.intermediate_size, config.hidden_size) - self.act = ACT2FN[config.hidden_act] - - def forward(self, hidden_states): - hidden_states = self.dense_h_to_4h(hidden_states) - hidden_states = self.act(hidden_states) - hidden_states = self.dense_4h_to_h(hidden_states) - return hidden_states - - -class GPTNeoXLayer(nn.Module): - def __init__(self, config): - super().__init__() - self.use_parallel_residual = config.use_parallel_residual - self.input_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.post_attention_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - self.attention = GPTNeoXAttention(config) - self.mlp = GPTNeoXMLP(config) - - def forward( - self, - hidden_states, - attention_mask=None, - position_ids=None, - head_mask=None, - use_cache=False, - layer_past=None, - output_attentions=False, - ): - attention_layer_outputs = self.attention( - self.input_layernorm(hidden_states), - attention_mask=attention_mask, - position_ids=position_ids, - layer_past=layer_past, - head_mask=head_mask, - use_cache=use_cache, - output_attentions=output_attentions, - ) - attn_output = attention_layer_outputs[0] # output_attn: attn_output, present, (attn_weights) - outputs = attention_layer_outputs[1:] - - if self.use_parallel_residual: - # pseudocode: - # x = x + attn(ln1(x)) + mlp(ln2(x)) - mlp_output = self.mlp(self.post_attention_layernorm(hidden_states)) - hidden_states = mlp_output + attn_output + hidden_states - else: - # pseudocode: - # x = x + attn(ln1(x)) - # x = x + mlp(ln2(x)) - attn_output = attn_output + hidden_states - mlp_output = self.mlp(self.post_attention_layernorm(attn_output)) - hidden_states = mlp_output + attn_output - - if use_cache: - outputs = (hidden_states,) + outputs # hidden_states, present, (attn_weights) - else: - outputs = (hidden_states,) + outputs[1:] # hidden_states, (attn_weights) - - return outputs - - -GPT_NEOX_START_DOCSTRING = r""" - This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use - it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and - behavior. - - Parameters: - config ([`~GPTNeoXConfig`]): 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. -""" - -GPT_NEOX_INPUTS_DOCSTRING = r""" - Args: - input_ids (`torch.LongTensor` of shape `({0})`): - Indices of input sequence tokens in the vocabulary. - - Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and - [`PreTrainedTokenizer.__call__`] for details. - - [What are input IDs?](../glossary#input-ids) - attention_mask (`torch.FloatTensor` of shape `({0})`, *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) - head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): - Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - - - 1 indicates the head is **not masked**, - - 0 indicates the head is **masked**. - - inputs_embeds (`torch.FloatTensor` of shape `({0}, 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. - 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 [`~file_utils.ModelOutput`] instead of a plain tuple. -""" - - -@add_start_docstrings( - "The bare GPTNeoX Model transformer outputting raw hidden-states without any specific head on top.", - GPT_NEOX_START_DOCSTRING, -) -class GPTNeoXModel(GPTNeoXPreTrainedModel): - def __init__(self, config): - super().__init__(config) - self.config = config - - self.embed_in = nn.Embedding(config.vocab_size, config.hidden_size) - self.layers = nn.ModuleList([GPTNeoXLayer(config) for _ in range(config.num_hidden_layers)]) - self.final_layer_norm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps) - - self.gradient_checkpointing = False - - # Initialize weights and apply final processing - self.post_init() - - def get_input_embeddings(self): - return self.embed_in - - def set_input_embeddings(self, value): - self.embed_in = value - - @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length")) - @add_code_sample_docstrings( - checkpoint=_CHECKPOINT_FOR_DOC, - real_checkpoint=_REAL_CHECKPOINT_FOR_DOC, - output_type=BaseModelOutputWithPast, - config_class=_CONFIG_FOR_DOC, - ) - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None, - use_cache: Optional[bool] = None, - output_attentions: Optional[bool] = None, - output_hidden_states: Optional[bool] = None, - return_dict: Optional[bool] = None, - position_ids=None - ) -> Union[Tuple, BaseModelOutputWithPast]: - r""" - past_key_values (`tuple(tuple(torch.FloatTensor))` of length `config.n_layers` with each tuple having 4 tensors of shape `(batch_size, num_heads, sequence_length - 1, embed_size_per_head)`): - Contains precomputed key and value hidden states of the attention blocks. Can be used to speed up 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)`. - 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 = 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 - use_cache = use_cache if use_cache is not None else self.config.use_cache - - if input_ids is not None and inputs_embeds is not None: - raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") - elif input_ids is not None: - input_shape = input_ids.size() - elif inputs_embeds is not None: - input_shape = inputs_embeds.size()[:-1] - else: - raise ValueError("You have to specify either input_ids or inputs_embeds") - - batch_size, seq_length = input_shape - - if past_key_values is None: - past_key_values = tuple([None] * self.config.num_hidden_layers) - - # Attention mask. - if attention_mask is not None: - assert batch_size > 0, "batch_size has to be defined and > 0" - attention_mask = attention_mask.view(batch_size, -1) - # We create a 3D attention mask from a 2D tensor mask. - # Sizes are [batch_size, 1, 1, to_seq_length] - # So we can broadcast to [batch_size, num_heads, from_seq_length, to_seq_length] - # this attention mask is more simple than the triangular masking of causal attention - # used in OpenAI GPT, we just need to prepare the broadcast dimension here. - attention_mask = attention_mask[:, None, None, :] - - # Since attention_mask is 1.0 for positions we want to attend and 0.0 for - # masked positions, this operation will create a tensor which is 0.0 for - # positions we want to attend and the dtype's smallest value for masked positions. - # Since we are adding it to the raw scores before the softmax, this is - # effectively the same as removing these entirely. - attention_mask = attention_mask.to(dtype=self.dtype) # fp16 compatibility - attention_mask = (1.0 - attention_mask) * torch.finfo(self.dtype).min - - # Prepare head mask if needed - # 1.0 in head_mask indicate we keep the head - # attention_probs has shape bsz x n_heads x N x N - # input head_mask has shape [num_heads] or [num_hidden_layers x num_heads] - # and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length] - head_mask = self.get_head_mask(head_mask, self.config.num_hidden_layers) - - if inputs_embeds is None: - inputs_embeds = self.embed_in(input_ids) - - hidden_states = inputs_embeds - - if self.gradient_checkpointing and self.training: - if use_cache: - logger.warning( - "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." - ) - use_cache = False - - presents = () if use_cache else None - all_attentions = () if output_attentions else None - all_hidden_states = () if output_hidden_states else None - for i, (layer, layer_past) in enumerate(zip(self.layers, past_key_values)): - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if self.gradient_checkpointing and self.training: - - def create_custom_forward(module): - def custom_forward(*inputs): - # None for layer_past - return module(*inputs, use_cache, None, output_attentions) - - return custom_forward - - outputs = torch.utils.checkpoint.checkpoint( - create_custom_forward(layer), - hidden_states, - attention_mask, - position_ids, - head_mask[i], - ) - else: - outputs = layer( - hidden_states, - attention_mask=attention_mask, - position_ids=position_ids, - head_mask=head_mask[i], - layer_past=layer_past, - use_cache=use_cache, - output_attentions=output_attentions, - ) - hidden_states = outputs[0] - if use_cache is True: - presents = presents + (outputs[1],) - if output_attentions: - all_attentions = all_attentions + (outputs[2 if use_cache else 1],) - - hidden_states = self.final_layer_norm(hidden_states) - # Add last hidden state - if output_hidden_states: - all_hidden_states = all_hidden_states + (hidden_states,) - - if not return_dict: - return tuple(v for v in [hidden_states, presents, all_hidden_states, all_attentions] if v is not None) - - return BaseModelOutputWithPast( - last_hidden_state=hidden_states, - past_key_values=presents, - hidden_states=all_hidden_states, - attentions=all_attentions, - ) - - -@add_start_docstrings( - """GPTNeoX Model with a `language modeling` head on top for CLM fine-tuning.""", GPT_NEOX_START_DOCSTRING -) -class GPTNeoXForCausalLM(GPTNeoXPreTrainedModel): - # _keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"] - _keys_to_ignore_on_load_missing = [r"predictions.decoder.bias"] - - def __init__(self, config): - super().__init__(config) - - self.gpt_neox = GPTNeoXModel(config) - self.embed_out = nn.Linear(config.hidden_size, config.vocab_size, bias=False) - - # Initialize weights and apply final processing - self.post_init() - - def get_output_embeddings(self): - return self.embed_out - - def set_output_embeddings(self, new_embeddings): - self.embed_out = new_embeddings - - @add_start_docstrings_to_model_forward(GPT_NEOX_INPUTS_DOCSTRING.format("batch_size, sequence_length")) - @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC) - def forward( - self, - input_ids: Optional[torch.LongTensor] = None, - attention_mask: Optional[torch.FloatTensor] = None, - inputs_embeds: Optional[torch.FloatTensor] = None, - head_mask: Optional[torch.FloatTensor] = None, - past_key_values: Optional[Tuple[Tuple[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, - position_ids=None, - ) -> Union[Tuple, CausalLMOutputWithPast]: - r""" - 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)`. The two additional tensors are - only required when the model is used as a decoder in a Sequence to Sequence model. - - Contains pre-computed hidden-states (key and values in the self-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)`. - labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): - Labels for computing the left-to-right language modeling loss (next word prediction). Indices should be in - `[-100, 0, ..., config.vocab_size]` (see `input_ids` docstring) Tokens with indices set to `-100` are - ignored (masked), the loss is only computed for the tokens with labels n `[0, ..., config.vocab_size]`. - 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`). - - Returns: - - Example: - - ```python - >>> from transformers import AutoTokenizer, GPTNeoXForCausalLM, GPTNeoXConfig - >>> import torch - - >>> tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b") - >>> config = GPTNeoXConfig.from_pretrained("EleutherAI/gpt-neox-20b") - >>> config.is_decoder = True - >>> model = GPTNeoXForCausalLM.from_pretrained("EleutherAI/gpt-neox-20b", config=config) - - >>> inputs = tokenizer("Hello, my dog is cute", return_tensors="pt") - >>> outputs = model(**inputs) - - >>> prediction_logits = outputs.logits - ```""" - return_dict = return_dict if return_dict is not None else self.config.use_return_dict - - outputs = self.gpt_neox( - input_ids, - attention_mask=attention_mask, - position_ids=position_ids, - head_mask=head_mask, - inputs_embeds=inputs_embeds, - past_key_values=past_key_values, - use_cache=use_cache, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - ) - - hidden_states = outputs[0] - lm_logits = self.embed_out(hidden_states) - - lm_loss = None - if labels is not None: - # move labels to correct device to enable model parallelism - labels = labels.to(lm_logits.device) - # we are doing next-token prediction; shift prediction scores and input ids by one - shift_logits = lm_logits[:, :-1, :].contiguous() - labels = labels[:, 1:].contiguous() - loss_fct = CrossEntropyLoss() - lm_loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), labels.view(-1)) - - if not return_dict: - output = (lm_logits,) + outputs[1:] - return ((lm_loss) + output) if lm_loss is not None else output - - return CausalLMOutputWithPast( - loss=lm_loss, - logits=lm_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, **model_kwargs): - input_shape = input_ids.shape - - # if model is used as a decoder in encoder-decoder model, the decoder attention mask is created on the fly - if attention_mask is None: - attention_mask = input_ids.new_ones(input_shape) - - # cut decoder_input_ids if past is used - if past_key_values and past_key_values[0] is not None: - input_ids = input_ids[:, -1:] - - return { - "input_ids": input_ids, - "attention_mask": attention_mask, - "past_key_values": past_key_values, - } - - def _reorder_cache(self, 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[:2]) + layer_past[2:], - ) - return reordered_past diff --git a/mftcoder_atorch/model/gpt_neox/tokenization_gpt_neox_fast.py b/mftcoder_atorch/model/gpt_neox/tokenization_gpt_neox_fast.py deleted file mode 100644 index c5b533a..0000000 --- a/mftcoder_atorch/model/gpt_neox/tokenization_gpt_neox_fast.py +++ /dev/null @@ -1,142 +0,0 @@ -# coding=utf-8 -# Copyright 2022 EleutherAI and The HuggingFace Inc. team. All rights reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Tokenization classes for GPTNeoX.""" -import json -from typing import TYPE_CHECKING, List, Optional, Tuple - -from tokenizers import pre_tokenizers - -from transformers import PreTrainedTokenizerFast -from transformers.utils import logging - - -if TYPE_CHECKING: - from transformers.pipelines.conversational import Conversation - - -logger = logging.get_logger(__name__) - -VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"} - -PRETRAINED_VOCAB_FILES_MAP = { - "tokenizer_file": { - "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json", - }, -} - -PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = { - "gpt-neox-20b": 2048, -} - - -class GPTNeoXTokenizerFast(PreTrainedTokenizerFast): - """ - Construct a "fast" GPT-NeoX-20B tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level - Byte-Pair-Encoding. - - This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will - be encoded differently whether it is at the beginning of the sentence (without space) or not: - - ``` - >>> from transformers import GPTNeoXTokenizerFast - >>> tokenizer = GPTNeoXTokenizerFast.from_pretrained("gpt2") - >>> tokenizer("Hello world")['input_ids'] - [15496, 995] - >>> tokenizer(" Hello world")['input_ids'] - [18435, 995] - ``` - - You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since - the model was not pretrained this way, it might yield a decrease in performance. - - - - When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`. - - - - This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should - refer to this superclass for more information regarding those methods. - - Args: - vocab_file (`str`): - Path to the vocabulary file. - merges_file (`str`): - Path to the merges file. - errors (`str`, *optional*, defaults to `"replace"`): - Paradigm to follow when decoding bytes to UTF-8. See - [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information. - unk_token (`str`, *optional*, defaults to `<|endoftext|>`): - The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this - token instead. - bos_token (`str`, *optional*, defaults to `<|endoftext|>`): - The beginning of sequence token. - eos_token (`str`, *optional*, defaults to `<|endoftext|>`): - The end of sequence token. - add_prefix_space (`bool`, *optional*, defaults to `False`): - Whether or not to add an initial space to the input. This allows to treat the leading word just as any - other word. (GPTNeoX tokenizer detect beginning of words by the preceding space). - trim_offsets (`bool`, *optional*, defaults to `True`): - Whether or not the post-processing step should trim offsets to avoid including whitespaces. - """ - - vocab_files_names = VOCAB_FILES_NAMES - pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP - max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES - model_input_names = ["input_ids", "attention_mask"] - - def __init__( - self, - vocab_file=None, - merges_file=None, - tokenizer_file=None, - unk_token="<|endoftext|>", - bos_token="<|endoftext|>", - eos_token="<|endoftext|>", - add_prefix_space=False, - **kwargs, - ): - super().__init__( - vocab_file, - merges_file, - tokenizer_file=tokenizer_file, - unk_token=unk_token, - bos_token=bos_token, - eos_token=eos_token, - add_prefix_space=add_prefix_space, - **kwargs, - ) - - pre_tok_state = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()) - if pre_tok_state.get("add_prefix_space", add_prefix_space) != add_prefix_space: - pre_tok_class = getattr(pre_tokenizers, pre_tok_state.pop("type")) - pre_tok_state["add_prefix_space"] = add_prefix_space - self.backend_tokenizer.pre_tokenizer = pre_tok_class(**pre_tok_state) - - self.add_prefix_space = add_prefix_space - - def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]: - files = self._tokenizer.model.save(save_directory, name=filename_prefix) - return tuple(files) - - def _build_conversation_input_ids(self, conversation: "Conversation") -> List[int]: - """This corresponds to DialoGPT variants of models.""" - input_ids = [] - for is_user, text in conversation.iter_texts(): - input_ids.extend(self.encode(text, add_special_tokens=False) + [self.eos_token_id]) - - if len(input_ids) > self.model_max_length: - input_ids = input_ids[-self.model_max_length :] - return input_ids \ No newline at end of file diff --git a/mftcoder_atorch/model/peft/__init__.py b/mftcoder_atorch/model/peft/__init__.py deleted file mode 100644 index daf63ee..0000000 --- a/mftcoder_atorch/model/peft/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -"""peft models interface.""" - -from . import utils, tuner -from peft.mapping import MODEL_TYPE_TO_PEFT_MODEL_MAPPING -from peft.utils import TaskType -from .modeling_peft import AntPeftForCausalLM, AntPeftForEmbedding - - -SUPPORTED_PEFT_TYPES = ["prefix", "lora", "adalora", "bitfit", "roem", "unipelt", "prompt", "ptuning"] - -# Register the Ant Causal Language Model -MODEL_TYPE_TO_PEFT_MODEL_MAPPING["ANT_CAUSAL_LM"] = AntPeftForCausalLM -TaskType.ANT_CAUSAL_LM = "ANT_CAUSAL_LM" - -MODEL_TYPE_TO_PEFT_MODEL_MAPPING["ANT_EMBEDDING"] = AntPeftForEmbedding -TaskType.ANT_EMBEDDING = "ANT_EMBEDDING" diff --git a/mftcoder_atorch/model/peft/modeling_peft.py b/mftcoder_atorch/model/peft/modeling_peft.py deleted file mode 100644 index df707a6..0000000 --- a/mftcoder_atorch/model/peft/modeling_peft.py +++ /dev/null @@ -1,653 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -import sys -sys.path.append("..") -import os -import torch -import inspect -import warnings -from accelerate.utils import get_balanced_memory -from accelerate import dispatch_model, infer_auto_device_map -from accelerate.hooks import AlignDevicesHook, add_hook_to_module, remove_hook_from_submodules - -from peft.peft_model import ( - PeftModel, - PeftModelForCausalLM, -) -from peft.utils import ( - PeftConfig, - PromptLearningConfig, - _set_trainable, -) -from model.peft.utils import ( - WEIGHTS_NAME, - get_peft_model_state_dict, - set_peft_model_state_dict -) -from model.peft.tuner import ( # noqa - AdaLoraModel, - RouteLoraModel, - PeftType, - PEFT_TYPE_TO_MODEL_MAPPING -) - - -class AntPeftForCausalLM(PeftModelForCausalLM): - def __init__(self, model, peft_config: PeftConfig, adapter_name: str = "default"): - super(PeftModel, self).__init__() - self.base_model = model - self.config = self.base_model.config - self.modules_to_save = None - self.peft_config = {} - self.active_adapter = adapter_name - self.peft_type = peft_config.peft_type - # compatible with peft==0.4.0 - try: - self.base_model_torch_dtype = getattr(model, "dtype", None) - except AttributeError: - pass - if not isinstance(peft_config, PromptLearningConfig): - self.peft_config[adapter_name] = peft_config - self.base_model = PEFT_TYPE_TO_MODEL_MAPPING[peft_config.peft_type]( - self.base_model, self.peft_config, adapter_name - ) - self.set_additional_trainable_modules(peft_config, adapter_name) - else: - self.add_adapter(adapter_name, peft_config) - - if getattr(self.peft_config[adapter_name], "modules_to_save", None) is not None: - self.modules_to_save = self.peft_config[adapter_name].modules_to_save - _set_trainable(self, adapter_name) - self.device = torch.device( - "cuda" if torch.cuda.is_available() else "cpu") - - self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation - self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation - - def set_route_id(self, route_id: int): - peft_config = self.active_peft_config - if peft_config.peft_type == PeftType.ROUTELORA: - self.base_model.activate_route_lora(route_id) - else: - warnings.warn("The route setting only support for Route Lora method," - f"but the current method is {peft_config.peft_type}") - - def expand_external_router(self, path: str): - peft_config = self.active_peft_config - if peft_config.peft_type == PeftType.ROUTELORA: - self.base_model.expand_external_router(path) - else: - warnings.warn("The route setting only support for Route Lora method," - f"but the current method is {peft_config.peft_type}") - - def forward( - self, - input_ids=None, - position_ids=None, - attention_mask=None, - inputs_embeds=None, - labels=None, - route_id: int = 0, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - **kwargs, - ): - peft_config = self.active_peft_config - if not isinstance(peft_config, PromptLearningConfig): - if peft_config.peft_type == PeftType.ROUTELORA: - self.base_model.activate_route_lora(route_id) - - return self.base_model( - input_ids=input_ids, - position_ids=position_ids, - attention_mask=attention_mask, - inputs_embeds=inputs_embeds, - labels=labels, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - **kwargs, - ) - - batch_size = input_ids.shape[0] - if attention_mask is not None: - # concat prompt attention mask - if len(attention_mask.size()) == 2: - prefix_attention_mask = torch.ones( - batch_size, peft_config.num_virtual_tokens).to(self.device) - attention_mask = torch.cat( - (prefix_attention_mask, attention_mask), dim=1) - elif len(attention_mask.size()) == 1: - for i in range(batch_size): - attention_mask[i] += peft_config.num_virtual_tokens - attention_mask = attention_mask.contiguous() - else: - assert ValueError( - f"The size of attention mask must in 1 or 2 dim, but get {len(attention_mask.size())}") - - # if kwargs.get("position_ids", None) is not None: - # warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.") - # kwargs["position_ids"] = None - if kwargs.get("token_type_ids", None) is not None: - warnings.warn( - "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids") - kwargs["token_type_ids"] = None - kwargs.update( - { - "attention_mask": attention_mask, - "labels": labels, - "position_ids": position_ids, - "output_attentions": output_attentions, - "output_hidden_states": output_hidden_states, - "return_dict": return_dict, - } - ) - - model_config = self.base_model.config.to_dict() - if peft_config.peft_type == PeftType.PREFIX_TUNING: - past_key_values = self.get_prompt(batch_size) - if model_config["model_type"] == "glm": - batch_size = past_key_values[0].size(1) - past_key_values = [feat[0].permute( - 0, 2, 1, 3).contiguous() for feat in past_key_values] - past_key_values = [ - feat.view( - batch_size, peft_config.num_virtual_tokens, -1) - for feat in past_key_values - ] - if model_config["block_position_encoding"] is True: - position_ids[:, 0] += peft_config.num_virtual_tokens - else: - position_ids += peft_config.num_virtual_tokens - kwargs["position_ids"] = position_ids.contiguous() - return self.base_model(input_ids=input_ids, mems=past_key_values, **kwargs) - - return self.base_model(input_ids=input_ids, past_key_values=past_key_values, **kwargs) - else: - # TODO: support p-tuning and prompt tuning for GLM - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(input_ids) - # concat prompt labels - if labels is not None: - prefix_labels = torch.full( - (batch_size, peft_config.num_virtual_tokens), -100).to(self.device) - kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1) - prompts = self.get_prompt(batch_size=batch_size) - prompts = prompts.to(inputs_embeds.dtype) - - attention_mask = attention_mask + peft_config.num_virtual_tokens - - virtual_token_position_ids = torch.arange( - peft_config.num_virtual_tokens, device=self.device, dtype=torch.long).unsqueeze(0) - virtual_token_position_ids = virtual_token_position_ids.expand(batch_size, -1) - if model_config["block_position_encoding"] is True: - position_ids[:, 0] += peft_config.num_virtual_tokens - virtual_token_position_ids = virtual_token_position_ids.unsqueeze(1) - block_virtual_token_position_ids = position_ids.new_zeros(( - batch_size, 1, peft_config.num_virtual_tokens)) - position_ids = torch.cat([ - torch.cat([virtual_token_position_ids, block_virtual_token_position_ids], dim=1), - position_ids], dim=-1 - ) - - else: - position_ids += peft_config.num_virtual_tokens - position_ids = torch.cat([virtual_token_position_ids, position_ids], dim=-1) - - kwargs["attention_mask"] = attention_mask.contiguous() - kwargs["position_ids"] = position_ids.contiguous() - - inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1) - return self.base_model(inputs_embeds=inputs_embeds, **kwargs) - - def save_pretrained(self, save_directory, **kwargs): - r""" - This function saves the adapter model and the adapter configuration files to a directory, so that it can be - reloaded using the [`LoraModel.from_pretrained`] class method, and also used by the [`LoraModel.push_to_hub`] - method. - - Args: - save_directory (`str`): - Directory where the adapter model and configuration files will be saved (will be created if it does not - exist). - kwargs (additional keyword arguments, *optional*): - Additional keyword arguments passed along to the `push_to_hub` method. - """ - if os.path.isfile(save_directory): - raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file") - os.makedirs(save_directory, exist_ok=True) - - for adapter_name, peft_config in self.peft_config.items(): - # save only the trainable weights - output_state_dict = get_peft_model_state_dict( - self, state_dict=kwargs.get("state_dict", None), adapter_name=adapter_name - ) - output_dir = os.path.join(save_directory, adapter_name) if adapter_name != "default" else save_directory - os.makedirs(output_dir, exist_ok=True) - torch.save(output_state_dict, os.path.join(output_dir, WEIGHTS_NAME)) - - # save the config and change the inference mode to `True` - if peft_config.base_model_name_or_path is None: - peft_config.base_model_name_or_path = ( - self.base_model.__dict__.get("name_or_path", None) - if isinstance(peft_config, PromptLearningConfig) - else self.base_model.model.__dict__.get("name_or_path", None) - ) - inference_mode = peft_config.inference_mode - peft_config.inference_mode = True - peft_config.save_pretrained(output_dir) - peft_config.inference_mode = inference_mode - - def load_adapter(self, model_id, adapter_name, is_trainable=False, **kwargs): - - from .tuner import PEFT_TYPE_TO_CONFIG_MAPPING - from huggingface_hub import hf_hub_download - - if adapter_name not in self.peft_config: - # load the config - peft_config = PEFT_TYPE_TO_CONFIG_MAPPING[ - PeftConfig.from_pretrained(model_id, subfolder=kwargs.get("subfolder", None)).peft_type - ].from_pretrained(model_id, subfolder=kwargs.get("subfolder", None)) - if isinstance(peft_config, PromptLearningConfig) and is_trainable: - raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.") - else: - peft_config.inference_mode = not is_trainable - self.add_adapter(adapter_name, peft_config) - - # load weights if any - path = os.path.join(model_id, kwargs["subfolder"]) if kwargs.get("subfolder", None) is not None else model_id - - if os.path.exists(os.path.join(path, WEIGHTS_NAME)): - filename = os.path.join(path, WEIGHTS_NAME) - else: - try: - filename = hf_hub_download(model_id, WEIGHTS_NAME, subfolder=kwargs.get("subfolder", None)) - except: # noqa - raise ValueError( - f"Can't find weights for {model_id} in {model_id} or in the Hugging Face Hub. " - f"Please check that the file {WEIGHTS_NAME} is present at {model_id}." - ) - - adapters_weights = torch.load( - filename, map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu") - ) - # load the weights into the model - set_peft_model_state_dict(self, adapters_weights, adapter_name=adapter_name) - if ( - (getattr(self, "hf_device_map", None) is not None) - and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0) - and len(self.peft_config) == 1 - ): - device_map = kwargs.get("device_map", "auto") - max_memory = kwargs.get("max_memory", None) - offload_dir = kwargs.get("offload_folder", None) - offload_index = kwargs.get("offload_index", None) - - dispatch_model_kwargs = {} - # Safety checker for previous `accelerate` versions - # `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/ - if "offload_index" in inspect.signature(dispatch_model).parameters: - dispatch_model_kwargs["offload_index"] = offload_index - - no_split_module_classes = self._no_split_modules - - if device_map != "sequential": - max_memory = get_balanced_memory( - self, - max_memory=max_memory, - no_split_module_classes=no_split_module_classes, - low_zero=(device_map == "balanced_low_0"), - ) - if isinstance(device_map, str): - device_map = infer_auto_device_map( - self, max_memory=max_memory, no_split_module_classes=no_split_module_classes - ) - dispatch_model( - self, - device_map=device_map, - offload_dir=offload_dir, - **dispatch_model_kwargs, - ) - hook = AlignDevicesHook(io_same_device=True) - if isinstance(self.peft_config[adapter_name], PromptLearningConfig): - remove_hook_from_submodules(self.prompt_encoder) - add_hook_to_module(self.get_base_model(), hook) - - # Set model in evaluation mode to deactivate Dropout modules by default - self.eval() - - @classmethod - def from_pretrained( - cls, - model, - model_id: str, - adapter_name: str = "default", - is_trainable: bool = False, - resume_from_checkpoint: bool = False, - **kwargs - ): - r""" - Instantiate a [`LoraModel`] from a pretrained Lora configuration and weights. - - Args: - model ([`~transformers.PreTrainedModel`]): - The model to be adapted. The model should be initialized with the - [`~transformers.PreTrainedModel.from_pretrained`] method from the 🤗 Transformers library. - model_id (`str` or `os.PathLike`): - The name of the Lora configuration to use. Can be either: - - A string, the `model id` of a Lora configuration hosted inside a model repo on the Hugging Face - Hub. - - A path to a directory containing a Lora configuration file saved using the `save_pretrained` - method (`./my_lora_config_directory/`). - """ - from .tuner import PEFT_TYPE_TO_CONFIG_MAPPING - from peft import MODEL_TYPE_TO_PEFT_MODEL_MAPPING - from transformers.trainer_utils import get_last_checkpoint - - if resume_from_checkpoint is True: - model_id = get_last_checkpoint(model_id) - print(f"The last checkpoint path is: {model_id}") - - # load the config - config = PEFT_TYPE_TO_CONFIG_MAPPING[ - PeftConfig.from_pretrained(model_id, subfolder=kwargs.get("subfolder", None)).peft_type - ].from_pretrained(model_id, subfolder=kwargs.get("subfolder", None)) - - if (getattr(model, "hf_device_map", None) is not None) and len( - set(model.hf_device_map.values()).intersection({"cpu", "disk"}) - ) > 0: - remove_hook_from_submodules(model) - - if isinstance(config, PromptLearningConfig) and is_trainable: - raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.") - else: - config.inference_mode = not is_trainable - - if config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys(): - model = cls(model, config, adapter_name) - else: - model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type](model, config, adapter_name) - model.load_adapter(model_id, adapter_name, **kwargs) - return model - - -class AntPeftForEmbedding(PeftModel): - def __init__(self, model, peft_config: PeftConfig, adapter_name: str = "default"): - super(PeftModel, self).__init__() - self.base_model = model - self.config = self.base_model.config - self.modules_to_save = None - self.peft_config = {} - self.active_adapter = adapter_name - self.peft_type = peft_config.peft_type - self.base_model_torch_dtype = getattr(model, "dtype", None) - if not isinstance(peft_config, PromptLearningConfig): - self.peft_config[adapter_name] = peft_config - self.base_model = PEFT_TYPE_TO_MODEL_MAPPING[peft_config.peft_type]( - self.base_model, self.peft_config, adapter_name - ) - self.set_additional_trainable_modules(peft_config, adapter_name) - else: - self.add_adapter(adapter_name, peft_config) - - if getattr(self.peft_config[adapter_name], "modules_to_save", None) is not None: - self.modules_to_save = self.peft_config[adapter_name].modules_to_save - _set_trainable(self, adapter_name) - self.device = torch.device( - "cuda" if torch.cuda.is_available() else "cpu") - - self.base_model_prepare_inputs_for_generation = self.base_model.prepare_inputs_for_generation - self.base_model.prepare_inputs_for_generation = self.prepare_inputs_for_generation - - def set_route_id(self, route_id: int): - peft_config = self.active_peft_config - if peft_config.peft_type == PeftType.ROUTELORA: - self.base_model.activate_route_lora(route_id) - else: - warnings.warn("The route setting only support for Route Lora method," - f"but the current method is {peft_config.peft_type}") - - def expand_external_router(self, path: str): - peft_config = self.active_peft_config - if peft_config.peft_type == PeftType.ROUTELORA: - self.base_model.expand_external_router(path) - else: - warnings.warn("The route setting only support for Route Lora method," - f"but the current method is {peft_config.peft_type}") - - def forward( - self, - query_ids: torch.Tensor, - query_position_ids: torch.Tensor = None, - query_attention_mask: torch.Tensor = None, - query_mask: torch.Tensor = None, - passage_ids: torch.Tensor = None, - passage_position_ids: torch.Tensor = None, - passage_attention_mask: torch.Tensor = None, - passage_mask: torch.Tensor = None, - route_id: int = 0, - inputs_embeds=None, - output_attentions=None, - output_hidden_states=None, - return_dict=None, - **kwargs, - ): - peft_config = self.active_peft_config - if not isinstance(peft_config, PromptLearningConfig): - if peft_config.peft_type == PeftType.ROUTELORA: - self.base_model.activate_route_lora(route_id) - - return self.base_model( - query_ids=query_ids, - query_position_ids=query_position_ids, - query_attention_mask=query_attention_mask, - query_mask=query_mask, - passage_ids=passage_ids, - passage_position_ids=passage_position_ids, - passage_attention_mask=passage_attention_mask, - passage_mask=passage_mask, - output_attentions=output_attentions, - output_hidden_states=output_hidden_states, - return_dict=return_dict, - **kwargs, - ) - - batch_size = query_ids.shape[0] - if query_attention_mask is not None and passage_attention_mask is not None: - # concat prompt attention mask - if len(query_attention_mask.size()) == 2: - prefix_attention_mask = torch.ones( - batch_size, peft_config.num_virtual_tokens).to(self.device) - query_attention_mask = torch.cat( - (prefix_attention_mask, query_attention_mask), dim=1) - passage_attention_mask = torch.cat( - (prefix_attention_mask, passage_attention_mask), dim=1) - elif len(query_attention_mask.size()) == 1: - for i in range(batch_size): - query_attention_mask[i] += peft_config.num_virtual_tokens - passage_attention_mask[i] += passage_attention_mask.num_virtual_tokens - query_attention_mask = query_attention_mask.contiguous() - passage_attention_mask = passage_attention_mask.contiguous() - else: - assert ValueError( - f"The size of attention mask must in 1 or 2 dim, " - f"but get {len(query_attention_mask.size())} and {len(passage_attention_mask.size())}" - ) - - # if kwargs.get("position_ids", None) is not None: - # warnings.warn("Position ids are not supported for parameter efficient tuning. Ignoring position ids.") - # kwargs["position_ids"] = None - if kwargs.get("token_type_ids", None) is not None: - warnings.warn( - "Token type ids are not supported for parameter efficient tuning. Ignoring token type ids") - kwargs["token_type_ids"] = None - kwargs.update( - { - "query_position_ids": query_position_ids, - "query_attention_mask": query_attention_mask, - "query_mask": query_mask, - "passage_ids": passage_ids, - "passage_position_ids": passage_position_ids, - "passage_attention_mask": passage_attention_mask, - "passage_mask": passage_mask, - "output_attentions": output_attentions, - "output_hidden_states": output_hidden_states, - "return_dict": return_dict, - } - ) - - if peft_config.peft_type == PeftType.PREFIX_TUNING: - past_key_values = self.get_prompt(batch_size) - model_config = self.base_model.config.to_dict() - if model_config["model_type"] == "glm": - batch_size = past_key_values[0].size(1) - past_key_values = [feat[0].permute( - 0, 2, 1, 3).contiguous() for feat in past_key_values] - past_key_values = [ - feat.view( - batch_size, peft_config.num_virtual_tokens, -1) - for feat in past_key_values - ] - if model_config["block_position_encoding"] is True: - query_position_ids[:, 0] += peft_config.num_virtual_tokens - passage_position_ids[:, 0] += peft_config.num_virtual_tokens - else: - query_position_ids += peft_config.num_virtual_tokens - passage_position_ids += peft_config.num_virtual_tokens - kwargs["query_position_ids"] = query_position_ids.contiguous() - kwargs["passage_position_ids"] = passage_position_ids.contiguous() - return self.base_model( - query_ids=query_ids, query_mems=past_key_values, passage_mems=past_key_values, **kwargs) - - return self.base_model(query_ids=query_ids, past_key_values=past_key_values, **kwargs) - else: - # TODO: support p-tuning and prompt tuning for GLM - if inputs_embeds is None: - inputs_embeds = self.word_embeddings(query_ids) - # concat prompt labels - # if labels is not None: - # prefix_labels = torch.full( - # (batch_size, peft_config.num_virtual_tokens), -100).to(self.device) - # kwargs["labels"] = torch.cat((prefix_labels, labels), dim=1) - prompts = self.get_prompt(batch_size=batch_size) - prompts = prompts.to(inputs_embeds.dtype) - inputs_embeds = torch.cat((prompts, inputs_embeds), dim=1) - return self.base_model(inputs_embeds=inputs_embeds, **kwargs) - - def save_pretrained(self, save_directory, **kwargs): - r""" - This function saves the adapter model and the adapter configuration files to a directory, so that it can be - reloaded using the [`LoraModel.from_pretrained`] class method, and also used by the [`LoraModel.push_to_hub`] - method. - - Args: - save_directory (`str`): - Directory where the adapter model and configuration files will be saved (will be created if it does not - exist). - kwargs (additional keyword arguments, *optional*): - Additional keyword arguments passed along to the `push_to_hub` method. - """ - if os.path.isfile(save_directory): - raise ValueError(f"Provided path ({save_directory}) should be a directory, not a file") - os.makedirs(save_directory, exist_ok=True) - - for adapter_name, peft_config in self.peft_config.items(): - # save only the trainable weights - output_state_dict = get_peft_model_state_dict( - self, state_dict=kwargs.get("state_dict", None), adapter_name=adapter_name - ) - output_dir = os.path.join(save_directory, adapter_name) if adapter_name != "default" else save_directory - os.makedirs(output_dir, exist_ok=True) - torch.save(output_state_dict, os.path.join(output_dir, WEIGHTS_NAME)) - - # save the config and change the inference mode to `True` - if peft_config.base_model_name_or_path is None: - peft_config.base_model_name_or_path = ( - self.base_model.__dict__.get("name_or_path", None) - if isinstance(peft_config, PromptLearningConfig) - else self.base_model.model.__dict__.get("name_or_path", None) - ) - inference_mode = peft_config.inference_mode - peft_config.inference_mode = True - peft_config.save_pretrained(output_dir) - peft_config.inference_mode = inference_mode - - def load_adapter(self, model_id, adapter_name, is_trainable=False, **kwargs): - - from .tuner import PEFT_TYPE_TO_CONFIG_MAPPING - from huggingface_hub import hf_hub_download - - if adapter_name not in self.peft_config: - # load the config - peft_config = PEFT_TYPE_TO_CONFIG_MAPPING[ - PeftConfig.from_pretrained(model_id, subfolder=kwargs.get("subfolder", None)).peft_type - ].from_pretrained(model_id, subfolder=kwargs.get("subfolder", None)) - if isinstance(peft_config, PromptLearningConfig) and is_trainable: - raise ValueError("Cannot set a prompt learning adapter to trainable when loading pretrained adapter.") - else: - peft_config.inference_mode = not is_trainable - self.add_adapter(adapter_name, peft_config) - - # load weights if any - path = os.path.join(model_id, kwargs["subfolder"]) if kwargs.get("subfolder", None) is not None else model_id - - if os.path.exists(os.path.join(path, WEIGHTS_NAME)): - filename = os.path.join(path, WEIGHTS_NAME) - else: - try: - filename = hf_hub_download(model_id, WEIGHTS_NAME, subfolder=kwargs.get("subfolder", None)) - except: # noqa - raise ValueError( - f"Can't find weights for {model_id} in {model_id} or in the Hugging Face Hub. " - f"Please check that the file {WEIGHTS_NAME} is present at {model_id}." - ) - - adapters_weights = torch.load( - filename, map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu") - ) - # load the weights into the model - set_peft_model_state_dict(self, adapters_weights, adapter_name=adapter_name) - if ( - (getattr(self, "hf_device_map", None) is not None) - and (len(set(self.hf_device_map.values()).intersection({"cpu", "disk"})) > 0) - and len(self.peft_config) == 1 - ): - device_map = kwargs.get("device_map", "auto") - max_memory = kwargs.get("max_memory", None) - offload_dir = kwargs.get("offload_folder", None) - offload_index = kwargs.get("offload_index", None) - - dispatch_model_kwargs = {} - # Safety checker for previous `accelerate` versions - # `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/ - if "offload_index" in inspect.signature(dispatch_model).parameters: - dispatch_model_kwargs["offload_index"] = offload_index - - no_split_module_classes = self._no_split_modules - - if device_map != "sequential": - max_memory = get_balanced_memory( - self, - max_memory=max_memory, - no_split_module_classes=no_split_module_classes, - low_zero=(device_map == "balanced_low_0"), - ) - if isinstance(device_map, str): - device_map = infer_auto_device_map( - self, max_memory=max_memory, no_split_module_classes=no_split_module_classes - ) - dispatch_model( - self, - device_map=device_map, - offload_dir=offload_dir, - **dispatch_model_kwargs, - ) - hook = AlignDevicesHook(io_same_device=True) - if isinstance(self.peft_config[adapter_name], PromptLearningConfig): - remove_hook_from_submodules(self.prompt_encoder) - add_hook_to_module(self.get_base_model(), hook) - - # Set model in evaluation mode to deactivate Dropout modules by default - self.eval() diff --git a/mftcoder_atorch/model/peft/tuner/__init__.py b/mftcoder_atorch/model/peft/tuner/__init__.py deleted file mode 100644 index 4d65d05..0000000 --- a/mftcoder_atorch/model/peft/tuner/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -"""peft tuner methods interface.""" - -from peft.utils import PeftType -from peft.peft_model import PEFT_TYPE_TO_MODEL_MAPPING -from peft.mapping import PEFT_TYPE_TO_CONFIG_MAPPING - -from .adalora import AdaLoraConfig, AdaLoraModel -from .routelora import RouteLoraConfig, RouteLoraModel -from .unipelt import UniPELTConfig, UniPELTModel, PEUniPELTModel -from .pe_base_model import PEBaseModel -from .bitfit import PeftBitfitConfig, PEBitfitModel, PeftBitfitModel -from .roem import PeftROEMConfig, PEROEMModel, PeftROEMModel - -# Register new ant peft methods -PeftType.ROUTELORA = "ROUTELORA" -PEFT_TYPE_TO_MODEL_MAPPING[PeftType.ROUTELORA] = RouteLoraModel -PEFT_TYPE_TO_CONFIG_MAPPING[PeftType.ROUTELORA] = RouteLoraConfig - -PeftType.UNIPELT = "UNIPELT" -PEFT_TYPE_TO_MODEL_MAPPING[PeftType.UNIPELT] = UniPELTModel -PEFT_TYPE_TO_CONFIG_MAPPING[PeftType.UNIPELT] = UniPELTConfig - -PeftType.ROEM = "ROEM" -PEFT_TYPE_TO_MODEL_MAPPING[PeftType.ROEM] = PeftROEMModel -PEFT_TYPE_TO_CONFIG_MAPPING[PeftType.ROEM] = PeftROEMConfig - -PeftType.BITFIT = "BITFIT" -PEFT_TYPE_TO_MODEL_MAPPING[PeftType.BITFIT] = PeftBitfitModel -PEFT_TYPE_TO_CONFIG_MAPPING[PeftType.BITFIT] = PeftBitfitConfig \ No newline at end of file diff --git a/mftcoder_atorch/model/peft/tuner/adalora.py b/mftcoder_atorch/model/peft/tuner/adalora.py deleted file mode 100644 index a61916f..0000000 --- a/mftcoder_atorch/model/peft/tuner/adalora.py +++ /dev/null @@ -1,721 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -import sys -sys.path.append("..") -sys.path.append("../..") -import importlib -import re -import warnings -from dataclasses import dataclass, field -from typing import Optional - -import torch -import torch.nn as nn -import torch.nn.functional as F -from peft.utils import transpose -from transformers.pytorch_utils import Conv1D -from model.peft.tuner import PeftType - -from peft.tuners.lora import ( - LoraConfig, - LoraLayer, - LoraModel, - mark_only_lora_as_trainable, -) - - -def is_bnb_available(): - return importlib.util.find_spec("bitsandbytes") is not None - - -if is_bnb_available(): - import bitsandbytes as bnb - - -TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING = { - "t5": ["q", "k", "v", "o", "wi", "wo"], - "mt5": ["q", "k", "v", "o", "wi_0", "wi_1", "wo"], - "bart": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"], - "opt": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"], - "roberta": ["query", "key", "value", "dense"], - "deberta-v2": ["query_proj", "key_proj", "value_proj", "dense"], - "glm": ["query_key_value", "dense"] -} - - -def _get_submodules(model, key): - parent = model.get_submodule(".".join(key.split(".")[:-1])) - target_name = key.split(".")[-1] - target = model.get_submodule(key) - return parent, target, target_name - - -def _freeze_adapter(model, adapter_name): - for n, p in model.named_parameters(): - if adapter_name in n: - p.requires_grad = False - - -@dataclass -class AdaLoraConfig(LoraConfig): - """ - This is the configuration class to store the configuration of a [`~peft.AdaLora`]. - - Args: - target_r (`int`): The target average rank of incremental matrix. - init_r (`int`): The initial rank for each incremental matrix. - tinit (`int`): The steps of initial fine-tuning warmup. - tfinal (`int`): The step of final fine-tuning. - deltaT (`int`): The time internval between two budget allocations. - beta1 (`float`): The hyperparameter of EMA for sensitivity smoothing. - beta2 (`float`): The hyperparameter of EMA for undertainty quantification. - orth_reg_weight (`float`): The coefficient of orthogonal regularization. - total_step (`int`): The total training steps that should be specified before training. - rank_pattern (`list`): The allocated rank for each weight matrix by RankAllocator. - """ - - target_r: int = field(default=8, metadata={"help": "Target Lora matrix dimension."}) - init_r: int = field(default=12, metadata={"help": "Intial Lora matrix dimension."}) - tinit: int = field(default=0, metadata={"help": "The steps of initial warmup."}) - tfinal: int = field(default=0, metadata={"help": "The steps of final warmup."}) - deltaT: int = field(default=1, metadata={"help": "Step interval of rank allocation."}) - beta1: float = field(default=0.85, metadata={"help": "Hyperparameter of EMA."}) - beta2: float = field(default=0.85, metadata={"help": "Hyperparameter of EMA."}) - orth_reg_weight: float = field(default=0.5, metadata={"help": "The orthogonal regularization coefficient."}) - total_step: Optional[int] = field(default=None, metadata={"help": "The total training steps."}) - rank_pattern: Optional[dict] = field(default=None, metadata={"help": "The saved rank pattern."}) - init_lora_weights: bool = field( - default=True, - metadata={"help": "Whether to initialize the weights of the Lora layers."}, - ) - - def __post_init__(self): - self.peft_type = PeftType.ADALORA - - -class AdaLoraModel(LoraModel): - """ - Creates AdaLoRA (Adaptive LoRA) model from a pretrained transformers model. Paper: - https://openreview.net/pdf?id=lq62uWRJjiY - - Args: - model ([`transformers.PreTrainedModel`]): The model to be adapted. - config ([`AdaLoraConfig`]): The configuration of the AdaLora model. - - Returns: - `torch.nn.Module`: The AdaLora model. - - Example:: - - >>> from transformers import AutoModelForSeq2SeqLM, LoraConfig >>> from peft import AdaLoraModel, AdaLoraConfig - >>> config = AdaLoraConfig( - peft_type="ADALORA", task_type="SEQ_2_SEQ_LM", r=8, lora_alpha=32, target_modules=["q", "v"], - lora_dropout=0.01, - ) - >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> model = AdaLoraModel(config, model) - - **Attributes**: - - **model** ([`transformers.PreTrainedModel`]) -- The model to be adapted. - - **peft_config** ([`AdaLoraConfig`]): The configuration of the AdaLora model. - """ - - def __init__(self, model, config, adapter_name): - nn.Module.__init__(self) - self.model = model - self.peft_config = config - self.add_adapter(adapter_name, self.peft_config[adapter_name]) - - def add_adapter(self, adapter_name, config=None): - if config is not None: - model_config = self.model.config.to_dict() if hasattr(self.model.config, "to_dict") else self.model.config - config = self._prepare_adalora_config(config, model_config) - self.peft_config[adapter_name] = config - self._find_and_replace(adapter_name) - if len(self.peft_config) > 1 and self.peft_config[adapter_name].bias != "none": - raise ValueError( - "AdaLoraModel supports only 1 adapter with bias." - "When using multiple adapters, set bias to 'none' for all adapters." - ) - traininable_mode_counter = 0 - for config in self.peft_config.values(): - if not config.inference_mode: - traininable_mode_counter += 1 - - if traininable_mode_counter > 1: - raise ValueError( - "AdaLoraModel supports only 1 trainable adapter. " - "When using multiple adapters, set inference_mode to" - "True for all adapters except the one you want to train." - ) - - mark_only_lora_as_trainable(self.model, self.peft_config[adapter_name].bias) - if self.peft_config[adapter_name].inference_mode: - _freeze_adapter(self.model, adapter_name) - else: - self.trainable_adapter_name = adapter_name - self.rankallocator = RankAllocator(self.model, self.peft_config[adapter_name], self.trainable_adapter_name) - - def _find_and_replace(self, adapter_name): - lora_config = self.peft_config[adapter_name] - loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False) - if loaded_in_8bit and not is_bnb_available(): - raise ImportError( - "To use Lora with 8-bit quantization, please install the `bitsandbytes` package. " - "You can install it with `pip install bitsandbytes`." - ) - is_target_modules_in_base_model = False - kwargs = { - "r": lora_config.init_r, - "lora_alpha": lora_config.lora_alpha, - "lora_dropout": lora_config.lora_dropout, - "fan_in_fan_out": lora_config.fan_in_fan_out, - "init_lora_weights": lora_config.init_lora_weights, - "merge_weights": lora_config.merge_weights or lora_config.inference_mode, - } - key_list = [key for key, _ in self.model.named_modules()] - for key in key_list: - if isinstance(lora_config.target_modules, str): - target_module_found = re.fullmatch(lora_config.target_modules, key) - else: - target_module_found = any(key.endswith(target_key) for target_key in lora_config.target_modules) - if target_module_found: - if not is_target_modules_in_base_model: - is_target_modules_in_base_model = True - parent, target, target_name = _get_submodules(self.model, key) - bias = target.bias is not None - if isinstance(target, LoraLayer): - target.update_layer( - adapter_name, - lora_config.init_r, - lora_config.lora_alpha, - lora_config.lora_dropout, - lora_config.init_lora_weights, - ) - else: - if loaded_in_8bit and isinstance(target, bnb.nn.Linear8bitLt): - kwargs.update( - { - "has_fp16_weights": target.state.has_fp16_weights, - "memory_efficient_backward": target.state.memory_efficient_backward, - "threshold": target.state.threshold, - "index": target.index, - } - ) - new_module = SVDLinear8bitLt( - adapter_name, target.in_features, target.out_features, bias=bias, **kwargs - ) - else: - if isinstance(target, torch.nn.Linear): - in_features, out_features = target.in_features, target.out_features - if kwargs["fan_in_fan_out"]: - warnings.warn( - "fan_in_fan_out is set to True but the target module is `torch.nn.Linear`. " - "Setting fan_in_fan_out to False." - ) - kwargs["fan_in_fan_out"] = lora_config.fan_in_fan_out = False - elif isinstance(target, Conv1D): - in_features, out_features = ( - target.weight.ds_shape if hasattr(target.weight, "ds_shape") else target.weight.shape - ) - if not kwargs["fan_in_fan_out"]: - warnings.warn( - "fan_in_fan_out is set to False but the target module is `Conv1D`. " - "Setting fan_in_fan_out to True." - ) - kwargs["fan_in_fan_out"] = lora_config.fan_in_fan_out = True - else: - raise ValueError( - f"Target module {target} is not supported. " - f"Currently, only `torch.nn.Linear` and `Conv1D` are supported." - ) - new_module = SVDLinear(adapter_name, in_features, out_features, bias=bias, **kwargs) - - self._replace_module(parent, target_name, new_module, target) - if not is_target_modules_in_base_model: - raise ValueError( - f"Target modules {lora_config.target_modules} not found in the base model. " - f"Please check the target modules and try again." - ) - - def __getattr__(self, name: str): - """Forward missing attributes to the wrapped module.""" - try: - return super().__getattr__(name) # defer to nn.Module's logic - except AttributeError: - return getattr(self.model, name) - - def forward(self, *args, **kwargs): - outputs = self.model.forward(*args, **kwargs) - - # Calculate the orthogonal regularization - orth_reg_weight = self.peft_config[self.trainable_adapter_name].orth_reg_weight - assert orth_reg_weight > 0 - - if hasattr(outputs, "loss"): - regu_loss = 0 - num_param = 0 - for n, p in self.model.named_parameters(): - if ("lora_A" in n or "lora_B" in n) and self.trainable_adapter_name in n: - para_cov = p @ p.T if "lora_A" in n else p.T @ p - EYE = torch.eye(*para_cov.size(), out=torch.empty_like(para_cov)) - EYE.requires_grad = False - num_param += 1 - regu_loss += torch.norm(para_cov - EYE, p="fro") - regu_loss = regu_loss / num_param - outputs.loss += orth_reg_weight * regu_loss - return outputs - - def resize_modules_by_rank_pattern(self, rank_pattern, adapter_name): - lora_config = self.peft_config[adapter_name] - for name, rank_idx in rank_pattern.items(): - if isinstance(rank_idx, list): - rank = sum(rank_idx) - elif isinstance(rank_idx, torch.Tensor): - rank_idx = rank_idx.view(-1) - rank = rank_idx.sum().item() - else: - raise ValueError("Unexcepted type of rank_idx") - key = ".".join(name.split(".")[0:-2]) if adapter_name in name else ".".join(name.split(".")[0:-1]) - _, target, _ = _get_submodules(self.model, key) - lora_E_weights = target.lora_E[adapter_name][rank_idx] - lora_A_weights = target.lora_A[adapter_name][rank_idx] - lora_B_weights = target.lora_B[adapter_name][:, rank_idx] - ranknum = target.ranknum[adapter_name] - target.update_layer( - adapter_name, - rank, - lora_config.lora_alpha, - lora_config.lora_dropout, - lora_config.init_lora_weights, - ) - with torch.no_grad(): - if rank > 0: - target.lora_E[adapter_name].copy_(lora_E_weights) - target.lora_A[adapter_name].copy_(lora_A_weights) - target.lora_B[adapter_name].copy_(lora_B_weights) - # The scaling is exactly as the previous - target.ranknum[adapter_name].copy_(ranknum) - - def resize_state_dict_by_rank_pattern(self, rank_pattern, state_dict, adapter_name): - for name, rank_idx in rank_pattern.items(): - rank = sum(rank_idx) - prefix = ".".join(name.split(".")[0:-2]) if adapter_name in name else ".".join(name.split(".")[0:-1]) - for layer in ["lora_E", "lora_A", "lora_B"]: - key = f"base_model.model.{prefix}.{layer}.{adapter_name}" - if layer != "lora_B": - state_dict[key] = ( - state_dict[key][rank_idx] if rank != state_dict[key].shape[0] else state_dict[key] - ) - else: - state_dict[key] = ( - state_dict[key][:, rank_idx] if rank != state_dict[key].shape[1] else state_dict[key] - ) - return state_dict - - def update_and_allocate(self, global_step): - lora_config = self.peft_config[self.trainable_adapter_name] - # Update the importance score and allocate the budget - if global_step < lora_config.total_step - lora_config.tfinal: - _, rank_pattern = self.rankallocator.update_and_allocate(self.model, global_step) - if rank_pattern: - lora_config.rank_pattern = rank_pattern - # Finalize the budget allocation - elif global_step == lora_config.total_step - lora_config.tfinal: - _, rank_pattern = self.rankallocator.update_and_allocate(self.model, global_step, force_mask=True) - # for some reason, this freezes the trainable parameters and nothing gets updates - # self.resize_modules_by_rank_pattern(rank_pattern, self.trainable_adapter_name) - lora_config.rank_pattern = rank_pattern - self.rankallocator.reset_ipt() - # Currently using inefficient way to mask the unimportant weights using the rank pattern - # due to problem mentioned above - elif global_step > lora_config.total_step - lora_config.tfinal: - self.rankallocator.mask_using_rank_pattern(self.model, lora_config.rank_pattern) - # Pass the function and do forward propagation - else: - return None - - @staticmethod - def _prepare_adalora_config(peft_config, model_config): - if peft_config.target_modules is None: - if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING: - raise ValueError("Please specify `target_modules` in `peft_config`") - peft_config.target_modules = TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING[ - model_config["model_type"] - ] - if peft_config.inference_mode: - peft_config.merge_weights = True - return peft_config - - -class AdaLoraLayer(LoraLayer): - def __init__( - self, - in_features: int, - out_features: int, - lora_dropout: float, - merge_weights: bool = False, - ): - super().__init__(in_features, out_features, lora_dropout, merge_weights) - self.lora_E = nn.ParameterDict({}) - self.lora_A = nn.ParameterDict({}) - self.lora_B = nn.ParameterDict({}) - self.ranknum = nn.ParameterDict({}) - self.r = {} - self.lora_alpha = {} - del self.lora_dropout - self.lora_dropout = None - self.lora_dropout = {} - self.scaling = {} - - def update_layer(self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights): - self.r[adapter_name] = r - self.lora_alpha[adapter_name] = lora_alpha - if lora_dropout > 0.0: - lora_dropout_layer = nn.Dropout(p=lora_dropout) - else: - - def lora_dropout_layer(x): - return x - - self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer})) - # Actual trainable parameters - # Right singular vectors - self.lora_A.update(nn.ParameterDict({adapter_name: nn.Parameter(torch.zeros(r, self.in_features))})) - # Singular values - self.lora_E.update(nn.ParameterDict({adapter_name: nn.Parameter(torch.zeros(r, 1))})) - # Left singular vectors - self.lora_B.update(nn.ParameterDict({adapter_name: nn.Parameter(torch.zeros(self.out_features, r))})) - # The current rank - self.ranknum.update(nn.ParameterDict({adapter_name: nn.Parameter(torch.zeros(1), requires_grad=False)})) - self.ranknum[adapter_name].data.fill_(float(r)) - self.ranknum[adapter_name].requires_grad = False - self.scaling[adapter_name] = lora_alpha if lora_alpha > 0 else float(r) - if init_lora_weights: - self.reset_lora_parameters(adapter_name) - self.to(self.weight.device) - - def reset_lora_parameters(self, adapter_name): - if adapter_name in self.lora_A.keys(): - nn.init.zeros_(self.lora_E[adapter_name]) - nn.init.normal_(self.lora_A[adapter_name], mean=0.0, std=0.02) - nn.init.normal_(self.lora_B[adapter_name], mean=0.0, std=0.02) - - -class SVDLinear(nn.Linear, AdaLoraLayer): - # SVD-based adaptation by a dense layer - def __init__( - self, - adapter_name: str, - in_features: int, - out_features: int, - r: int = 0, - lora_alpha: int = 1, - lora_dropout: float = 0.0, - fan_in_fan_out: bool = False, - merge_weights: bool = False, - **kwargs, - ): - init_lora_weights = kwargs.pop("init_lora_weights", True) - nn.Linear.__init__(self, in_features, out_features, **kwargs) - AdaLoraLayer.__init__( - self, - in_features=in_features, - out_features=out_features, - lora_dropout=lora_dropout, - merge_weights=merge_weights - ) - # Freezing the pre-trained weight matrix - self.weight.requires_grad = False - - self.fan_in_fan_out = fan_in_fan_out - if fan_in_fan_out: - self.weight.data = self.weight.data.T - - nn.Linear.reset_parameters(self) - self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights) - self.active_adapter = adapter_name - - def merge(self): - if self.active_adapter not in self.lora_A.keys(): - return - if self.merged: - warnings.warn("Already merged. Nothing to do.") - return - if self.r[self.active_adapter] > 0: - self.weight.data += ( - transpose( - self.lora_B[self.active_adapter] - @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]) - ) - * self.scaling[self.active_adapter] - / (self.ranknum[self.active_adapter] + 1e-5) - ) - self.merged = True - - def unmerge(self): - if self.active_adapter not in self.lora_A.keys(): - return - if not self.merged: - warnings.warn("Already unmerged. Nothing to do.") - return - if self.r[self.active_adapter] > 0: - self.weight.data -= ( - transpose( - self.lora_B[self.active_adapter] - @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]) - ) - * self.scaling[self.active_adapter] - / (self.ranknum[self.active_adapter] + 1e-5) - ) - self.merged = False - - def forward(self, x: torch.Tensor): - if self.active_adapter not in self.lora_A.keys(): - return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) - if self.disable_adapters: - if self.r[self.active_adapter] > 0 and self.merged: - self.unmerge() - result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) - elif self.r[self.active_adapter] > 0 and not self.merged: - result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) - result += ( - ( - self.lora_dropout[self.active_adapter](x) - @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]).T - @ self.lora_B[self.active_adapter].T - ) - * self.scaling[self.active_adapter] - / (self.ranknum[self.active_adapter] + 1e-5) - ) - else: - result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) - return result - - -if is_bnb_available(): - - class SVDLinear8bitLt(bnb.nn.Linear8bitLt, AdaLoraLayer): - # Low-rank matrix for SVD-based adaptation - def __init__( - self, - adapter_name, - in_features, - out_features, - r: int = 0, - lora_alpha: int = 1, - lora_dropout: float = 0.0, - **kwargs, - ): - bnb.nn.Linear8bitLt.__init__( - self, - in_features, - out_features, - bias=kwargs.get("bias", True), - has_fp16_weights=kwargs.get("has_fp16_weights", True), - memory_efficient_backward=kwargs.get("memory_efficient_backward", False), - threshold=kwargs.get("threshold", 0.0), - index=kwargs.get("index", None), - ) - AdaLoraLayer.__init__(self, in_features=in_features, out_features=out_features) - # Freezing the pre-trained weight matrix - self.weight.requires_grad = False - - init_lora_weights = kwargs.pop("init_lora_weights", True) - self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights) - self.active_adapter = adapter_name - - def forward(self, x: torch.Tensor): - result = super().forward(x) - - if self.disable_adapters or self.active_adapter not in self.lora_A.keys(): - return result - elif self.r[self.active_adapter] > 0: - if not torch.is_autocast_enabled(): - expected_dtype = result.dtype - - if x.dtype != torch.float32: - x = x.float() - output = ( - ( - self.lora_dropout[self.active_adapter](x) - @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]).T - @ self.lora_B[self.active_adapter].T - ).to(expected_dtype) - * self.scaling[self.active_adapter] - / (self.ranknum[self.active_adapter] + 1e-5) - ) - else: - output = ( - ( - self.lora_dropout[self.active_adapter](x) - @ (self.lora_A[self.active_adapter] * self.lora_E[self.active_adapter]).T - @ self.lora_B[self.active_adapter].T - ) - * self.scaling[self.active_adapter] - / (self.ranknum[self.active_adapter] + 1e-5) - ) - result += output - return result - - -class RankAllocator(object): - """ - The RankAllocator for AdaLoraModel. Paper: https://openreview.net/pdf?id=lq62uWRJjiY - - Args: - config ([`AdaLoraConfig`]): The configuration of the AdaLora model. - model: the model that we apply AdaLoRA to. - - """ - - def __init__(self, model, peft_config, adapter_name): - self.peft_config = peft_config - self.adapter_name = adapter_name - self.beta1 = peft_config.beta1 - self.beta2 = peft_config.beta2 - assert self.beta1 > 0 and self.beta1 < 1 - assert self.beta2 > 0 and self.beta2 < 1 - - self.reset_ipt() - self._set_budget_scheduler(model) - - def set_total_step(self, total_step): - self.peft_config.total_step = total_step - - def reset_ipt(self): - self.ipt = {} - self.exp_avg_ipt = {} - self.exp_avg_unc = {} - - def _set_budget_scheduler(self, model): - self.init_bgt = 0 - self.name_set = set() - for n, p in model.named_parameters(): - if f"lora_A.{self.adapter_name}" in n: - self.init_bgt += p.size(0) - self.name_set.add(n.replace("lora_A", "%s")) - self.name_set = sorted(self.name_set) - # The total final rank budget - self.target_bgt = self.peft_config.target_r * len(self.name_set) - - def budget_schedule(self, step: int): - tinit = self.peft_config.tinit - tfinal = self.peft_config.tfinal - total_step = self.peft_config.total_step - # Initial warmup - if step <= tinit: - budget = self.init_bgt - mask_ind = False - # Final fine-tuning - elif step > total_step - tfinal: - budget = self.target_bgt - mask_ind = True - else: - # Budget decreasing with a cubic scheduler - mul_coeff = 1 - (step - tinit) / (total_step - tfinal - tinit) - budget = int((self.init_bgt - self.target_bgt) * (mul_coeff**3) + self.target_bgt) - mask_ind = True if step % self.peft_config.deltaT == 0 else False - return budget, mask_ind - - def update_ipt(self, model): - # Update the sensitivity and uncertainty for every weight - for n, p in model.named_parameters(): - if "lora_" in n and self.adapter_name in n: - if n not in self.ipt: - self.ipt[n] = torch.zeros_like(p) - self.exp_avg_ipt[n] = torch.zeros_like(p) - self.exp_avg_unc[n] = torch.zeros_like(p) - with torch.no_grad(): - self.ipt[n] = (p * p.grad).abs().detach() - # Sensitivity smoothing - self.exp_avg_ipt[n] = self.beta1 * self.exp_avg_ipt[n] + (1 - self.beta1) * self.ipt[n] - # Uncertainty quantification - self.exp_avg_unc[n] = ( - self.beta2 * self.exp_avg_unc[n] + (1 - self.beta2) * (self.ipt[n] - self.exp_avg_ipt[n]).abs() - ) - - def _element_score(self, n): - return self.exp_avg_ipt[n] * self.exp_avg_unc[n] - - def _combine_ipt(self, ipt_E, ipt_AB): - ipt_AB = ipt_AB.sum(dim=1, keepdim=False) - sum_ipt = ipt_E.view(-1) + ipt_AB.view(-1) - return sum_ipt - - def mask_to_budget(self, model, budget): - value_ipt = {} - vector_ipt = {} - triplet_ipt = {} - # Get the importance score for A, E, B - for n, p in model.named_parameters(): - if f"lora_A.{self.adapter_name}" in n: - entry_ipt = self._element_score(n) - comb_ipt = torch.mean(entry_ipt, dim=1, keepdim=True) - name_m = n.replace("lora_A", "%s") - if name_m not in vector_ipt: - vector_ipt[name_m] = [comb_ipt] - else: - vector_ipt[name_m].append(comb_ipt) - if f"lora_B.{self.adapter_name}" in n: - entry_ipt = self._element_score(n) - comb_ipt = torch.mean(entry_ipt, dim=0, keepdim=False).view(-1, 1) - name_m = n.replace("lora_B", "%s") - if name_m not in vector_ipt: - vector_ipt[name_m] = [comb_ipt] - else: - vector_ipt[name_m].append(comb_ipt) - if f"lora_E.{self.adapter_name}" in n: - entry_ipt = self._element_score(n) - name_m = n.replace("lora_E", "%s") - value_ipt[name_m] = entry_ipt - - all_score = [] - # Calculate the score for each triplet - for name_m in vector_ipt: - ipt_E = value_ipt[name_m] - ipt_AB = torch.cat(vector_ipt[name_m], dim=1) - sum_ipt = self._combine_ipt(ipt_E, ipt_AB) - name_E = name_m % "lora_E" - triplet_ipt[name_E] = sum_ipt.view(-1, 1) - all_score.append(sum_ipt.view(-1)) - - # Get the threshold by ranking ipt - mask_threshold = torch.kthvalue( - torch.cat(all_score), - k=self.init_bgt - budget, - )[0].item() - - rank_pattern = {} - # Mask the unimportant triplets - with torch.no_grad(): - for n, p in model.named_parameters(): - if f"lora_E.{self.adapter_name}" in n: - p.masked_fill_(triplet_ipt[n] <= mask_threshold, 0.0) - rank_pattern[n] = (~(triplet_ipt[n] <= mask_threshold)).view(-1).tolist() - return rank_pattern - - def update_and_allocate(self, model, global_step, force_mask=False): - # # Update the importance score and allocate the budget - if global_step < self.peft_config.total_step - self.peft_config.tfinal: - self.update_ipt(model) - budget, mask_ind = self.budget_schedule(global_step) - # Allocate the budget according to importance scores - if mask_ind or force_mask: - rank_pattern = self.mask_to_budget(model, budget) - else: - rank_pattern = None - return budget, rank_pattern - - def mask_using_rank_pattern(self, model, rank_pattern): - # Mask the unimportant triplets - is_adapter_name_truncated = False - if self.adapter_name not in next(iter(rank_pattern.keys())): - is_adapter_name_truncated = True - - with torch.no_grad(): - for n, p in model.named_parameters(): - if f"lora_E.{self.adapter_name}" in n: - key = n if not is_adapter_name_truncated else n.replace(f".{self.adapter_name}", "") - mask = torch.Tensor(rank_pattern[key]).unsqueeze(-1).to(p.device) - p.masked_fill_(~mask.bool(), 0.0) \ No newline at end of file diff --git a/mftcoder_atorch/model/peft/tuner/bitfit.py b/mftcoder_atorch/model/peft/tuner/bitfit.py deleted file mode 100644 index 2e2a12f..0000000 --- a/mftcoder_atorch/model/peft/tuner/bitfit.py +++ /dev/null @@ -1,154 +0,0 @@ -import sys -sys.path.append("..") -sys.path.append("../..") -import torch -import importlib -from enum import Enum -from peft.utils import PeftType -from dataclasses import dataclass, field, asdict -from typing import Optional, List - -from .pe_base_model import PEBaseModel -from model.peft.utils import PetuningConfig -from model.peft.utils.others import _freeze_model - - -def is_alps_available(): - return importlib.util.find_spec("alps") is not None - - -if is_alps_available(): - from alps.util import logger -else: - import logging - logger = logging.getLogger(__file__) - - -class PEBitfitModel(PEBaseModel): - """ - 只训练模型bias：参考 https://arxiv.org/pdf/2106.10199.pdf - model: huggingface transformers model - tokenizer: huggingface transformers tokenizer - """ - - def __init__(self, model): - self.model = model - - def get_model(self): - not_freeze_param_name = ["bias"] - set_parameter_requires_grad(self.model, not_freeze_param_name) - return self.model - - @classmethod - def restore(self, model=None, path=None): - logger.info("bitfit不需要额外加载参数") - return model - - -# 根据名称锁定参数层 -def set_parameter_requires_grad(model, freeze_param_name=[]): - if not isinstance(freeze_param_name, list): - freeze_param_name = [freeze_param_name] - - for idx, (name, param) in enumerate(model.named_parameters()): - for p in freeze_param_name: - if p not in name: - param.requires_grad = False - # 打印参数层名 - for idx, (name, param) in enumerate(model.named_parameters()): - for p in freeze_param_name: - if p in name: - print("trainable parameter name is:") - print(name) - param.requires_grad = True - - -@dataclass -class PeftBitfitConfig(PetuningConfig): - """ - This is the configuration class to store the configuration of a [`PeftBitfitModel`]. - - Args: - modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable - and saved in the final checkpoint. - """ - - modules_to_save: Optional[List[str]] = field( - default=None, - metadata={ - "help": "List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. " - "For example, in Sequence Classification or Token Classification tasks, " - "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved." - }, - ) - - def __post_init__(self): - self.peft_type = PeftType.BITFIT - - -class PeftBitfitModel(torch.nn.Module): - """ - Creates Bitfit model for ant peft. - - Args: - model ([`~transformers.PreTrainedModel`]): The model to be freeze with some layers. - config ([`PeftBitfitConfig`]): The configuration of the Bitfit model. - - Returns: - `torch.nn.Module`: The Bitfit model. - - **Attributes**: - - **model** ([`~transformers.PreTrainedModel`]) -- The model to be freezed. - - **peft_config** ([`PeftBitfitConfig`]): The configuration of the Bitfit model. - """ - - def __init__(self, model, config, adapter_name): - super().__init__() - self.model = model - - self.forward = self.model.forward - self.peft_config = config - self.add_adapter(adapter_name, self.peft_config[adapter_name]) - - def add_adapter(self, adapter_name, config=None): - if not isinstance(config, PeftBitfitConfig): - raise ValueError( - f"The PeftBitfitModel need PeftBitfitConfig, but get {type(config)}." - ) - - if config is not None: - config = self._prepare_lora_config(config) - self.peft_config[adapter_name] = config - - if len(self.peft_config) > 1: - raise ValueError( - "BitfitModel supports only 1 peft config or name." - "Because it only freeze the shallow layers without any additional parameters." - ) - - self.model = PEBitfitModel(self.model).get_model() - - if self.peft_config[adapter_name].inference_mode: - _freeze_model(self.model) - - @staticmethod - def _prepare_lora_config(peft_config): - if peft_config.inference_mode: - peft_config.merge_weights = True - return peft_config - - def __getattr__(self, name: str): - """Forward missing attributes to the wrapped module.""" - try: - return super().__getattr__(name) # defer to nn.Module's logic - except AttributeError: - return getattr(self.model, name) - - def get_peft_config_as_dict(self, inference: bool = False): - config_dict = {} - for key, value in self.peft_config.items(): - config = {k: v.value if isinstance(v, Enum) else v for k, v in asdict(value).items()} - if inference: - config["inference_mode"] = True - config_dict[key] = config - return config \ No newline at end of file diff --git a/mftcoder_atorch/model/peft/tuner/pe_base_model.py b/mftcoder_atorch/model/peft/tuner/pe_base_model.py deleted file mode 100644 index bd4082a..0000000 --- a/mftcoder_atorch/model/peft/tuner/pe_base_model.py +++ /dev/null @@ -1,19 +0,0 @@ -class PEBaseModel: - """PEtuning的基类模型，定义了PEtuning模型都该有的方法""" - - def __init__(): - return - - def get_model(self): - """对模型进行修改，冻结参数或者插入可训模块""" - pass - - @classmethod - def restore(self, model=None, path=None): - """从path恢复PE模型 - - Args: - model (_type_, optional): 原始模型. Defaults to None. - path (_type_, optional): 增量路径. Defaults to None. - """ - pass diff --git a/mftcoder_atorch/model/peft/tuner/roem.py b/mftcoder_atorch/model/peft/tuner/roem.py deleted file mode 100644 index 9b455bf..0000000 --- a/mftcoder_atorch/model/peft/tuner/roem.py +++ /dev/null @@ -1,168 +0,0 @@ -import sys -sys.path.append("..") -sys.path.append("../..") -import torch -import importlib -from enum import Enum -from peft.utils import PeftType -from dataclasses import dataclass, field, asdict -from typing import Optional, List, Union - -from .pe_base_model import PEBaseModel -from model.peft.utils import ( - PetuningConfig, - TRANSFORMERS_MODELS_ROME_LAYER_MODULES_MAPPING -) -from model.peft.utils.others import _freeze_model - - -def is_alps_available(): - return importlib.util.find_spec("alps") is not None - - -if is_alps_available(): - from alps.util import logger -else: - import logging - logger = logging.getLogger(__file__) - - -class PEROEMModel(PEBaseModel): - """ - 只训练模型中间偏上层mlp：参考 https://arxiv.org/pdf/2202.05262.pdf ； https://arxiv.org/abs/2012.14913 - model: huggingface transformers model - tokenizer: huggingface transformers tokenizer - """ - - def __init__(self, model, model_name, task_type=None): - self.model = model - self.model_name = model_name - - def get_model(self): - layer_mapping = TRANSFORMERS_MODELS_ROME_LAYER_MODULES_MAPPING[self.model_name] - assert len(layer_mapping) == 2 - not_freeze_param_name = [] - for i in range(layer_mapping[0], layer_mapping[1]): - no_freeze_name = str(i) + ".mlp" - logger.info(f"Freeze the {no_freeze_name} layer of model") - not_freeze_param_name.append(no_freeze_name) - set_parameter_requires_grad(self.model, not_freeze_param_name) - return self.model - - @classmethod - def restore(self, model=None, path=None): - logger.info("roem不需要额外加载参数") - return model - - -# 根据名称锁定参数层 -def set_parameter_requires_grad(model, freeze_param_name=[]): - if not isinstance(freeze_param_name, list): - freeze_param_name = [freeze_param_name] - - for idx, (name, param) in enumerate(model.named_parameters()): - for p in freeze_param_name: - if p not in name: - param.requires_grad = False - # 打印参数层名 - for idx, (name, param) in enumerate(model.named_parameters()): - for p in freeze_param_name: - if p in name: - print("The name of used parameter used by ROEM is:") - print(name) - param.requires_grad = True - - -@dataclass -class PeftROEMConfig(PetuningConfig): - """ - This is the configuration class to store the configuration of a [`PeftROEMModel`]. - - Args: - target_layers (`Union[List[int], int]`): The names of the modules to apply Lora to. - """ - - target_layers: Optional[Union[List[int], int]] = field( - default=None, - metadata={ - "help": "List of layers of the model to freeze the parameters." - "For example, [20, 30] or '30' " - }, - ) - - def __post_init__(self): - self.peft_type = PeftType.ROEM - - -class PeftROEMModel(torch.nn.Module): - """ - Creates ROEM model for ant peft. - - Args: - model ([`~transformers.PreTrainedModel`]): The model to be freeze with some layers. - config ([`PeftROEMConfig`]): The configuration of the ROEM model. - - Returns: - `torch.nn.Module`: The ROEM model. - - **Attributes**: - - **model** ([`~transformers.PreTrainedModel`]) -- The model to be freezed. - - **peft_config** ([`PeftROEMConfig`]): The configuration of the ROEM model. - """ - - def __init__(self, model, config, adapter_name): - super().__init__() - self.model = model - - self.forward = self.model.forward - self.peft_config = config - self.add_adapter(adapter_name, self.peft_config[adapter_name]) - - def add_adapter(self, adapter_name, config=None): - if not isinstance(config, PeftROEMConfig): - raise ValueError( - f"The PeftROEMModel need PeftROEMConfig, but get {type(config)}." - ) - - model_config = self.model.config.to_dict() if hasattr(self.model.config, "to_dict") else self.model.config - if config is not None: - config = self._prepare_lora_config(config, model_config) - self.peft_config[adapter_name] = config - - if len(self.peft_config) > 1: - raise ValueError( - "ROEMModel supports only 1 peft config or name." - "Because it only freeze the shallow layers without any additional parameters." - ) - - model_name = model_config["model_type"] - self.model = PEROEMModel(self.model, model_name).get_model() - - if self.peft_config[adapter_name].inference_mode: - _freeze_model(self.model) - - @staticmethod - def _prepare_lora_config(peft_config, model_config): - if peft_config.target_layers is None: - if model_config["model_type"] not in TRANSFORMERS_MODELS_ROME_LAYER_MODULES_MAPPING: - raise ValueError("Please specify `target_layers` in `peft_config`") - peft_config.target_layers = TRANSFORMERS_MODELS_ROME_LAYER_MODULES_MAPPING[model_config["model_type"]] - if peft_config.inference_mode: - peft_config.merge_weights = True - return peft_config - - def __getattr__(self, name: str): - """Forward missing attributes to the wrapped module.""" - try: - return super().__getattr__(name) # defer to nn.Module's logic - except AttributeError: - return getattr(self.model, name) - - def get_peft_config_as_dict(self, inference: bool = False): - config_dict = {} - for key, value in self.peft_config.items(): - config = {k: v.value if isinstance(v, Enum) else v for k, v in asdict(value).items()} - if inference: - config["inference_mode"] = True - config_dict[key] = config - return config \ No newline at end of file diff --git a/mftcoder_atorch/model/peft/tuner/routelora.py b/mftcoder_atorch/model/peft/tuner/routelora.py deleted file mode 100644 index ccbe46f..0000000 --- a/mftcoder_atorch/model/peft/tuner/routelora.py +++ /dev/null @@ -1,622 +0,0 @@ -import sys -sys.path.append("..") -sys.path.append("../..") -import importlib -import re -import math -import warnings -from dataclasses import dataclass, field -from typing import Optional, Dict, List, Any, Union # noqa - -import torch -import torch.nn as nn -import torch.nn.functional as F -from peft.utils import transpose -from transformers.pytorch_utils import Conv1D -from model.peft.utils import TRANSFORMERS_MODELS_TO_ROUTELORA_TARGET_MODULES_MAPPING -from model.peft.tuner import PeftType - -from peft.tuners.lora import ( - LoraConfig, - LoraLayer, - LoraModel -) - - -def is_bnb_available(): - return importlib.util.find_spec("bitsandbytes") is not None - - -if is_bnb_available(): - import bitsandbytes as bnb - - -@dataclass -class RouteLoraConfig(LoraConfig): - """ - This is the configuration class to store the configuration of a [`~peft.RouteLora`]. - - Args: - - r (`int`): Lora attention dimension - - route_size (`int`): The number of router models. - - target_modules (`Union[List[str],str]`): The names of the modules to apply Lora to. - - lora_alpha (`float`): The alpha parameter for Lora scaling. - - lora_dropout (`float`): The dropout probability for Lora layers. - - merge_weights (`bool`): - Whether to merge the weights of the Lora layers with the base transformer model in `eval` mode. - - fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out) - - enable_lora ( `List[bool]`): Used with `lora.MergedLinear`. - - bias (`str`): Bias type for Lora. Can be 'none', 'all' or 'lora_only' - - modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable - and saved in the final checkpoint. - """ - - route_size: int = field(default=1, metadata={"help": "The size of router"}) - - def __post_init__(self): - self.peft_type = PeftType.ROUTELORA - - -class RouteLoraModel(LoraModel): - """ - Creates Low Rank Adapter (Lora) model from a pretrained transformers model. - - Args: - model ([`transformers.PreTrainedModel`]): The model to be adapted. - config ([`LoraConfig`]): The configuration of the Lora model. - - Returns: - `torch.nn.Module`: The Lora model. - - Example:: - - >>> from transformers import AutoModelForSeq2SeqLM, LoraConfig >>> from peft import LoraModel, LoraConfig >>> - config = LoraConfig( - peft_type="LORA", task_type="SEQ_2_SEQ_LM", r=8, lora_alpha=32, target_modules=["q", "v"], - lora_dropout=0.01, ) - >>> model = AutoModelForSeq2SeqLM.from_pretrained("t5-base") >>> lora_model = LoraModel(config, model) - - **Attributes**: - - **model** ([`transformers.PreTrainedModel`]) -- The model to be adapted. - - **peft_config** ([`LoraConfig`]): The configuration of the Lora model. - """ - - def __init__(self, config, model): - super(RouteLoraModel, self).__init__(config, model) - - self.route_size = self.peft_config.route_size - if self.route_size > 0: - self.activate_route_lora(self.route_size - 1) - - def _find_and_replace(self): - loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False) - if loaded_in_8bit and not is_bnb_available(): - raise ImportError( - "To use Lora with 8-bit quantization, please install the `bitsandbytes` package. " - "You can install it with `pip install bitsandbytes`." - ) - is_target_modules_in_base_model = False - kwargs = { - "r": self.peft_config.r, - "lora_alpha": self.peft_config.lora_alpha, - "lora_dropout": self.peft_config.lora_dropout, - "fan_in_fan_out": self.peft_config.fan_in_fan_out, - "merge_weights": self.peft_config.merge_weights or self.peft_config.inference_mode, - } - key_list = [key for key, _ in self.model.named_modules()] - for key in key_list: - if isinstance(self.peft_config.target_modules, str): - target_module_found = re.fullmatch(self.peft_config.target_modules, key) - else: - target_module_found = any(key.endswith(target_key) for target_key in self.peft_config.target_modules) - if target_module_found: - if not is_target_modules_in_base_model: - is_target_modules_in_base_model = True - parent, target, target_name = self._get_submodules(key) - bias = target.bias is not None - if loaded_in_8bit and isinstance(target, bnb.nn.Linear8bitLt): - kwargs.update( - { - "has_fp16_weights": target.state.has_fp16_weights, - "memory_efficient_backward": target.state.memory_efficient_backward, - "threshold": target.state.threshold, - "index": target.index, - } - ) - if self.peft_config.enable_lora is None: - new_module = Linear8bitLt(target.in_features, target.out_features, bias=bias, **kwargs) - else: - kwargs.update({ - "enable_lora": self.peft_config.enable_lora, - "route_size": self.peft_config.route_size - }) - new_module = MergedLinear8bitLt(target.in_features, target.out_features, bias=bias, **kwargs) - elif isinstance(target, torch.nn.Linear) and self.peft_config.enable_lora is None: - new_module = RouteLinear(target.in_features, target.out_features, bias=bias, **kwargs) - elif self.peft_config.enable_lora is not None: - kwargs.update({ - "enable_lora": self.peft_config.enable_lora, - "route_size": self.peft_config.route_size - }) - if isinstance(target, Conv1D): - in_features, out_features = ( - target.weight.ds_shape if hasattr(target.weight, "ds_shape") else target.weight.shape - ) - else: - in_features, out_features = target.in_features, target.out_features - if kwargs["fan_in_fan_out"]: - warnings.warn( - "fan_in_fan_out is set to True but the target module is not a Conv1D. " - "Setting fan_in_fan_out to False." - ) - kwargs["fan_in_fan_out"] = False - new_module = MergedRouteLinear(in_features, out_features, bias=bias, **kwargs) - self._replace_module(parent, target_name, new_module, target) - if not is_target_modules_in_base_model: - raise ValueError( - f"Target modules {self.peft_config.target_modules} not found in the base model. " - f"Please check the target modules and try again." - ) - - def expand_external_router(self, weight_path: str): - loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False) - if loaded_in_8bit: - raise NotImplementedError( - "The route lora method is not support for int quantization, " - "we will implement this fuction in the future." - ) - - states_dict = torch.load(weight_path) - external_key_list = states_dict.keys() - if "0" in external_key_list[0]: - raise NotImplementedError("The merge with other router is not support, pls wait.") - self.peft_config.route_size += 1 - - key_list = [key for key, _ in self.model.named_modules()] - for key in key_list: - if isinstance(self.peft_config.target_modules, str): - target_module_found = re.fullmatch(self.peft_config.target_modules, key) - else: - target_module_found = any(key.endswith(target_key) for target_key in self.peft_config.target_modules) - - if target_module_found: - expand_cnt = 0 - for external_key in external_key_list: - if external_key.beginwith(key): - if "0" in external_key: - raise NotImplementedError("The merge with other router is not support, pls wait.") - else: - _, target, _ = self._get_submodules(key) - target.route_size += 1 - weights = states_dict[external_key] - new_linear_moudle = nn.Linear(weights.size(0), weights.size(1)) - new_linear_moudle.weight.data = weights - new_linear_moudle.to(target.weights.device) - - if "lora_A" in external_key: - target.lora_A.append(new_linear_moudle) - else: - target.lora_B.append(new_linear_moudle) - expand_cnt += 1 - - assert expand_cnt == 2, ValueError("123") - - def activate_route_lora(self, route_id: int): - loaded_in_8bit = getattr(self.model, "is_loaded_in_8bit", False) - if loaded_in_8bit: - raise NotImplementedError( - "The route lora method is not support for int quantization, " - "we will implement this fuction in the future." - ) - - if route_id < self.route_size: - key_list = [key for key, _ in self.model.named_modules()] - for key in key_list: - if isinstance(self.peft_config.target_modules, str): - target_module_found = re.fullmatch(self.peft_config.target_modules, key) - else: - target_module_found = any( - key.endswith(target_key) for target_key in self.peft_config.target_modules) - if target_module_found: - _, target, _ = self._get_submodules(key) - target.activate_route_lora(route_id) - else: - warnings.warn("The route id need less than the route size," - f"but the route id is {route_id} " - f"and the route size is {self.route_size}.") - - @staticmethod - def _prepare_lora_config(peft_config, model_config): - if peft_config.target_modules is None: - if model_config["model_type"] not in TRANSFORMERS_MODELS_TO_ROUTELORA_TARGET_MODULES_MAPPING: - raise ValueError("Please specify `target_modules` in `peft_config`") - peft_config.target_modules = TRANSFORMERS_MODELS_TO_ROUTELORA_TARGET_MODULES_MAPPING[ - model_config["model_type"]] - if peft_config.inference_mode: - peft_config.merge_weights = True - return peft_config - - -# 以下代码基于https://github.com/microsoft/LoRA/blob/main/loralib/layers.py改进， -# 用于扩展RouteLora方法并适配AntNLP框架 - - -class RouteLinear(nn.Linear, LoraLayer): - # Lora implemented in a dense layer - def __init__( - self, - in_features: int, - out_features: int, - r: int = 0, - route_size: int = 1, - lora_alpha: int = 1, - lora_dropout: float = 0.0, - fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out) - merge_weights: bool = True, - **kwargs, - ): - nn.Linear.__init__(self, in_features, out_features, **kwargs) - LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights) - - self.fan_in_fan_out = fan_in_fan_out - self.route_size = route_size - - # Actual trainable parameters - self.active_route_id = None - if r > 0 and route_size > 0: - self.lora_A = nn.ParameterList() - self.lora_B = nn.ParameterList() - for _ in range(route_size): - self.lora_A.append(nn.Linear(in_features, r, bias=False)) - self.lora_B.append(nn.Linear(r, out_features, bias=False)) - self.scaling = self.lora_alpha / self.r - - # Freezing the pre-trained weight matrix - self.weight.requires_grad = False - self.reset_parameters() - if fan_in_fan_out: - self.weight.data = self.weight.data.T - - def reset_parameters(self): - nn.Linear.reset_parameters(self) - if hasattr(self, "lora_A"): - # initialize A the same way as the default for nn.Linear and B to zero - for sub_lora_A, sub_lora_B in zip(self.lora_A, self.lora_B): - nn.init.kaiming_uniform_(sub_lora_A, a=math.sqrt(5)) - nn.init.zeros_(sub_lora_B.weight) - - def activate_route_lora(self, route_id: int): - if route_id != self.active_route_id: - if route_id >= self.route_size: - warnings.warn(f"The choice route id is great than route size," - f"where the route id is {route_id} and route size is {self.route_size}.") - elif not self.merged: - self.active_route_id = route_id - elif self.merged and self.r > 0 and self.active_route_id is not None: - self.weight.data -= ( - transpose( - self.lora_B[self.active_route_id].weight @ self.lora_A[self.active_route_id].weight, - self.fan_in_fan_out - ) * self.scaling - ) - self.merged = False - - def train(self, mode: bool = True): - nn.Linear.train(self, mode) - self.lora_A.train(mode) - self.lora_B.train(mode) - if not mode and self.merge_weights and not self.merged and self.active_route_id is not None: - # Merge the weights and mark it - if self.r > 0: - self.weight.data += ( - transpose( - self.lora_B[self.active_route_id].weight @ self.lora_A[self.active_route_id].weight, - self.fan_in_fan_out - ) * self.scaling - ) - self.merged = True - elif self.merge_weights and self.merged and self.active_route_id is not None: - # Make sure that the weights are not merged - if self.r > 0: - self.weight.data -= ( - transpose( - self.lora_B[self.active_route_id].weight @ self.lora_A[self.active_route_id].weight, - self.fan_in_fan_out - ) * self.scaling - ) - self.merged = False - - def eval(self): - nn.Linear.eval(self) - self.lora_A.eval() - self.lora_B.eval() - - def forward(self, x: torch.Tensor): - if self.disable_adapters: - if self.r > 0 and self.merged and self.active_route_id is not None: - self.weight.data -= ( - transpose( - self.lora_B[self.active_route_id].weight @ self.lora_A[self.active_route_id].weight, - self.fan_in_fan_out - ) * self.scaling - ) - self.merged = False - return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) - elif self.r > 0 and not self.merged and self.active_route_id is not None: - result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) - if self.r > 0: - result += self.lora_B[self.active_route_id]( - self.lora_A[self.active_route_id](self.lora_dropout(x)) - ) * self.scaling - return result - else: - return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) - - -class MergedRouteLinear(nn.Linear, LoraLayer): - # Lora implemented in a dense layer - def __init__( - self, - in_features: int, - out_features: int, - r: int = 0, - route_size: int = 1, - lora_alpha: int = 1, - lora_dropout: float = 0.0, - enable_lora: List[bool] = [False], - fan_in_fan_out: bool = False, - merge_weights: bool = True, - **kwargs, - ): - nn.Linear.__init__(self, in_features, out_features, **kwargs) - LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=merge_weights) - if out_features % len(enable_lora) != 0: - raise ValueError("The length of enable_lora must divide out_features") - self.enable_lora = enable_lora - self.fan_in_fan_out = fan_in_fan_out - self.route_size = route_size - - # Actual trainable parameters - self.active_route_id = None - if r > 0 and route_size > 0 and any(enable_lora): - self.lora_A = nn.ParameterList() - self.lora_B = nn.ParameterList() - for _ in range(route_size): - self.lora_A.append(nn.Linear(in_features, r * sum(enable_lora), bias=False)) - self.lora_B.append(nn.Conv1d( - r * sum(enable_lora), - out_features // len(enable_lora) * sum(enable_lora), - kernel_size=1, - groups=2, - bias=False, - )) - self.scaling = self.lora_alpha / self.r - - # Freezing the pre-trained weight matrix - self.weight.requires_grad = False - - # Compute the indices - self.lora_ind = self.weight.new_zeros((out_features,), dtype=torch.bool).view(len(enable_lora), -1) - self.lora_ind[enable_lora, :] = True - self.lora_ind = self.lora_ind.view(-1) - self.reset_parameters() - if fan_in_fan_out: - self.weight.data = self.weight.data.T - - def reset_parameters(self): - nn.Linear.reset_parameters(self) - if hasattr(self, "lora_A"): - # initialize A the same way as the default for nn.Linear and B to zero - for sub_lora_A, sub_lora_B in zip(self.lora_A, self.lora_B): - nn.init.kaiming_uniform_(sub_lora_A.weight, a=math.sqrt(5)) - nn.init.zeros_(sub_lora_B.weight) - - def zero_pad(self, x): - result = x.new_zeros((*x.shape[:-1], self.out_features)) - result = result.view(-1, self.out_features) - result[:, self.lora_ind] = x.reshape(-1, self.out_features // len(self.enable_lora) * sum(self.enable_lora)) - return result.view((*x.shape[:-1], self.out_features)) - - def activate_route_lora(self, route_id: int): - if route_id != self.active_route_id: - if route_id >= self.route_size: - warnings.warn(f"The choice route id is great than route size," - f"where the route id is {route_id} and route size is {self.route_size}.") - elif not self.merged: - self.active_route_id = route_id - elif self.merged and self.r > 0 and self.active_route_id is not None: - delta_w = F.conv1d( - self.lora_A[self.active_route_id].weight.data.unsqueeze(0), - self.lora_B[self.active_route_id].weight.data.unsqueeze(-1), - groups=sum(self.enable_lora), - ).squeeze(0) - self.weight.data -= self.zero_pad(transpose(delta_w * self.scaling, self.fan_in_fan_out)) - self.merged = False - - def train(self, mode: bool = True): - nn.Linear.train(self, mode) - self.lora_A.train(mode) - self.lora_B.train(mode) - if not mode and self.merge_weights and not self.merged: - # Merge the weights and mark it - if self.r > 0 and self.active_route_id is not None and any(self.enable_lora): - delta_w = F.conv1d( - self.lora_A[self.active_route_id].weight.data.unsqueeze(0), - self.lora_B[self.active_route_id].weight.data.unsqueeze(-1), - groups=sum(self.enable_lora), - ).squeeze(0) - self.weight.data += self.zero_pad(transpose(delta_w * self.scaling, self.fan_in_fan_out)) - self.merged = True - elif self.merge_weights and self.merged: - # Make sure that the weights are not merged - if self.r > 0 and self.active_route_id is not None and any(self.enable_lora): - delta_w = F.conv1d( - self.lora_A[self.active_route_id].weight.data.unsqueeze(0), - self.lora_B[self.active_route_id].weight.data.unsqueeze(-1), - groups=sum(self.enable_lora), - ).squeeze(0) - self.weight.data -= self.zero_pad(transpose(delta_w * self.scaling, self.fan_in_fan_out)) - self.merged = False - - def eval(self): - nn.Linear.eval(self) - self.lora_A.eval() - self.lora_B.eval() - - def forward(self, x: torch.Tensor): - if self.disable_adapters: - if self.r > 0 and self.merged and any(self.enable_lora) and self.active_route_id is not None: - delta_w = F.conv1d( - self.lora_A[self.active_route_id].weight.data.unsqueeze(0), - self.lora_B[self.active_route_id].weight.data.unsqueeze(-1), - groups=sum(self.enable_lora), - ).squeeze(0) - self.weight.data -= self.zero_pad(transpose(delta_w * self.scaling, self.fan_in_fan_out)) - self.merged = False - return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) - elif self.merged: - return F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) - else: - result = F.linear(x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias) - if self.r > 0 and self.active_route_id is not None: - after_A = self.lora_A[self.active_route_id](self.lora_dropout(x)) - after_B = self.lora_B[self.active_route_id](after_A.transpose(-2, -1)).transpose(-2, -1) - result += self.zero_pad(after_B) * self.scaling - return result - - -if is_bnb_available(): - - class Linear8bitLt(bnb.nn.Linear8bitLt, LoraLayer): - # Lora implemented in a dense layer - def __init__( - self, - in_features, - out_features, - r: int = 0, - lora_alpha: int = 1, - lora_dropout: float = 0.0, - **kwargs, - ): - bnb.nn.Linear8bitLt.__init__( - self, - in_features, - out_features, - bias=kwargs.get("bias", True), - has_fp16_weights=kwargs.get("has_fp16_weights", True), - memory_efficient_backward=kwargs.get("memory_efficient_backward", False), - threshold=kwargs.get("threshold", 0.0), - index=kwargs.get("index", None), - ) - LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=False) - # Actual trainable parameters - if r > 0: - self.lora_A = nn.Linear(in_features, r, bias=False) - self.lora_B = nn.Linear(r, out_features, bias=False) - self.scaling = self.lora_alpha / self.r - # Freezing the pre-trained weight matrix - self.weight.requires_grad = False - self.reset_parameters() - - def reset_parameters(self): - if hasattr(self, "lora_A"): - # initialize A the same way as the default for nn.Linear and B to zero - nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5)) - nn.init.zeros_(self.lora_B.weight) - - def forward(self, x: torch.Tensor): - result = super().forward(x) - - if self.disable_adapters: - return result - elif self.r > 0: - if not torch.is_autocast_enabled(): - expected_dtype = result.dtype - - if x.dtype != torch.float32: - x = x.float() - output = self.lora_B(self.lora_A(self.lora_dropout(x))).to(expected_dtype) * self.scaling - result += output - else: - output = self.lora_B(self.lora_A(self.lora_dropout(x))) * self.scaling - result += output - return result - - class MergedLinear8bitLt(bnb.nn.Linear8bitLt, LoraLayer): - # Lora implemented in a dense layer - def __init__( - self, - in_features: int, - out_features: int, - r: int = 0, - lora_alpha: int = 1, - lora_dropout: float = 0.0, - enable_lora: List[bool] = [False], - **kwargs, - ): - bnb.nn.Linear8bitLt.__init__( - self, - in_features, - out_features, - bias=kwargs.get("bias", True), - has_fp16_weights=kwargs.get("has_fp16_weights", True), - memory_efficient_backward=kwargs.get("memory_efficient_backward", False), - threshold=kwargs.get("threshold", 0.0), - index=kwargs.get("index", None), - ) - LoraLayer.__init__(self, r=r, lora_alpha=lora_alpha, lora_dropout=lora_dropout, merge_weights=False) - if out_features % len(enable_lora) != 0: - raise ValueError("The length of enable_lora must divide out_features") - self.enable_lora = enable_lora - # Actual trainable parameters - if r > 0 and any(enable_lora): - self.lora_A = nn.Linear(in_features, r * sum(enable_lora), bias=False) - self.lora_B = nn.Conv1d( - r * sum(enable_lora), - out_features // len(enable_lora) * sum(enable_lora), - kernel_size=1, - groups=2, - bias=False, - ) - self.scaling = self.lora_alpha / self.r - # Freezing the pre-trained weight matrix - self.weight.requires_grad = False - # Compute the indices - self.lora_ind = self.weight.new_zeros((out_features,), dtype=torch.bool).view(len(enable_lora), -1) - self.lora_ind[enable_lora, :] = True - self.lora_ind = self.lora_ind.view(-1) - self.reset_parameters() - - def reset_parameters(self): - if hasattr(self, "lora_A"): - # initialize A the same way as the default for nn.Linear and B to zero - nn.init.kaiming_uniform_(self.lora_A.weight, a=math.sqrt(5)) - nn.init.zeros_(self.lora_B.weight) - - def zero_pad(self, x): - result = x.new_zeros((*x.shape[:-1], self.out_features)) - result = result.view(-1, self.out_features) - result[:, self.lora_ind] = x.reshape( - -1, self.out_features // len(self.enable_lora) * sum(self.enable_lora) - ) - return result.view((*x.shape[:-1], self.out_features)) - - def forward(self, x: torch.Tensor): - result = super().forward(x) - if self.disable_adapters: - return result - elif self.r > 0: - if not torch.is_autocast_enabled(): - expected_dtype = result.dtype - if x.dtype != torch.float32: - x = x.float() - after_A = self.lora_A(self.lora_dropout(x)) - after_B = self.lora_B(after_A.transpose(-2, -1)).transpose(-2, -1) - output = self.zero_pad(after_B).to(expected_dtype) * self.scaling - result += output - else: - after_A = self.lora_A(self.lora_dropout(x)) - after_B = self.lora_B(after_A.transpose(-2, -1)).transpose(-2, -1) - output = self.zero_pad(after_B) * self.scaling - result += output - return result \ No newline at end of file diff --git a/mftcoder_atorch/model/peft/tuner/unipelt.py b/mftcoder_atorch/model/peft/tuner/unipelt.py deleted file mode 100644 index a8445c3..0000000 --- a/mftcoder_atorch/model/peft/tuner/unipelt.py +++ /dev/null @@ -1,1021 +0,0 @@ -import sys -sys.path.append("..") -sys.path.append("../..") -from peft import get_peft_model, PeftModel -import math -import re -import warnings -import os -from dataclasses import asdict, dataclass, field -from enum import Enum -from typing import List, Optional, Union -from accelerate import dispatch_model, infer_auto_device_map -from accelerate.utils import get_balanced_memory -import inspect -import torch -import torch.nn as nn -import torch.nn.functional as F -from transformers.pytorch_utils import Conv1D - -from accelerate.hooks import ( - AlignDevicesHook, - add_hook_to_module, - remove_hook_from_submodules, -) -from peft.utils import ( - TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING, - ModulesToSaveWrapper, - PeftConfig, - WEIGHTS_NAME, - _freeze_adapter, - _get_submodules, - transpose, -) -from model.peft.tuner import PeftType -from peft.mapping import PEFT_TYPE_TO_CONFIG_MAPPING - - -from .pe_base_model import PEBaseModel - - -# coding=utf-8 -# Copyright 2023-present the HuggingFace Inc. team. -# -# 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. - - -class UniPELTModel(torch.nn.Module): - r""" - 改编自LoraModel - Args: - model ([`~transformers.PreTrainedModel`]): The model to be adapted. - config ([`LoraConfig`]): The configuration of the Lora model. - - Returns: - `torch.nn.Module`: The Lora model. - - **Attributes**: - - **model** ([`~transformers.PreTrainedModel`]) -- The model to be adapted. - - **peft_config** ([`LoraConfig`]): The configuration of the Lora model. - """ - - def __init__(self, model, config, adapter_name): - super().__init__() - self.model = model - self.forward = self.model.forward - self.peft_config = config - self.add_adapter(adapter_name, self.peft_config[adapter_name]) - - def add_adapter(self, adapter_name, config=None): - if config is not None: - model_config = ( - self.model.config.to_dict() - if hasattr(self.model.config, "to_dict") - else self.model.config - ) - config = self._prepare_lora_config(config, model_config) - self.peft_config[adapter_name] = config - self._find_and_replace(adapter_name) - if len(self.peft_config) > 1 and self.peft_config[adapter_name].bias != "none": - raise ValueError( - "LoraModel supports only 1 adapter with bias. \ - When using multiple adapters, set bias to 'none' for all adapters." - ) - mark_only_lora_as_trainable(self.model, self.peft_config[adapter_name].bias) - if self.peft_config[adapter_name].inference_mode: - _freeze_adapter(self.model, adapter_name) - - def _find_and_replace(self, adapter_name): - lora_config = self.peft_config[adapter_name] - - is_target_modules_in_base_model = False - kwargs = { - "r": lora_config.r, - "lora_alpha": lora_config.lora_alpha, - "lora_dropout": lora_config.lora_dropout, - "fan_in_fan_out": lora_config.fan_in_fan_out, - "init_lora_weights": lora_config.init_lora_weights, - } - key_list = [key for key, _ in self.model.named_modules()] - for key in key_list: - if isinstance(lora_config.target_modules, str): - target_module_found = re.fullmatch(lora_config.target_modules, key) - else: - target_module_found = any( - key.endswith(target_key) - for target_key in lora_config.target_modules - ) - if target_module_found: - if not is_target_modules_in_base_model: - is_target_modules_in_base_model = True - parent, target, target_name = _get_submodules(self.model, key) - if hasattr(target, "bias"): - bias = target.bias is not None - - if isinstance(target, GatedLoraLayer): - target.update_layer( - adapter_name, - lora_config.r, - lora_config.lora_alpha, - lora_config.lora_dropout, - lora_config.init_lora_weights, - ) - else: - if True: # lazy modification - if isinstance(target, torch.nn.Linear): - in_features, out_features = ( - target.in_features, - target.out_features, - ) - if kwargs["fan_in_fan_out"]: - warnings.warn( - "fan_in_fan_out is set to True but the target module is `torch.nn.Linear`. " - "Setting fan_in_fan_out to False." - ) - kwargs[ - "fan_in_fan_out" - ] = lora_config.fan_in_fan_out = False - elif isinstance(target, Conv1D): - in_features, out_features = ( - target.weight.ds_shape - if hasattr(target.weight, "ds_shape") - else target.weight.shape - ) - if not kwargs["fan_in_fan_out"]: - warnings.warn( - "fan_in_fan_out is set to False but the target module is `Conv1D`. " - "Setting fan_in_fan_out to True." - ) - kwargs[ - "fan_in_fan_out" - ] = lora_config.fan_in_fan_out = True - else: - raise ValueError( - f"Target module {target} is not supported. " - f"Currently, only `torch.nn.Linear` and `Conv1D` are supported." - ) - new_module = PELTLinear( - adapter_name, in_features, out_features, bias=bias, **kwargs - ) - - self._replace_module(parent, target_name, new_module, target) - if not is_target_modules_in_base_model: - raise ValueError( - f"Target modules {lora_config.target_modules} not found in the base model. " - f"Please check the target modules and try again." - ) - - def _replace_module(self, parent_module, child_name, new_module, old_module): - setattr(parent_module, child_name, new_module) - new_module.weight = old_module.weight - if hasattr(old_module, "bias"): - if old_module.bias is not None: - new_module.bias = old_module.bias - - if getattr(old_module, "state", None) is not None: - new_module.state = old_module.state - new_module.to(old_module.weight.device) - - # dispatch to correct device - for name, module in new_module.named_modules(): - if "lora_" in name: - module.to(old_module.weight.device) - - def __getattr__(self, name: str): - """Forward missing attributes to the wrapped module.""" - try: - return super().__getattr__(name) # defer to nn.Module's logic - except AttributeError: - return getattr(self.model, name) - - def get_peft_config_as_dict(self, inference: bool = False): - config_dict = {} - for key, value in self.peft_config.items(): - config = { - k: v.value if isinstance(v, Enum) else v - for k, v in asdict(value).items() - } - if inference: - config["inference_mode"] = True - config_dict[key] = config - return config - - def _set_adapter_layers(self, enabled=True): - for module in self.model.modules(): - if isinstance(module, GatedLoraLayer): - module.disable_adapters = False if enabled else True - - def enable_adapter_layers(self): - self._set_adapter_layers(enabled=True) - - def disable_adapter_layers(self): - self._set_adapter_layers(enabled=False) - - def set_adapter(self, adapter_name): - for module in self.model.modules(): - if isinstance(module, GatedLoraLayer): - if module.merged: - warnings.warn( - "Adapter cannot be set when the model is merged. Unmerging the model first." - ) - module.unmerge() - module.active_adapter = adapter_name - - def merge_adapter(self): - for module in self.model.modules(): - if isinstance(module, GatedLoraLayer): - module.merge() - - def unmerge_adapter(self): - for module in self.model.modules(): - if isinstance(module, GatedLoraLayer): - module.unmerge() - - @staticmethod - def _prepare_lora_config(peft_config, model_config): - if peft_config.target_modules is None: - if ( - model_config["model_type"] - not in TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING - ): - raise ValueError("Please specify `target_modules` in `peft_config`") - peft_config.target_modules = ( - TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING[ - model_config["model_type"] - ] - ) - if peft_config.inference_mode: - peft_config.merge_weights = True - return peft_config - - def merge_and_unload(self): - r""" - This method merges the LoRa layers into the base model. This is needed if someone wants to use the base model - as a standalone model. - """ - if getattr(self.config, "model_type", None) == "gpt2": - raise ValueError("GPT2 models are not supported for merging LORA layers") - - if getattr(self.model, "is_loaded_in_8bit", False): - raise ValueError( - "Cannot merge LORA layers when the model is loaded in 8-bit mode" - ) - - key_list = [key for key, _ in self.model.named_modules() if "lora" not in key] - for key in key_list: - try: - parent, target, target_name = _get_submodules(self.model, key) - except AttributeError: - continue - if isinstance(target, GatedLoraLayer): - bias = target.bias is not None - new_module = torch.nn.Linear( - target.in_features, target.out_features, bias=bias - ) - target.merge() - self._replace_module(parent, target_name, new_module, target) - - # save any additional trainable modules part of `modules_to_save` - if isinstance(target, ModulesToSaveWrapper): - setattr( - parent, target_name, target.modules_to_save[target.active_adapter] - ) - - return self.model - - def add_weighted_adapter(self, adapters, weights, adapter_name): - if len({self.peft_config[adapter].r for adapter in adapters}) != 1: - raise ValueError("All adapters must have the same r value") - self.peft_config[adapter_name] = self.peft_config[adapters[0]] - self.peft_config[adapter_name].lora_alpha = self.peft_config[adapters[0]].r - self._find_and_replace(adapter_name) - mark_only_lora_as_trainable(self.model, self.peft_config[adapter_name].bias) - _freeze_adapter(self.model, adapter_name) - key_list = [key for key, _ in self.model.named_modules() if "lora" not in key] - for key in key_list: - _, target, _ = _get_submodules(self.model, key) - if isinstance(target, GatedLoraLayer): - if adapter_name in target.lora_A: - target.lora_A[adapter_name].weight.data = ( - target.lora_A[adapter_name].weight.data * 0.0 - ) - target.lora_B[adapter_name].weight.data = ( - target.lora_B[adapter_name].weight.data * 0.0 - ) - for adapter, weight in zip(adapters, weights): - if adapter not in target.lora_A: - continue - target.lora_A[adapter_name].weight.data += ( - target.lora_A[adapter].weight.data - * weight - * target.scaling[adapter] - ) - target.lora_B[adapter_name].weight.data += ( - target.lora_B[adapter].weight.data * weight - ) - - elif adapter_name in target.lora_embedding_A: - target.lora_embedding_A[adapter_name].data = ( - target.lora_embedding_A[adapter_name].data * 0.0 - ) - target.lora_embedding_B[adapter_name].data = ( - target.lora_embedding_B[adapter_name].data * 0.0 - ) - for adapter, weight in zip(adapters, weights): - if adapter not in target.lora_embedding_A: - continue - target.lora_embedding_A[adapter_name].data += ( - target.lora_embedding_A[adapter].data - * weight - * target.scaling[adapter] - ) - target.lora_embedding_B[adapter_name].data += ( - target.lora_embedding_B[adapter].data * weight - ) - - -# Below code is based on https://github.com/microsoft/LoRA/blob/main/loralib/layers.py -# and modified to work with PyTorch FSDP - - -# ------------------------------------------------------------------------------------------ -# Copyright (c) Microsoft Corporation. All rights reserved. -# Licensed under the MIT License (MIT). See LICENSE in the repo root for license information. -# ------------------------------------------------------------------------------------------ - - -# had to adapt it for `lora_only` to work -def mark_only_lora_as_trainable(model: nn.Module, bias: str = "none") -> None: - for n, p in model.named_parameters(): - if "lora_" not in n: - p.requires_grad = False - if bias == "none": - return - elif bias == "all": - for n, p in model.named_parameters(): - if "bias" in n: - p.requires_grad = True - elif bias == "lora_only": - for m in model.modules(): - if ( - isinstance(m, GatedLoraLayer) - and hasattr(m, "bias") - and m.bias is not None - ): - m.bias.requires_grad = True - else: - raise NotImplementedError - - -class GatedLoraLayer: - def __init__( - self, - in_features: int, - out_features: int, - ): - """UniPELT里使用了带gate的Lora，在peft Lora上增加了`self.lora_gate`作为门控 - - Args: - in_features (int): _description_ - out_features (int): _description_ - """ - self.r = {} - self.lora_alpha = {} - self.scaling = {} - self.lora_dropout = nn.ModuleDict({}) - self.lora_A = nn.ModuleDict({}) - self.lora_B = nn.ModuleDict({}) - self.lora_gate = nn.ModuleDict({}) - # For Embedding layer - self.lora_embedding_A = nn.ParameterDict({}) - self.lora_embedding_B = nn.ParameterDict({}) - # Mark the weight as unmerged - self.merged = False - self.disable_adapters = False - self.in_features = in_features - self.out_features = out_features - - def update_layer( - self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights - ): - """在lora dict里添加新的实例。 - - Args: - adapter_name (_type_): _description_ - r (_type_): _description_ - lora_alpha (_type_): _description_ - lora_dropout (_type_): _description_ - init_lora_weights (_type_): _description_ - """ - self.r[adapter_name] = r - self.lora_alpha[adapter_name] = lora_alpha - if lora_dropout > 0.0: - lora_dropout_layer = nn.Dropout(p=lora_dropout) - else: - lora_dropout_layer = nn.Identity() - - self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer})) - # Actual trainable parameters - if r > 0: - self.lora_A.update( - nn.ModuleDict( - {adapter_name: nn.Linear(self.in_features, r, bias=False)} - ) - ) - self.lora_B.update( - nn.ModuleDict( - {adapter_name: nn.Linear(r, self.out_features, bias=False)} - ) - ) - self.scaling[adapter_name] = lora_alpha / r - self.lora_gate.update( - nn.ModuleDict( - {adapter_name: nn.Linear(self.in_features, 1, bias=False)} - ) - ) - if init_lora_weights: - self.reset_lora_parameters(adapter_name) - self.to(self.weight.device) - - def update_layer_embedding( - self, adapter_name, r, lora_alpha, lora_dropout, init_lora_weights - ): - self.r[adapter_name] = r - self.lora_alpha[adapter_name] = lora_alpha - if lora_dropout > 0.0: - lora_dropout_layer = nn.Dropout(p=lora_dropout) - else: - lora_dropout_layer = nn.Identity() - - self.lora_dropout.update(nn.ModuleDict({adapter_name: lora_dropout_layer})) - # Actual trainable parameters - if r > 0: - self.lora_embedding_A.update( - nn.ParameterDict( - { - adapter_name: nn.Parameter( - self.weight.new_zeros((r, self.in_features)) - ) - } - ) - ) - self.lora_embedding_B.update( - nn.ParameterDict( - { - adapter_name: nn.Parameter( - self.weight.new_zeros((self.out_features, r)) - ) - } - ) - ) - self.scaling[adapter_name] = lora_alpha / r - if init_lora_weights: - self.reset_lora_parameters(adapter_name) - self.to(self.weight.device) - - def reset_lora_parameters(self, adapter_name): - if adapter_name in self.lora_A.keys(): - # initialize A the same way as the default for nn.Linear and B to zero - nn.init.kaiming_uniform_(self.lora_A[adapter_name].weight, a=math.sqrt(5)) - nn.init.kaiming_uniform_(self.lora_gate[adapter_name].weight) - nn.init.zeros_(self.lora_B[adapter_name].weight) - if adapter_name in self.lora_embedding_A.keys(): - # initialize a the same way as the default for nn.linear and b to zero - nn.init.zeros_(self.lora_embedding_A[adapter_name]) - nn.init.normal_(self.lora_embedding_B[adapter_name]) - - -class PELTLinear(nn.Linear, GatedLoraLayer): - # GatedLora implemented in a dense layer - def __init__( - self, - adapter_name: str, - in_features: int, - out_features: int, - r: int = 0, - lora_alpha: int = 1, - lora_dropout: float = 0.0, - fan_in_fan_out: bool = False, # Set this to True if the layer to replace stores weight like (fan_in, fan_out) - **kwargs, - ): - init_lora_weights = kwargs.pop("init_lora_weights", True) - - nn.Linear.__init__(self, in_features, out_features, **kwargs) - GatedLoraLayer.__init__( - self, in_features=in_features, out_features=out_features - ) - # Freezing the pre-trained weight matrix - self.weight.requires_grad = False - - self.fan_in_fan_out = fan_in_fan_out - if fan_in_fan_out: - self.weight.data = self.weight.data.T - - nn.Linear.reset_parameters(self) - self.update_layer(adapter_name, r, lora_alpha, lora_dropout, init_lora_weights) - self.active_adapter = adapter_name - - def merge(self): - if self.active_adapter not in self.lora_A.keys(): - return - if self.merged: - warnings.warn("Already merged. Nothing to do.") - return - if self.r[self.active_adapter] > 0: - self.weight.data += ( - transpose( - self.lora_B[self.active_adapter].weight - @ self.lora_A[self.active_adapter].weight, - self.fan_in_fan_out, - ) - * self.scaling[self.active_adapter] - ) - self.merged = True - - def unmerge(self): - if self.active_adapter not in self.lora_A.keys(): - return - if not self.merged: - warnings.warn("Already unmerged. Nothing to do.") - return - if self.r[self.active_adapter] > 0: - self.weight.data -= ( - transpose( - self.lora_B[self.active_adapter].weight - @ self.lora_A[self.active_adapter].weight, - self.fan_in_fan_out, - ) - * self.scaling[self.active_adapter] - ) - self.merged = False - - def forward(self, x: torch.Tensor): - previous_dtype = x.dtype - - if self.active_adapter not in self.lora_A.keys(): - return F.linear( - x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias - ) - if self.disable_adapters: - if self.r[self.active_adapter] > 0 and self.merged: - self.unmerge() - result = F.linear( - x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias - ) - elif self.r[self.active_adapter] > 0 and not self.merged: - result = F.linear( - x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias - ) - - x = x.to(self.lora_A[self.active_adapter].weight.dtype) - - gate = self.lora_gate[self.active_adapter](x) - - result += ( - gate - * self.lora_B[self.active_adapter]( - self.lora_A[self.active_adapter]( - self.lora_dropout[self.active_adapter](x) - ) - ) - * self.scaling[self.active_adapter] - ) - else: - result = F.linear( - x, transpose(self.weight, self.fan_in_fan_out), bias=self.bias - ) - - result = result.to(previous_dtype) - - return result - - -@dataclass -class UniPELTConfig(PeftConfig): - """ - 因为是在Lor上增加门控，所以其他结构不变 - This is the configuration class to store the configuration of a [`LoraModel`]. - - Args: - r (`int`): Lora attention dimension. - target_modules (`Union[List[str],str]`): The names of the modules to apply Lora to. - lora_alpha (`float`): The alpha parameter for Lora scaling. - lora_dropout (`float`): The dropout probability for Lora layers. - fan_in_fan_out (`bool`): Set this to True if the layer to replace stores weight like (fan_in, fan_out). - For example, gpt-2 uses `Conv1D` which stores weights like (fan_in, fan_out) - and hence this should be set to `True`.: - bias (`str`): Bias type for Lora. Can be 'none', 'all' or 'lora_only' - modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable - and saved in the final checkpoint. - """ - - r: int = field(default=8, metadata={"help": "Lora attention dimension"}) - target_modules: Optional[Union[List[str], str]] = field( - default=None, - metadata={ - "help": "List of module names or regex expression of the module names to replace with Lora." - "For example, ['q', 'v'] or '.*decoder.*(SelfAttention|EncDecAttention).*(q|v)$' " - }, - ) - lora_alpha: int = field(default=None, metadata={"help": "Lora alpha"}) - lora_dropout: float = field(default=None, metadata={"help": "Lora dropout"}) - fan_in_fan_out: bool = field( - default=False, - metadata={ - "help": "Set this to True if the layer to replace stores weight like (fan_in, fan_out)" - }, - ) - bias: str = field( - default="none", - metadata={"help": "Bias type for Lora. Can be 'none', 'all' or 'lora_only'"}, - ) - modules_to_save: Optional[List[str]] = field( - default=None, - metadata={ - "help": "List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. " - "For example, in Sequence Classification or Token Classification tasks, " - "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved." - }, - ) - init_lora_weights: bool = field( - default=True, - metadata={"help": "Whether to initialize the weights of the Lora layers."}, - ) - - def __post_init__(self): - self.peft_type = PeftType.UNIPELT - - -class PEUniPELTModel(PEBaseModel): - def __init__(self, model, task_type, r, lora_alpha, lora_dropout, model_name): - """ - 实现了UniPELT: A Unified Framework for Parameter-Efficient Language Model Tuninghttps://arxiv.org/abs/2110.07577 - - - Args: - model (_type_): huggingface transformers model - task_type (_type_): "SEQ_CLS", "SEQ_2_SEQ_LM","CAUSAL_LM","TOKEN_CLS" - r (_type_): lora rank - lora_alpha (_type_): lora alpha - lora_dropout (_type_): The dropout probability for Lora layers. - model_name (_type_): model_name - - Raises: - NotImplementedError: _description_ - """ - self.base_model = model - if task_type not in ["SEQ_CLS", "SEQ_2_SEQ_LM", "CAUSAL_LM", "TOKEN_CLS"]: - raise NotImplementedError("this task_type is not supported") - from solutions.antllm.antllm.models.peft.utils import ( - TRANSFORMERS_MODELS_TO_LORA_LAGE_TARGET_MODULES_MAPPING, - ) - - self.config = UniPELTConfig( - task_type=task_type, - target_modules=TRANSFORMERS_MODELS_TO_LORA_LAGE_TARGET_MODULES_MAPPING[ - model_name - ], - inference_mode=False, - lora_alpha=lora_alpha, - r=r, - lora_dropout=lora_dropout, - ) - - def get_model(self): - self.pe_model = get_peft_model(model=self.base_model, peft_config=self.config) - return self.pe_model - - def get_model_state_dict(self, model, state_dict=None, adapter_name="default"): - """ - 改了PeftModel.save_pretrained，使其支持UniPELT - Get the state dict of the Peft model. - - Args: - model ([`PeftModel`]): The Peft model. When using torch.nn.DistributedDataParallel, DeepSpeed or FSDP, - the model should be the underlying model/unwrapped model (i.e. model.module). - state_dict (`dict`, *optional*, defaults to `None`): - The state dict of the model. If not provided, the state dict of the model - will be used. - """ - config = model.peft_config[adapter_name] - if state_dict is None: - state_dict = model.state_dict() - # if config.peft_type in (PeftType.LORA, PeftType.ADALORA): - # to_return = lora_state_dict(model, bias=model.peft_config.bias) - # adapted from `https://github.com/microsoft/LoRA/blob/main/loralib/utils.py` - # to be used directly with the state dict which is necessary when using DeepSpeed or FSDP - bias = config.bias - if bias == "none": - to_return = {k: state_dict[k] for k in state_dict if "lora_" in k} - elif bias == "all": - to_return = { - k: state_dict[k] for k in state_dict if "lora_" in k or "bias" in k - } - elif bias == "lora_only": - to_return = {} - for k in state_dict: - if "lora_" in k: - to_return[k] = state_dict[k] - bias_name = k.split("lora_")[0] + "bias" - if bias_name in state_dict: - to_return[bias_name] = state_dict[bias_name] - else: - raise NotImplementedError - to_return = { - k: v - for k, v in to_return.items() - if (("lora_" in k and adapter_name in k) or ("bias" in k)) - } - if config.peft_type == PeftType.ADALORA: - rank_pattern = config.rank_pattern - if rank_pattern is not None: - rank_pattern = { - k.replace(f".{adapter_name}", ""): v - for k, v in rank_pattern.items() - } - config.rank_pattern = rank_pattern - to_return = model.resize_state_dict_by_rank_pattern( - rank_pattern, to_return, adapter_name - ) - if model.modules_to_save is not None: - for key, value in state_dict.items(): - if any( - f"{module_name}.modules_to_save.{adapter_name}" in key - for module_name in model.modules_to_save - ): - to_return[key.replace("modules_to_save.", "")] = value - - to_return = {k.replace(f".{adapter_name}", ""): v for k, v in to_return.items()} - return to_return - - def save(self, save_directory, **kwargs): - r""" - 改了PeftModel.save_pretrained，使其支持UniPELT - This function saves the adapter model and the adapter configuration files to a directory, so that it can be - reloaded using the [`LoraModel.from_pretrained`] class method, and also used by the [`LoraModel.push_to_hub`] - method. - - Args: - save_directory (`str`): - Directory where the adapter model and configuration files will be saved (will be created if it does not - exist). - kwargs (additional keyword arguments, *optional*): - Additional keyword arguments passed along to the `push_to_hub` method. - """ - if os.path.isfile(save_directory): - raise ValueError( - f"Provided path ({save_directory}) should be a directory, not a file" - ) - os.makedirs(save_directory, exist_ok=True) - - for adapter_name, peft_config in self.pe_model.peft_config.items(): - # save only the trainable weights - output_state_dict = self.get_model_state_dict( - self.pe_model, - state_dict=kwargs.get("state_dict", None), - adapter_name=adapter_name, - ) - # logger.info(output_state_dict) - output_dir = ( - os.path.join(save_directory, adapter_name) - if adapter_name != "default" - else save_directory - ) - os.makedirs(output_dir, exist_ok=True) - torch.save(output_state_dict, os.path.join(output_dir, WEIGHTS_NAME)) - - # save the config and change the inference mode to `True` - if peft_config.base_model_name_or_path is None: - peft_config.base_model_name_or_path = ( - self.base_model.model.__dict__.get("name_or_path", None) - ) - inference_mode = peft_config.inference_mode - peft_config.inference_mode = True - peft_config.save_pretrained(output_dir) - peft_config.inference_mode = inference_mode - - @classmethod - def restore( - cls, - model=None, - path=None, - adapter_name="default", - is_trainable=False, - **kwargs, - ): - r""" - 改写了PeftModel.from_pretrained，使其支持UniPELT - Instantiate a [`LoraModel`] from a pretrained Lora configuration and weights. - - Args: - model ([`~transformers.PreTrainedModel`]): - The model to be adapted. The model should be initialized with the - [`~transformers.PreTrainedModel.from_pretrained`] method from the 🤗 Transformers library. - model_id (`str` or `os.PathLike`): - The name of the Lora configuration to use. Can be either: - - A string, the `model id` of a Lora configuration hosted inside a model repo on the Hugging Face - Hub. - - A path to a directory containing a Lora configuration file saved using the `save_pretrained` - method (`./my_lora_config_directory/`). - """ - from peft.mapping import ( - MODEL_TYPE_TO_PEFT_MODEL_MAPPING, - ) - - # load the config - config = PEFT_TYPE_TO_CONFIG_MAPPING[ - PeftConfig.from_pretrained( - path, subfolder=kwargs.get("subfolder", None) - ).peft_type - ].from_pretrained(path, subfolder=kwargs.get("subfolder", None)) - - if (getattr(model, "hf_device_map", None) is not None) and len( - set(model.hf_device_map.values()).intersection({"cpu", "disk"}) - ) > 0: - remove_hook_from_submodules(model) - - config.inference_mode = not is_trainable - - if config.task_type not in MODEL_TYPE_TO_PEFT_MODEL_MAPPING.keys(): - peft_model = cls(model, config, adapter_name) - else: - # for example model is of PeftModelForSeq2SeqLM - peft_model = MODEL_TYPE_TO_PEFT_MODEL_MAPPING[config.task_type]( - model, config, adapter_name - ) - peft_model = load_adapter(peft_model, path, adapter_name, **kwargs) - return peft_model - - -def load_adapter( - peft_model: PeftModel, - path, - adapter_name, - is_trainable=False, - **kwargs, -): - """改写了PeftModel.load_adapter，使其支持UniPELT - - Args: - peft_model (PeftModel): _description_ - path (_type_): _description_ - adapter_name (_type_): _description_ - is_trainable (bool, optional): _description_. Defaults to False. - - Returns: - _type_: _description_ - """ - if adapter_name not in peft_model.peft_config: - # load the config - peft_config = UniPELTConfig.from_pretrained( - path, subfolder=kwargs.get("subfolder", None) - ) - peft_config.inference_mode = not is_trainable - # base model is pretrained model - peft_model.base_model.add_adapter(adapter_name, peft_config) - - peft_model.set_additional_trainable_modules(peft_config, adapter_name) - - # load weights if any - path = ( - os.path.join(path, kwargs["subfolder"]) - if kwargs.get("subfolder", None) is not None - else path - ) - - if os.path.exists(os.path.join(path, WEIGHTS_NAME)): - filename = os.path.join(path, WEIGHTS_NAME) - else: - pass - - adapters_weights = torch.load( - filename, - map_location=torch.device("cuda" if torch.cuda.is_available() else "cpu"), - ) - # load the weights into the model - set_model_state_dict(peft_model, adapters_weights, adapter_name=adapter_name) - if ( - (getattr(peft_model, "hf_device_map", None) is not None) - and ( - len(set(peft_model.hf_device_map.values()).intersection({"cpu", "disk"})) - > 0 - ) - and len(peft_model.peft_config) == 1 - ): - device_map = kwargs.get("device_map", "auto") - max_memory = kwargs.get("max_memory", None) - offload_dir = kwargs.get("offload_folder", None) - offload_index = kwargs.get("offload_index", None) - - dispatch_model_kwargs = {} - # Safety checker for previous `accelerate` versions - # `offload_index` was introduced in https://github.com/huggingface/accelerate/pull/873/ - if "offload_index" in inspect.signature(dispatch_model).parameters: - dispatch_model_kwargs["offload_index"] = offload_index - - no_split_module_classes = peft_model._no_split_modules - - if device_map != "sequential": - max_memory = get_balanced_memory( - peft_model, - max_memory=max_memory, - no_split_module_classes=no_split_module_classes, - low_zero=(device_map == "balanced_low_0"), - ) - if isinstance(device_map, str): - device_map = infer_auto_device_map( - peft_model, - max_memory=max_memory, - no_split_module_classes=no_split_module_classes, - ) - dispatch_model( - peft_model, - device_map=device_map, - offload_dir=offload_dir, - **dispatch_model_kwargs, - ) - hook = AlignDevicesHook(io_same_device=True) - add_hook_to_module(peft_model.get_base_model(), hook) - - # Set model in evaluation mode to deactivate Dropout modules by default - peft_model.eval() - return peft_model - - -def set_model_state_dict( - model: PeftModel, peft_model_state_dict, adapter_name="default" -): - """ - 改写了peft.uitls下的set_peft_model_state_dict，使其支持UniPELT - Set the state dict of the Peft model. - - Args: - model ([`PeftModel`]): The Peft model. - peft_model_state_dict (`dict`): The state dict of the Peft model. - """ - config = model.peft_config[adapter_name] - state_dict = {} - if model.modules_to_save is not None: - for key, value in peft_model_state_dict.items(): - if any(module_name in key for module_name in model.modules_to_save): - for module_name in model.modules_to_save: - if module_name in key: - key = key.replace( - module_name, f"{module_name}.modules_to_save.{adapter_name}" - ) - break - state_dict[key] = value - else: - state_dict = peft_model_state_dict - - if config.peft_type in (PeftType.LORA, PeftType.ADALORA, PeftType.UNIPELT): - peft_model_state_dict = {} - for k, v in state_dict.items(): - if "lora_" in k: - suffix = k.split("lora_")[1] - if "." in suffix: - suffix_to_replace = ".".join(suffix.split(".")[1:]) - k = k.replace( - suffix_to_replace, f"{adapter_name}.{suffix_to_replace}" - ) - else: - k = f"{k}.{adapter_name}" - peft_model_state_dict[k] = v - else: - peft_model_state_dict[k] = v - if config.peft_type == PeftType.ADALORA: - rank_pattern = config.rank_pattern - if rank_pattern is not None: - model.resize_modules_by_rank_pattern(rank_pattern, adapter_name) - model.load_state_dict(peft_model_state_dict, strict=False) - - -def print_lora_parameters(model): - """debug 用，查看权重是否正确加载 - - Args: - model (_type_): _description_ - """ - for n, p in model.named_parameters(): - if "lora_B" in n: - print(n) - print(p) - # break - if "lora_A" in n: - print(n) - print(p) - # break - if "lora_gate" in n: - print(n) - print(p) - break diff --git a/mftcoder_atorch/model/peft/utils/__init__.py b/mftcoder_atorch/model/peft/utils/__init__.py deleted file mode 100644 index 240c9e9..0000000 --- a/mftcoder_atorch/model/peft/utils/__init__.py +++ /dev/null @@ -1,14 +0,0 @@ -"""peft utils interface.""" - -from .config import PeftConfig, PetuningConfig - -from .mapping import TRANSFORMERS_MODELS_ROME_LAYER_MODULES_MAPPING -from .mapping import TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING -from .mapping import TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING -from .mapping import TRANSFORMERS_MODELS_TO_LORA_LAGE_TARGET_MODULES_MAPPING -from .mapping import TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING -from .mapping import TRANSFORMERS_MODELS_TO_ROUTELORA_TARGET_MODULES_MAPPING -from .mapping import WEIGHTS_NAME, CONFIG_NAME -from .mapping import bloom_model_postprocess_past_key_value - -from .others import get_peft_model_state_dict, set_peft_model_state_dict, _freeze_model, prepare_model_for_kbit_training \ No newline at end of file diff --git a/mftcoder_atorch/model/peft/utils/config.py b/mftcoder_atorch/model/peft/utils/config.py deleted file mode 100644 index cc316a6..0000000 --- a/mftcoder_atorch/model/peft/utils/config.py +++ /dev/null @@ -1,29 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -import sys -sys.path.append("..") -sys.path.append("../..") -from typing import List, Optional -from dataclasses import dataclass, field -from peft.utils import PeftConfig - - -@dataclass -class PetuningConfig(PeftConfig): - """ - This is the base configuration class to store the configuration of [`ROEM`], or [`BitFit`]. - - Args: - modules_to_save (`List[str]`):List of modules apart from LoRA layers to be set as trainable - and saved in the final checkpoint. - """ - - modules_to_save: Optional[List[str]] = field( - default=None, - metadata={ - "help": "List of modules apart from LoRA layers to be set as trainable and saved in the final checkpoint. " - "For example, in Sequence Classification or Token Classification tasks, " - "the final layer `classifier/score` are randomly initialized and as such need to be trainable and saved." - }, - ) \ No newline at end of file diff --git a/mftcoder_atorch/model/peft/utils/mapping.py b/mftcoder_atorch/model/peft/utils/mapping.py deleted file mode 100644 index fc14125..0000000 --- a/mftcoder_atorch/model/peft/utils/mapping.py +++ /dev/null @@ -1,141 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -import sys -sys.path.append("..") -sys.path.append("../..") -import torch -from peft.utils import ( - TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING, - TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING -) - - -# needed for prefix-tuning of bloom model -def bloom_model_postprocess_past_key_value(past_key_values): - past_key_values = torch.cat(past_key_values) - ( - total_layers, - batch_size, - num_attention_heads, - num_virtual_tokens, - head_dim, - ) = past_key_values.shape - keys = past_key_values[: total_layers // 2] - keys = keys.transpose(2, 3).reshape( - total_layers // 2, - batch_size * num_attention_heads, - head_dim, - num_virtual_tokens, - ) - values = past_key_values[total_layers // 2 :] - values = values.reshape( - total_layers // 2, - batch_size * num_attention_heads, - num_virtual_tokens, - head_dim, - ) - - return tuple(zip(keys, values)) - - -NEW_TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING = { - "t5": ["q", "v"], - "mt5": ["q", "v"], - "bart": ["q_proj", "v_proj"], - "gpt2": ["c_attn"], - "bloom": ["query_key_value"], - "bloomz": ["query_key_value"], - "blip-2": ["q", "v", "q_proj", "v_proj"], - "opt": ["q_proj", "v_proj"], - "gptj": ["q_proj", "v_proj"], - "gpt_neox": ["query_key_value"], - "gpt_neo": ["q_proj", "v_proj"], - "bert": ["query", "value"], - "roberta": ["query", "value"], - "xlm-roberta": ["query", "value"], - "electra": ["query", "value"], - "deberta-v2": ["query_proj", "value_proj"], - "deberta": ["in_proj"], - "layoutlm": ["query", "value"], - "llama": ["q_proj", "v_proj"], - "chatglm": ["query_key_value"], - "glm": ["query_key_value"], -} - -NEW_TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING = { - "t5": ["q", "k", "v", "o", "wi", "wo"], - "mt5": ["q", "k", "v", "o", "wi_0", "wi_1", "wo"], - "bart": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"], - # "gpt2": ["c_attn"], - "bloom": ["query_key_value"], - "bloomz": ["query_key_value"], - "opt": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"], - # "gptj": ["q_proj", "v_proj"], - # "gpt_neox": ["query_key_value"], - # "gpt_neo": ["q_proj", "v_proj"], - # "bert": ["query", "value"], - "roberta": ["query", "key", "value", "dense"], - # "xlm-roberta": ["query", "value"], - # "electra": ["query", "value"], - "deberta-v2": ["query_proj", "key_proj", "value_proj", "dense"], - "chatglm": ["query_key_value"], - "glm": ["query_key_value"], - # "deberta": ["in_proj"], - # "layoutlm": ["query", "value"], -} - -TRANSFORMERS_MODELS_TO_LORA_LAGE_TARGET_MODULES_MAPPING = { - "t5": ["q", "k", "v", "o", "wi", "wo"], - "mt5": ["q", "k", "v", "o", "wi_0", "wi_1", "wo"], - "bart": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"], - # "gpt2": ["c_attn"], - "bloom": ["query_key_value", "dense", "dense_h_to_4h", "dense_4h_to_h"], - "bloomz": ["query_key_value", "dense", "dense_h_to_4h", "dense_4h_to_h"], - "opt": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"], - # "gptj": ["q_proj", "v_proj"], - # "gpt_neox": ["query_key_value"], - # "gpt_neo": ["q_proj", "v_proj"], - # "bert": ["query", "value"], - "roberta": ["query", "key", "value", "dense"], - # "xlm-roberta": ["query", "value"], - # "electra": ["query", "value"], - "llama": ["q_proj", "v_proj"], - "deberta-v2": ["query_proj", "key_proj", "value_proj", "dense"], - "glm": ["query_key_value", "dense"] - # "deberta": ["in_proj"], - # "layoutlm": ["query", "value"], -} - -TRANSFORMERS_MODELS_TO_ROUTELORA_TARGET_MODULES_MAPPING = { - "t5": ["q", "k", "v", "o", "wi", "wo"], - "mt5": ["q", "k", "v", "o", "wi_0", "wi_1", "wo"], - "bart": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"], - "opt": ["q_proj", "k_proj", "v_proj", "out_proj", "fc1", "fc2"], - "roberta": ["query", "key", "value", "dense"], - "deberta-v2": ["query_proj", "key_proj", "value_proj", "dense"], - "chatglm": ["query_key_value"], - "glm": ["query_key_value"] -} - -TRANSFORMERS_MODELS_ROME_LAYER_MODULES_MAPPING = { - "glm": [0, 22], - "bloom": [17, 22], - "bloomz": [17, 22], -} - -TRANSFORMERS_MODELS_TO_PREFIX_TUNING_POSTPROCESS_MAPPING = { - "bloom": bloom_model_postprocess_past_key_value, - "bloomz": bloom_model_postprocess_past_key_value, -} - -WEIGHTS_NAME = "adapter_model.bin" -CONFIG_NAME = "adapter_config.json" - - -TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING.update( - NEW_TRANSFORMERS_MODELS_TO_LORA_TARGET_MODULES_MAPPING -) -TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING.update( - NEW_TRANSFORMERS_MODELS_TO_ADALORA_TARGET_MODULES_MAPPING -) diff --git a/mftcoder_atorch/model/peft/utils/others.py b/mftcoder_atorch/model/peft/utils/others.py deleted file mode 100644 index 65da580..0000000 --- a/mftcoder_atorch/model/peft/utils/others.py +++ /dev/null @@ -1,318 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -import sys -sys.path.append("..") -sys.path.append("../..") -import copy - -import torch -from .config import PetuningConfig -from peft.utils import PromptLearningConfig, PeftType - - -def prepare_model_for_int8_training(model, use_gradient_checkpointing=True): - r""" - This method wraps the entire protocol for preparing a model before running a training. This includes: - 1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm - head to fp32 - - Args: - model, (`transformers.PreTrainedModel`): - The loaded model from `transformers` - """ - loaded_in_8bit = getattr(model, "is_loaded_in_8bit", False) - - for name, param in model.named_parameters(): - # freeze base model's layers - param.requires_grad = False - - # cast all non INT8 parameters to fp32 - for param in model.parameters(): - if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16): - param.data = param.data.to(torch.float32) - - if loaded_in_8bit and use_gradient_checkpointing: - # For backward compatibility - if hasattr(model, "enable_input_require_grads"): - model.enable_input_require_grads() - else: - - def make_inputs_require_grad(module, input, output): - output.requires_grad_(True) - - model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) - - # enable gradient checkpointing for memory efficiency - model.gradient_checkpointing_enable() - - return model - - -def prepare_model_for_kbit_training(model, use_gradient_checkpointing=True): - r""" - This method wraps the entire protocol for preparing a model before running a training. This includes: - 1- Cast the layernorm in fp32 2- making output embedding layer require grads 3- Add the upcasting of the lm - head to fp32 - - Args: - model, (`transformers.PreTrainedModel`): - The loaded model from `transformers` - """ - loaded_in_kbit = getattr(model, "is_loaded_in_8bit", False) or getattr(model, "is_loaded_in_4bit", False) - - for name, param in model.named_parameters(): - # freeze base model's layers - param.requires_grad = False - - # cast all non INT8 parameters to fp32 - for param in model.parameters(): - if (param.dtype == torch.float16) or (param.dtype == torch.bfloat16): - param.data = param.data.to(torch.float32) - - if loaded_in_kbit and use_gradient_checkpointing: - # For backward compatibility - if hasattr(model, "enable_input_require_grads"): - model.enable_input_require_grads() - else: - - def make_inputs_require_grad(module, input, output): - output.requires_grad_(True) - - model.get_input_embeddings().register_forward_hook(make_inputs_require_grad) - - # enable gradient checkpointing for memory efficiency - model.gradient_checkpointing_enable() - - return model - - -# copied from transformers.models.bart.modeling_bart -def shift_tokens_right(input_ids: torch.Tensor, pad_token_id: int, decoder_start_token_id: int): - """ - Shift input ids one token to the right. - - Args: - input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): input ids - pad_token_id (`int`): The id of the `padding` token. - decoder_start_token_id (`int`): The id of the `start` token. - """ - shifted_input_ids = input_ids.new_zeros(input_ids.shape) - shifted_input_ids[:, 1:] = input_ids[:, :-1].clone() - shifted_input_ids[:, 0] = decoder_start_token_id - - if pad_token_id is None: - raise ValueError("self.model.config.pad_token_id has to be defined.") - # replace possible -100 values in labels by `pad_token_id` - shifted_input_ids.masked_fill_(shifted_input_ids == -100, pad_token_id) - - return shifted_input_ids - - -class ModulesToSaveWrapper(torch.nn.Module): - def __init__(self, module_to_save, adapter_name): - super().__init__() - self.original_module = module_to_save - self.modules_to_save = torch.nn.ModuleDict({}) - self.update(adapter_name) - self.active_adapter = adapter_name - - def update(self, adapter_name): - self.modules_to_save.update(torch.nn.ModuleDict({adapter_name: copy.deepcopy(self.original_module)})) - - def forward(self, *args, **kwargs): - if self.active_adapter not in self.modules_to_save: - return self.original_module(*args, **kwargs) - return self.modules_to_save[self.active_adapter](*args, **kwargs) - - -def _get_submodules(model, key): - parent = model.get_submodule(".".join(key.split(".")[:-1])) - target_name = key.split(".")[-1] - target = model.get_submodule(key) - return parent, target, target_name - - -def _freeze_adapter(model, adapter_name): - for n, p in model.named_parameters(): - if adapter_name in n: - p.requires_grad = False - - -def _freeze_model(model): - for n, p in model.named_parameters(): - p.requires_grad = False - - -def _set_trainable(model, adapter_name): - key_list = [key for key, _ in model.named_modules()] - for key in key_list: - target_module_found = any(key.endswith(target_key) for target_key in model.modules_to_save) - if target_module_found: - parent, target, target_name = _get_submodules(model, key) - if isinstance(target, ModulesToSaveWrapper): - target.update(adapter_name) - else: - for param in target.parameters(): - param.requires_grad = True - setattr(parent, target_name, ModulesToSaveWrapper(target, adapter_name)) - - -def _set_adapter(model, adapter_name): - for module in model.modules(): - if isinstance(module, ModulesToSaveWrapper): - module.active_adapter = adapter_name - - -def fsdp_auto_wrap_policy(model): - import functools - import os - - from accelerate import FullyShardedDataParallelPlugin - from torch.distributed.fsdp.wrap import _or_policy, lambda_auto_wrap_policy, transformer_auto_wrap_policy - - from peft.tuners import PrefixEncoder, PromptEmbedding, PromptEncoder - - def lambda_policy_fn(module): - if ( - len(list(module.named_children())) == 0 - and getattr(module, "weight", None) is not None - and module.weight.requires_grad - ): - return True - return False - - lambda_policy = functools.partial(lambda_auto_wrap_policy, lambda_fn=lambda_policy_fn) - transformer_wrap_policy = functools.partial( - transformer_auto_wrap_policy, - transformer_layer_cls=( - PrefixEncoder, - PromptEncoder, - PromptEmbedding, - FullyShardedDataParallelPlugin.get_module_class_from_name( - model, os.environ.get("FSDP_TRANSFORMER_CLS_TO_WRAP", "") - ), - ), - ) - - auto_wrap_policy = functools.partial(_or_policy, policies=[lambda_policy, transformer_wrap_policy]) - return auto_wrap_policy - - -def transpose(weight, fan_in_fan_out): - return weight.T if fan_in_fan_out else weight - - -def get_peft_model_state_dict(model, state_dict=None, adapter_name="default"): - """ - Get the state dict of the Peft model. - - Args: - model ([`PeftModel`]): The Peft model. When using torch.nn.DistributedDataParallel, DeepSpeed or FSDP, - the model should be the underlying model/unwrapped model (i.e. model.module). - state_dict (`dict`, *optional*, defaults to `None`): - The state dict of the model. If not provided, the state dict of the model - will be used. - """ - config = model.peft_config[adapter_name] - if state_dict is None: - state_dict = model.state_dict() - if config.peft_type in (PeftType.LORA, PeftType.ADALORA, PeftType.ROUTELORA, PeftType.UNIPELT): - # to_return = lora_state_dict(model, bias=model.peft_config.bias) - # adapted from `https://github.com/microsoft/LoRA/blob/main/loralib/utils.py` - # to be used directly with the state dict which is necessary when using DeepSpeed or FSDP - bias = config.bias - if bias == "none": - to_return = {k: state_dict[k] for k in state_dict if "lora_" in k} - elif bias == "all": - to_return = {k: state_dict[k] for k in state_dict if "lora_" in k or "bias" in k} - elif bias == "lora_only": - to_return = {} - for k in state_dict: - if "lora_" in k: - to_return[k] = state_dict[k] - bias_name = k.split("lora_")[0] + "bias" - if bias_name in state_dict: - to_return[bias_name] = state_dict[bias_name] - else: - raise NotImplementedError - to_return = {k: v for k, v in to_return.items() if (("lora_" in k and adapter_name in k) or ("bias" in k))} - if config.peft_type == PeftType.ADALORA: - rank_pattern = config.rank_pattern - if rank_pattern is not None: - rank_pattern = {k.replace(f".{adapter_name}", ""): v for k, v in rank_pattern.items()} - config.rank_pattern = rank_pattern - to_return = model.resize_state_dict_by_rank_pattern(rank_pattern, to_return, adapter_name) - - elif config.peft_type == PeftType.ADAPTION_PROMPT: - to_return = {k: state_dict[k] for k in state_dict if k.split(".")[-1].startswith("adaption_")} - elif isinstance(config, PromptLearningConfig): - to_return = {} - if config.inference_mode: - prompt_embeddings = model.prompt_encoder[adapter_name].embedding.weight - else: - prompt_embeddings = model.get_prompt_embedding_to_save(adapter_name) - to_return["prompt_embeddings"] = prompt_embeddings - elif isinstance(config, PetuningConfig): - to_return = state_dict - else: - raise NotImplementedError - if model.modules_to_save is not None: - for key, value in state_dict.items(): - if any(f"{module_name}.modules_to_save.{adapter_name}" in key for module_name in model.modules_to_save): - to_return[key.replace("modules_to_save.", "")] = value - - to_return = {k.replace(f".{adapter_name}", ""): v for k, v in to_return.items()} - return to_return - - -def set_peft_model_state_dict(model, peft_model_state_dict, adapter_name="default"): - """ - Set the state dict of the Peft model. - - Args: - model ([`PeftModel`]): The Peft model. - peft_model_state_dict (`dict`): The state dict of the Peft model. - """ - config = model.peft_config[adapter_name] - state_dict = {} - if model.modules_to_save is not None: - for key, value in peft_model_state_dict.items(): - if any(module_name in key for module_name in model.modules_to_save): - for module_name in model.modules_to_save: - if module_name in key: - key = key.replace(module_name, f"{module_name}.modules_to_save.{adapter_name}") - break - state_dict[key] = value - else: - state_dict = peft_model_state_dict - - if config.peft_type in (PeftType.LORA, PeftType.ADALORA, PeftType.ROUTELORA, PeftType.UNIPELT): - peft_model_state_dict = {} - for k, v in state_dict.items(): - if "lora_" in k: - suffix = k.split("lora_")[1] - if "." in suffix: - suffix_to_replace = ".".join(suffix.split(".")[1:]) - k = k.replace(suffix_to_replace, f"{adapter_name}.{suffix_to_replace}") - else: - k = f"{k}.{adapter_name}" - peft_model_state_dict[k] = v - else: - peft_model_state_dict[k] = v - if config.peft_type == PeftType.ADALORA: - rank_pattern = config.rank_pattern - if rank_pattern is not None: - model.resize_modules_by_rank_pattern(rank_pattern, adapter_name) - elif isinstance(config, PromptLearningConfig) or config.peft_type == PeftType.ADAPTION_PROMPT: - peft_model_state_dict = state_dict - elif isinstance(config, PetuningConfig): - peft_model_state_dict = state_dict - else: - raise NotImplementedError - - model.load_state_dict(peft_model_state_dict, strict=False) - if isinstance(config, PromptLearningConfig): - model.prompt_encoder[adapter_name].embedding.load_state_dict( - {"weight": peft_model_state_dict["prompt_embeddings"]}, strict=True - ) diff --git a/mftcoder_atorch/tokenizer/__init__.py b/mftcoder_atorch/tokenizer/__init__.py deleted file mode 100644 index 22b0f7b..0000000 --- a/mftcoder_atorch/tokenizer/__init__.py +++ /dev/null @@ -1,16 +0,0 @@ -# Copyright (c) 2020, NVIDIA CORPORATION. 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. - - -from .tokenizer import build_tokenizer diff --git a/mftcoder_atorch/tokenizer/gpt2_tokenization.py b/mftcoder_atorch/tokenizer/gpt2_tokenization.py deleted file mode 100644 index f8a1128..0000000 --- a/mftcoder_atorch/tokenizer/gpt2_tokenization.py +++ /dev/null @@ -1,368 +0,0 @@ -# Copyright (c) 2021, EleutherAI -# This file is based on code by the authors denoted below and has been modified from its original version. -# -# Copyright 2018 The Open AI Team Authors and The HuggingFace Inc. team. -# -# 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 OpenAI GPT.""" - -from __future__ import absolute_import, division, print_function, unicode_literals - -import sys -import json -import logging -import os -import regex as re -from io import open - -from functools import lru_cache - - -logger = logging.getLogger(__name__) - -PRETRAINED_VOCAB_ARCHIVE_MAP = { - "gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-vocab.json", -} -PRETRAINED_MERGES_ARCHIVE_MAP = { - "gpt2": "https://s3.amazonaws.com/models.huggingface.co/bert/gpt2-merges.txt", -} -PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = { - "gpt2": 1024, -} - -VOCAB_NAME = "vocab.json" -MERGES_NAME = "merges.txt" -SPECIAL_TOKENS_NAME = "special_tokens.txt" - - -@lru_cache() -def bytes_to_unicode(): - """ - Returns list of utf-8 byte and a corresponding list of unicode strings. - The reversible bpe codes work on unicode strings. - This means you need a large # of unicode characters in your vocab if you want to avoid UNKs. - When you're at something like a 10B token dataset you end up needing around 5K for decent coverage. - This is a significant percentage of your normal, say, 32K bpe vocab. - To avoid that, we want lookup tables between utf-8 bytes and unicode strings. - And avoids mapping to whitespace/control characters the bpe code barfs on. - """ - _chr = unichr if sys.version_info[0] == 2 else chr - bs = ( - list(range(ord("!"), ord("~") + 1)) - + list(range(ord("¡"), ord("¬") + 1)) - + list(range(ord("®"), ord("ÿ") + 1)) - ) - cs = bs[:] - n = 0 - for b in range(2**8): - if b not in bs: - bs.append(b) - cs.append(2**8 + n) - n += 1 - cs = [_chr(n) for n in cs] - return dict(zip(bs, cs)) - - -def get_pairs(word): - """Return set of symbol pairs in a word. - - Word is represented as tuple of symbols (symbols being variable-length strings). - """ - pairs = set() - prev_char = word[0] - for char in word[1:]: - pairs.add((prev_char, char)) - prev_char = char - return pairs - - -class GPT2Tokenizer(object): - """ - GPT-2 BPE tokenizer. Peculiarities: - - Byte-level BPE - """ - - @classmethod - def from_pretrained( - cls, pretrained_model_name_or_path, cache_dir=None, *inputs, **kwargs - ): - """ - Instantiate a PreTrainedBertModel from a pre-trained model file. - Download and cache the pre-trained model file if needed. - """ - if pretrained_model_name_or_path in PRETRAINED_VOCAB_ARCHIVE_MAP: - vocab_file = PRETRAINED_VOCAB_ARCHIVE_MAP[pretrained_model_name_or_path] - merges_file = PRETRAINED_MERGES_ARCHIVE_MAP[pretrained_model_name_or_path] - special_tokens_file = None - else: - vocab_file = os.path.join(pretrained_model_name_or_path, VOCAB_NAME) - merges_file = os.path.join(pretrained_model_name_or_path, MERGES_NAME) - special_tokens_file = os.path.join( - pretrained_model_name_or_path, SPECIAL_TOKENS_NAME - ) - if not os.path.exists(special_tokens_file): - special_tokens_file = None - else: - logger.info( - "loading special tokens file {}".format(special_tokens_file) - ) - # redirect to the cache, if necessary - try: - from .file_utils import cached_path - - resolved_vocab_file = cached_path(vocab_file, cache_dir=cache_dir) - resolved_merges_file = cached_path(merges_file, cache_dir=cache_dir) - except EnvironmentError: - logger.error( - "Model name '{}' was not found in model name list ({}). " - "We assumed '{}' was a path or url but couldn't find files {} and {} " - "at this path or url.".format( - pretrained_model_name_or_path, - ", ".join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()), - pretrained_model_name_or_path, - vocab_file, - merges_file, - ) - ) - return None - if resolved_vocab_file == vocab_file and resolved_merges_file == merges_file: - logger.info("loading vocabulary file {}".format(vocab_file)) - logger.info("loading merges file {}".format(merges_file)) - else: - logger.info( - "loading vocabulary file {} from cache at {}".format( - vocab_file, resolved_vocab_file - ) - ) - logger.info( - "loading merges file {} from cache at {}".format( - merges_file, resolved_merges_file - ) - ) - if ( - pretrained_model_name_or_path - in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP - ): - # if we're using a pretrained model, ensure the tokenizer won't index sequences longer - # than the number of positional embeddings - max_len = PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP[ - pretrained_model_name_or_path - ] - kwargs["max_len"] = min(kwargs.get("max_len", int(1e12)), max_len) - # Instantiate tokenizer. - if special_tokens_file and "special_tokens" not in kwargs: - special_tokens = ( - open(special_tokens_file, encoding="utf-8").read().split("\n")[:-1] - ) - else: - special_tokens = kwargs.pop("special_tokens", []) - tokenizer = cls( - resolved_vocab_file, - resolved_merges_file, - special_tokens=special_tokens, - *inputs, - **kwargs - ) - return tokenizer - - def __init__( - self, - vocab_file, - merges_file, - errors="replace", - special_tokens=None, - max_len=None, - ): - self.max_len = max_len if max_len is not None else int(1e12) - self.encoder = json.load(open(vocab_file)) - self.decoder = {v: k for k, v in self.encoder.items()} - self.errors = errors # how to handle errors in decoding - self.byte_encoder = bytes_to_unicode() - self.byte_decoder = {v: k for k, v in self.byte_encoder.items()} - bpe_data = open(merges_file, encoding="utf-8").read().split("\n")[1:-1] - bpe_merges = [tuple(merge.split()) for merge in bpe_data] - self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges)))) - - # Should haved added re.IGNORECASE so BPE merges can happen for - # capitalized versions of contractions - self.pat = re.compile( - r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""" - ) - - self.special_tokens = {} - self.special_tokens_decoder = {} - self.set_special_tokens(special_tokens) - - def __len__(self): - return len(self.encoder) + len(self.special_tokens) - - def set_special_tokens(self, special_tokens): - """Add a list of additional tokens to the encoder. - The additional tokens are indexed starting from the last index of the - current vocabulary in the order of the `special_tokens` list. - """ - if not special_tokens: - self.special_tokens = {} - self.special_tokens_decoder = {} - return - self.special_tokens = dict( - (tok, len(self.encoder) + i) for i, tok in enumerate(special_tokens) - ) - self.special_tokens_decoder = {v: k for k, v in self.special_tokens.items()} - logger.info("Special tokens {}".format(self.special_tokens)) - - @lru_cache(maxsize=131072) - def bpe(self, token): - word = tuple(token) - pairs = get_pairs(word) - - if not pairs: - return token - - while True: - bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf"))) - if bigram not in self.bpe_ranks: - break - first, second = bigram - new_word = [] - i = 0 - while i < len(word): - try: - j = word.index(first, i) - new_word.extend(word[i:j]) - i = j - except BaseException: - new_word.extend(word[i:]) - break - - if word[i] == first and i < len(word) - 1 and word[i + 1] == second: - new_word.append(first + second) - i += 2 - else: - new_word.append(word[i]) - i += 1 - new_word = tuple(new_word) - word = new_word - if len(word) == 1: - break - else: - pairs = get_pairs(word) - word = " ".join(word) - return word - - def tokenize(self, text): - """Tokenize a string.""" - bpe_tokens = [] - for token in re.findall(self.pat, text): - if sys.version_info[0] == 2: - token = "".join(self.byte_encoder[ord(b)] for b in token) - else: - token = "".join(self.byte_encoder[b] for b in token.encode("utf-8")) - bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" ")) - return bpe_tokens - - def convert_tokens_to_ids(self, tokens): - """Converts a sequence of tokens into ids using the vocab.""" - ids = [] - if isinstance(tokens, str) or ( - sys.version_info[0] == 2 and isinstance(tokens, unicode) - ): - if tokens in self.special_tokens: - return self.special_tokens[tokens] - else: - return self.encoder.get(tokens, 0) - for token in tokens: - if token in self.special_tokens: - ids.append(self.special_tokens[token]) - else: - ids.append(self.encoder.get(token, 0)) - if len(ids) > self.max_len: - logger.warning( - "Token indices sequence length is longer than the specified maximum " - " sequence length for this OpenAI GPT model ({} > {}). Running this" - " sequence through the model will result in indexing errors".format( - len(ids), self.max_len - ) - ) - return ids - - def convert_ids_to_tokens(self, ids, skip_special_tokens=False): - """Converts a sequence of ids in BPE tokens using the vocab.""" - tokens = [] - for i in ids: - if i in self.special_tokens_decoder: - if not skip_special_tokens: - tokens.append(self.special_tokens_decoder[i]) - else: - tokens.append(self.decoder[i]) - return tokens - - def encode(self, text): - return self.convert_tokens_to_ids(self.tokenize(text)) - - def decode(self, tokens): - text = "".join([self.decoder[token] for token in tokens]) - text = bytearray([self.byte_decoder[c] for c in text]).decode( - "utf-8", errors=self.errors - ) - return text - - def save_vocabulary(self, vocab_path): - """Save the tokenizer vocabulary and merge files to a directory.""" - if not os.path.isdir(vocab_path): - logger.error( - "Vocabulary path ({}) should be a directory".format(vocab_path) - ) - return - vocab_file = os.path.join(vocab_path, VOCAB_NAME) - merge_file = os.path.join(vocab_path, MERGES_NAME) - special_tokens_file = os.path.join(vocab_path, SPECIAL_TOKENS_NAME) - - with open(vocab_file, "w", encoding="utf-8") as f: - f.write(json.dumps(self.encoder, ensure_ascii=False)) - - index = 0 - with open(merge_file, "w", encoding="utf-8") as writer: - writer.write("#version: 0.2\n") - for bpe_tokens, token_index in sorted( - self.bpe_ranks.items(), key=lambda kv: kv[1] - ): - if index != token_index: - logger.warning( - "Saving vocabulary to {}: BPE merge indices are not consecutive." - " Please check that the tokenizer is not corrupted!".format( - merge_file - ) - ) - index = token_index - writer.write(" ".join(bpe_tokens) + "\n") - index += 1 - - index = len(self.encoder) - with open(special_tokens_file, "w", encoding="utf-8") as writer: - for token, token_index in sorted( - self.special_tokens.items(), key=lambda kv: kv[1] - ): - if index != token_index: - logger.warning( - "Saving special tokens vocabulary to {}: BPE indices are not consecutive." - " Please check that the tokenizer is not corrupted!".format( - special_tokens_file - ) - ) - index = token_index - writer.write(token + "\n") - index += 1 - - return vocab_file, merge_file, special_tokens_file diff --git a/mftcoder_atorch/tokenizer/tokenizer.py b/mftcoder_atorch/tokenizer/tokenizer.py deleted file mode 100644 index 12b1bda..0000000 --- a/mftcoder_atorch/tokenizer/tokenizer.py +++ /dev/null @@ -1,409 +0,0 @@ -# Copyright (c) 2021, EleutherAI -# This file is based on code by the authors denoted below and has been modified from its original version. -# -# Copyright (c) 2020, NVIDIA CORPORATION. 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. - -"""Megatron tokenizers.""" - -from abc import ABC -from abc import abstractmethod - -from tokenizers import Tokenizer -from transformers import GPT2Tokenizer, GPT2TokenizerFast -import numpy as np -import sentencepiece as spm -from typing import List, Union -from .gpt2_tokenization import GPT2Tokenizer -from utils.common_utils import print_rank_0, is_old_version - -def build_tokenizer(args): - """Initialize tokenizer.""" - print_rank_0("> building {} tokenizer ...".format(args.tokenizer_type)) - # if args.rank == 0: - # print("> building {} tokenizer ...".format(args.tokenizer_type), flush=True) - - # Select and instantiate the tokenizer. - if args.tokenizer_type.lower() == "GPT2BPETokenizer".lower(): - assert args.vocab_file is not None - assert args.merge_file is not None - tokenizer = _GPT2BPETokenizer(args.vocab_file, args.merge_file) - elif args.tokenizer_type.lower() == "SPMTokenizer".lower(): - assert args.vocab_file is not None - tokenizer = SentencePieceTokenizer(args.vocab_file) - elif args.tokenizer_type.lower() == "HFTokenizer".lower(): - assert args.vocab_file is not None - tokenizer = HFTokenizer(args.vocab_file) - elif args.tokenizer_type.lower() == "HFGPT2Tokenizer".lower(): - if args.vocab_file is None: - print( - "WARNING: No vocab file found, loading Huggingface's pretrained GPT2Tokenizer" - ) - tokenizer = HFGPT2Tokenizer(args.vocab_file) - elif args.tokenizer_type.lower() == "CharLevelTokenizer".lower(): - tokenizer = CharLevelTokenizer(vocab_size=512) - elif args.tokenizer_type.lower() == "TiktokenTokenizer".lower(): - assert args.vocab_file is not None - tokenizer = TiktokenTokenizer(args.vocab_file) - else: - raise NotImplementedError( - "{} tokenizer is not " "implemented.".format(args.tokenizer_type) - ) - - # Add vocab size. - args.padded_vocab_size = _vocab_size_with_padding(tokenizer.vocab_size, args) - - return tokenizer - - -def _vocab_size_with_padding(orig_vocab_size, args): - """Pad vocab size so it is divisible by model parallel size and - still having GPU friendly size.""" - - after = orig_vocab_size - multiple = args.make_vocab_size_divisible_by * args.model_parallel_size - while (after % multiple) != 0: - after += 1 - print_rank_0( - " > padded vocab (size: {}) with {} dummy tokens " - "(new size: {})".format(orig_vocab_size, after - orig_vocab_size, after) - ) - return after - - -class AbstractTokenizer(ABC): - """Abstract class for tokenizer.""" - - def __init__(self, name): - self.name = name - super().__init__() - - @property - @abstractmethod - def vocab_size(self): - pass - - @property - @abstractmethod - def vocab(self): - """Dictionary from vocab text token to id token.""" - pass - - @property - @abstractmethod - def inv_vocab(self): - """Dictionary from vocab id token to text token.""" - pass - - @abstractmethod - def tokenize(self, text): - pass - - def detokenize(self, token_ids): - raise NotImplementedError( - "detokenizer is not implemented for {} " "tokenizer".format(self.name) - ) - - @property - def cls(self): - raise NotImplementedError( - "CLS is not provided for {} " "tokenizer".format(self.name) - ) - - @property - def sep(self): - raise NotImplementedError( - "SEP is not provided for {} " "tokenizer".format(self.name) - ) - - @property - def pad(self): - raise NotImplementedError( - "PAD is not provided for {} " "tokenizer".format(self.name) - ) - - @property - def eod(self): - raise NotImplementedError( - "EOD is not provided for {} " "tokenizer".format(self.name) - ) - - @property - def mask(self): - raise NotImplementedError( - "MASK is not provided for {} " "tokenizer".format(self.name) - ) - - -class _GPT2BPETokenizer(AbstractTokenizer): - """Original GPT2 BPE tokenizer.""" - - def __init__(self, vocab_file, merge_file): - name = "GPT2 BPE" - super().__init__(name) - - self.tokenizer = GPT2Tokenizer( - vocab_file, merge_file, errors="replace", special_tokens=[], max_len=None - ) - self.eod_id = self.tokenizer.encoder["<|endoftext|>"] - - @property - def vocab_size(self): - return len(self.tokenizer.encoder) - - @property - def vocab(self): - return self.tokenizer.encoder - - @property - def inv_vocab(self): - return self.tokenizer.decoder - - def tokenize(self, text): - return self.tokenizer.encode(text) - - def detokenize(self, token_ids): - return self.tokenizer.decode(token_ids) - - @property - def eod(self): - return self.eod_id - - -class SentencePieceTokenizer(AbstractTokenizer): - """Designed to Integrate SP's Tokenizer.""" - - def __init__(self, vocab_file): - name = "SPM" - super().__init__(name) - - self.tokenizer = spm.SentencePieceProcessor(model_file=vocab_file) - # self.eod_id = self.tokenizer.piece_to_id("<|endoftext|>") - self.eod_id = self.tokenizer.piece_to_id("") - self.pad_id = self.tokenizer.piece_to_id("[PAD]") - self.unk_id = self.tokenizer.piece_to_id("") - - @property - def vocab_size(self): - return self.tokenizer.get_piece_size() - - @property - def vocab(self): - return { - self.tokenizer.id_to_piece(idx): idx - for idx in range(self.tokenizer.get_piece_size()) - } - - @property - def inv_vocab(self): - return { - idx: self.tokenizer.id_to_piece(idx) - for idx in range(self.tokenizer.get_piece_size()) - } - - def tokenize(self, text): - return self.tokenizer.encode(text) - - def detokenize(self, token_ids): - return self.tokenizer.decode(token_ids) - - @property - def eod(self): - return self.eod_id - - -class HFTokenizer(AbstractTokenizer): - """Designed to Integrate HF's Tokenizer library.""" - - def __init__(self, vocab_file): - name = "HFTokenizer" - super().__init__(name) - - self.tokenizer = Tokenizer.from_file(vocab_file) - # self.eod_id = self.tokenizer.token_to_id("<|endoftext|>") - self.eod_id = self.tokenizer.token_to_id("<|end|>") - # self.pad_id = self.tokenizer.token_to_id("<|padding|>") - - # 新词表没有<|padding|>, 用<|extratoken_1|>代替，和tokenization一致 - # self.pad_id = self.tokenizer.token_to_id("<|extratoken_1|>") - self.pad_id = self.tokenizer.token_to_id("<|pad|>") - - @property - def vocab_size(self): - return self.tokenizer.get_vocab_size() - - @property - def vocab(self): - return self.tokenizer.get_vocab() - - @property - def inv_vocab(self): - return self.tokenizer.decoder - - def tokenize(self, text: str): - return self.tokenizer.encode(text).ids - - def tokenize_batch(self, text_batch: Union[List[str], str]): - return self.tokenizer.encode_batch(text_batch) - - def detokenize(self, token_ids): - return self.tokenizer.decode(token_ids) - - @property - def eod(self): - return self.eod_id - - -class HFGPT2Tokenizer(AbstractTokenizer): - """Designed to Integrate the pretrained OpenAI GPT2 Tokenizers from HF""" - - def __init__(self, vocab_file=None, fast=True): - name = "HFGPT2Tokenizer" - if fast: - name += "Fast" - super().__init__(name) - if vocab_file is None: - vocab_file = "gpt2" - if fast: - self.tokenizer = GPT2TokenizerFast.from_pretrained(vocab_file) - else: - self.tokenizer = GPT2Tokenizer.from_pretrained(vocab_file) - - self.tokenizer.add_special_tokens({"pad_token": "<|padding|>"}) - self.eod_id = self.tokenizer.eos_token_id - self.pad_id = self.tokenizer.pad_token_id - - @property - def vocab_size(self): - return len(self.tokenizer) - - @property - def vocab(self): - return self.tokenizer.get_vocab() - - @property - def inv_vocab(self): - return self.tokenizer._tokenizer.decoder - - def tokenize(self, text: str): - return self.tokenizer.encode(text) - - def tokenize_batch(self, text_batch: Union[List[str], str]): - if isinstance(text_batch, str): - text_batch = [text_batch] - return [self.tokenize(t) for t in text_batch] - - def detokenize(self, token_ids): - return self.tokenizer.decode(token_ids) - - @property - def eod(self): - return self.eod_id - - -class CharLevelTokenizer(AbstractTokenizer): - """Character Level Tokenizer""" - - def __init__(self, vocab_size): - name = "CharLevelTokenizer" - super().__init__(name) - self._vocab_size = vocab_size - self.eod_id = 0 - self.pad_id = 1 - - def clamp(self, n): - return max(32, min(n, self.vocab_size)) - - @property - def vocab_size(self): - return self._vocab_size - - @property - def vocab(self): - raise NotImplementedError - - @property - def inv_vocab(self): - raise NotImplementedError - - def decode_token(self, token: int): - return str(chr(self.clamp(token))) - - def tokenize(self, text: str): - return list(np.fromstring(text, dtype=np.uint8)) - - def tokenize_batch(self, text_batch: Union[List[str], str]): - if isinstance(text_batch, list): - return [self.tokenize(s) for s in text_batch] - else: - return self.tokenize(text_batch) - - def detokenize(self, token_ids): - return "".join(list(map(self.decode_token, token_ids))) - - @property - def eod(self): - return self.eod_id - - -class TiktokenTokenizer(AbstractTokenizer): - """Tokenizer from OpenAI's tiktoken implementation""" - - def __init__(self, vocab_file): - try: - import tiktoken - except ModuleNotFoundError: - print("Please install tiktoken: (https://github.com/openai/tiktoken)") - raise Exception - - name = "TiktokenTokenizer" - super().__init__(name) - - self.tokenizer = tiktoken.get_encoding(vocab_file) - self.eod_id = self.tokenizer.eot_token - self.pad_id = None - - @property - def vocab_size(self): - return self.tokenizer.n_vocab - - @property - def vocab(self): - raise NotImplementedError( - "TiktokenTokenizer does not implement vocabulary access." - ) - - @property - def inv_vocab(self): - raise NotImplementedError( - "TiktokenTokenizer does not implement vocabulary access. \ - To get the idx-th token in vocabulary, use tokenizer.decode([idx]) ." - ) - - def tokenize(self, text: str): - return self.tokenizer.encode(text) # , allowed_special="all") - - def tokenize_batch(self, text_batch: List[str]): - return self.tokenizer.encode_batch(text_batch, allowed_special="all") - - def detokenize(self, token_ids): - return self.tokenizer.decode(tokens=token_ids, errors="strict") - - @property - def eod(self): - return self.eod_id - - @property - def pad(self): - raise NotImplementedError diff --git a/mftcoder_atorch/tokenizer/train_tokenizer.py b/mftcoder_atorch/tokenizer/train_tokenizer.py deleted file mode 100644 index 14a8e29..0000000 --- a/mftcoder_atorch/tokenizer/train_tokenizer.py +++ /dev/null @@ -1,126 +0,0 @@ -# Copyright (c) 2021, EleutherAI -# -# 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. - -""" -Assumes a dataset of jsonl files in the same format as the neox training set. -""" - -from tokenizers import Tokenizer, decoders, models, pre_tokenizers, processors, trainers -from tokenizers.normalizers import NFKC - -from glob import glob -import os -import json -import argparse - - -def load_jsonl(input_path, quiet=True) -> list: - """ - Read list of objects from a JSON lines file. - """ - data = [] - with open(input_path, "r", encoding="utf-8") as f: - for line in f: - data.append(json.loads(line.rstrip("\n|\r"))) - if not quiet: - print("Loaded {} records from {}".format(len(data), input_path)) - return data - - -def json_iterator(input_dir, text_key="text"): - all_jsonls = glob(f"{input_dir}/*.jsonl") + glob(f"{input_dir}/*.json") - for j in all_jsonls: - data = load_jsonl(j) - for doc in data: - yield doc[text_key] - - -def train_tokenizer( - input_dir: str, save_path: str, tokenizer_type: str = "BPE", vocab_size: int = 52000 -): - """ - Trains a tokenizer on all the json files in `input_dir` and saves it to `save_path` - - :param input_dir: input directory containing jsonl files - :param save_path: path to save tokenizer to - :param tokenizer_type: type of tokenizer to train. - :param vocab_size: int, size of tokenizer's vocab - :return: - """ - - if tokenizer_type == "BPE": - model = models.BPE() - else: - raise NotImplementedError(f"Tokenizer type {tokenizer_type} not implemented") - tokenizer = Tokenizer(model) - - # Customize pre-tokenization and decoding - tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=True) - tokenizer.decoder = decoders.ByteLevel() - tokenizer.post_processor = processors.ByteLevel(trim_offsets=True) - tokenizer.normalizer = NFKC() - - # And then train - trainer = trainers.BpeTrainer( - vocab_size=vocab_size, special_tokens=["<|endoftext|>", "<|padding|>"] - ) - tokenizer.train_from_iterator(json_iterator(input_dir), trainer) - - # And Save it - tokenizer.save(save_path, pretty=True) - print(f"Tokenizer saved at {save_path}") - - -def parse_args(): - parser = argparse.ArgumentParser( - description="script for training a multilingual " - "HF tokenizer on CC dumps with upweighting for low resource languages" - ) - parser.add_argument( - "--json_input_dir", - type=str, - help="Path to folder containing tokenizer training data in jsonl format", - ) - parser.add_argument( - "--tokenizer_output_path", - type=str, - help="Path to which your trained tokenizer will be saved (should end in .json)", - ) - parser.add_argument( - "--tokenizer_type", - type=str, - help="type of tokenizer to train, currently only BPE is supported", - choices=["BPE"], - default=["BPE"], - ) - parser.add_argument( - "-v", - "--vocab_size", - help="vocabulary size of tokenizer, default=52k", - type=int, - default=52000, - ) - return parser.parse_args() - - -if __name__ == "__main__": - - args = parse_args() - - train_tokenizer( - args.json_input_dir, - save_path=args.tokenizer_output_path, - tokenizer_type=args.tokenizer_type, - vocab_size=args.vocab_size, - ) diff --git a/mftcoder_atorch/train/__init__.py b/mftcoder_atorch/train/__init__.py deleted file mode 100644 index d2045bb..0000000 --- a/mftcoder_atorch/train/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from .run_train import * \ No newline at end of file diff --git a/mftcoder_atorch/train/run_gpt_mft.sh b/mftcoder_atorch/train/run_gpt_mft.sh deleted file mode 100644 index de0a58b..0000000 --- a/mftcoder_atorch/train/run_gpt_mft.sh +++ /dev/null @@ -1,96 +0,0 @@ -#!/bin/bash -LOAD_RAW_DATASET=True -if [ ${LOAD_RAW_DATASET} = "True" ]; then - LOAD_RAW_DATASET="--load_raw_dataset" - DATA_PATHS="$DATA_PATHS" - DATA_WEIGHTS="[1.,1.,...,1.]" - DATA_SPLIT="95,5,0" - SHUFFLE_BEFORE_SPLIT="" - USE_RANDOM_SAMPLER="" - USE_WEIGHTED_LOSS="" - WEIGHT_BY_NUM_DOCUMENTS="" -else - LOAD_RAW_DATASET="" - DATA_PATHS="$DATA_PATHS" - DATA_WEIGHTS="[1.,1.,...,1.]" - DATA_SPLIT="95,5,0" - SHUFFLE_BEFORE_SPLIT="--shuffle_before_split" - USE_RANDOM_SAMPLER="--use_random_sampler" - USE_WEIGHTED_LOSS="--use_weighted_loss" - WEIGHT_BY_NUM_DOCUMENTS="--weight_by_num_documents" -fi - -VOCAB_FILE="../utils/vocab.json" -MODEL_TYPE="gpt_neox" - -PRETRAINED_MODEL_PATH="$MODEL_NAME_OR_PATH" -RESUME_FROM_CHECKPOINT="false" - -PER_DEVICE_BATCH_SIZE=$1 -TP=$2 -DP=$3 -EPOCH=$4 -TOTAL_TRAIN_BATCH_SIZE=$(($PER_DEVICE_BATCH_SIZE * $TP * $DP)) -GPU=$(($TP * $DP)) -OUTPUT="$OUTPUT_DIR" -TENSORBOARD_PATH="$TensorBoard_DIR" -PREFIX="master-0" -mkdir -p $OUTPUT || true -echo "output to $OUTPUT" -mkdir -p $TENSORBOARD_PATH -chmod 777 $OUTPUT -chmod 777 $TENSORBOARD_PATH - -python -m atorch.distributed.launch \ - --nproc_per_node=$(nvidia-smi -L | wc -l) \ - run_train.py \ - ${LOAD_RAW_DATASET} \ - --tokenize_mode 'sft' \ - --train_mode 'sft' \ - --padding_mode 'padding' \ - --pretrained_model_path $PRETRAINED_MODEL_PATH \ - --vocab_file $VOCAB_FILE \ - --model_type $MODEL_TYPE \ - --padding \ - --data_paths $DATA_PATHS \ - --data_weights $DATA_WEIGHTS \ - --data_split $DATA_SPLIT \ - ${SHUFFLE_BEFORE_SPLIT} \ - ${USE_RANDOM_SAMPLER} \ - ${USE_WEIGHTED_LOSS} \ - ${WEIGHT_BY_NUM_DOCUMENTS} \ - --train_iters 100 \ - --num_warmup_steps 500 \ - --custom_lr_scheduler_type 'cosine' \ - --learning_rate 1.0e-4 \ - --min_lr 1.0e-5 \ - --valid_iters 10 \ - --valid_interval 2000 \ - --num_train_epochs $EPOCH \ - --seq_length 4096 \ - --total_train_batch_size $TOTAL_TRAIN_BATCH_SIZE \ - --per_device_valid_batch_size $PER_DEVICE_BATCH_SIZE \ - --seed 42 \ - --preprocessing_num_workers 6 \ - --num_workers 8 \ - --output_dir $OUTPUT \ - --tensorboard_dir $TENSORBOARD_PATH \ - --ignore_mismatched_sizes \ - --skip_atorch_autoacc_dryrun \ - --tp $TP \ - --dp $DP \ - --bf16 \ - --checkpointing_steps 2000 \ - --log_interval 10 \ - --make_vocab_size_divisible_by 128 \ - --weighted_loss_mode 'case3' \ - --checkpoint_activations \ - --resume_from_checkpoint $RESUME_FROM_CHECKPOINT \ - --max_grad_norm 1 \ - --evaluation_strategy "steps,epoch" \ - --save_strategy "steps" \ - --save_total_limit 2 \ - --extra_save_by_epoch \ - --metric_for_best_model 'loss' \ - --greater_is_better 'false' \ - --early_stopping_patience 10 2>&1 | tee $OUTPUT/$PREFIX-output.txt \ No newline at end of file diff --git a/mftcoder_atorch/train/run_gpt_mft_peft.sh b/mftcoder_atorch/train/run_gpt_mft_peft.sh deleted file mode 100644 index d43405c..0000000 --- a/mftcoder_atorch/train/run_gpt_mft_peft.sh +++ /dev/null @@ -1,97 +0,0 @@ -#!/bin/bash -LOAD_RAW_DATASET=True -if [ ${LOAD_RAW_DATASET} = "True" ]; then - LOAD_RAW_DATASET="--load_raw_dataset" - DATA_PATHS="$DATA_PATHS" - DATA_WEIGHTS="[1.,1.,...,1.]" - DATA_SPLIT="90,10,0" - SHUFFLE_BEFORE_SPLIT="" - USE_RANDOM_SAMPLER="" - USE_WEIGHTED_LOSS="" - WEIGHT_BY_NUM_DOCUMENTS="" -else - LOAD_RAW_DATASET="" - DATA_PATHS="$DATA_PATHS" - DATA_WEIGHTS="[1.,1.,...,1.]" - DATA_SPLIT="95,5,0" - SHUFFLE_BEFORE_SPLIT="--shuffle_before_split" - USE_RANDOM_SAMPLER="--use_random_sampler" - USE_WEIGHTED_LOSS="--use_weighted_loss" - WEIGHT_BY_NUM_DOCUMENTS="--weight_by_num_documents" -fi - -VOCAB_FILE="../utils/vocab.json" -MODEL_TYPE="gpt_neox" - -PRETRAINED_MODEL_PATH="$MODEL_NAME_OR_PATH" -RESUME_FROM_CHECKPOINT="false" - -PER_DEVICE_BATCH_SIZE=$1 -TP=$2 -DP=$3 -EPOCH=$4 -TOTAL_TRAIN_BATCH_SIZE=$(($PER_DEVICE_BATCH_SIZE * $TP * $DP)) -GPU=$(($TP * $DP)) -OUTPUT="$OUTPUT_DIR" -TENSORBOARD_PATH="$TensorBoard_DIR" -PREFIX="master-0" -mkdir -p $OUTPUT || true -echo "output to $OUTPUT" -mkdir -p $TENSORBOARD_PATH -chmod 777 $OUTPUT -chmod 777 $TENSORBOARD_PATH - -python -m atorch.distributed.launch \ - --nproc_per_node=$(nvidia-smi -L | wc -l) \ - run_train.py \ - ${LOAD_RAW_DATASET} \ - --tokenize_mode 'sft' \ - --padding_mode 'padding' \ - --pretrained_model_path $PRETRAINED_MODEL_PATH \ - --vocab_file $VOCAB_FILE \ - --model_type $MODEL_TYPE \ - --padding \ - --data_paths $DATA_PATHS \ - --data_weights $DATA_WEIGHTS \ - --data_split $DATA_SPLIT \ - ${SHUFFLE_BEFORE_SPLIT} \ - ${USE_RANDOM_SAMPLER} \ - ${USE_WEIGHTED_LOSS} \ - ${WEIGHT_BY_NUM_DOCUMENTS} \ - --train_iters 100 \ - --num_warmup_steps 30 \ - --custom_lr_scheduler_type 'cosine' \ - --learning_rate 5.0e-5 \ - --min_lr 1.0e-6 \ - --valid_iters 400 \ - --valid_interval 500 \ - --num_train_epochs $EPOCH \ - --seq_length 4096 \ - --total_train_batch_size $TOTAL_TRAIN_BATCH_SIZE \ - --per_device_valid_batch_size $PER_DEVICE_BATCH_SIZE \ - --seed 42 \ - --preprocessing_num_workers 6 \ - --num_workers 8 \ - --output_dir $OUTPUT \ - --tensorboard_dir $TENSORBOARD_PATH \ - --ignore_mismatched_sizes \ - --skip_atorch_autoacc_dryrun \ - --tp $TP \ - --dp $DP \ - --bf16 \ - --checkpointing_steps 500 \ - --log_interval 10 \ - --make_vocab_size_divisible_by 128 \ - --weighted_loss_mode 'case3' \ - --peft_type 'lora' \ - --checkpoint_activations \ - --resume_from_checkpoint $RESUME_FROM_CHECKPOINT \ - --max_grad_norm 1 \ - --evaluation_strategy "steps,epoch" \ - --save_strategy "steps" \ - --save_total_limit 2 \ - --extra_save_by_epoch \ - --metric_for_best_model 'loss' \ - --greater_is_better 'false' \ - --early_stopping_patience 5 \ - --use_dynamic_padding 2>&1 | tee $OUTPUT/$PREFIX-output.txt diff --git a/mftcoder_atorch/train/run_train.py b/mftcoder_atorch/train/run_train.py deleted file mode 100644 index 9e4fcbe..0000000 --- a/mftcoder_atorch/train/run_train.py +++ /dev/null @@ -1,136 +0,0 @@ -import sys -sys.path.append("..") -import json -import logging -import math -import os -import numpy as np -os.environ["TOKENIZERS_PARALLELISM"] = "false" - -class cb: - def __init__(self, path): - self.path = path - def __call__(self, s): - with open(f"{self.path}/fsdp_mapping.html", "w") as f: - f.write(s) - -# handle multi-processing writing -os.environ["HF_MODULES_CACHE"] = os.path.join("/root/.cache/huggingface/modules", os.getenv("RANK", "")) -import random # noqa: E402 -import datasets # noqa: E402 -import transformers # noqa: E402 -from torch.utils.data import DataLoader # noqa: E402 -from torch.utils.data.distributed import DistributedSampler # noqa: E402 -from transformers import ( # noqa: E402 - default_data_collator, - # get_scheduler, - set_seed, -) -from transformers.utils.versions import require_version # noqa: E402 -from atorch.utils.meta_model_utils import init_empty_weights_with_disk_offload # noqa: E402 - -from transformers import AutoTokenizer - -from torch.distributed.fsdp import ( - FullyShardedDataParallel as FSDP, -) - -from utils.common_utils import ( - is_local_main_process, generate_task_id, print_rank_0, is_old_version, - atorch_init_distributed, atorch_reset_distributed, TASK2ID, ID2TASK, - get_rank, get_world_size -) -from utils.auto_accelerate_utils import DataCollatorForMFTDataset, loss_func_mft -from arguments.get_arguments import parse_args -from model.build_model import setup_model -from data.gpt2_multi_task_dataset import load_dataset_from_jsonl -from train.trainer.atorch_trainer import AtorchTrainer -from pathlib import Path - - -def main(): - args = parse_args() - - # Make one log on every process with the configuration for debugging. - logging.basicConfig( - # format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - format="%(asctime)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO, - ) - logger = logging.getLogger(__name__) - if is_local_main_process(): - datasets.utils.logging.set_verbosity_warning() - transformers.utils.logging.set_verbosity_info() - else: - datasets.utils.logging.set_verbosity_error() - transformers.utils.logging.set_verbosity_error() - - # If passed along, set the training seed now. - if args.seed is not None: - set_seed(args.seed) - - generate_task_id(args.data_paths, args.train_mode) # generate TASK2ID, ID2TASK mapping - print(TASK2ID) - print(ID2TASK) - - model, model_config, tokenizer = setup_model(args, logger, use_cache=False) - print(f'args.total_model_param: {args.total_model_param}') - - train_dataset, dataloader_args = None, None - train_dataloader, valid_dataloader, test_dataloader = None, None, None - - args.world_size = get_world_size() - global_rank = get_rank() - print(f'world_size: {args.world_size}, global_rank: {global_rank}') - args.per_device_train_batch_size = args.total_train_batch_size // args.world_size - if args.load_raw_dataset: - print_rank_0('load raw dataset') - if args.model_type in ['gpt_neox']: - train_dataset, valid_dataset = load_dataset_from_jsonl(args, tokenizer, shard_data=True, world_size=args.world_size, global_rank=global_rank) - - if train_dataset is not None: - args.do_train = True - if valid_dataset is not None: - args.do_valid = True - else: - print_rank_0('please set load_raw_dataset to True and rerun') - - if args.resume_from_checkpoint == 'true': - logger.info(f'Resume from {args.output_dir}') - resume_from_checkpoint = True - else: - logger.info(f'Train from scratch') - resume_from_checkpoint = False - if args.model_type in ['gpt_neox']: - gpt_data = True - else: - gpt_data = False - data_collator = DataCollatorForMFTDataset(args.model_type, args.weighted_loss_mode, args.use_dynamic_padding) - my_loss_function = loss_func_mft - trainer = AtorchTrainer( - model=model, - args=args, - train_dataset=train_dataset, - valid_dataset=valid_dataset, - tokenizer=tokenizer, - # files_to_save=files_to_save, - args_to_save={ - # 'max_length': args.max_length, - 'max_length': args.seq_length, - 'peft_type': args.peft_type, - 'gpt_model': gpt_data - }, - data_collator=data_collator, - my_loss_func=my_loss_function, - custom_lr_scheduler_type=args.custom_lr_scheduler_type, - rank=global_rank - ) - if args.do_train: - trainer.train(resume_from_checkpoint=resume_from_checkpoint) - - -if __name__ == "__main__": - atorch_init_distributed("nccl") - main() - atorch_reset_distributed() diff --git a/mftcoder_atorch/train/trainer/atorch_trainer.py b/mftcoder_atorch/train/trainer/atorch_trainer.py deleted file mode 100644 index 110a060..0000000 --- a/mftcoder_atorch/train/trainer/atorch_trainer.py +++ /dev/null @@ -1,1041 +0,0 @@ -#!/usr/bin/env python -# coding=utf-8 - -import datetime -import json -import logging -import math -import os -import random -import re -import shutil -import time -import warnings -from functools import partial -from pathlib import Path -import gc - -import numpy as np - -import atorch -import torch -from deepspeed.ops.adam import DeepSpeedCPUAdam -from torch.distributed.fsdp import FullStateDictConfig -from torch.distributed.fsdp import FullyShardedDataParallel as FSDP -from torch.distributed.fsdp import StateDictType -from torch.optim.lr_scheduler import LambdaLR, CosineAnnealingLR, CosineAnnealingWarmRestarts -from torch.utils.data import DataLoader -from torch.utils.data.distributed import DistributedSampler -from torch.utils.tensorboard import SummaryWriter -from tqdm.auto import tqdm -from transformers import get_scheduler as get_scheduler_trans -from transformers.modeling_utils import PreTrainedModel, unwrap_model -from transformers.trainer import ( - OPTIMIZER_NAME, - SCHEDULER_NAME, - TRAINER_STATE_NAME, - TRAINING_ARGS_NAME -) -from transformers.trainer_pt_utils import reissue_pt_warnings -from transformers.trainer_utils import ( - PREFIX_CHECKPOINT_DIR, -) -from transformers.utils import WEIGHTS_NAME -from torch.nn import CrossEntropyLoss -from utils.common_utils import print_rank_0, get_tflops_megatron, get_computation_speed, TASK2ID, ID2TASK, EarlyStopping -from utils.auto_accelerate_utils import get_ltor_masks_and_position_ids - -from atorch.auto import auto_accelerate -from atorch.utils.version import torch_version -from model.gpt_neox.modeling_gpt_neox import GPTNeoXLayer, GPTNeoXAttention, GPTNeoXMLP -from model.peft.modeling_peft import PeftModel - -HYPER_PARAMETER_NAME = 'hyper_parameters.json' -ATORCH_CHECKPOINT_NAME = 'atorch_checkpoint.bin' -EPOCH_CHECKPOINT_NAME = 'epoch' - -logger = logging.getLogger(__name__) - - -def is_local_main_process(): - return atorch.local_rank() == 0 - - -def is_global_main_process(): - return atorch.rank() == 0 - - -def has_inf_or_nan(x): - try: - # if x is half, the .float() incurs an additional deep copy, but it's necessary if - # Pytorch's .sum() creates a one-element tensor of the same type as x - # (which is true for some recent version of pytorch). - cpu_sum = float(x.float().sum()) - # More efficient version that can be used if .sum() returns a Python scalar - # cpu_sum = float(x.sum()) - except RuntimeError as instance: - # We want to check if inst is actually an overflow exception. - # RuntimeError could come from a different error. - # If so, we still want the exception to propagate. - if "value cannot be converted" not in instance.args[0]: - raise - return True - else: - if cpu_sum == float('inf') or cpu_sum == -float('inf') or cpu_sum != cpu_sum: - return True - return False - - -def count_model_params(model): - trainable_params = 0 - all_params = 0 - for param in model.parameters(): - num_params = param.numel() - all_params += num_params - if param.requires_grad: - trainable_params += num_params - return all_params, trainable_params - - -class AtorchArguments: - def __init__(self, **kwargs): - for key, value in kwargs.items(): - setattr(self, key, value) - - -def get_linear_schedule_with_log_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1): - - def lr_lambda(current_step: int): - inverse_log_warm_up = 1.0 / math.log(num_warmup_steps) - if current_step == 0: - return 0.0 - if current_step < num_warmup_steps: - return inverse_log_warm_up * math.log(current_step) - return max( - 0.0, float(num_training_steps - current_step) / - float(max(1, num_training_steps - num_warmup_steps)) - ) - - return LambdaLR(optimizer, lr_lambda, last_epoch) - - -def get_scheduler(name, optimizer, num_warmup_steps, num_training_steps): - scheduler_map = { - 'log_warmup_linear_decay': get_linear_schedule_with_log_warmup} - try: - lr_scheduler = get_scheduler_trans( - name, optimizer, num_warmup_steps, num_training_steps) - return lr_scheduler - except Exception: - schedule_func = scheduler_map[name] - return schedule_func(optimizer, num_warmup_steps, num_training_steps) - - -class AtorchTrainer: - def __init__(self, - model, - args, - train_dataset, - valid_dataset, - tokenizer=None, - callbacks=None, - no_save_atorch_checkpoint=None, - save_pytorch_model_bin_checkpoint=True, - train_peft=False, - rank=0, - max_shard_size='10GB', - files_to_save=None, - args_to_save=None, - data_collator=None, - my_loss_func=None, - **kwargs, - ): - self.args = args - self.TASK2ID = TASK2ID - self.ID2TASK = ID2TASK - print('in atorch trainer') - print(TASK2ID) - print(ID2TASK) - self.model = model - self.no_save_atorch_checkpoint = no_save_atorch_checkpoint - self.save_pytorch_model_bin_checkpoint = save_pytorch_model_bin_checkpoint - self.train_peft = train_peft - self.rank = rank - self.kwargs = kwargs - self.train_dataset = train_dataset - self.valid_dataset = valid_dataset - self.tokenizer = tokenizer - self.max_shard_size = max_shard_size - self.files_to_save = files_to_save - self.args_to_save = args_to_save - self.best_metric = None - self.best_model_checkpoint = None - self.no_save_base_model = True - - self.device = f"cuda:{atorch.local_rank()}" - - self.total_train_batch_size = self.args.per_device_train_batch_size * \ - self.args.gradient_accumulation_steps * \ - atorch.world_size() - - self.data_collator = data_collator - self.my_loss_func = my_loss_func - if self.args.early_stopping_patience > 0: - print(f'early_stopping_patience: {self.args.early_stopping_patience}') - patience = self.args.early_stopping_patience - self.early_stopping = EarlyStopping(patience, verbose=True) - - self.train_dataloader_args = { - "shuffle": True, - "batch_size": self.total_train_batch_size, - "pin_memory": True, - "collate_fn": data_collator, - "drop_last": True, - # "num_workers": args.num_workers, - # "persistent_workers": args.num_workers > 0, - } - - self.valid_dataloader = DataLoader( - valid_dataset, - sampler=DistributedSampler(valid_dataset, shuffle=True), - batch_size=args.per_device_valid_batch_size, - pin_memory=True, - collate_fn=data_collator - ) - self.valid_dataloader_length = len(self.valid_dataloader) - - if self.args.resume_from_checkpoint == 'true': - self.resume_checkpoint_dir = self.get_last_checkpoint( - self.args.output_dir) - - self.atorch_args = AtorchArguments( - lr=args.learning_rate, - weight_decay=args.weight_decay, - adam_eps=args.adam_epsilon, - adam_beta1=args.adam_beta1, - adam_beta2=args.adam_beta2) - - self.atorch_init() - - self.num_update_steps_per_epoch = math.ceil( - len(self.train_dataloader) / self.args.gradient_accumulation_steps) - print(f'number of update steps per epoch: {self.num_update_steps_per_epoch}') - if self.args.max_steps == -1: - self.args.max_steps = int( - self.args.num_train_epochs * self.num_update_steps_per_epoch) - else: - self.args.num_train_epochs = math.ceil( - self.args.max_steps / self.num_update_steps_per_epoch) - - custom_lr_scheduler_type = self.kwargs.get( - 'custom_lr_scheduler_type', None) - self.lr_scheduler = get_scheduler( - name=custom_lr_scheduler_type if custom_lr_scheduler_type else self.args.lr_scheduler_type, - optimizer=self.optimizer, - num_warmup_steps=self.args.num_warmup_steps, - num_training_steps=self.args.max_steps, - ) - print_rank_0(f'lr_scheduler{self.lr_scheduler}') - if self.args.resume_from_checkpoint == 'true': - with warnings.catch_warnings(record=True): - self.lr_scheduler.load_state_dict(torch.load( - os.path.join(self.resume_checkpoint_dir, SCHEDULER_NAME))) - self._load_rng_state(self.resume_checkpoint_dir) - torch.distributed.barrier() - now_datetime = datetime.datetime.now() - timestr = datetime.datetime.strftime(now_datetime, '%Y%m%d-%H%M%S') - self.log_dir = os.path.join(self.args.output_dir, 'runs', timestr) - self.summary_writer = None - if torch.distributed.get_rank() == 0: - self.summary_writer = SummaryWriter(log_dir=self.log_dir) - - def get_last_checkpoint(self, folder): - _re_checkpoint = re.compile(r"^" + PREFIX_CHECKPOINT_DIR + r"\-(\d+)") - content = sorted(os.listdir(folder)) - checkpoints = [ - path - for path in content - if _re_checkpoint.search(path) is not None and os.path.isdir(os.path.join(folder, path)) - ] - if len(checkpoints) == 0: - return - return os.path.join(folder, max(checkpoints, key=lambda x: int(_re_checkpoint.search(x).groups()[0]))) - - def _load_rng_state(self, resume_checkpoint_dir): - # Load RNG states from `checkpoint` - if resume_checkpoint_dir is None: - return - - if self.args.world_size > 1: - rng_file = os.path.join( - resume_checkpoint_dir, f"rng_state_{self.rank}.pth") - if not os.path.isfile(rng_file): - logger.info( - f"Didn't find an RNG file for process {self.rnak}, if you are resuming a training that " - "wasn't launched in a distributed fashion, reproducibility is not guaranteed." - ) - return - else: - rng_file = os.path.join(resume_checkpoint_dir, "rng_state.pth") - if not os.path.isfile(rng_file): - logger.info( - "Didn't find an RNG file, if you are resuming a training that was launched in a distributed " - "fashion, reproducibility is not guaranteed." - ) - return - - checkpoint_rng_state = torch.load(rng_file) - random.setstate(checkpoint_rng_state["python"]) - np.random.set_state(checkpoint_rng_state["numpy"]) - torch.random.set_rng_state(checkpoint_rng_state["cpu"]) - if torch.cuda.is_available(): - if self.args.local_rank != -1: - torch.cuda.random.set_rng_state(checkpoint_rng_state["cuda"]) - else: - try: - torch.cuda.random.set_rng_state_all( - checkpoint_rng_state["cuda"]) - except Exception as e: - logger.info( - f"Didn't manage to set back the RNG states of the GPU because of the following error:\n {e}" - "\nThis won't yield the same results as if the training had not been interrupted." - ) - - def load_atorch_model_state(self, model_state_dict, **kwargs): - print('resume atorch model state') - if self.is_rank0(): - self.model.load_state_dict(model_state_dict) - # load on rank 0，then distribute params via sync_module_states - torch.distributed.barrier() - # self.model = FSDP(self.model, sync_module_states=True, **kwargs) - - def load_atorch_optim_state(self, optim_state_dict): - - print('resume optimizer state') - optim_state_dict = FSDP.scatter_full_optim_state_dict( - optim_state_dict, self.model) # may be removed after PyTorch 2.2 - - def move_optim_state_to_cpu(optim_state_dict): - for k in optim_state_dict: - if isinstance(optim_state_dict[k], torch.Tensor): - optim_state_dict[k] = optim_state_dict[k].cpu() - elif isinstance(optim_state_dict[k], dict): - move_optim_state_to_cpu(optim_state_dict[k]) - - move_optim_state_to_cpu(optim_state_dict) - - self.optimizer.load_state_dict(optim_state_dict) - - - def atorch_init(self): - assert torch_version() >= (2, 0, 0), "use pt2.0 for use orig param if fsdp" - - if self.args.model_type == 'gpt_neox': - # wrap_class = (GPTNeoXAttention, GPTNeoXMLP) - wrap_class = (GPTNeoXLayer,) - - parallel_mode = [] - if self.args.dp: - parallel_mode.append(("data", self.args.dp)) - if self.args.tp: - parallel_mode.append(("tensor_parallel", self.args.tp)) - strategy = [ - ("parallel_mode", (parallel_mode, None)), - "module_replace", - ] - if self.args.peft_type is None or self.args.peft_type == 'lora': - cpu_offload = False - fsdp_config = { - "atorch_wrap_cls": wrap_class, - "sync_module_states": True, - "use_orig_params": True, - "limit_all_gathers": True, - # "cpu_offload": True, - } - print(fsdp_config) - fsdp_opt = ("fsdp", fsdp_config) - strategy.append(fsdp_opt) - self.args.atorch_opt = "fsdp" - else: - # TODO: qlora - self.args.atorch_opt = "ddp" - - if self.args.bf16 or self.args.fp16: - if self.args.bf16: - amp_config = {"dtype": torch.bfloat16, "skip_if_nonfinite": True} - # TODO: qlora - elif self.args.fp16: - amp_config = {"dtype": torch.float16} - strategy.append(("amp_native", amp_config)) - - if self.args.checkpoint_activations: - strategy.append(("checkpoint", wrap_class)) - print(f"Manually loaded auto acc strategy: {strategy}") - - def prepare_input(batch, device): - batch = {k: v.to(device=device, non_blocking=True) if v is not None else None - for k, v in batch.items()} - return batch - - def optim_param_func(model, args): - no_decay = ["bias", "LayerNorm.weight", "layernorm.weight"] - optimizer_grouped_parameters = [ - { - "params": [ - p - for n, p in model.named_parameters() - if not any(nd in n for nd in no_decay) - ], - "weight_decay": args.weight_decay, - }, - { - "params": [ - p - for n, p in model.named_parameters() - if any(nd in n for nd in no_decay) - ], - "weight_decay": 0.0, - }, - ] - return optimizer_grouped_parameters - - # load fsdp checkpoint - if self.args.resume_from_checkpoint == 'true': - logger.info(f'Resume training from {self.resume_checkpoint_dir}') - if self.is_rank0(): - sd = torch.load(os.path.join( - self.resume_checkpoint_dir, ATORCH_CHECKPOINT_NAME), map_location='cpu') - model_state_dict, optim_state_dict = sd['model_state_dict'], sd['optimizer_state_dict'] - else: - model_state_dict, optim_state_dict = None, None - torch.distributed.barrier() # other rank waiting - ########## - self.load_atorch_model_state(model_state_dict) - ########## - - if self.is_rank0(): - print(f'GPU mem before fsdp:') - print(torch.cuda.memory_summary(device=self.device, abbreviated=False)) - optim_func = torch.optim.AdamW - print(f'optimizer before fsdp: {optim_func}') - - ddp_find_unused_parameters = None - if self.args.atorch_opt == "ddp" and not (self.args.peft_type in ["lora", "qlora"] and self.args.checkpoint_activations): - ddp_find_unused_parameters = True - - status, result, best_strategy = auto_accelerate( - self.model, - optim_func, - self.train_dataset, - dataloader_args=self.train_dataloader_args, - loss_func=self.my_loss_func, - prepare_input=prepare_input, - optim_args={ - "lr": self.atorch_args.lr, - "weight_decay": self.atorch_args.weight_decay, - "eps": self.atorch_args.adam_eps, - "betas": (self.atorch_args.adam_beta1, self.atorch_args.adam_beta2), - }, - optim_param_func=partial( - optim_param_func, args=self.atorch_args), - load_strategy=strategy, - ignore_dryrun_on_load_strategy=True, - find_unused_parameters=ddp_find_unused_parameters, - ) - assert ( - status - ), f"auto_accelerate failed. status: {status}, result: {result}, best_strategy: {best_strategy}" - print(f"Best strategy is: {best_strategy}") - - self.model = result.model - self.optimizer = result.optim - print(f'optimizer after fsdp: {self.optimizer}') - self.loss_func = result.loss_func - self.train_dataloader = result.dataloader - self.prepare_input = result.prepare_input - - if self.args.resume_from_checkpoint == 'true': - self.load_atorch_optim_state(optim_state_dict) - print(f"atorch use optimizer: {self.optimizer}") - if self.is_rank0(): - print(f'GPU mem after fsdp:') - print(torch.cuda.memory_summary(device=self.device, abbreviated=False)) - - def evaluate(self): - logger.info(f"Start evaluation") - if self.is_rank0(): - print(torch.cuda.memory_summary(device=self.device, abbreviated=False)) - print(f'valid dataset length is: {len(self.valid_dataset)}') - print(f'valid dataloader length is: {len(self.valid_dataloader)}') - print(f'per device batch size: {self.args.per_device_valid_batch_size}') - progress_bar = tqdm(range(len(self.valid_dataloader)), - disable=not is_local_main_process(), - smoothing=0) - self.model.eval() - losses = [] - accumulated_task_loss_np = np.zeros(len(self.ID2TASK)) - accumulated_task_num_np = np.zeros(len(self.ID2TASK)) - accumulated_step = 0 - for step, batch in enumerate(self.valid_dataloader): - if step >= self.args.valid_iters: - break - with torch.no_grad(): - outputs = self.model( - input_ids=batch['input_ids'].to(self.device), - attention_mask=batch['attention_mask'].to(self.device), - position_ids=batch['position_ids'].to(self.device) - ) - loss, task_loss, task_num = self.loss_func(outputs, batch, self.args.weighted_loss_mode) - - repeated_loss = loss.repeat( - self.args.per_device_valid_batch_size) - if repeated_loss.ndim == 0: - repeated_loss = repeated_loss.clone()[None] - output_tensors = [repeated_loss.clone() - for _ in range(atorch.world_size())] - torch.distributed.all_gather(output_tensors, repeated_loss) - for tensor in output_tensors: - if torch.isnan(tensor).any() or torch.isinf(tensor).any(): - accumulated_step -= 1 - continue - losses.append(torch.cat(output_tensors, dim=0).cpu()) - task_loss = task_loss.cpu().numpy() - task_num = task_num.cpu().numpy() - accumulated_task_loss_np += task_loss - accumulated_task_num_np += task_num - - accumulated_step += 1 - progress_bar.update(1) - - losses = torch.cat(losses) - losses = losses[: len(self.valid_dataset)] - mean_loss = torch.mean(losses).item() - accumulated_task_loss = torch.tensor(accumulated_task_loss_np).to(self.device) - accumulated_task_num = torch.tensor(accumulated_task_num_np).to(self.device) - torch.distributed.all_reduce(accumulated_task_loss, op=torch.distributed.ReduceOp.SUM) - torch.distributed.all_reduce(accumulated_task_num, op=torch.distributed.ReduceOp.SUM) - accumulated_task_loss /= torch.distributed.get_world_size() - valid_task_loss = accumulated_task_loss / (accumulated_step - 1) - logs = {'valid_loss': mean_loss} - per_task_valid_loss = {self.ID2TASK[i]+'_loss': valid_task_loss[i].item() for i in range(len(self.ID2TASK))} - logs.update(per_task_valid_loss) - if is_global_main_process(): - logger.info('log point') - for i in range(len(self.ID2TASK)): - if accumulated_task_num[i] != 0: - logger.info(f"{self.ID2TASK[i]}_loss: {valid_task_loss[i]}, sample nums: {accumulated_task_num[i]}") - self.log(logs, step=self.global_steps, phase='Evaluation') - metrics = {'valid_loss': mean_loss, 'valid_task_loss': valid_task_loss} - logger.info(f"Finish evaluation") - if self.is_rank0(): - print(torch.cuda.memory_summary(device=self.device, abbreviated=False)) - - return metrics - - def log(self, logs, step, phase='Train'): - if not self.summary_writer: - return - logger.info(json.dumps(logs)) - for key, value in logs.items(): - self.summary_writer.add_scalar(f'{phase}/{key}', value, step) - - def _sorted_checkpoints( - self, - output_dir=None, - checkpoint_prefix=PREFIX_CHECKPOINT_DIR, - checkpoint_name_pattern='([0-9]+)', - use_mtime=False - ): - ordering_and_checkpoint_path = [] - - glob_checkpoints = [str(x) for x in Path(output_dir).glob( - f"{checkpoint_prefix}-*") if os.path.isdir(x)] - - for path in glob_checkpoints: - if use_mtime: - ordering_and_checkpoint_path.append( - (os.path.getmtime(path), path)) - else: - regex_match = re.search( - f".*{checkpoint_prefix}-({checkpoint_name_pattern})", path) - if regex_match is not None and regex_match.groups() is not None: - ordering_and_checkpoint_path.append( - (int(regex_match.groups()[0]), path)) - - checkpoints_sorted = sorted(ordering_and_checkpoint_path) - checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted] - # Make sure we don't delete the best model. - if self.best_model_checkpoint is not None: - best_model_index = checkpoints_sorted.index(str(Path(self.best_model_checkpoint))) - for i in range(best_model_index, len(checkpoints_sorted) - 1): - checkpoints_sorted[i], checkpoints_sorted[i + 1] = checkpoints_sorted[i + 1], checkpoints_sorted[i] - print_rank_0(f'checkpoints sorted list: {checkpoints_sorted}') - return checkpoints_sorted - - def _rotate_checkpoints( - self, - use_mtime=False, - output_dir=None, - prefix=PREFIX_CHECKPOINT_DIR, - checkpoint_name_pattern='.*') -> None: - if self.args.save_total_limit is None or self.args.save_total_limit <= 0: - return - - # Check if we should delete older checkpoint(s) - checkpoints_sorted = self._sorted_checkpoints( - use_mtime=use_mtime, - output_dir=output_dir, - checkpoint_prefix=prefix, - checkpoint_name_pattern=checkpoint_name_pattern) - if len(checkpoints_sorted) <= self.args.save_total_limit: - return - - # If save_total_limit=1 with load_best_model_at_end=True, we could end up deleting the last checkpoint, which - # we don't do to allow resuming. - save_total_limit = self.args.save_total_limit - - number_of_checkpoints_to_delete = max( - 0, len(checkpoints_sorted) - save_total_limit) - checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete] - for checkpoint in checkpoints_to_be_deleted: - logger.info( - f"Deleting older checkpoint [{checkpoint}] due to args.save_total_limit") - shutil.rmtree(checkpoint, ignore_errors=True) - - def _clean_atorch_checkpoints(self, output_dir=None, prefix=PREFIX_CHECKPOINT_DIR): - # Check if we should delete older checkpoint(s) - checkpoints_sorted = self._sorted_checkpoints( - output_dir=output_dir, - checkpoint_prefix=prefix, - checkpoint_name_pattern='([0-9]+)') - - # If save_total_limit=1 with load_best_model_at_end=True, we could end up deleting the last checkpoint, which - # we don't do to allow resuming. - - for checkpoint in checkpoints_sorted[:-1]: - logger.info( - f"Deleting older atorch checkpoint [{checkpoint}] due to self.args.save_total_limit") - try: - os.remove(os.path.join(checkpoint, ATORCH_CHECKPOINT_NAME)) - except Exception: - continue - - def _save_peft_model(self, output_dir, state_dict=None): - logger.info(f"Start saving peft model to {output_dir}") - output_dir = output_dir if output_dir is not None else self.args.output_dir - os.makedirs(output_dir, exist_ok=True) - model = unwrap_model(self.model) - if isinstance(model, PeftModel): - if state_dict is None: - state_dict = model.state_dict() - model.save_pretrained( - output_dir, state_dict=state_dict, is_main_process=self.is_rank0()) - else: - if state_dict is None: - state_dict = self.model.state_dict() - if self.is_rank0(): - torch.save(state_dict, os.path.join( - output_dir, "pytorch_model.bin")) - logger.info(f"Saving peft model done.") - - def _save_model(self, output_dir=None, state_dict=None): - # If we are executing this function, we are the process zero, so we don't check for that. - output_dir = output_dir if output_dir is not None else self.args.output_dir - os.makedirs(output_dir, exist_ok=True) - logger.info(f"Saving model checkpoint to {output_dir}") - # Save a trained model and configuration using `save_pretrained()`. - # They can then be reloaded using `from_pretrained()` - if not isinstance(self.model, PreTrainedModel): - if isinstance(unwrap_model(self.model), PreTrainedModel): - print_rank_0('save in not pretrained model~~~~~~~') - if state_dict is None: - state_dict = self.model.state_dict() - state_dict = {key: value.bfloat16() if self.args.bf16 else value.half() for key, value in state_dict.items()} - model = unwrap_model(self.model) - model.save_pretrained( - output_dir, - state_dict=state_dict, - max_shard_size=self.max_shard_size, - is_main_process=self.is_rank0()) - elif isinstance(unwrap_model(self.model), PeftModel): - if state_dict is None: - state_dict = unwrap_model(self.model).base_model.model.state_dict() - # Filter the peft params ... - param_keys = list(state_dict.keys()) - base_model_state_dict = {} - for key in param_keys: - if LORA_KEY in key: - # state_dict.pop(key) - continue - elif PEFT_PARAM_PREFIX in key: - value = state_dict[key] - new_key = key.replace(PEFT_PARAM_PREFIX, "") - base_model_state_dict[new_key] = value - else: - base_model_state_dict[key] = value - if self.is_rank0(): - torch.save(base_model_state_dict, - os.path.join(output_dir, WEIGHTS_NAME)) - else: - logger.info( - "Trainer.model is not a `PreTrainedModel`, only saving its state dict.") - if state_dict is None: - state_dict = self.model.state_dict() - state_dict = {key: value.bfloat16() if self.args.bf16 else value.half() for key, value in state_dict.items()} - torch.save(state_dict, os.path.join(output_dir, WEIGHTS_NAME)) - else: - print(f'save in pretrained model!!!!!!') - if state_dict is None: - state_dict = self.model.state_dict() - state_dict = {key: value.bfloat16() if self.args.bf16 else value.half() for key, value in state_dict.items()} - self.model.save_pretrained( - output_dir, - state_dict=state_dict, - max_shard_size=self.max_shard_size) - if self.tokenizer is not None: - self.tokenizer.save_pretrained(output_dir) - - # Good practice: save your training arguments together with the trained model - torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME)) - - def is_rank0(self): - return self.rank == 0 - - def _save_atorch_checkpoint(self, output_dir): - - # StateDictType.FULL_STATE_DICT - # FullStateDictConfig save to CPU，save on rank 0 only - save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) - with FSDP.state_dict_type(self.model, StateDictType.FULL_STATE_DICT, save_policy): - model_state_dict = self.model.state_dict() - optim_state_dict = FSDP.full_optim_state_dict(self.model, self.optimizer) # may be removed after PyTorch 2.2 - - if self.is_rank0(): - os.makedirs(output_dir, exist_ok=True) - torch.save( - { - "model_state_dict": model_state_dict, - "optimizer_state_dict": optim_state_dict, - "global_steps": self.global_steps, - }, - os.path.join(output_dir, ATORCH_CHECKPOINT_NAME), - ) - torch.distributed.barrier() # other rank waiting - - - def save(self, suffix=None, metrics=None): - logger.info('Save start') - if self.is_rank0(): - print(torch.cuda.memory_summary(device=self.device, abbreviated=False)) - if not self.save_pytorch_model_bin_checkpoint: - return - if suffix is None: - checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{self.global_steps}" - else: - checkpoint_folder = f"{PREFIX_CHECKPOINT_DIR}-{suffix}" - - run_dir = self.args.output_dir - output_dir = os.path.join(run_dir, checkpoint_folder) - # get state_dict, by every rank - if isinstance(self.model, FSDP): - save_policy = FullStateDictConfig(offload_to_cpu=atorch.world_size() > 1, rank0_only=atorch.world_size() > 1) - with FSDP.state_dict_type(self.model, StateDictType.FULL_STATE_DICT, save_policy): - model_state_dict = self.model.state_dict() - optim_state_dict = FSDP.full_optim_state_dict(self.model, self.optimizer) # may be removed after PyTorch 2.2 - else: - model_state_dict = unwrap_model(self.model).state_dict() - optim_state_dict = self.optimizer.state_dict() - if not self.no_save_atorch_checkpoint: - if self.args.peft_type is None or not self.no_save_base_model: - if self.is_rank0(): - os.makedirs(output_dir, exist_ok=True) - torch.save( - { - "model_state_dict": model_state_dict, - "optimizer_state_dict": optim_state_dict, - "global_steps": self.global_steps, - }, - os.path.join(output_dir, ATORCH_CHECKPOINT_NAME), - ) - torch.distributed.barrier() # other rank waiting - if self.args.peft_type is not None: - print(f'no_save_base_model: {self.no_save_base_model}') - if not self.no_save_base_model: - self._save_model(output_dir=output_dir) - self._save_peft_model(output_dir=output_dir) - else: - self._save_model(output_dir=output_dir) - - with warnings.catch_warnings(record=True) as caught_warnings: - torch.save(self.lr_scheduler.state_dict(), - os.path.join(output_dir, SCHEDULER_NAME)) - reissue_pt_warnings(caught_warnings) - - # Save RNG state in non-distributed training - rng_states = { - "python": random.getstate(), - "numpy": np.random.get_state(), - "cpu": torch.random.get_rng_state(), - } - if torch.cuda.is_available(): - if self.args.local_rank == -1: - # In non distributed, we save the global CUDA RNG state (will take care of DataParallel) - rng_states["cuda"] = torch.cuda.random.get_rng_state_all() - else: - rng_states["cuda"] = torch.cuda.random.get_rng_state() - - os.makedirs(output_dir, exist_ok=True) - - if torch.distributed.get_world_size() <= 1: - torch.save(rng_states, os.path.join(output_dir, "rng_state.pth")) - else: - torch.save(rng_states, os.path.join( - output_dir, f"rng_state_{self.rank}.pth")) - - if self.args_to_save: - json.dump(self.args_to_save, open(os.path.join(output_dir, - HYPER_PARAMETER_NAME), 'w'), ensure_ascii=False, indent=2) - # save state - state = {'global_steps': self.global_steps} - json.dump(state, open(os.path.join( - output_dir, TRAINER_STATE_NAME), 'w'), ensure_ascii=False, indent=2) - - # Determine the new best metric / best model checkpoint - if metrics is not None and self.args.metric_for_best_model is not None: - metric_to_check = self.args.metric_for_best_model - if not metric_to_check.startswith("valid_"): - metric_to_check = f"valid_{metric_to_check}" - metric_value = metrics[metric_to_check] - - operator = np.greater if self.args.greater_is_better == 'true' else np.less - if ( - self.best_metric is None - or self.best_model_checkpoint is None - or operator(metric_value, self.best_metric) - ): - self.best_metric = metric_value - self.best_model_checkpoint = output_dir - print_rank_0(f'current best model checkpoint is: {self.best_model_checkpoint}, valid_loss: {self.best_metric}') - - if self.is_rank0(): - if self.args.extra_save_by_epoch: - print('extra_save_by_epoch') - # for epoch saving，checkpoint will not be deleted，not affected by save_total_limit - # for step saving，only keep save_total_limit ckpts - self._rotate_checkpoints( - output_dir=run_dir, prefix=PREFIX_CHECKPOINT_DIR, checkpoint_name_pattern='([0-9]+)$') - else: - self._rotate_checkpoints( - output_dir=run_dir, prefix=PREFIX_CHECKPOINT_DIR) - # only save atorch checkpoint of the latest checkpoint - self._clean_atorch_checkpoints( - output_dir=run_dir, prefix=PREFIX_CHECKPOINT_DIR) - - print(torch.cuda.memory_summary(device=self.device, abbreviated=False)) - torch.distributed.barrier() - logger.info('Save finished') - - - def train(self, **kwargs): - logger.info("***** Running training *****") - logger.info(f" Num examples = {len(self.train_dataset)}") - logger.info(f" Num Epochs = {self.args.num_train_epochs}") - logger.info( - f" Instantaneous batch size per device = {self.args.per_device_train_batch_size}") - logger.info( - f" Total train batch size (w. parallel, distributed & accumulation) = {self.total_train_batch_size}") - logger.info( - f" Gradient Accumulation steps = {self.args.gradient_accumulation_steps}") - logger.info(f" Total optimization steps = {self.args.max_steps}") - - progress_bar = tqdm(range(self.args.max_steps), - disable=not is_local_main_process(), - smoothing=0) - training_time = 0 - - self.global_steps = 0 - start_epoch = 0 - steps_trained_in_current_epoch = 0 - exit_flag = False - if self.args.resume_from_checkpoint == 'true': - state = json.load( - open(os.path.join(self.resume_checkpoint_dir, TRAINER_STATE_NAME), 'r')) - self.global_steps = state.get('global_steps', 0) - progress_bar = tqdm(range(self.args.max_steps), - disable=not is_local_main_process(), - initial=self.global_steps, - smoothing=0) - start_epoch = self.global_steps // self.num_update_steps_per_epoch - steps_trained_in_current_epoch = self.global_steps % self.num_update_steps_per_epoch - steps_trained_in_current_epoch *= self.args.gradient_accumulation_steps - print(f'Start training at step {self.global_steps}') - self.last_step_logged = self.global_steps - self.skipped_steps = 0 - self.accumulated_loss = 0 - self.accumulated_task_loss = np.zeros(len(self.ID2TASK)) - self.accumulated_task_num = np.zeros(len(self.ID2TASK)) - - self.train_task_loss_prev = None - self.valid_task_loss_prev = None # L_valid at step t-1 - self.ema_valid_task_loss_prev = None # L_valid_ema at step t-2 - self.ratio_valid_task_loss_prev = torch.zeros(len(self.ID2TASK)).to(self.device) # ema ratio at step t-1 - - for epoch in range(start_epoch, int(self.args.num_train_epochs)): - self.train_dataloader.set_epoch(epoch) - self.model.train() - start_time = time.time() - - valid_iterator = iter(self.valid_dataloader) - for step, batch in enumerate(self.train_dataloader): - if step == 0: - print_rank_0(f"step 1 batch shape: {batch['input_ids'].shape},\n" - f"last 10 tokens: {batch['input_ids'][:, -10:]}") - print_rank_0(f"first 1000 tokens") - for pt in range(10): - print_rank_0(f"{batch['input_ids'][:, 10 * pt:10 * pt + 10]}") - skipped = False - self.model.train() - step_start = time.time() - if steps_trained_in_current_epoch and step < steps_trained_in_current_epoch: - continue - steps_trained_in_current_epoch = 0 - outputs = self.model( - input_ids=batch['input_ids'].to(self.device), - attention_mask=batch['attention_mask'].to(self.device), - position_ids=batch['position_ids'].to(self.device), - ) - - loss, task_loss, task_num = self.loss_func(outputs, batch, self.args.weighted_loss_mode) - - loss = loss / self.args.gradient_accumulation_steps - loss_tensor = torch.zeros( - [1], device=loss.device, dtype=loss.dtype) - loss_tensor[0] = loss.item() - torch.distributed.all_reduce(loss_tensor) - torch.distributed.all_reduce(task_loss, op=torch.distributed.ReduceOp.SUM) - torch.distributed.all_reduce(task_num, op=torch.distributed.ReduceOp.SUM) - reduce_loss = loss_tensor.sum() / torch.distributed.get_world_size() - if has_inf_or_nan(reduce_loss): - print_rank_0(f'There have nan loss.') - self.skipped_steps += 1 - skipped = True - else: - self.accumulated_loss += reduce_loss.item() - mean_task_loss = task_loss / torch.distributed.get_world_size() - self.accumulated_task_loss += mean_task_loss.cpu().numpy() - self.accumulated_task_num += task_num.cpu().numpy() - loss.backward() - self.global_steps += 1 - if step % self.args.gradient_accumulation_steps == 0 or step == len(self.train_dataloader) - 1: - if self.args.max_grad_norm is not None and self.args.max_grad_norm > 0: - # unscale for fp16 - try: - self.optimizer.unscale_() - except Exception: - pass - if isinstance(self.model, FSDP): - self.model.clip_grad_norm_(self.args.max_grad_norm) - else: - torch.nn.utils.clip_grad_norm_( - self.model.parameters(), self.args.max_grad_norm) - self.optimizer.step() - overflow = hasattr(self.optimizer, "step_was_skipped") and self.optimizer.step_was_skipped - if skipped != overflow: - print(f'skipped != overflow!!!!!!!!!!!!!!!!') - if not overflow: - self.lr_scheduler.step() - - self.optimizer.zero_grad() - - step_time = time.time() - step_start - step_tflops = get_tflops_megatron(self.args.total_model_param, self.args.hidden_size, self.args.num_hidden_layers, - self.args.per_device_train_batch_size, self.args.seq_length, step_time) - step_speed = get_computation_speed(self.args.per_device_train_batch_size, self.args.seq_length, step_time) - progress_bar.update(1) - - if self.global_steps % self.args.log_interval == 0: - print_rank_0(f'max memory allocated: {torch.cuda.max_memory_allocated()}') - if (self.global_steps - self.last_step_logged - self.skipped_steps) == 0: - self.accumulated_loss = 0 - self.skipped_steps = 0 - self.accumulated_task_loss = torch.zeros(len(self.ID2TASK)).to(self.device) - self.accumulated_task_num = torch.zeros(len(self.ID2TASK)).to(self.device) - self.last_step_logged = self.global_steps - self.epoch = self.global_steps / self.num_update_steps_per_epoch - print_rank_0(f'this log interval is skipped!') - continue - - train_loss = round( - self.accumulated_loss / (self.global_steps - self.last_step_logged - self.skipped_steps), 4) - - train_task_loss = self.accumulated_task_loss / (self.global_steps - self.last_step_logged - self.skipped_steps) - - if is_global_main_process(): - logger.info('log point') - logger.info(f'skipped steps: {self.skipped_steps}') - for i in range(len(self.ID2TASK)): - if self.accumulated_task_num[i] != 0: - logger.info(f"{self.ID2TASK[i]}_loss: {train_task_loss[i]}, sample nums: {self.accumulated_task_num[i]}") - self.accumulated_loss = 0 - self.skipped_steps = 0 - self.accumulated_task_loss = np.zeros(len(self.ID2TASK)) - self.accumulated_task_num = np.zeros(len(self.ID2TASK)) - self.last_step_logged = self.global_steps - self.epoch = self.global_steps / self.num_update_steps_per_epoch - learning_rate = self.lr_scheduler.get_last_lr()[0] - if torch.is_tensor(learning_rate): - learning_rate = learning_rate.item() - logs = {'train_loss': train_loss, - 'epoch': self.epoch, - 'learning_rate': learning_rate, - } - per_task_train_loss = {self.ID2TASK[i]+'_loss': train_task_loss[i].item() for i in range(len(self.ID2TASK))} - logs.update(per_task_train_loss) - if is_global_main_process(): - compute_mode = 'labels = -100' if batch['loss_mask'] is None else 'loss mask' - logger.info(f'weighted loss mode: {self.args.weighted_loss_mode}, compute mode: {compute_mode}') - self.log(logs, step=self.global_steps, - phase='train') - logger.info(f"tflops: {step_tflops} | token speed: {step_speed:.2f} tokens/gpu/s | sample speed: {step_speed / self.args.seq_length:.2f}") - - if 'steps' in self.args.evaluation_strategy.split(',') and self.global_steps % self.args.valid_interval == 0: - exit_flag = False - del loss, outputs - metrics = self.evaluate() - print_rank_0(f'Global steps: {self.global_steps} evaluate metrics: {metrics}') - if self.args.early_stopping_patience > 0: - self.early_stopping(metrics['valid_loss'], self.model) - # if early stopping - if self.early_stopping.early_stop: - exit_flag = True - print("Early stopping") - # exit model training - break - if 'steps' in self.args.save_strategy.split(',') and self.global_steps % self.args.checkpointing_steps == 0: - self.save(metrics=metrics) - - if self.global_steps >= self.args.max_steps: - break - - if exit_flag: - print("Early stopping") - break - logger.info(f"Training of epoch {epoch + 1} finished") - - training_time += time.time() - start_time - if 'epoch' in self.args.evaluation_strategy.split(','): - metrics = self.evaluate() - if self.args.early_stopping_patience > 0: - self.early_stopping(metrics['valid_loss'], self.model) - # if early stopping - if self.early_stopping.early_stop: - exit_flag = True - print("Early stopping") - # exit model training - break - if 'epoch' in self.args.save_strategy.split(',') or self.args.extra_save_by_epoch: - if epoch + 1 < (int(self.args.num_train_epochs) // 3) and self.args.total_model_param < 1e9: - continue - print_rank_0(f'Global steps: {self.global_steps} | Epoch {epoch + 1} checkpoint metrics: {metrics}') - self.save( - suffix=f'{self.global_steps}-{EPOCH_CHECKPOINT_NAME}-{epoch + 1}') diff --git a/mftcoder_atorch/utils/__init__.py b/mftcoder_atorch/utils/__init__.py deleted file mode 100644 index 0cf9434..0000000 --- a/mftcoder_atorch/utils/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .common_utils import * -from .auto_accelerate_utils import * \ No newline at end of file diff --git a/mftcoder_atorch/utils/auto_accelerate_utils.py b/mftcoder_atorch/utils/auto_accelerate_utils.py deleted file mode 100644 index ea1cc13..0000000 --- a/mftcoder_atorch/utils/auto_accelerate_utils.py +++ /dev/null @@ -1,226 +0,0 @@ -import sys -sys.path.append("..") -import torch -import atorch -from utils.common_utils import print_rank_0, TASK2ID, ID2TASK -from torch.nn import CrossEntropyLoss -from dataclasses import dataclass -import numpy as np -import torch.nn.functional as F -from typing import Dict, List, Tuple, Union - -torch.set_printoptions(threshold=np.inf) - - -def get_task_mask(task_id): - task_num = len(TASK2ID) - task_mask = torch.zeros(task_id.shape[0], task_num) - task_mask[torch.arange(task_id.size(0)).unsqueeze(1), task_id] = 1 - - return task_mask - - -def get_task_loss(task_losses, task_id): # TODO - # Fix a printing order. - task_loss_per_batch = torch.zeros(len(ID2TASK)).to(device=task_id.device) - # Count the occurrences of each task. - task_num_per_batch = torch.zeros(len(ID2TASK)).to(device=task_id.device) - for i in range(len(task_id)): - task_num_per_batch[task_id[i][0]] += 1 - task_loss_per_batch[task_id[i][0]] = task_losses[task_id[i][0]] - - return task_loss_per_batch, task_num_per_batch - - -def loss_func_mft(outputs, inputs, weighted_loss_mode): - # task_id shape: [[1], [2], [4], [3], ..., [1]] - labels, task_mask, task_id, loss_mask, weights = inputs['labels'], inputs['task_mask'], inputs['task_id'], inputs['loss_mask'], inputs['weights'] - weighted = weighted_loss_mode - lm_logits = outputs["logits"] - labels = labels.to(device=lm_logits.device) - task_mask = task_mask.to(device=lm_logits.device) - task_id = task_id.to(device=lm_logits.device) - bsz, seq_len = labels.shape - if loss_mask is None: - ineffective_tokens_per_sample = (labels==-100).sum(dim=1) - effective_tokens_per_sample = - (ineffective_tokens_per_sample - seq_len) - effective_tokens = bsz * seq_len - ineffective_tokens_per_sample.sum() - loss_fct = CrossEntropyLoss(reduction='none', ignore_index=-100) - else: - loss_mask = loss_mask.to(device=lm_logits.device) - effective_tokens_per_sample = torch.sum(loss_mask, dim=1, dtype=torch.int) - effective_tokens = torch.sum(loss_mask).item() - loss_fct = CrossEntropyLoss(reduction='none') - if weighted_loss_mode.endswith('focalloss'): - losses = loss_fct(lm_logits, labels) - else: - losses = loss_fct(lm_logits.view(-1, lm_logits.size(-1)), labels.view(-1)) # [B * L, 1] - # losses = losses.contiguous().view(bsz, -1) - losses = losses.view(bsz, -1) - token_losses = losses.clone().detach().float() if loss_mask is None else losses.clone().detach().float() # [B, L] - task_mask_trans = torch.transpose(task_mask, 0, 1) - unique_weights = torch.unique(weights) - unique_id = torch.unique(task_id) - if weighted_loss_mode == "case3" or weighted_loss_mode == "case4": - loss = 0.0 - for i, w in enumerate(unique_weights): - row_idx = torch.squeeze(weights) == w.item() - if weighted_loss_mode == "case3": - if loss_mask is None: - loss += torch.sum(losses[row_idx, :]) / torch.sum(effective_tokens_per_sample[row_idx]) - else: - loss += torch.sum((losses * loss_mask)[row_idx, :]) / torch.sum(loss_mask[row_idx, :]) - elif weighted_loss_mode == "case4": - if loss_mask is None: - loss += torch.mean(torch.sum(losses, dim=1)[row_idx] / effective_tokens_per_sample[row_idx]) - else: - loss += torch.mean(torch.sum(losses * loss_mask, dim=1)[row_idx] / torch.sum(loss_mask, dim=1)[row_idx]) - loss /= len(unique_weights) - elif weighted_loss_mode == "case2": - if loss_mask is None: - loss = torch.mean(torch.sum(losses, dim=1) / effective_tokens_per_sample) - else: - loss = torch.mean(torch.sum(losses * loss_mask, dim=1) / torch.sum(loss_mask, dim=1)) - elif weighted_loss_mode == "case1": - # flatten losses & loss_mask tensor - if loss_mask is None: - loss = torch.sum(losses.view(-1)) / effective_tokens - else: - loss = torch.sum(losses.view(-1) * loss_mask.view(-1)) / loss_mask.sum() - - # Fix a printing order. - task_loss = torch.zeros(len(ID2TASK)).to(device=task_id.device) # per task loss - task_num = torch.zeros(len(ID2TASK)).to(device=task_id.device) - # unique_id = torch.unique(task_id) - for i, w in enumerate(unique_id): - row_idx = torch.squeeze(task_id) == w.item() - if loss_mask is None: - task_loss[w] = torch.sum(token_losses[row_idx, :]) / torch.sum(effective_tokens_per_sample[row_idx]) - task_num[w] = len(effective_tokens_per_sample[row_idx]) - else: # TODO: - task_loss[w] = torch.sum((token_losses * loss_mask)[row_idx, :]) / torch.sum(loss_mask[row_idx, :]) - task_num[w] = len(torch.sum(loss_mask, dim=1)[row_idx]) - - return loss, task_loss, task_num - - -def get_attn_mask(seq_length, device): - """ - Get triangular attention mask for a given sequence length / device. - """ - # lower triangular attention mask - mask = torch.tril(torch.ones((1, seq_length, seq_length), device=device)).view( - 1, 1, seq_length, seq_length - ) - - # convert to binary - return mask < 0.5 - - -def get_ltor_masks_and_position_ids(data): - """Build masks and position id for left to right model.""" - - # Extract batch size and sequence length. - batch_size, seq_length = data.size() - - # Attention mask (lower triangular). - # attention_mask = get_attn_mask( - # seq_length=seq_length, - # device=data.device, - # ) - attention_mask = torch.ones((batch_size, seq_length), device=data.device) - - # Position ids. - position_ids = torch.arange(seq_length, dtype=torch.long, device=data.device) - position_ids = position_ids.unsqueeze(0).expand_as(data).clone() - - return attention_mask, position_ids - - -def prepare_gpt_input(batch, device): - batch = {k: v.to(device=device, non_blocking=True) for k, v in batch.items()} - - if 'loss_mask' in batch and 'labels' not in batch: - # if 'loss_mask' in batch: - print_rank_0('loss mask in batch') - input_ids = batch['input_ids'].long() - batch['input_ids'] = input_ids[:, :-1].contiguous().to(device=device) - batch['labels'] = input_ids[:, 1:].contiguous().to(device=device) - batch['loss_mask'] = batch['loss_mask'].float()[:, 1:].contiguous() - else: - batch['input_ids'] = batch['input_ids'].long() - batch['labels'] = batch['labels'].long() - batch['loss_mask'] = None - - # Get the masks and position ids. - batch['attention_mask'], batch['position_ids'] = get_ltor_masks_and_position_ids(data=batch['input_ids']) - - if self.args.weighted_loss_mode: - weights = batch['weight'].float().to(device=device) # [2, 4, 6, 3, ..., 2] - # batch['loss_mask'] *= weights - - if 'task_id' in batch and batch['task_id'] is not None: # task_id: bsz * 1, [[1], [2], [4], [3], ..., [1]] - batch['task_mask'] = get_task_mask(batch['task_id']).to(device=device) # bsz * task_num - - return batch - - -@dataclass -class DataCollatorForMFTDataset(object): - def __init__(self, model_type, weighted_loss_mode, use_dynamic_padding): - self.model_type = model_type - self.weighted_loss_mode = weighted_loss_mode - self.use_dynamic_padding = use_dynamic_padding - - # tokenizer: None - - def __call__(self, instances): - input_ids, attention_mask, position_ids, labels, loss_mask, weights, task_id = tuple( - [instance[key] if key in instance else None for instance in instances] for key in - ("input_ids", "attention_mask", "position_ids", "labels", "loss_mask", "weight", "task_id")) - # input_ids, loss_mask, weights, task_id = tuple(instances[key] for key in ("input_ids", "loss_mask", "weight", "task_id")) - - result_batch = {} - ''' - outputs = model( - input_ids=batch['input_ids'], - attention_mask=batch['attention_mask'], - # labels=(batch['labels'], batch['loss_mask'], batch['task_mask']), - # labels=(batch['labels'], batch['loss_mask']), - position_ids=batch['position_ids'], - ) - ''' - - input_ids = torch.tensor(np.array(input_ids)).long() - # input_ids = input_ids.long() - if loss_mask[0] is None: - result_batch['input_ids'] = input_ids.contiguous() - labels = torch.tensor(np.array(labels)).long() - result_batch['labels'] = labels.contiguous() - result_batch['loss_mask'] = None - else: - loss_mask = torch.tensor(np.array(loss_mask)) - if self.use_dynamic_padding: - last_one_pos = (loss_mask == 1).long().cumsum(dim=1).argmax(dim=1) - max_pos = last_one_pos.max().item() + 1 - else: - max_pos = loss_mask.shape[-1] - result_batch['input_ids'] = input_ids[:, :max_pos-1].contiguous() # [B, L + 1] -> [B, L] - result_batch['labels'] = input_ids[:, 1:max_pos].contiguous() - result_batch['loss_mask'] = loss_mask.float()[:, 1:max_pos].contiguous() - - if self.weighted_loss_mode and weights is not None: - weights = torch.tensor(np.array(weights)) - result_batch['weights'] = weights - # if result_batch['loss_mask'] is not None: - # result_batch['loss_mask'] *= weights - - # Get the masks and position ids. - result_batch['attention_mask'], result_batch['position_ids'] = get_ltor_masks_and_position_ids(data=result_batch['input_ids']) - - if task_id is not None: - task_id = torch.tensor(np.array(task_id)) - result_batch['task_mask'] = get_task_mask(task_id) # bsz * task_num - result_batch['task_id'] = task_id - - return result_batch diff --git a/mftcoder_atorch/utils/common_utils.py b/mftcoder_atorch/utils/common_utils.py deleted file mode 100644 index 7d36ad5..0000000 --- a/mftcoder_atorch/utils/common_utils.py +++ /dev/null @@ -1,371 +0,0 @@ -import os -import math -import torch -import atorch -import numpy as np -from collections.abc import Mapping # noqa: E402 -from contextlib import contextmanager # noqa: E402 -from torch.distributed.fsdp import ( - FullyShardedDataParallel as FSDP, - # BackwardPrefetch, - FullStateDictConfig, - StateDictType, -) -from transformers import get_scheduler -from utils.learning_rates import AnnealingLR -TASK2ID = {} -ID2TASK = {} - - -def get_rank(): - return atorch.rank() - - -def get_local_rank(): - return atorch.local_rank() - - -def is_main_process(): - return atorch.rank() == 0 - - -def is_local_main_process(): - return atorch.local_rank() == 0 - - -def print_rank_0(*message): - """If distributed is initialized print only on rank 0.""" - if torch.distributed.is_initialized(): - if torch.distributed.get_rank() == 0: - print(*message, flush=True) - else: - print(*message, flush=True) - - -def get_world_size(): - return atorch.world_size() - - -def wait_for_everyone(): - torch.distributed.barrier() - - -def atorch_init_distributed(backend="nccl"): - atorch.init_distributed(backend, set_cuda_device_using_local_rank=True) - # atorch.init_distributed(backend) - - -def atorch_reset_distributed(): - atorch.reset_distributed() - - -def _goes_first(is_main): - if is_main is False: - wait_for_everyone() - yield - if is_main is True: - wait_for_everyone() - - -def get_model_params_num(model): - """ - Get params number of the model - Args: - model: model(required) - Returns: - the number of parameters of model - """ - num = 0 - for _, param in model.named_parameters(): - num += param.nelement() - return num - - -@contextmanager -def main_process_first(): - yield from _goes_first(is_main_process()) - - -def unwrap_model(model): - """ - Recursively unwraps a model from potential containers (as used in distributed training). - - Args: - model (`torch.nn.Module`): The model to unwrap. - """ - # since there could be multiple levels of wrapping, unwrap recursively - if hasattr(model, "module"): - return unwrap_model(model.module) - else: - return model - - -def honor_type(obj, generator): - """ - Cast a generator to the same type as obj (list, tuple or namedtuple) - """ - try: - return type(obj)(generator) - except TypeError: - # Some objects may not be able to instantiate from a generator directly - return type(obj)(*list(generator)) - - -def recursively_apply( - func, - data, - *args, - test_type=lambda t: isinstance(t, torch.Tensor), - error_on_other_type=False, - **kwargs, -): - if isinstance(data, (tuple, list)): - return honor_type( - data, - ( - recursively_apply( - func, - o, - *args, - test_type=test_type, - error_on_other_type=error_on_other_type, - **kwargs, - ) - for o in data - ), - ) - elif isinstance(data, Mapping): - return type(data)( - { - k: recursively_apply( - func, - v, - *args, - test_type=test_type, - error_on_other_type=error_on_other_type, - **kwargs, - ) - for k, v in data.items() - } - ) - elif test_type(data): - return func(data, *args, **kwargs) - elif error_on_other_type: - raise TypeError( - f"Can't apply {func.__name__} on object of type {type(data)}, only of nested list/tuple/dicts of objects " - f"that satisfy {test_type.__name__}." - ) - return data - - -def gather(tensor): - def _gpu_gather_one(tensor): - if tensor.ndim == 0: - tensor = tensor.clone()[None] - output_tensors = [tensor.clone() for _ in range(torch.distributed.get_world_size())] - torch.distributed.all_gather(output_tensors, tensor) - return torch.cat(output_tensors, dim=0) - - return recursively_apply(_gpu_gather_one, tensor, error_on_other_type=True) - - -def save_ckpt(model, optimizer, lr_scheduler, epoch, steps, save_path, logger): - if isinstance(model, FSDP): - print('Saving a FSDP model') - optim_state_dict = FSDP.full_optim_state_dict(model, optimizer) - save_policy = FullStateDictConfig(offload_to_cpu=True, rank0_only=True) - with FSDP.state_dict_type(model, StateDictType.FULL_STATE_DICT, save_policy): - model_state_dict = model.state_dict() - lrs_state_dict = lr_scheduler.state_dict() - else: - print('Saving a normal model') - model_state_dict = model.state_dict() - optim_state_dict = optimizer.state_dict() - lrs_state_dict = lr_scheduler.state_dict() - # rank0 save - if is_main_process(): - torch.save( - { - "epoch": epoch + 1, - "step": steps, - "state_dict": model_state_dict, - "optimizer": optim_state_dict, - "lrs_state_dict": lrs_state_dict, - }, - save_path, - ) - logger.info(f"Saved checkpoint {save_path} (epoch {epoch + 1} @ {steps} steps)") - wait_for_everyone() - # torch.distributed.barrier() # other rank waiting - - -def scheduler_and_resume(args, train_dataloader, model, optimizer, checkpoint): - # Scheduler and math around the number of training steps. - overrode_max_steps = False - args.num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if args.max_steps == -1: - args.max_steps = args.num_train_epochs * args.num_update_steps_per_epoch - overrode_max_steps = True - - lr_scheduler = AnnealingLR( - optimizer, - start_lr=args.learning_rate, - warmup_iter=args.num_warmup_steps, - total_iters=args.max_steps * args.gradient_accumulation_steps, - decay_style=args.lr_scheduler_type, - last_iter=0, - min_lr=args.min_lr, - use_checkpoint_lr_scheduler=True, - ) - - if args.resume_from_checkpoint is not None: - if os.path.isfile(args.resume_from_checkpoint): - starting_epoch = checkpoint["epoch"] - 1 - steps = checkpoint["step"] - args.resume_step = steps - # Restore the optim state - if optimizer is not None: - if isinstance(model, FSDP): - print('Loading optimizer for a FSDP model') - full_osd = checkpoint["optimizer"] - sharded_osd = FSDP.scatter_full_optim_state_dict(full_osd, model) - optimizer.load_state_dict(sharded_osd) - else: - print('Loading optimizer for a normal model') - optimizer.load_state_dict(checkpoint["optimizer"]) - logging.info("Optimizer state is restored from the checkpoint") - if lr_scheduler is not None: - lr_scheduler.load_state_dict(checkpoint["lrs_state_dict"]) - logging.info(f"Loaded checkpoint '{args.resume_from_checkpoint}' (epoch {checkpoint['epoch']} @ {steps} steps)") - else: - logger.info(f"No optimizer and lr scheduler checkpoint found at '{args.resume_from_checkpoint}'") - - # We need to recalculate our total training steps as the size of the training dataloader may have changed. - args.num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if overrode_max_steps: - args.max_steps = args.num_train_epochs * args.num_update_steps_per_epoch - # Afterwards we recalculate our number of training epochs - args.num_train_epochs = math.ceil(args.max_steps / args.num_update_steps_per_epoch) - - return args, lr_scheduler, optimizer - - -def get_computation_speed(batch_size_per_device, seq_len, step_time): - - return batch_size_per_device * seq_len / (step_time + 1e-12) - - -def human_readable_flops(num): - for unit in [ - "", - "KFLOPS", - "MFLOPS", - "GFLOPS", - "TFLOPS", - "PFLOPS", - "EFLOPS", - "ZFLOPS", - ]: - if abs(num) < 1000.0: - return "%3.1f%s" % (num, unit) - num /= 1000.0 - return "%.1f%s" % (num, "Yi") - - -def get_tflops_new(args, batch_size, seq_len, step_time): - sl = seq_len - L = args.num_hidden_layers - h = args.hidden_size - V = args.vocab_size - flops = (96 * batch_size * sl * L * h * h * (1 + sl / (6 * h) + V / (16 * L * h)) / step_time) - return human_readable_flops(flops) - - -def get_tflops_megatron(total_model_param, hidden_size, num_hidden_layers, - batch_size_per_device, seq_len, step_time): - - ff = total_model_param * 6 - attn = seq_len * hidden_size * num_hidden_layers * 60 - flops = ( - batch_size_per_device - * seq_len - * (ff + attn) - / step_time - ) - return human_readable_flops(flops) - - -def is_old_version(path): - new_vocab_files = ['merge.model'] - new_vocab_file_exists = [] - for filename in new_vocab_files: - if not os.path.exists(os.path.join(path, filename)): - new_vocab_file_exists.append(False) - else: - new_vocab_file_exists.append(True) - if all(new_vocab_file_exists): - return False - if any(new_vocab_file_exists): - return 'new_version_file_absent' - else: - return True - - -def generate_task_id(data_paths, train_mode): - data_prefixes = list(data_paths[1:-1].split(',')) - print("data paths: ") - print(data_prefixes) - - for i, prefix in enumerate(data_prefixes): - if train_mode == 'sft': - task_name = prefix.split('/')[-1] - else: - task_name = prefix.split('/')[-2] - TASK2ID[task_name] = i - ID2TASK[i] = task_name - - -class EarlyStopping: - """Early stops the training if validation loss doesn't improve after a given patience.""" - def __init__(self, patience=7, verbose=False, delta=0): - """ - Args: - patience (int): How long to wait after last time validation loss improved. - Default: 7 - verbose (bool): If True, prints a message for each validation loss improvement. - Default: False - delta (float): Minimum change in the monitored quantity to qualify as an improvement. - Default: 0 - """ - self.patience = patience - self.verbose = verbose - self.counter = 0 - self.best_score = None - self.early_stop = False - self.val_loss_min = np.Inf - self.delta = delta - - def __call__(self, val_loss, model): - - score = -val_loss - - if self.best_score is None: - self.best_score = score - # self.save_checkpoint(val_loss, model) - elif score < self.best_score + self.delta: - self.counter += 1 - print(f'EarlyStopping counter: {self.counter} out of {self.patience}') - if self.counter >= self.patience: - self.early_stop = True - else: - self.best_score = score - # self.save_checkpoint(val_loss, model) - self.counter = 0 - - def save_checkpoint(self, val_loss, model): - '''Saves model when validation loss decrease.''' - if self.verbose: - print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...') - torch.save(model.state_dict(), 'checkpoint.pt') - self.val_loss_min = val_loss - diff --git a/mftcoder_atorch/utils/learning_rates.py b/mftcoder_atorch/utils/learning_rates.py deleted file mode 100644 index eb644e1..0000000 --- a/mftcoder_atorch/utils/learning_rates.py +++ /dev/null @@ -1,148 +0,0 @@ -# Copyright (c) 2021, EleutherAI -# This file is based on code by the authors denoted below and has been modified from its original version. -# -# Copyright (c) 2020, NVIDIA CORPORATION. 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. - -"""Learning rate decay functions.""" - -import math - -# from .common_utils import print_rank_0 - - -class AnnealingLR(object): - """Anneals the learning rate.""" - - def __init__( - self, - optimizer, - start_lr, - warmup_iter, - total_iters, - decay_style, - last_iter, - min_lr=0.0, - use_checkpoint_lr_scheduler=True, - override_lr_scheduler=False, - use_mup=False, - ): - - # Class values. - self.optimizer = optimizer - self.start_lr = start_lr - self.min_lr = min_lr - self.warmup_iter = warmup_iter - self.num_iters = last_iter - self.end_iter = total_iters - assert self.end_iter > 0 - self.decay_style = decay_style - self.override_lr_scheduler = override_lr_scheduler - self.use_checkpoint_lr_scheduler = use_checkpoint_lr_scheduler - self.use_mup = use_mup - if self.override_lr_scheduler: - assert not self.use_checkpoint_lr_scheduler, ( - "both override and " "use-checkpoint are set." - ) - # Set the learning rate - self.step(self.num_iters) - - print("> learning rate decay style: {}".format(self.decay_style)) - - def update_lr(self, lr): - self.start_lr = lr - - def get_lr(self): - """Learning rate decay functions from: - https://openreview.net/pdf?id=BJYwwY9ll pg. 4""" - - num_iters_ = min(self.num_iters, self.end_iter - self.warmup_iter) - # Warmup. - if self.warmup_iter > 0 and self.num_iters <= self.warmup_iter: - return float(self.start_lr) * num_iters_ / self.warmup_iter - - num_iters_ = num_iters_ - self.warmup_iter - if self.decay_style == "linear": - lr = self.start_lr * (self.end_iter - num_iters_) / self.end_iter - elif self.decay_style == "cosine": - lr = ( - self.start_lr - / 2.0 - * (math.cos(math.pi * num_iters_ / self.end_iter) + 1) - ) - elif self.decay_style == "exponential": - # exp(-0.693) = 1/2 - lr = self.start_lr * math.exp(-0.693 * num_iters_ / self.end_iter) - else: - lr = self.start_lr - return max(lr, self.min_lr) - - def step(self, step_num=None): - """Set lr for all parameters groups.""" - if step_num is None: - step_num = self.num_iters + 1 - self.num_iters = step_num - new_lr = self.get_lr() - for group in self.optimizer.param_groups: - if self.use_mup and "width_mult" in group: - group["lr"] = new_lr / group["width_mult"] - else: - group["lr"] = new_lr - - def state_dict(self): - state_dict = { - "start_lr": self.start_lr, - "warmup_iter": self.warmup_iter, - "num_iters": self.num_iters, - "decay_style": self.decay_style, - "end_iter": self.end_iter, - "min_lr": self.min_lr, - } - return state_dict - - def _check_and_set(self, cls_value, sd_value, name): - """Auxiliary function for checking the values in the checkpoint and - setting them.""" - if self.override_lr_scheduler: - print_rank_0(" > overriding {} value to {}".format(name, cls_value)) - return cls_value - - if not self.use_checkpoint_lr_scheduler: - assert cls_value == sd_value, ( - "AnnealingLR: class input value" - "and checkpoint values for {} do not match".format(name) - ) - print_rank_0(" > using checkpoint value {} for {}".format(sd_value, name)) - return sd_value - - def load_state_dict(self, sd): - - self.start_lr = self._check_and_set( - self.start_lr, sd["start_lr"], "learning rate" - ) - self.min_lr = self._check_and_set( - self.min_lr, sd["min_lr"], "minimum learning rate" - ) - self.warmup_iter = self._check_and_set( - self.warmup_iter, sd["warmup_iter"], "warmup iterations" - ) - self.end_iter = self._check_and_set( - self.end_iter, sd["end_iter"], "total number of iterations" - ) - self.decay_style = self._check_and_set( - self.decay_style, sd["decay_style"], "decay style" - ) - - self.num_iters = sd["num_iters"] - self.step(self.num_iters) diff --git a/mftcoder_atorch/utils/merge_base_and_lora_to_hf.py b/mftcoder_atorch/utils/merge_base_and_lora_to_hf.py deleted file mode 100644 index 29378e3..0000000 --- a/mftcoder_atorch/utils/merge_base_and_lora_to_hf.py +++ /dev/null @@ -1,38 +0,0 @@ -import os -import sys -import time -import shutil -import torch -import transformers -sys.path.append("..") -from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModel -from peft import LoraConfig, get_peft_model -from peft import PeftModel -from model_mapping import MODEL_SPECIAL_TOKENS - - -model_path='path to base model' -lora_adapter='path to lora adaptor ckpt' -save_path='path to new merged model' -model_type = 'gpt_neox' - -t0 = time.time() -config = {"model_type": model_type} -tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True) - -base_model = AutoModelForCausalLM.from_pretrained( - model_path, - trust_remote_code=True, - torch_dtype=torch.bfloat16, - return_dict=True, - device_map="auto" -) -print(base_model) - -# merge, save model and tokenizer -model_to_merge = PeftModel.from_pretrained(base_model, lora_adapter) -merged_model = model_to_merge.merge_and_unload() -print(merged_model.config) -merged_model.save_pretrained(save_path) -tokenizer.save_pretrained(save_path) -print(f"Merge finised: {save_path} saved, Cost {time.time()-t0:.2f}s") \ No newline at end of file diff --git a/mftcoder_atorch/utils/vocab.json b/mftcoder_atorch/utils/vocab.json deleted file mode 100644 index fb3b7bb..0000000 --- a/mftcoder_atorch/utils/vocab.json +++ /dev/null @@ -1,205457 +0,0 @@ -{ - "version": "1.0", - "truncation": null, - "padding": null, - "added_tokens": [ - { - "id": 100256, - "content": "<|endoftext|>", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": false, - "special": true - }, - { - "id": 100257, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100258, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100259, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100260, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100261, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100262, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100263, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100264, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100265, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100266, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100267, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100268, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100269, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100270, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100271, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100272, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100273, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100274, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100275, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100276, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100277, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100278, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100279, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100280, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100281, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100282, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100283, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100284, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100285, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100286, - "content": " ", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100287, - "content": "\t\t\t\t\t\t\t\t\t", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100288, - "content": "\t\t\t\t\t\t\t\t", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100289, - "content": "\t\t\t\t\t\t\t", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100290, - "content": "\t\t\t\t\t\t", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100291, - "content": "\t\t\t\t\t", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100292, - "content": "\t\t\t\t", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100293, - "content": "\t\t\t", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100294, - "content": "\t\t", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100295, - "content": "// language: Java", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100296, - "content": "// language: C", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100297, - "content": "// language: C++", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100298, - "content": "// language: JavaScript", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100299, - "content": "// language: PHP", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100300, - "content": "# language: Python", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100301, - "content": "// language: C#", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100302, - "content": "# language: Ruby", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100303, - "content": "// language: GO", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100304, - "content": "// language: TypeScript", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100305, - "content": "/* language: CSS */", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100306, - "content": "# language: Shell", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100307, - "content": "// language: Scala", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100308, - "content": "-- language: SQL", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100309, - "content": "// language: Kotlin", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100310, - "content": "", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100311, - "content": "", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100312, - "content": "", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100313, - "content": "sitof", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100314, - "content": "inlinehint", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100315, - "content": "fpex", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100316, - "content": "local_unnamed_addr", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100317, - "content": "cleanupret", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100318, - "content": "swifterror", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100319, - "content": "getelementptr", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100320, - "content": "x86_amx", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100321, - "content": "dereferenceable_or_null", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100322, - "content": "v64", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100323, - "content": "i1", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100324, - "content": "extern_weak", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100325, - "content": "trun", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100326, - "content": "inttopt", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100327, - "content": "fptrun", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100328, - "content": "addrspacecas", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100329, - "content": "fsub", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100330, - "content": "memprof", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100331, - "content": "nobuiltin", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100332, - "content": "unnamed_addr", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100333, - "content": "absolute_symbol", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100334, - "content": "invariant.group", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100335, - "content": "swiftself", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100336, - "content": "shl", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100337, - "content": "va_arg", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100338, - "content": "noduplicate", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100339, - "content": "shufflevector", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100340, - "content": "tbaa", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100341, - "content": "fcmp", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100342, - "content": "ppc_fp128", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100343, - "content": "callbr", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100344, - "content": "v128", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100345, - "content": "f64", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100346, - "content": "onversio", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100347, - "content": "ashr", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100348, - "content": "cleanuppad", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100349, - "content": "nuw", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100350, - "content": "i32", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100351, - "content": "itwis", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100352, - "content": "x86_mmx", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100353, - "content": "lshr", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100354, - "content": "nounwind", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100355, - "content": "ifunc", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100356, - "content": "indirectbr", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100357, - "content": "linker_private_weak", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100358, - "content": "norecurse", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100359, - "content": "strictfp", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100360, - "content": "returns_twice", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100361, - "content": "fptos", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100362, - "content": "tbaa.struct", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100363, - "content": "uwtable", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100364, - "content": "linkonce", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100365, - "content": "insertvalue", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100366, - "content": "i16", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100367, - "content": "f32", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100368, - "content": "nsw", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100369, - "content": "fp128", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100370, - "content": "dereferenceable", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100371, - "content": "udiv", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100372, - "content": "speculatable", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100373, - "content": "irr_loop", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100374, - "content": "catchswitch", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100375, - "content": "sanitize_memory", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100376, - "content": "sanitize_hwaddress", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100377, - "content": "func_sanitize", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100378, - "content": "noreturn", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100379, - "content": "ggregat", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100380, - "content": "urem", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100381, - "content": "inbounds", - "single_word": false, - "lstrip": false, - "rstrip": false, - "normalized": true, - "special": false - }, - { - "id": 100382, - "content": "sdiv", - "single_word": false, - "lstrip": false, - "rstrip": false, - 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"dropdown": 11385, - "_DEBUG": 11386, - "260": 11387, - "\u0120detect": 11388, - "country": 11389, - "!\");\u010a": 11390, - "\u0109R": 11391, - "appy": 11392, - "\u0120cref": 11393, - "('<": 11394, - "\"=>": 11395, - "\u0120LE": 11396, - "reader": 11397, - "\u0120administr": 11398, - "\u00c3\u00b5": 11399, - "ucket": 11400, - "\u0120fashion": 11401, - ".char": 11402, - "izar": 11403, - "\u0120disable": 11404, - "\u0120suc": 11405, - "\u0120Live": 11406, - "issue": 11407, - "\u0120metadata": 11408, - "flags": 11409, - "\u0120\u00f0\u0141": 11410, - "\u0120committed": 11411, - "\u0120va": 11412, - "\u0120rough": 11413, - "\u0120'''\u010a": 11414, - "\u0120highlight": 11415, - "_vars": 11416, - "VO": 11417, - "\u0120encoding": 11418, - "-Z": 11419, - "_sign": 11420, - "$(\"#": 11421, - "\u0120rain": 11422, - "reatest": 11423, - "\u0120END": 11424, - "Selection": 11425, - "\u0120candidates": 11426, - "\u0120sav": 11427, - ".Empty": 11428, - "\u0120decisions": 11429, - "\u0120collabor": 11430, - 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"\u0120intelligence": 11478, - ".IS": 11479, - "\u0120Observable": 11480, - "\u0120Hard": 11481, - "Bool": 11482, - "211": 11483, - "idential": 11484, - ".Anchor": 11485, - "\u0120selling": 11486, - "CI": 11487, - "AGES": 11488, - "tle": 11489, - "bur": 11490, - "UFFER": 11491, - "RY": 11492, - "\u0120bigger": 11493, - "\u0120rat": 11494, - "\u0120famous": 11495, - "\u0120typename": 11496, - "\u0120explained": 11497, - "}}\u010a": 11498, - "\u0120nuclear": 11499, - "-N": 11500, - "\u0120crisis": 11501, - "\u0120Enter": 11502, - "\u0120answers": 11503, - "/${": 11504, - "/pl": 11505, - "\u0120sequ": 11506, - "_next": 11507, - "mask": 11508, - "\u0120standing": 11509, - "\u0120plenty": 11510, - "\u0120Cross": 11511, - "\u0109ret": 11512, - "dro": 11513, - "\u0120Cast": 11514, - "167": 11515, - "=true": 11516, - "\u0120Chris": 11517, - "icio": 11518, - "\u0120Mike": 11519, - "Decimal": 11520, - "addComponent": 11521, - "Len": 11522, - "\u0120cock": 11523, - "\u0120#{": 11524, - "URN": 11525, - "": 11657, - "\u0120*=": 11658, - "\u0120PS": 11659, - "\u0120dangerous": 11660, - "[p": 11661, - "OME": 11662, - "Other": 11663, - "\u0120StringBuilder": 11664, - "Points": 11665, - "heading": 11666, - "\u0120currency": 11667, - "\u0120percentage": 11668, - "_API": 11669, - "\u0120classic": 11670, - "thead": 11671, - "\u0120MO": 11672, - "FE": 11673, - "Idx": 11674, - "await": 11675, - "\u0120\u00c3\u00a8": 11676, - "\u0120accident": 11677, - "\u0120variant": 11678, - "\u0120myst": 11679, - "\u0120Land": 11680, - "\u0120Bre": 11681, - "\u0120harm": 11682, - "\u0120Acc": 11683, - "\u0120charged": 11684, - "iones": 11685, - "Visibility": 11686, - "arry": 11687, - "\u0120Language": 11688, - "\u0120walking": 11689, - "\".\u010a\u010a": 11690, - "ifer": 11691, - "\u0120leadership": 11692, - ".From": 11693, - "ynam": 11694, - "\u0120timestamp": 11695, - "ipt": 11696, - "\u0120Has": 11697, - "REFER": 11698, - "\u0120Its": 11699, - "\u0120listener": 11700, - "UTE": 11701, - "213": 11702, - "_description": 11703, - 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"282": 16544, - ".Has": 16545, - "341": 16546, - "intage": 16547, - "big": 16548, - "ologist": 16549, - "ennis": 16550, - "460": 16551, - "\u00d9\u0129": 16552, - "\u0120chicken": 16553, - "\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u010a": 16554, - "\u00e7\u013d": 16555, - "\u00e3\u0123\u00a7": 16556, - "\u0120peak": 16557, - "\u0120drinking": 16558, - "\u0120encode": 16559, - "\u0120NEW": 16560, - "malloc": 16561, - "\u0109fprintf": 16562, - "\u0120=================================================================": 16563, - "including": 16564, - "\u0120principles": 16565, - "\u0120Mah": 16566, - "267": 16567, - "storage": 16568, - "-key": 16569, - "\u0120keyword": 16570, - "%;": 16571, - "\u0120trained": 16572, - ".contrib": 16573, - "\u0120kv": 16574, - "__':\u010a": 16575, - "\u0120Boy": 16576, - "parameter": 16577, - "\u0120suite": 16578, - "\u0120thousand": 16579, - "\u0120coordinate": 16580, - "-generated": 16581, - "\u00ed\u0137\u013a": 16582, - "generated": 16583, - "\u0120admitted": 16584, - "\u0120pussy": 16585, - "#w": 16586, - "\u0120swim": 16587, - "union": 16588, - "Na": 16589, - "274": 16590, - "\u0120Royal": 16591, - ".channel": 16592, - "Updated": 16593, - "_ROOT": 16594, - "\u0120vital": 16595, - "335": 16596, - "raction": 16597, - "\u0120Crusher": 16598, - "\u0120preced": 16599, - "\u0120horizontal": 16600, - "Blueprint": 16601, - "\u0120attrs": 16602, - "\u0120smoke": 16603, - "\u00d0\u0134": 16604, - ".Equals": 16605, - "FB": 16606, - "\u0120Resources": 16607, - "rolling": 16608, - "\u0120passes": 16609, - "\u0120Num": 16610, - "rotate": 16611, - "etype": 16612, - "\\\",": 16613, - "\u0120sensitive": 16614, - "\u0120tall": 16615, - "?\u00e2\u0122\u013f\u010a\u010a": 16616, - "Proxy": 16617, - "iy": 16618, - "_section": 16619, - "\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136\u00e2\u0122\u0136": 16620, - "brid": 16621, - "\u0120circuit": 16622, - "atan": 16623, - "ENC": 16624, - "\u0120driven": 16625, - "\u0120voted": 16626, - "\u0120educational": 16627, - "\u0120interaction": 16628, - "abetes": 16629, - "\u0120tone": 16630, - "\u0120InitializeComponent": 16631, - "\u0120merely": 16632, - "\u0120\u00ec\u0140": 16633, - "cookie": 16634, - "_div": 16635, - "\u0120UILabel": 16636, - "vely": 16637, - "});\u010d\u010a": 16638, - "_ENT": 16639, - "#+#+": 16640, - "articles": 16641, - "\u0120Southern": 16642, - "\u0120stronger": 16643, - "\u0120Given": 16644, - "\u0120Eric": 16645, - "\u0120IR": 16646, - "abstract": 16647, - "Under": 16648, - "nable": 16649, - "\u0120increment": 16650, - "oven": 16651, - "\u0120coin": 16652, - "_timer": 16653, - "\u0120suffered": 16654, - "\u0120FREE": 16655, - "'].\"": 16656, - "\u0120Queen": 16657, - "stats": 16658, - "\u0120meetings": 16659, - "276": 16660, - "\u0120entering": 16661, - "\u0120alongside": 16662, - "(session": 16663, - "itals": 16664, - "\u0120foundation": 16665, - "\u0120Credit": 16666, - ".div": 16667, - "_ALL": 16668, - "pcion": 16669, - "_stat": 16670, - "icking": 16671, - 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"imiento": 16721, - "\u0120Frame": 16722, - "324": 16723, - "_gen": 16724, - "inery": 16725, - "_)": 16726, - "messages": 16727, - ".settings": 16728, - "\u0120Mean": 16729, - "\u0120Museum": 16730, - "irq": 16731, - "attach": 16732, - "\u0120Palestin": 16733, - "_QU": 16734, - "_tags": 16735, - "\u0120casual": 16736, - "emen": 16737, - "ASSWORD": 16738, - "432": 16739, - "$s": 16740, - "\u0120Circ": 16741, - "\u00d0\u00be\u00d0\u00b9": 16742, - "etric": 16743, - "/P": 16744, - "018": 16745, - "\u0120epoch": 16746, - "The": 16761, - "\u0120Ak": 16762, - "\u0120grass": 16763, - "/*\u010d\u010a": 16764, - "(dis": 16765, - "\u0120guns": 16766, - "\u0120tb": 16767, - "\u0120Kevin": 16768, - ".args": 16769, - "\u0120Ah": 16770, - "oped": 16771, - "(J": 16772, - "columns": 16773, - "arguments": 16774, - "\u0120WithEvents": 16775, - "_full": 16776, - "\u0120Defense": 16777, - "Simple": 16778, - "\u0120deaths": 16779, - "295": 16780, - "\u0120extensive": 16781, - "\u0120Still": 16782, - "\u0120Expression": 16783, - "\u0120Agency": 16784, - "\u0120performing": 16785, - "FX": 16786, - "\u0120usuario": 16787, - "UAL": 16788, - "Side": 16789, - "odos": 16790, - "aptop": 16791, - "\u0120credentials": 16792, - "_cap": 16793, - "atient": 16794, - "\u0120Disney": 16795, - "\u0120ai": 16796, - "\u0120chip": 16797, - "\u0120volt": 16798, - ".makeText": 16799, - "%%%%%%%%%%%%%%%%": 16800, - "\u0120belief": 16801, - "_LOC": 16802, - "\u0120Civil": 16803, - "Navigation": 16804, - "\u0120reveal": 16805, - "\u0120violent": 16806, - "\u0120Fil": 16807, - "\u0120catalog": 16808, - "emed": 16809, - "scan": 16810, - ".control": 16811, - "\u0120constitution": 16812, - "Country": 16813, - "Separator": 16814, - "_APP": 16815, - "topic": 16816, - "uetooth": 16817, - "MIN": 16818, - "\u0120descriptor": 16819, - "yt": 16820, - "ETHER": 16821, - "\u0120distribute": 16822, - "'}\u010a": 16823, - ".trim": 16824, - ".Line": 16825, - "\u0120lbl": 16826, - "assertEquals": 16827, - "\u0120Det": 16828, - "ombok": 16829, - "(width": 16830, - 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"posable": 17877, - "-fluid": 17878, - "\u0120thorough": 17879, - "\u0120publicly": 17880, - "apters": 17881, - "\u0120Wilson": 17882, - "_PRE": 17883, - "yard": 17884, - "\u00e4\u00bc": 17885, - "\u0109in": 17886, - "339": 17887, - "\u0120revers": 17888, - "\u0120bullet": 17889, - "cribed": 17890, - "nesota": 17891, - "\u0120($_": 17892, - "annon": 17893, - "cursor": 17894, - "\u0120clothing": 17895, - "\u0120Multi": 17896, - "287": 17897, - ":',": 17898, - "\u0120vess": 17899, - "ordinator": 17900, - "\u0120einem": 17901, - "Cannot": 17902, - "\u0120armed": 17903, - "\u0109V": 17904, - "\u00e4\u00b8\u012c": 17905, - ".Flat": 17906, - "\u0120Sep": 17907, - "\u0120Subject": 17908, - "_font": 17909, - "\u0120characteristics": 17910, - "Done": 17911, - "eln": 17912, - "############": 17913, - "POS": 17914, - "\u0120density": 17915, - "\u0120Platform": 17916, - "-items": 17917, - "\u0120overs": 17918, - "\u0120pushing": 17919, - "\u00e7\u00a4": 17920, - ".Connection": 17921, - "_term": 17922, - "\u0120initialization": 17923, - "________________________________": 17924, - "\u00e7\u00ac": 17925, - ".document": 17926, - "lesh": 17927, - "\u0109document": 17928, - "\u0120Pin": 17929, - "\u00c3\u00a7a": 17930, - "\u0120definitions": 17931, - ".Path": 17932, - "_WRITE": 17933, - "\u0120\u0109\u010a": 17934, - "?>\u010a\u010a": 17935, - "\u0120terrible": 17936, - "bean": 17937, - "ickets": 17938, - "\u0120SV": 17939, - "Buy": 17940, - "(task": 17941, - "\u0120regime": 17942, - "google": 17943, - "\u0120crack": 17944, - ".visit": 17945, - "NUM": 17946, - "energy": 17947, - "\u0120struck": 17948, - "_sample": 17949, - ".payload": 17950, - "\u0120revis": 17951, - "\u0120Scene": 17952, - "\u0120pg": 17953, - "\u0120breakfast": 17954, - "URRENT": 17955, - ".charAt": 17956, - "_exception": 17957, - "\u0120Anton": 17958, - "\u0120guidelines": 17959, - "\u0120exhaust": 17960, - "\u0120Financial": 17961, - "\u0120indent": 17962, - "\u0120desktop": 17963, - "Hidden": 17964, - "Failure": 17965, - "\u0120principle": 17966, - "\u0120iv": 17967, - "\u0120seks": 17968, - "network": 17969, - "\u0120numberOf": 17970, - "\u0120Albert": 17971, - "\u0109long": 17972, - "801": 17973, - ",.": 17974, - "\u0120zeros": 17975, - "fade": 17976, - "\u0120Typ": 17977, - "\u0120Term": 17978, - "\u0120Arts": 17979, - ".Application": 17980, - "\u0120behalf": 17981, - "\u00e6\u012a\u00b7": 17982, - "\u0120mere": 17983, - "(`${": 17984, - "\u0120awareness": 17985, - "elpers": 17986, - "flix": 17987, - "\u0120weigh": 17988, - "\u0120estimates": 17989, - ".child": 17990, - "/O": 17991, - "\u0120Bitmap": 17992, - ".bottom": 17993, - "\u0120**************************************************************************": 17994, - "Expect": 17995, - "ento": 17996, - "\u0120Forum": 17997, - "veral": 17998, - "\u0120jail": 17999, - "\u0120abilities": 18000, - "\u0120HOLD": 18001, - "\u0120Cit": 18002, - "\u0120dynam": 18003, - "\u0120gray": 18004, - "\u0109\u0109\u0109\u0109\u0109\u0109\u0109\u0109\u0109\u0109\u0109\u0109\u0109": 18005, - 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"(cur": 18053, - "\u0120removing": 18054, - "\u0120coeff": 18055, - "\u0120Though": 18056, - "\u0120forecast": 18057, - "408": 18058, - "\u0120Vegas": 18059, - "Site": 18060, - "346": 18061, - "\u0120trab": 18062, - "\u0120Henry": 18063, - "-i": 18064, - "\u0120involves": 18065, - "BT": 18066, - "\u0120slo": 18067, - "Invoke": 18068, - "\u0120lucky": 18069, - "025": 18070, - "rat": 18071, - "\u0120?\u010a": 18072, - "\u0120handled": 18073, - "(fd": 18074, - "contents": 18075, - "\u0120OFF": 18076, - "RF": 18077, - "\u0120sty": 18078, - "\u0120Motor": 18079, - "tery": 18080, - "tax": 18081, - "MAP": 18082, - "\u0120Mrs": 18083, - "\u0120phones": 18084, - "\u0120UIView": 18085, - "\")));\u010a": 18086, - "(dev": 18087, - "\u0120Irish": 18088, - "019": 18089, - "\u0120ws": 18090, - "DI": 18091, - "_OFFSET": 18092, - "\u0120Events": 18093, - "\u0120stages": 18094, - "\u0120}//": 18095, - "\u0120haben": 18096, - "STANCE": 18097, - "\u0120Sin": 18098, - "\u0120Money": 18099, - "(top": 18100, - "\u0120appointment": 18101, - "VERSION": 18102, - "metadata": 18103, - "_comment": 18104, - "\u0120colleagues": 18105, - "maps": 18106, - "\u00e2\u013a": 18107, - "\u010a\u0109\u010a": 18108, - "(al": 18109, - "_req": 18110, - "\u0120fut": 18111, - "\u0120architecture": 18112, - "351": 18113, - "\u0120WHETHER": 18114, - "\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120": 18115, - "_screen": 18116, - "\u0120styleUrls": 18117, - "\u0120monster": 18118, - ".up": 18119, - "phia": 18120, - "\u0120processor": 18121, - "\u0120Terr": 18122, - "=',": 18123, - "\u0120Manufact": 18124, - "\u0120NT": 18125, - "kel": 18126, - "ibern": 18127, - "\u0109file": 18128, - "Ali": 18129, - "rientation": 18130, - "\u0120//!": 18131, - "apore": 18132, - "aneous": 18133, - "\u0120Creat": 18134, - "folder": 18135, - "415": 18136, - "\u0120hay": 18137, - "Suppress": 18138, - "(left": 18139, - "\u0120euro": 18140, - "\u0120disclaimer": 18141, - "ustry": 18142, - "ships": 18143, - "_fd": 18144, - "\u0120Fa": 18145, - "_insert": 18146, - "\u0120rol": 18147, - "ifting": 18148, - "\u0120Comments": 18149, - "_br": 18150, - "\u0120losses": 18151, - "\u0120Added": 18152, - "charg": 18153, - "\u0120\u00d0\u00bf\u00d0\u00be": 18154, - "_system": 18155, - "\u0120Sometimes": 18156, - "\u0120Spain": 18157, - "(group": 18158, - "ialis": 18159, - "\u0120dollar": 18160, - "\u0120Args": 18161, - "499": 18162, - "297": 18163, - "quires": 18164, - "\u0120Ten": 18165, - ".scss": 18166, - "\u0120survive": 18167, - "usage": 18168, - "\u0120jun": 18169, - "imiter": 18170, - "\u00ef\u00bc\u0123\u010a\u010a": 18171, - "\u0120fifth": 18172, - "toggle": 18173, - "\u0120decline": 18174, - "($\"": 18175, - "(Long": 18176, - "inge": 18177, - "\u0120pilot": 18178, - "-light": 18179, - "-radius": 18180, - "\u0120podcast": 18181, - "\u0120naturally": 18182, - "Pages": 18183, - "\u00e4\u00b8\u00ba": 18184, - "\u0120Despite": 18185, - "\u0120lighting": 18186, - "\u0120crate": 18187, - "\u0120Binary": 18188, - "\u0120reducing": 18189, - "\u0120eleg": 18190, - "\u0120Mouse": 18191, - "\u0120TestBed": 18192, - "\u0120beforeEach": 18193, - "_ARRAY": 18194, - "Redirect": 18195, - "329": 18196, - "\u0120flood": 18197, - "\u0120ships": 18198, - "363": 18199, - "\u0120electricity": 18200, - ")*(": 18201, - "\u00ea\u00b8": 18202, - "\u0120Viet": 18203, - "hero": 18204, - "\u0120dia": 18205, - "\u0120Kent": 18206, - "heart": 18207, - "\u0120threats": 18208, - "_acc": 18209, - "\u0120symbols": 18210, - "ischen": 18211, - "_inst": 18212, - "Criterion": 18213, - "\u0120TIM": 18214, - ".Height": 18215, - "580": 18216, - "\u0120\u00e2\u0122\u013b": 18217, - "();\u010a\u010a\u010a": 18218, - "Products": 18219, - "_SP": 18220, - "\u0120Cy": 18221, - "\u0120dependent": 18222, - "este": 18223, - "\u0120datos": 18224, - "dit": 18225, - "\u00d0\u00b0\u00d0\u00b2": 18226, - "IGNAL": 18227, - "\u0120lesson": 18228, - "\">'": 18229, - "\u0120Cover": 18230, - "\u0120Hope": 18231, - "\u0120Timer": 18232, - "\u0120dad": 18233, - "viders": 18234, - "\u0120Phot": 18235, - "/?": 18236, - "ropy": 18237, - "oming": 18238, - "asion": 18239, - "\u0120\\(": 18240, - "\u0120ET": 18241, - "\u0120Reading": 18242, - "\u0120episodes": 18243, - "lm": 18244, - "421": 18245, - "echa": 18246, - "\u0120neuro": 18247, - "820": 18248, - "\u0120harmon": 18249, - "\u0120liberal": 18250, - "-ind": 18251, - "393": 18252, - "DATA": 18253, - "\u0120everyday": 18254, - "\u0120divided": 18255, - "\u0120ActiveRecord": 18256, - "figure": 18257, - "UA": 18258, - "\u00e4\u00b9": 18259, - "riendly": 18260, - "tech": 18261, - "601": 18262, - ".gameObject": 18263, - "\u00d0\u00b8\u00d1\u0124\u00d1\u012e": 18264, - "374": 18265, - "\u0120moon": 18266, - "ftime": 18267, - "\u0120noch": 18268, - "\u0120TORT": 18269, - "\u0120VM": 18270, - ".initial": 18271, - "(child": 18272, - "\u0120musical": 18273, - "\u0120oc": 18274, - "bas": 18275, - "\u0120Hay": 18276, - "361": 18277, - "_long": 18278, - "\u0120memset": 18279, - "iley": 18280, - "adelphia": 18281, - "SV": 18282, - "roat": 18283, - "_tx": 18284, - "\u0120lon": 18285, - "\u0120ngOnInit": 18286, - "bp": 18287, - "\u0120Golden": 18288, - "ACHE": 18289, - "\u0120worried": 18290, - "azi": 18291, - "Ear": 18292, - "Take": 18293, - "(fp": 18294, - "burgh": 18295, - "_Data": 18296, - "gres": 18297, - "\u0120Ont": 18298, - "pus": 18299, - "\u0120transparent": 18300, - "\u0120pocket": 18301, - "\u0120ram": 18302, - "igrations": 18303, - ".\u010d\u010a\u010d\u010a": 18304, - "\u0120[(": 18305, - "\u0120adopted": 18306, - "\u0120reportedly": 18307, - "\u0120Dream": 18308, - "\u0120}));\u010a": 18309, - "losing": 18310, - "\u0120teeth": 18311, - "\u0120Books": 18312, - "\",&": 18313, - "enny": 18314, - "LEMENT": 18315, - "\u0120gel": 18316, - "\u0120Plant": 18317, - "437": 18318, - "!\u00e2\u0122\u013f": 18319, - ".host": 18320, - "\u0120Reply": 18321, - "376": 18322, - "rength": 18323, - "\u0120recognition": 18324, - "\u0120}}>\u010a": 18325, - "LA": 18326, - "\u0120mirror": 18327, - "\u0120assistant": 18328, - "(device": 18329, - "\u0120spiritual": 18330, - "builder": 18331, - "\u00c2\u00a7": 18332, - "\u0120outr": 18333, - "\u0120tt": 18334, - "\u0120PER": 18335, - "\u0120radical": 18336, - "Methods": 18337, - "\u0120pace": 18338, - "udy": 18339, - "\u0120gut": 18340, - "\u0120Greek": 18341, - "\u0120nonatomic": 18342, - "\u0120Paper": 18343, - "_GPIO": 18344, - "\u0120obst": 18345, - ".Ad": 18346, - "vironments": 18347, - "\u0120Sov": 18348, - "356": 18349, - "(con": 18350, - "\u0120Transaction": 18351, - ".assign": 18352, - "\u0109catch": 18353, - "elter": 18354, - "\u0120bitcoin": 18355, - "_GR": 18356, - "\u0120\u010d\u010a": 18473, - "metic": 18474, - "\u0120transformation": 18475, - "\u00e5\u0131\u00b7": 18476, - "\u0120rgb": 18477, - "istributions": 18478, - "\u0120implicit": 18479, - "/in": 18480, - "destination": 18481, - "\u00d0\u00b0\u00d1\u0124\u00d1\u012e": 18482, - "Zero": 18483, - "\u0120unset": 18484, - "920": 18485, - ".where": 18486, - ".go": 18487, - "\u0120formation": 18488, - "\u0120declaration": 18489, - "()\u010d\u010a\u010d\u010a": 18490, - "\u0120Expl": 18491, - "\u0109\u0109\u0109\u0120\u0120": 18492, - "/pro": 18493, - ".JSON": 18494, - "441": 18495, - "\u0120desk": 18496, - ".substr": 18497, - "//----------------------------------------------------------------------------": 18498, - "lyn": 18499, - "pson": 18500, - "407": 18501, - "disable": 18502, - "\u0120Func": 18503, - "\u0109Assert": 18504, - "\u0120MARK": 18505, - "\u0120defeat": 18506, - "\u0120blind": 18507, - "\u0120constants": 18508, - "362": 18509, - ".headers": 18510, - "UILD": 18511, - "\u0120expenses": 18512, - "Pixel": 18513, - "\u0120hr": 18514, - "\u0120fel": 18515, - "\u0120Eastern": 18516, - "424": 18517, - "490": 18518, - "_del": 18519, - "357": 18520, - "\u0120Cub": 18521, - "\u0120sq": 18522, - "\u0109count": 18523, - "\u0120Directory": 18524, - "\u0120exclus": 18525, - "\u0120historic": 18526, - "\u0120------------------------------------------------": 18527, - "\u0120composition": 18528, - "\u0120dataGridView": 18529, - "\u0120Burn": 18530, - "\u0120BC": 18531, - "Master": 18532, - "\u0120spawn": 18533, - "\u0120bearing": 18534, - ".SetActive": 18535, - "ilo": 18536, - "\u0120gallery": 18537, - "\u0120founded": 18538, - "\u0120availability": 18539, - ".sqrt": 18540, - "\u0120pes": 18541, - "\u0120DOM": 18542, - "mate": 18543, - "Oct": 18544, - "\u0120matched": 18545, - "itivity": 18546, - "\u0120anxiety": 18547, - ".price": 18548, - "\u0120Instant": 18549, - "\u00ec\u012c": 18550, - "\u0120tut": 18551, - "ICollection": 18552, - ".shared": 18553, - "_sql": 18554, - "tbl": 18555, - "library": 18556, - "_destroy": 18557, - "ermal": 18558, - "\u0120Notes": 18559, - "\u0120Ein": 18560, - "\u0120southern": 18561, - "\u0120OTHERWISE": 18562, - 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"\u0120northeast": 41929, - "esium": 41930, - "\u0120Cliente": 41931, - "ulance": 41932, - "nty": 41933, - "\u0120tecn": 41934, - "Devices": 41935, - "\u0120grains": 41936, - "\u0120Og": 41937, - "\u0120SEL": 41938, - "udiant": 41939, - "\u0120++;\u010a": 41940, - "\u0120explanations": 41941, - "occo": 41942, - "\u0120diets": 41943, - "\u0120cohort": 41944, - "(controller": 41945, - ".Iterator": 41946, - "-rich": 41947, - "rocess": 41948, - "GD": 41949, - "\u0120carbohydr": 41950, - "\u0120fried": 41951, - "\u0120Employment": 41952, - "\u00ec\u0140\u00a5": 41953, - "\u0120Leonard": 41954, - "_${": 41955, - "quares": 41956, - "\u0120companions": 41957, - "\u0120paris": 41958, - "\u0120stimulation": 41959, - "\u0120Zoo": 41960, - "\u0120relevance": 41961, - "\u0120Colour": 41962, - "\u0120spear": 41963, - "otional": 41964, - "\u0120Lite": 41965, - "\u0120Kosten": 41966, - "\u0120\u00c3\u00b3": 41967, - "_attachment": 41968, - "orphic": 41969, - "\u0120damit": 41970, - "\u0120dlg": 41971, - "\u0120thrive": 41972, - "CHANGE": 41973, - "\u0120Apparently": 41974, - "\u0120atual": 41975, - "\u0120rooted": 41976, - "(images": 41977, - "awi": 41978, - "ariat": 41979, - "\u0120cherry": 41980, - "STATIC": 41981, - "mnt": 41982, - "\u0120UserId": 41983, - "illet": 41984, - "\u0120Hispanic": 41985, - "\u0120nak": 41986, - "\u0120centro": 41987, - "\u0120dims": 41988, - "_initialize": 41989, - "\u00c4\u00b1k": 41990, - "\u0120Centers": 41991, - "REN": 41992, - "\u0120evolutionary": 41993, - "\u0120Topics": 41994, - "_damage": 41995, - "emer": 41996, - "\u0120rund": 41997, - "\u0120punished": 41998, - "\u0120cubic": 41999, - "fair": 42000, - "[];\u010a\u010a": 42001, - "\u0120instantiate": 42002, - "\u0120oversee": 42003, - "-delete": 42004, - "unteer": 42005, - "startTime": 42006, - "\u0120Pipeline": 42007, - "_GAME": 42008, - "\u0120Cir": 42009, - "\u0109Null": 42010, - ".Formatting": 42011, - "ucumber": 42012, - "\u0120Ride": 42013, - "\u0120zoo": 42014, - "\u0120checker": 42015, - "\u00e5\u0132\u012e": 42016, - "=C": 42017, - "\u0120grit": 42018, - "\");//": 42019, - "_xy": 42020, - "\u0120Declaration": 42021, - "\u0120callable": 42022, - "Foo": 42023, - "\u0120ListItem": 42024, - "\u0120inaccur": 42025, - "mlin": 42026, - "\u0109Data": 42027, - "\u0120evolving": 42028, - "awan": 42029, - "\u0120cafe": 42030, - "folk": 42031, - "_IDX": 42032, - "\u0120Anything": 42033, - "\u0120Palestine": 42034, - "\u0120GridView": 42035, - "\u0120colony": 42036, - "\u0120Germans": 42037, - "(+": 42038, - ".pid": 42039, - ".jsx": 42040, - "\u0120Superior": 42041, - "Christian": 42042, - "\u0120Lect": 42043, - "\u0109Game": 42044, - "\u0120instrumental": 42045, - "Animations": 42046, - "\u00d0\u00b4\u00d0\u00b0\u00d0\u00bb": 42047, - "\u0120Moses": 42048, - "\u0109\u0109\u010d\u010a\u0109\u0109\u010d\u010a": 42049, - "zs": 42050, - "kte": 42051, - "\u00e4\u00b8\u013c": 42052, - "_DIST": 42053, - "bitmap": 42054, - "dB": 42055, - "\u0120persistence": 42056, - "\u00d1\u0122\u00d0\u00be\u00d1\u0123": 42057, - "$l": 42058, - "Bron": 42059, - "\u0120{|": 42060, - "_chart": 42061, - "\u0120Consum": 42062, - "\u0120hemp": 42063, - "\u0120\"))\u010a": 42064, - "\u0120attackers": 42065, - "\u0120knowledgeable": 42066, - "\u0120cet": 42067, - "\u0120viruses": 42068, - "'I": 42069, - "\u0120pitcher": 42070, - "\u0120sweeping": 42071, - "=list": 42072, - "aptops": 42073, - ".depth": 42074, - "\u0120instructed": 42075, - "\u0120Rus": 42076, - "benhavn": 42077, - "\u0120\u00d0\u00b8\u00d0\u00bd": 42078, - "Sports": 42079, - "\u0120onset": 42080, - "\u00e6\u013f\u0125": 42081, - ".RED": 42082, - "_si": 42083, - "\u0120PST": 42084, - ".onChange": 42085, - ">tag": 42086, - "\u0120Roh": 42087, - "_character": 42088, - "\u0120Laws": 42089, - "\u0120Bachelor": 42090, - "_swap": 42091, - ".reactivex": 42092, - "\u0120rewarding": 42093, - "Medium": 42094, - "-[": 42095, - "\u0120Recently": 42096, - "Joint": 42097, - "partition": 42098, - "\u0120Minutes": 42099, - "\u0120indo": 42100, - "\u0120absorbed": 42101, - "\u0120GN": 42102, - "_IND": 42103, - "\u0120saber": 42104, - "Spawn": 42105, - "outputs": 42106, - "\u0120Jeffrey": 42107, - "\u0120medieval": 42108, - "hed": 42109, - "Guide": 42110, - "\u0120psycho": 42111, - "\u0120glam": 42112, - "Elim": 42113, - "\u00c3\u00a4dchen": 42114, - "_plain": 42115, - "\u0120Sau": 42116, - "-four": 42117, - "\u0120analyzing": 42118, - "QUERY": 42119, - "\u0120tomato": 42120, - "_buttons": 42121, - "VEN": 42122, - ".setStatus": 42123, - ".Url": 42124, - "+\u010a\u010a": 42125, - "\u0120complaining": 42126, - "degree": 42127, - "confirmed": 42128, - "\u0120subt": 42129, - "parsed": 42130, - "\u0120torque": 42131, - "\u0120troubled": 42132, - "\u0120TARGET": 42133, - "\u0120trademarks": 42134, - "\u0120Coordinate": 42135, - "\u0120Viv": 42136, - "\u0120//}\u010a\u010a": 42137, - "\u0120apr\u00c3\u00a8s": 42138, - ".getPosition": 42139, - "(KeyCode": 42140, - "\u0120Silva": 42141, - "\u0120meteor": 42142, - "\u0120endorsement": 42143, - "Overview": 42144, - "\u0120Poss": 42145, - ".Inject": 42146, - "\u0120evenly": 42147, - "\u0120visualization": 42148, - "\u0120wchar": 42149, - "\u0120HDMI": 42150, - "\u0120funct": 42151, - "ickname": 42152, - "','','": 42153, - "\u0120forwards": 42154, - "ManagedObject": 42155, - "\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120": 42156, - "\u0109server": 42157, - "\u0120Outlook": 42158, - "\u0120Chronicle": 42159, - "\u0120dubbed": 42160, - "\u0120dok": 42161, - "\u0120Wear": 42162, - ".AL": 42163, - "paren": 42164, - ".Interface": 42165, - "Interfaces": 42166, - ".cod": 42167, - "\u0120dib": 42168, - ".Globalization": 42169, - 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"losures": 42306, - "_REL": 42307, - "\u0120exclusion": 42308, - "\u0120Skype": 42309, - "weise": 42310, - "-stop": 42311, - "nothing": 42312, - "\u0120Egg": 42313, - "isors": 42314, - "Richard": 42315, - "\u0120counseling": 42316, - "\u0120commem": 42317, - "\u0120QMessageBox": 42318, - "\u0120Synd": 42319, - "\u0120Frost": 42320, - "\u0120Competition": 42321, - "\u0120Awake": 42322, - "\u0120ted": 42323, - "iciones": 42324, - "\u0120DevComponents": 42325, - "VERTISEMENT": 42326, - "otti": 42327, - ".runner": 42328, - "\u0120uniquely": 42329, - ".flag": 42330, - "\u0109rs": 42331, - "_generic": 42332, - "\u0120```\u010a": 42333, - "ACHINE": 42334, - "\u0120mein": 42335, - "(Application": 42336, - "(br": 42337, - "\u0120ratios": 42338, - ":,": 42339, - "\u0120XCTest": 42340, - "ustainable": 42341, - "-www": 42342, - "itles": 42343, - "_TEMP": 42344, - "\u0120syst": 42345, - "umericUpDown": 42346, - "\u0109assertTrue": 42347, - "\u0120wf": 42348, - ".peek": 42349, - "\u0120Bulg": 42350, - 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"_Button": 42397, - "Harry": 42398, - "\u00e8\u00bf\u013d": 42399, - "\u0120strains": 42400, - "\u0120AppBar": 42401, - "\u0120Chan": 42402, - "/video": 42403, - "\u0120bam": 42404, - ".Progress": 42405, - "$f": 42406, - "lemen": 42407, - "\u0120irregular": 42408, - "\u0120Duncan": 42409, - "\u0120Mint": 42410, - "-video": 42411, - "\u00e0\u00a6\u00be": 42412, - "\u00c3\u00b3wn": 42413, - "\u0120EMPTY": 42414, - "\u0120stacked": 42415, - "\u0120HA": 42416, - "_cut": 42417, - "\u0120wherein": 42418, - "\u0120Ways": 42419, - "(counter": 42420, - "\u00e8\u00af\u0137": 42421, - "FormGroup": 42422, - "\u0120blew": 42423, - "courses": 42424, - "\u0120productos": 42425, - "rys": 42426, - "\u0120Restr": 42427, - "\u0120styling": 42428, - ">s": 42429, - "\u0120piv": 42430, - "\u0120itertools": 42431, - "getRepository": 42432, - "\u0120Ik": 42433, - "_devices": 42434, - "layui": 42435, - "\u0120halfway": 42436, - "\u0120fran\u00c3\u00a7": 42437, - "\u0120tuning": 42438, - "OA": 42439, - "_Node": 42440, - "arde": 42441, - "\u0120fierce": 42442, - "licted": 42443, - "#\u010d\u010a": 42444, - "\u0120breakthrough": 42445, - "\u0120Erik": 42446, - "\u0120bride": 42447, - "\u0120.\"": 42448, - "culus": 42449, - "inside": 42450, - "\u0120Indianapolis": 42451, - "\u0120EE": 42452, - "\u0120yog": 42453, - "urret": 42454, - ".fs": 42455, - ".grad": 42456, - "_cards": 42457, - "_accuracy": 42458, - "_epi": 42459, - "queda": 42460, - "/org": 42461, - "\u00e9\u00aa\u012e": 42462, - "\u0120compte": 42463, - "))[": 42464, - "Outside": 42465, - "Greater": 42466, - "\u0120Renderer": 42467, - ".actor": 42468, - "Accounts": 42469, - "Idle": 42470, - "_hours": 42471, - "erner": 42472, - "Joined": 42473, - "\u0120menj": 42474, - "requires": 42475, - "\u0120OPER": 42476, - ".removeChild": 42477, - "\u0109sp": 42478, - "\u0120esse": 42479, - "rift": 42480, - "xFE": 42481, - "\u0120Shakespeare": 42482, - "____________": 42483, - "\u0120budgets": 42484, - "ModelState": 42485, - "fillable": 42486, - "-component": 42487, - "ocos": 42488, - "\u0120BUTTON": 42489, - "/io": 42490, - ",out": 42491, - "sms": 42492, - "Thomas": 42493, - "\u0120Armed": 42494, - "resume": 42495, - "\u0120rotating": 42496, - "\u0120Vault": 42497, - "\u0120seus": 42498, - ".(*": 42499, - "\u0120amino": 42500, - "\u0120[]);\u010a\u010a": 42501, - "\u0120provoc": 42502, - "nox": 42503, - ".GetEnumerator": 42504, - "=======\u010a": 42505, - "\u00e6\u0138\u013b": 42506, - "_scroll": 42507, - "\u0120filmed": 42508, - "\u0120Soci": 42509, - "gap": 42510, - "gro": 42511, - "Vote": 42512, - "\"But": 42513, - "_RC": 42514, - "Animal": 42515, - "\u00c2\u0122": 42516, - "ibile": 42517, - "\u0120awaken": 42518, - "orest": 42519, - "inja": 42520, - "\u0120Ivan": 42521, - "(Command": 42522, - "\u0120*****": 42523, - "\u00ce\u00b7": 42524, - "\u0120kvinder": 42525, - "/helpers": 42526, - "_cases": 42527, - "tg": 42528, - "\u00ec\u0126\u00b8": 42529, - "Registered": 42530, - "\u0109pass": 42531, - "_digits": 42532, - "\u0120contour": 42533, - "\u0120infants": 42534, - "\u0120justification": 42535, - "\u0120Fortunately": 42536, - "Contr": 42537, - "\u0120onCreateView": 42538, - "_SAMPLE": 42539, - "\u0120allowNull": 42540, - "\u0120nud": 42541, - "\u0120fetched": 42542, - "_equ": 42543, - "\u0120Unable": 42544, - "=\\\"\"": 42545, - ">{\u010a": 42546, - "\u0120committees": 42547, - "istema": 42548, - "+\".": 42549, - "\u00c3\u0143an": 42550, - "mant": 42551, - "\u0120southeast": 42552, - "\u00ef\u00bc\u012e\u010a": 42553, - "dialogs": 42554, - "PROJECT": 42555, - "charger": 42556, - "-port": 42557, - "(uuid": 42558, - ".export": 42559, - "Six": 42560, - "\u0120RP": 42561, - "Prem": 42562, - "\u0120conscience": 42563, - "\u0120marginRight": 42564, - "_distribution": 42565, - "yaml": 42566, - "resizing": 42567, - "Dock": 42568, - "\u0120Locations": 42569, - "GY": 42570, - "Seed": 42571, - "BUFFER": 42572, - "ossip": 42573, - "ullen": 42574, - "Things": 42575, - "-self": 42576, - ".poll": 42577, - "PLAYER": 42578, - "\u0120\u00e5\u00ae": 42579, - "GROUP": 42580, - "\u0120Away": 42581, - "\u0120gospel": 42582, - "xfd": 42583, - "Mary": 42584, - "\u0120Portable": 42585, - "TURE": 42586, - "\u0120utilis": 42587, - "\u0120seit": 42588, - "\u0120strand": 42589, - "\u0120transc": 42590, - "\u0120(^": 42591, - "\u0120Alfred": 42592, - ".mem": 42593, - ".circle": 42594, - "\u0120~/": 42595, - "forcing": 42596, - "\u0120riot": 42597, - "prox": 42598, - "THON": 42599, - "izaci\u00c3\u00b3n": 42600, - "\u0120NI": 42601, - "rost": 42602, - "\u0120dispro": 42603, - "_instances": 42604, - "\u00ef\u00bc\u012e\u00e2\u0122\u013e": 42605, - "ographer": 42606, - "endas": 42607, - "\u0120Isaac": 42608, - "\u0120Pine": 42609, - "/dis": 42610, - "\u0120colorWith": 42611, - "iterate": 42612, - "_stride": 42613, - "\u0120punto": 42614, - ".EventArgs": 42615, - "(center": 42616, - "\u0120neighboring": 42617, - "\u0120Prison": 42618, - "\u0120Messenger": 42619, - "\u0120epidemic": 42620, - "dao": 42621, - "_complex": 42622, - "\u0120gravel": 42623, - "_DIP": 42624, - "\u00c3\u00a9ment": 42625, - "\u0120Ari": 42626, - "_bitmap": 42627, - ".quit": 42628, - "(valid": 42629, - "\u0120pend": 42630, - "\u0120respiratory": 42631, - "\u0120rebound": 42632, - "DefaultValue": 42633, - "\u00e3\u0125\u0143": 42634, - "\u0120commits": 42635, - ".tests": 42636, - "_fr": 42637, - "itet": 42638, - ".sf": 42639, - "\u0120spacecraft": 42640, - "critical": 42641, - "\u0120depressed": 42642, - "\u0120AnyObject": 42643, - "\u0120unb": 42644, - "\u0120discern": 42645, - "(mysql": 42646, - "Latin": 42647, - "\u0120Bog": 42648, - "\u0120Wildlife": 42649, - "ToFile": 42650, - "ioxid": 42651, - "@RestController": 42652, - "\u0120\"$(": 42653, - "\u0120<<\"": 42654, - "\u0120defects": 42655, - "\u0120datum": 42656, - "hin": 42657, - "\u0120realizar": 42658, - "anyahu": 42659, - "\u0120Sig": 42660, - "@Data": 42661, - "adaptive": 42662, - "\u0120Catherine": 42663, - ".cr": 42664, - "\u0120COOKIE": 42665, - "\u0120pictured": 42666, - "\u0120Fighter": 42667, - "Queryable": 42668, - "\u0120Anyway": 42669, - "\u0120GLFW": 42670, - "_namespace": 42671, - "_ft": 42672, - "\u0120])": 42673, - "Organization": 42674, - "\u0120constitutes": 42675, - "\u0120quand": 42676, - "(chunk": 42677, - "\"/>\u010d\u010a": 42678, - "\u0120Lakes": 42679, - "mainwindow": 42680, - "Carthy": 42681, - "spin": 42682, - "(csv": 42683, - ":red": 42684, - "-commerce": 42685, - "\u00e0\u00b8\u00b9": 42686, - "\u0120discovering": 42687, - "\u0120eco": 42688, - "_fac": 42689, - "inceton": 42690, - "\u0120Greens": 42691, - "jwt": 42692, - "\u00d8\u00b5": 42693, - "\u0120Broncos": 42694, - "\u0120Goods": 42695, - "(GTK": 42696, - "\u0120returnValue": 42697, - "\u0120siempre": 42698, - "\u0120neutr": 42699, - "went": 42700, - "\u0120Natal": 42701, - "\u0120enthusiastic": 42702, - "\u00e1\u00bb\u012f": 42703, - "FN": 42704, - "/database": 42705, - "Catalog": 42706, - "\u0120brun": 42707, - "\u0120Kash": 42708, - "_Pl": 42709, - "iscrim": 42710, - ",width": 42711, - "\u0120inmates": 42712, - "Assignment": 42713, - "\u0120Haven": 42714, - "\u0120playground": 42715, - "exam": 42716, - "@Controller": 42717, - "uliar": 42718, - ".getParent": 42719, - "\u0120\";\u010a\u010a": 42720, - ":size": 42721, - "issors": 42722, - "\u0120fis": 42723, - "\u0120alc": 42724, - "ensation": 42725, - "\u0120Nixon": 42726, - "\u0120mighty": 42727, - "-str": 42728, - "_special": 42729, - "_ADC": 42730, - "\u0120Twig": 42731, - "umbling": 42732, - "-address": 42733, - "\u0120heroin": 42734, - "YTE": 42735, - "\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u0120\u010a": 42736, - "Friend": 42737, - "\u0120ave": 42738, - "\u0120PNG": 42739, - "\u0120Kurdish": 42740, - "DataSetChanged": 42741, - "\u0120blades": 42742, - "bral": 42743, - "Steam": 42744, - "\u0120sigu": 42745, - "IRTUAL": 42746, - "acos": 42747, - "UDP": 42748, - "(database": 42749, - "hec": 42750, - "\u0120Strings": 42751, - "_scalar": 42752, - "\u0109desc": 42753, - "\u0120TLS": 42754, - ";\"\u010a": 42755, - "\u0120Corbyn": 42756, - "SimpleName": 42757, - "uell": 42758, - "\u0120Entre": 42759, - "ellites": 42760, - "-place": 42761, - "\u0120frankly": 42762, - "\u0120Erf": 42763, - "CEL": 42764, - "\u0120pa\u00c3\u0143s": 42765, - "\u0120hedge": 42766, - "\u0120latent": 42767, - "\u0120IRQ": 42768, - "\u0120Herald": 42769, - "\u0120Prec": 42770, - "\u00eb\u00b3\u00b4": 42771, - ".TEXT": 42772, - "Salary": 42773, - "\u0120autumn": 42774, - "\u0120travail": 42775, - ".Sum": 42776, - "\u0120cared": 42777, - "Mor": 42778, - "\u0120intuitive": 42779, - "\u0120journals": 42780, - "_IT": 42781, - "\u0120Trou": 42782, - "\u00e4\u00bc\u0142": 42783, - "HasColumnName": 42784, - "Composite": 42785, - "\u0120spice": 42786, - "_disk": 42787, - "_CODES": 42788, - "\u0120Introduced": 42789, - "iona": 42790, - "\u0120nuestra": 42791, - "oct": 42792, - "\u0120\u0120\u0120\u0120\u010a\u0120\u0120\u0120\u0120\u010a\u0120\u0120\u0120\u0120\u010a": 42793, - "(parameter": 42794, - "\u0120studios": 42795, - "\u0120projectId": 42796, - "\u0120bdsm": 42797, - ".SqlClient": 42798, - "imizer": 42799, - "\u0120CARD": 42800, - "+t": 42801, - "aan": 42802, - ".sol": 42803, - "_Adjust": 42804, - "\u0120righteous": 42805, - "\u0120Logging": 42806, - ".filters": 42807, - "_TAB": 42808, - "\u0109sys": 42809, - "rophic": 42810, - "otherapy": 42811, - "\u0120Browse": 42812, - "keyboard": 42813, - "RON": 42814, - "+\\": 42815, - "ropped": 42816, - "\u0120extensively": 42817, - "fk": 42818, - "\u0120lime": 42819, - "years": 42820, - "Exc": 42821, - "\u0120sph": 42822, - "\u0120cheating": 42823, - "andro": 42824, - "\u00c3\u0143o": 42825, - "\u0120prince": 42826, - "oire": 42827, - "\u0120Destination": 42828, - "\u0120Converts": 42829, - "\u0120upstream": 42830, - "oled": 42831, - "\u0120servants": 42832, - "\u0120semantic": 42833, - "\u0120crunch": 42834, - "\u0120eventual": 42835, - "runner": 42836, - "/error": 42837, - "Spin": 42838, - "\u0120secretly": 42839, - "\u0120assemble": 42840, - ".Person": 42841, - "enderror": 42842, - "_<": 42843, - "\u0120pendant": 42844, - "Sleep": 42845, - "\u0120Chemistry": 42846, - "\u0120bosses": 42847, - "lk": 42848, - "))),\u010a": 42849, - "Blockly": 42850, - "DEVICE": 42851, - "\u0120reflecting": 42852, - "\u0120ample": 42853, - "Milliseconds": 42854, - "\u0120Presidential": 42855, - "\u0120usuarios": 42856, - "\u0120NZ": 42857, - "\u0120Salary": 42858, - "\u0120Amanda": 42859, - "_np": 42860, - "jury": 42861, - "\u0120k\u00c3\u00b6n": 42862, - "\u0120therapist": 42863, - "\u0120homosexual": 42864, - "\u0120Drake": 42865, - "-window": 42866, - "\u0120Located": 42867, - ".Driver": 42868, - "\u0120VIDEO": 42869, - "\u0120merchants": 42870, - "\u0120Chest": 42871, - "-lock": 42872, - "/php": 42873, - "\u0120milano": 42874, - "_STYLE": 42875, - "arger": 42876, - "idea": 42877, - "GUID": 42878, - "advanced": 42879, - "meal": 42880, - "OptionsItemSelected": 42881, - "='%": 42882, - "\u0120Cham": 42883, - ":data": 42884, - "(stat": 42885, - "WillAppear": 42886, - "\u0120informal": 42887, - "aji": 42888, - "\u0120reproductive": 42889, - "\u0120CAS": 42890, - "\u00e3\u0123\u00a3": 42891, - "FUNC": 42892, - "\u0120Ruth": 42893, - ")+(": 42894, - "CONST": 42895, - "\u0120Fans": 42896, - "\u0120groupId": 42897, - "xffffffff": 42898, - "\u0120sampler": 42899, - "\u0120}}\">": 42900, - ".the": 42901, - "\u0120hollow": 42902, - "WAY": 42903, - "\u0120Faculty": 42904, - "AttributedString": 42905, - "\u0120Looks": 42906, - "\u0120Rex": 42907, - "jk": 42908, - "\u0120MIL": 42909, - "\u0120bard": 42910, - ".Long": 42911, - "\u0120livest": 42912, - "\u0120skal": 42913, - "icism": 42914, - "MAIN": 42915, - "\u0120mucho": 42916, - "BODY": 42917, - "\u0120ese": 42918, - "\u0109use": 42919, - "Foot": 42920, - ".SQLException": 42921, - "\u0120inheritance": 42922, - "received": 42923, - "\u0120putas": 42924, - "edis": 42925, - "alsa": 42926, - "\u0120ErrorMessage": 42927, - "Booking": 42928, - "\u0120tract": 42929, - "acz": 42930, - "\u0120Cant": 42931, - "_regex": 42932, - "\u0120ideological": 42933, - "\u0120jihad": 42934, - "hos": 42935, - 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"(direction": 43159, - "\u0120crews": 43160, - "arked": 43161, - "\u0120Memphis": 43162, - "\u0120Sell": 43163, - "GTK": 43164, - "\u0120maid": 43165, - ":disable": 43166, - "\u00e9\u013d\u0128": 43167, - "\u0120Pf": 43168, - "\u0120albeit": 43169, - "openh": 43170, - "?>\">\u010a": 43171, - ".getSource": 43172, - "(scale": 43173, - "Du": 43174, - "\u0120PIL": 43175, - "_refresh": 43176, - "\u0120bets": 43177, - "(car": 43178, - "\u0120Von": 43179, - "|--------------------------------------------------------------------------\u010a": 43180, - "\u0120Grat": 43181, - "Much": 43182, - "(Dialog": 43183, - ".stopPropagation": 43184, - "\u0120tek": 43185, - "\u0120exits": 43186, - "'],$": 43187, - "\u0120phoneNumber": 43188, - "ucs": 43189, - "ecimal": 43190, - "--------------": 43191, - "inp": 43192, - ".pojo": 43193, - "\u0120corpus": 43194, - "\u0120practitioners": 43195, - ".pic": 43196, - "\"testing": 43197, - "\u0120stringBy": 43198, - ".NotNull": 43199, - "\u0120rang": 43200, - ".Dynamic": 43201, - 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"\u00ef\u00bc \u012e", - "Wh at", - ". 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"er m", - "\u0120V ector", - "w ith", - "T ER", - "( str", - "\u0120res pons", - "\u0120h it", - ".S et", - "gr id", - "ri a", - "\u0120c lick", - "und le", - "C ase", - "ins ert", - "Util s", - "\u0120\"\" \"", - "\u0120im plement", - "at al", - "tem pt", - "tem plate", - "oc r", - "return s", - "\u0120play ers", - "us ers", - "ed ef", - "\u0120Th ese", - "\u0120am ong", - "\u0120de b", - "h a", - ".get Element", - "\u0120c irc", - "\u0120an swer", - "\u0120w alk", - "\u0120t reat", - "\u0120G e", - "\u0120C reate", - "\u0120a ge", - "\u0120re q", - "O ST", - "ang ular", - "\u00d1 \u0131", - "\u0120f ive", - "5 3", - "\u0120distrib uted", - "\u0120fri end", - "T P", - "\u0120c lean", - "ow s", - ".Control s", - "d is", - "\u0120w ords", - ". io", - "z y", - "\u0120he ader", - "\u0120C heck", - "\u00e2\u0122\u013b m", - "j ust", - "h older", - "=\" \u010d\u010a", - ". annot", - "\u0120col lection", - "' .", - "\u0120sim ilar", - "\u0120t aken", - "(\" %", - "Or der", - "'] \u010a", - "-m d", - 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Util", - "\u0120w ow", - "LO Y", - "\u0120Mar shal", - "\"> '.$", - "\u0120B ak", - "\u0120mod ifiers", - "d ictionary", - "\u0120St re", - "m ultiple", - "\")) ,", - "\u0120C ort", - "'] \").", - "( admin", - "\u0120Cre ator", - "Int ernet", - "( ms", - "log y", - "DECL ARE", - "\u0120Marc us", - "<< <<", - "\u00e3\u0123 \u0142", - "_m y", - "(in st", - "\u0120sc iences", - "ND ER", - ". enter", - "\u0120it u", - "\u0120beh ave", - "P an", - "omb ies", - "=' <", - "')) ;\u010d\u010a", - "\u0120M ENU", - "\u0120Work ers", - ".No Error", - "\u0120bind ings", - "\u0120dis abilities", - "{ \\", - "\u0120M unicip", - "\u0120co res", - "ur ple", - "\u0120N okia", - "us ions", - "\u0120F itness", - ".handle Change", - "\u0120jav ascript", - "\u00ec\u013c \u0136", - "( dec", - "\u0120pack ing", - "-de pend", - "\u0120trans cript", - "z eros", - "_ alert", - "? \",\u010a", - "lib s", - "\u00b1 \u00d0\u00be\u00d1\u0124", - "\u0120| \u010a\u010a", - "tr ained", - "\u0120G ent", - "\u0120R ab", - "x p", - "_config uration", - "\u00e5\u00a4 \u00a9", - "_ accept", - ".rec yclerview", - ": url", - "\u0120Mu hammad", - "\u0120privile ges", - "_b ank", - "uk u", - "w allet", - "\u0120RO OT", - "\u0120enc uent", - "? family", - "\u0109 position", - "\u0120c g", - "\u0120prec ip", - "method s", - "_f ast", - "in crement", - "\u0120T iger", - "_OCC URRED", - "qu ip", - "\u0120H AS", - "_d om", - "\u0120w reck", - "b j", - "\u0120d ern", - "\u0120org ans", - ". entries", - "\u0120_ ('", - "ram ento", - "\u0120Jam ie", - "\u0120p unk", - "IP P", - "\u0120program a", - "\u0120att ain", - "\u0120pro ves", - "/s ign", - "\u0120answer ing", - "\u0120l adder", - "************************ ****", - "\u0120W almart", - "\u0120CONT ENT", - "duct or", - "\u0120ver bal", - "\u0120P ID", - "c rypto", - "_CALL BACK", - "\u0120= ================================", - "\u0120pot ent", - "\u0120short s", - ".U ri", - ".un iform", - "; border", - "\u0120W er", - "\u0120here in", - "ll a", - "\u0120I hr", - "P ixmap", - "l iteral", - "! )\u010a\u010a", - "g eneric", - "r ust", - "_script s", - "ost o", - "it us", - "\u0120Coal ition", - "\u0120rem ot", - "de ploy", - "\u0120Eag le", - "\u00e3\u0122\u0123 \u00e3\u0122\u012e", - "\u0120important e", - "\u0109 object", - "\u0120season al", - "ne j", - "aid u", - "Bind View", - "\u0120Si erra", - "-b g", - "\u0120make Styles", - "[ offset", - "G ames", - "\u0120horm one", - "AR IO", - "head s", - "( select", - "\u0120Start ed", - "@ param", - "_de cl", - "_b log", - "\u0120a \u00c3\u00b1o", - "\\ Api", - "\u0120Mil waukee", - "Pro vid", - "An imated", - "\u0120cool er", - "\u0120Se ed", - ". Edit", - "\u00cf \u0126", - "\u0120T aking", - "\u0120border Color", - "-found er", - ".Logger Factory", - "\u0120\"\" \u010a\u010a", - "AL T", - "\u0120L ate", - "EDI ATE", - "\u0120);\u010a\u010a \u010a", - "af a", - "\u0120cancell ation", - "At om", - "\u0120B irmingham", - "emp resa", - "HE MA", - "asc al", - "\u0120up side", - ".V ersion", - "\u0120F older", - "\u0120E ight", - "\u0120V intage", - "\u0120App Delegate", - "\u0120Pre vention", - ".se parator", - "ST M", - "( room", - "gener ator", - "\u0120c attle", - "\u0109 Z", - "\u0120Part icle", - "' };\u010a", - "\u0120neighb ours", - "\u0120State less", - "\u0120alt itude", - "\u0120sa int", - "\u00d0\u00be\u00d0\u00b1 \u00d0\u00b0\u00d0\u00b2", - "\u0120conv inc", - "\u0120Cont ents", - "\u0120je une", - "(t s", - "Serial ization", - "(c ollection", - "\u0120J azz", - "\u0120D od", - "\u0120R och", - "ac io", - "comm ended", - "DEF INE", - ".on load", - "\u0120special ty", - "PL ACE", - "_MO VE", - "\u0120account able", - "Re uters", - "\u0120f icken", - "\u0120de pr", - "W ow", - "V oid", - ".s pace", - "\u00e0\u00b8 \u0139", - "\u0120t q", - "\u0120P ets", - "< $", - "(C urrent", - "ber ries", - "plan ation", - "\u0120list Of", - "\u0120Th u", - "\u0120PR INT", - "\u0120m ismo", - "\u0120do i", - "ch k", - "\u0120Un icode", - "( role", - "\u0120vir gin", - "< Point", - "_RESP ONSE", - "-h ouse", - "\u0120Venez uela", - "EM AIL", - "\u0120p \u00c3\u00bab", - "_ex ist", - "B all", - ".C L", - "re ferences", - "\u0120Beautiful Soup", - "\u0109 Expect", - "TH IS", - "\u00d1\u0125 \u00d0\u00b4", - "b ane", - "\u0120temp oral", - "ER IC", - "et as", - "\u0120refresh ing", - "\u0120sec ular", - "@ synthesize", - "ac cur", - "\u0120n ella", - "\u0120S OL", - ".p ipe", - "Ch annels", - "\u00e8\u0129 \u00aa", - "\u0120insert ion", - "\u00e1\u00bb \u012d", - "el ia", - "\u0120adjust able", - "Can ada", - "\u0120I TEM", - "\u0120cur ves", - "\u0120Che ap", - "let ing", - "\u0120optim istic", - "al lo", - "\u0120polit ician", - "_down load", - "= edge", - "ORT H", - "\u0120model o", - "art o", - ". rotate", - "\u0120s elenium", - "\u00e6\u012a \u0133", - "_al ias", - "\u0120renown ed", - ".' .", - "\u0120c zy", - "\u0120al les", - ".Com piler", - "\u0120B ass", - "Conn ector", - ".R ole", - "L INK", - "\u0120c riterion", - "lem etry", - "Success fully", - "/p ng", - "\u0120ey eb", - "asp berry", - "( gr", - "\u0120d angers", - "\u0120correct ed", - "\u0120gl ow", - "\u0120elabor ate", - "\u0120B ears", - "aw ai", - "=\" '+", - "\u0120promot ions", - "\u0120mathematic al", - "\u0120\" `", - "_Generic Class", - "\u0120Che f", - ".S ort", - "table Name", - "R IC", - "\u0120volunt ary", - "\u0120Bl ade", - "-e lect", - "\u0120Com bat", - "\u0120Ab ility", - "\u0120ab dom", - "\u0120d uck", - "T mp", - "\u00e5\u0127 \u00a8", - "\u0120er ase", - ".P h", - "\u0120Default s", - "p artment", - "_US B", - "\u00c3\u00aa te", - "; 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