pax_global_header00006660000000000000000000000064143033442400014507gustar00rootroot0000000000000052 comment=30e8e9b9859b4ed7fb92c2fa7bb753d01628b8c3 pangu/000077500000000000000000000000001430334424000121655ustar00rootroot00000000000000pangu/.gitignore000066400000000000000000000034261430334424000141620ustar00rootroot00000000000000# ---> Python # Byte-compiled / optimized / DLL files __pycache__/ *.py[cod] *$py.class # C extensions *.so # Distribution / packaging .Python build/ develop-eggs/ dist/ downloads/ eggs/ .eggs/ lib/ lib64/ parts/ sdist/ var/ wheels/ pip-wheel-metadata/ share/python-wheels/ *.egg-info/ .installed.cfg *.egg MANIFEST # PyInstaller # Usually these files are written by a python script from a template # before PyInstaller builds the exe, so as to inject date/other infos into it. *.manifest *.spec # Installer logs pip-log.txt pip-delete-this-directory.txt # Unit test / coverage reports htmlcov/ .tox/ .nox/ .coverage .coverage.* .cache nosetests.xml coverage.xml *.cover *.py,cover .hypothesis/ .pytest_cache/ # Translations *.mo *.pot # Django stuff: *.log local_settings.py db.sqlite3 db.sqlite3-journal # Flask stuff: instance/ .webassets-cache # Scrapy stuff: .scrapy # Sphinx documentation docs/_build/ # PyBuilder target/ # Jupyter Notebook .ipynb_checkpoints # IPython profile_default/ ipython_config.py # pyenv .python-version # pipenv # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. # However, in case of collaboration, if having platform-specific dependencies or dependencies # having no cross-platform support, pipenv may install dependencies that don't work, or not # install all needed dependencies. #Pipfile.lock # PEP 582; used by e.g. github.com/David-OConnor/pyflow __pypackages__/ # Celery stuff celerybeat-schedule celerybeat.pid # SageMath parsed files *.sage.py # Environments .env .venv env/ venv/ ENV/ env.bak/ venv.bak/ # Spyder project settings .spyderproject .spyproject # Rope project settings .ropeproject # mkdocs documentation /site # mypy .mypy_cache/ .dmypy.json dmypy.json # Pyre type checker .pyre/ pangu/README.md000066400000000000000000000045541430334424000134540ustar00rootroot00000000000000# PanGu-Alpha-Applications 中文|[English](README_EN.md) ## 简介 本项目旨在为鹏程系列超大规模预训练模型提供从算法层到应用层的算法示例和应用示范，以大模型为AI基础设施，加速大模型的应用技术创新和应用生态构建。 目前已提供盘古系列模型的训练推理、finetune\promptune\fewshot learning、模型压缩以及下游任务应用等示例。 ## 内容导引 | | 项目 | 描述 | | --- | :----------------: | :--------------- | | 预训练模型 | [[鹏程系列](./model)] | 鹏程系列预训练模型如鹏程.盘古、鹏程.盘古增强版等| | 算法层&任务层 | [[模型迁移](./method)]
[[模型压缩](./method)] |算法层与任务层位置| | 应用层 | [[应用](./app)] | 应用层项目位置 | ## 进展 - **2021.08.18**
* 小样本模型迁移第一版baseline发布。 * 任务层：支持盘古模型nli任务，baseline：cmnli。 * 算法层：支持盘古模型fine-tune，prompt-tune，incontext-learning。 - **2021.11.25**
* 鹏程.盘古增强版beta版发布。 - **2022.01.25**
* 鹏程.盘古small版版发布。 - **2022.09.01**
* 鹏程.盘古对话生成模型发布。 ## 总体框架 **模型层**：基于分布训练模架、大数据、高效算法训练大模型，构建文本、多语言、多模型、知识图谱等领域的AI底层基础设施。
**算法层**：基于预训练模型做模型压缩、小样本模型迁移、持续学习等方面的算法创新，构建基础算法模块，为预训练模型落地应用提供底层算法支撑。
**任务层**：基于算法层基础算法模块，构建两大基础任务NLU和NLG的实现样例，为上层应用提供底层任务建模支撑。
**应用层**：基于任务层基础任务，设计对话机器人、写作助手、多语翻译、知识问答、专利辅助生成等应用，为预训练模型落地应用提供示范应用，促进模型的加速落地和深度应用。
## 代码说明 - **代码结构** ```bash PanGu-Alpha-Application |-- README.md |-- app # 应用层目录 |-- com # 总共通用目录 |-- megatron # 引用的megatron代码 |-- method # 算法层&任务层目录 |-- requirements.txt `-- resource ``` ## License ```bash 正在添加中 ``` pangu/README_EN.md000066400000000000000000000045021430334424000140270ustar00rootroot00000000000000# PanGu-Alpha-Applications [中文](README.md)|English ## Introduction This project aims to provide algorithm examples and application demonstrations from algorithm layer to application layer for Pengcheng series super large-scale pre training models, take the large model as AI infrastructure, and accelerate the application technology innovation and application ecology construction of the large model. ## Navigation | | Project | Describe | | --- | :---------------- | :--------------- | | pretrained models | [[PanGu-Alpha](https:\//git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha)]
[[PanGu-Alpha-Evolution](./model/pangu_evolution)]| Pengcheng series pre-training models | | algorithm&task | [[model transfer](./method)]
[[model compression](./method)] |The path of algorithm&task| | application | [[application](./app)] | The path of application | ## Progress - **2021.08.18**
* Relase the first version of baseline for few shot model transfer. * Task layer：cmnli baseline. * Algorithm layer：fine-tune，prompt-tune，incontext-learning. ## Framework **Model layer**：Training models based on distributed training, big data and efficient algorithm. Building AI underlying infrastructure in the fields of text, multilingual, multimodal and knowledge graph.
**Algorithm layer**：Based on the pre-trained models, make algorithm innovation in model compression, small sample model migration and continuous learning, and build a basic algorithm module to provide underlying algorithm support for the landing application of the pre training model.
**Task layer**：Based on the basic algorithm module of the algorithm layer, the implementation examples of the two basic tasks NLU and NLG are constructed to provide the underlying task modeling support for the upper application.
**Application layer**：Applications such as dialogue robot, writing assistant, multilingual translation, Knowledge Q & A and patent aided generation are designed to provide demonstration applications for the landing application of the pre training model and promote the accelerated landing and in-depth application of the model. ## Codes - **structure** ```bash PanGu-Alpha-Application |-- README.md |-- app |-- com |-- megatron |-- method |-- requirements.txt `-- resource ``` ## License ```bash adding ```pangu/app/000077500000000000000000000000001430334424000127455ustar00rootroot00000000000000pangu/app/README.md000066400000000000000000000010651430334424000142260ustar00rootroot00000000000000# 应用 ## 简介 基于大模型+大数数据的范式，设计对话机器人、写作助手、多语翻译、知识问答、专利辅助生成等应用，为预训练模型落地应用提供示范应用，促进模型的加速落地和深度应用。 ## 应用模型列表 | 序号 | 类型 | 语言 | 模型 | 参数 | 结构 | 导航 | | --- | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | | 1 | 对话生成 | 中文 | 鹏程.盘古-Dialog | 2.6B | Transformer decoder | [[链接](chat)] | ## 进展 ## License ```bash 正在添加中 ``` pangu/app/__init__.py000066400000000000000000000001211430334424000150500ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/5 # @Author: qing pangu/app/chat/000077500000000000000000000000001430334424000136645ustar00rootroot00000000000000pangu/app/chat/README.md000066400000000000000000000065761430334424000151610ustar00rootroot00000000000000# 鹏程.盘古-Dialog 中文|[English](README_EN.md) ## 1 简介 鹏程.盘古对话生成大模型(PanGu-Dialog)。PanGu-Dialog是以大数据和大模型为显著特征的大规模开放域对话生成模型，充分利用了大规模预训练语言模型的知识和语言能力，基于预训练+持续微调的学习策略融合大规模普通文本和对话数据训练而成。在各项指标上达到了中文纯模型生成式对话SOTA水平，在知识性和信息量方面优势明显。 **要说明的是，目前生成式对话仍处于较低水平，大模型+大数据的范式对话整体质量、知识正确性有明显优势，但安全性、可靠、可信、可控、智慧等方面的提升并不明显，与人类对话能力存在明显的差距，这仍是对话生成一道难题。后续我们将在现有基础上针对不同的维度不断优化迭代，不断进步。** ## 2 方法 ### 2.1 基本信息 基础模型：鹏程.盘古2.6B，1024最大序列长度。 模型结构：decoder only，简单高效、生成能力更强、预训练更简单。 数据格式：采用特殊token将同一对话session中每一个对话轮次的语句分割，用另一特殊token分割不同的对话session。为了提升数据利用率，context和response都会参与loss计算。为了提升训练效率，不同的对话session将被拼接起来作为最大序列长度的自回归式语言模型的输入，并通过attention mask隔离session间的干扰。 训练数据：收集开源数据集，并进行初步的清洗过滤。第一阶段最终整理出3000w多轮对话、7000w句子、16亿文字的对话数据，覆盖社交媒体、闲聊、知识对话、知识问答10个数据集。详细信息如下表。 ### 2.2 训练 基于鹏程.盘古预训练语言的大规模中文对话模型，采用多阶段持续训练的方式在对话数据进行学习，充分利用训练语言模型语言能力和知识。整个训练框架如下图所示，PLM实际为鹏程.盘古模型，多数据是指收集的多源的对话数据，统一任务格式和Prompt tune和持续学习采用与[鹏程.盘古增强版](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha-Evolution)一致的方法。
## 模型文件 | 模型文件 | Md5 | 大小 | 参数配置 | | ------ | ---- | ---- | -------- | | [pangu_dialog_fp16_2b6.zip](https://git.openi.org.cn/attachments/9c403892-39d5-48c4-96dc-cf8a9c8e91b2?type=0) | *** | 4.6G | num-layers : 31
hidden-size : 2560
num-attention-heads : 32 | ```txt pangu_dialog_fp16_2b6 # 模型目录 -- iter_0001000 # 迭代次数目录 --mp_rank_00 # 模型并行时各个 GPU 的目录 --model_optim_rng.pt # 模型文件 --latest_checkpointed_iteration.txt # 记录 ckpt 的迭代次数文件 ``` **注：`num-layers` 等于 [Pangu](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha) 项目中的 `num-layers - 1`** ## 3 使用 ### 推理代码 ```bash bash scripts/pangu_dialog_infer.sh ``` ### 训练或微调 ```bash bash scripts/pangu_dialog_tune.sh ``` ## 4 环境 ### pytorch版 支持 python >= 3.6, pytorch >= 1.5, cuda >= 10, nccl >= 2.6, and nltk。 推荐使用英伟达的官方 docker 镜像`docker pull nvcr.io/nvidia/pytorch:20.03-py3`。 pangu/app/chat/__init__.py000066400000000000000000000001221430334424000157700ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/16 # @Author: qing pangu/app/chat/datadefine/000077500000000000000000000000001430334424000157505ustar00rootroot00000000000000pangu/app/chat/datadefine/__init__.py000066400000000000000000000001221430334424000200540ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/22 # @Author: qing pangu/app/chat/datadefine/dialog_dataset.py000066400000000000000000000046431430334424000212750ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/22 # @Author: qing import os import torch class DialogDataset(object): def __init__(self, args, tokenizer, split="", ratio=1, num=-1): self.args = args self.tokenizer = tokenizer self.ratio = ratio self.split = split self.pad_id = tokenizer.pad_id self.eod_id = self.tokenizer.eot_id self.eot_id = self.tokenizer.eod_id self.data, self.max_len = self.process_data() if num > 0: self.data = self.data[:num] def context_to_id(self, utter_list): context_ids = [] for utter in utter_list: utter_ids = self.tokenizer.encode(utter) context_ids += utter_ids + [self.eot_id] context_ids += [self.eod_id] return context_ids def collate(self, samples): bs = len(samples) model_data = { "input_ids": torch.ones(bs, self.args.seq_length, dtype=torch.long) * self.pad_id, "label_ids": torch.ones(bs, self.args.seq_length, dtype=torch.long) * self.pad_id, "context_lengths": torch.ones(bs, 1, dtype=torch.long), "loss_mask": torch.zeros(bs, self.args.seq_length, dtype=torch.long) } for i, samp in enumerate(samples): input_id, label_id = samp[:-1], samp[1:] seq_len = len(input_id) model_data["input_ids"][i][:seq_len] = torch.tensor(input_id, dtype=torch.long) model_data["label_ids"][i][:seq_len] = torch.tensor(label_id, dtype=torch.long) model_data["context_lengths"][i][0] = seq_len model_data["loss_mask"][i, :seq_len] = 1 if self.args.eod_mask_loss: model_data["loss_mask"][i][input_id == self.eod_id] = 0.0 def process_data(self): data = [] sizes = [] data_path = os.path.join(self.args.data_path) with open(data_path, "r", encoding="utf-8") as f: data_lines = f.readlines() print(f"All case num: {len(data_lines)}.") for i, instance in enumerate(data_lines): utter_list = instance.split("\t") utter_ids = self.context_to_id(utter_list) sizes.append(len(utter_ids)) data.append(utter_ids) max_len = max(sizes) return data, max_len def __len__(self): return len(self.data) def __getitem__(self, idx): return self.data[idx]pangu/app/chat/inference/000077500000000000000000000000001430334424000156225ustar00rootroot00000000000000pangu/app/chat/inference/__init__.py000066400000000000000000000001221430334424000177260ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/16 # @Author: qing pangu/app/chat/inference/infer_generate_dialog.py000066400000000000000000000147521430334424000225010ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/6/6 # @Author: qing import os import sys import re import torch import numpy as np import torch.nn.functional as F sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))) from megatron.utils import get_ltor_masks_and_position_ids from model.pangu.inference.infer_generate import InferGenerate class InferGenerateDialog(InferGenerate): def __init__(self, model_name="pangu_prompt", post_process_flag=True): super().__init__(model_name=model_name) self.post_process_flag = post_process_flag self.set_adjust_paras() def set_adjust_paras(self): self.adjust_flag = True self.calc_banned_puncs_ids = [] self.repetition_penalty = 1.0 self.max_conv_repetition_penalty = 1 # static: 1.2 or 1, selfchat: topp0.7 with 2, selfchat: topp0.9 with 1 self.no_repeat_ngram_size = 3 self.repetition_conv_penalty = True self.bad_words_ids = None self.antonym_dict = None self.min_length = 2 self.mid_length = 20 self.length_penalty = 2 self.filter_value = -float('Inf') self.confine_value = -10000 self.set_banned_puncs_ids() def set_banned_puncs_ids(self): punc_zh = """！？｡＂＃＄％＆＇（）＊＋，－／：；＜＝＞＠［＼］＾＿｀｛｜｝～｟｠｢｣､、〃》「」『』【】〔〕〖〗〘〙〚〛〜〝〞〟〰〾〿–—‘’‛“”„‟…‧﹏.""" punc_en = """!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~\n\t""" spec_ch = """�+;"-.!/\\_,$%^*(+"')]+|[+——()?【】“”；！，。？、~@#￥%……&*（）▂●""" puncs = punc_zh + punc_en + spec_ch for punc in puncs: token_id = self.tokenizer.encode(punc) if not token_id: continue if token_id[0] in self.calc_banned_puncs_ids: continue self.calc_banned_puncs_ids.append(token_id[0]) def context_to_id(self, context, eot): context_ids = [] for utter in context: utter_ids = self.tokenizer.encode(utter) context_ids += utter_ids if eot: context_ids += [eot] return context_ids def post_process(self, generate_texts): generate_texts_processed = [] if not self.post_process_flag: return generate_texts for generate_text in generate_texts: text_tmp = generate_text.replace("\n", "").replace(" ", "") text_tmp = text_tmp.replace("\u2047", "") text_tmp = re.sub(r'(.+?)\1{1,}$', r'\1', text_tmp) generate_texts_processed.append(text_tmp) return generate_texts_processed def get_single_generate_data(self, input_str, max_len, eot=None): origin_input_ids = self.context_to_id(input_str, eot) origin_input_ids = np.array(origin_input_ids).reshape(1, -1) if origin_input_ids.shape[-1] >= max_len: origin_input_ids = origin_input_ids[:, -max_len:] bs, valid_length = origin_input_ids.shape context_len = valid_length pad_length = self.args.seq_length - valid_length input_ids = np.pad(origin_input_ids, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, self.tokenizer.pad_id)) input_ids = torch.tensor(input_ids, dtype=torch.long).contiguous().cuda() attention_mask, _, position_ids = get_ltor_masks_and_position_ids(input_ids, self.tokenizer.eod, self.args.reset_position_ids, self.args.reset_attention_mask, self.args.eod_mask_loss) input_data = (input_ids, position_ids, attention_mask, valid_length) return input_data, context_len def do_single_generate(self, input_data, temperature=1, top_k=None, top_p=None, max_num=50, eot=None): input_ids, position_ids, attention_mask, valid_length = input_data inputs = input_ids cnt = 0 with torch.no_grad(): while valid_length < self.args.seq_length: logits = self.model(inputs, position_ids, attention_mask) next_token_logits = logits[:, valid_length - 1, :] / temperature next_token_logits = self.adjust_next_token_scores(input_ids[:, :valid_length], next_token_logits, cnt + 1, eot=eot) if top_k is None and top_p is None: next_token = torch.argmax(next_token_logits, dim=-1) else: next_token_logscores = self.top_k_logits(next_token_logits, top_k=top_k, top_p=top_p) probs = F.softmax(next_token_logscores, dim=-1) # probs = next_token_logscores / torch.sum(next_token_logscores, dim=-1) next_token = torch.multinomial(probs.float(), num_samples=1).squeeze(1) if next_token[0] == self.tokenizer.eod or valid_length == self.args.seq_length - 1 or cnt >= max_num: outputs = input_ids break if eot and next_token[0] == eot: outputs = input_ids break input_ids[:, valid_length] = next_token valid_length += 1 cnt += 1 return outputs def generate(self, input_str, temperature=1, top_k=None, top_p=None, max_len=1000, max_num=50, eot=None): input_data, context_len = self.get_single_generate_data(input_str, max_len, eot) outputs = self.do_single_generate(input_data, temperature=temperature, top_k=top_k, top_p=top_p, max_num=max_num, eot=eot) outputs = outputs.cpu().numpy() length = np.sum(outputs != self.tokenizer.pad_id) outputs = outputs[0, context_len:length] generate_text = "".join(self.tokenizer.convert_ids_to_tokens(outputs.tolist())) return generate_text if __name__=="__main__": examples = {"对话1": ["你好", "你好", "今天天气真好，想出去玩"]} generater = InferGenerateDialog() context = examples["对话1"] output_text = generater.generate(context, top_k=0, top_p=0.9, max_num=50, eot=8) print(f"input_text: \n{context} \noutput_text: \n{output_text}")pangu/app/chat/resource/000077500000000000000000000000001430334424000155135ustar00rootroot00000000000000pangu/app/chat/resource/method.png000066400000000000000000001205441430334424000175070ustar00rootroot00000000000000PNG  IHDR lo?sRGBgAMA a pHYsttfxIDATx^i%W}r"nOC87awtu7:66m@ ll F[ bhaI@$@ P&& $!1!$Y`WO/Vq~wf>Oܙk\_r4"""""""""ǐPDDDDDDDDdLCBcH(""""""""2q EDDDDDDDD&!1$8"""""""""ǐPDDDDDDDDdLCBcH(""""""""2q EDDDDDDDD&!1$8"""""""""ǐPDDDDDDDDdLCBcH(""""""""2qKU' CBU ^Bc'ӐPDDDDDDdj EDDDDDD!VѐPDDDDDDdj EDDDDDD!Vq!5OzғTU__=K Uuȸ0$T*X,\Uu*4qaHUB8KHxUuB1$T*X4$mÐPU>YCBUECB6 Uu1$T*X4$mÐPU꾚x;UVkϐPU8b1:RmaRAU꾚D_ĐPU8bNTDd/E_ Tu5l#}MZ!x"m _"xBWkHF UC(-Ddz})DP:&֐"7jP,щ>#w|_\{::ԞwuW:GI/:^Pd}>&CBUŚ??޽{ႪK;ＳyGOu裸$B]3WUz^=ל94O꾚 qGdHUBX% M})D]W?c?͓ bMHPG幬q}:CǼ|zL #xu˕dCqu_M8ְ n9|ډOic:9y}SzA;)0$T*XщFAW TuF?P>M})D$#$KTG(% :! 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-*- coding: utf-8 -*- # @Date: 2022/8/16 # @Author: qing pangu/app/chat/scripts/pangu_dialog_infer.sh000066400000000000000000000014221430334424000215220ustar00rootroot00000000000000#! /bin/bash export CUDA_VISIBLE_DEVICES="0,1" export MASTER_PORT=7700 WORKING_DIR=/userhome/gpt3/PanGu-Alpha-Applications MODEL_PATH=/raid0/pangu/model/dialog/pangu_dialog_fp16_2b6/ # 2.6B Layers=31 Hsize=2560 Heads=32 # 350M #Layers=23 #Hsize=1024 #Heads=16 python -u ${WORKING_DIR}/app/chat/inference/infer_generate_dialog.py \ --model-parallel-size 1 \ --num-layers $Layers \ --hidden-size $Hsize \ --num-attention-heads $Heads \ --max-position-embeddings 1024 \ --tokenizer-type GPT2BPETokenizer \ --seq-length 1024 \ --load $MODEL_PATH \ --vocab-file ${WORKING_DIR}/megatron/bpe_4w_pcl/vocab \ --merge-file gpt2-merges.txt \ --no-load-rng \ --make-vocab-size-divisible-by 1 \ --fp16 \ pangu/app/chat/scripts/pangu_dialog_tune.sh000066400000000000000000000033411430334424000213740ustar00rootroot00000000000000#! /bin/bash WORKING_DIR=/userhome/gpt3/PanGu-Alpha-Applications MODEL_PATH=/raid0/pangu/model/dialog/pangu_dialog_fp16_2b6/ MODEL_SAVE_PATH=/raid0/pangu/model/dialog/pangu_dialog_fp16_2b6_new/ DATA_PATH=/you_data_path GPUS_PER_NODE=8 MASTER_ADDR=localhost MASTER_PORT=6000 NNODES=1 NODE_RANK=0 WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT" python -u -m torch.distributed.launch $DISTRIBUTED_ARGS \ ${WORKING_DIR}/app/chat/train/training_main.py \ --model-parallel-size 8 \ --num-layers 31 \ --hidden-size 2560 \ --num-attention-heads 32 \ --batch-size 64 \ --seq-length 1024 \ --max-position-embeddings 1024 \ --train-iters 50000 \ --lr-decay-iters 1000 \ --save $MODEL_SAVE_PATH \ --load $MODEL_PATH \ --data-path $DATA_PATH \ --vocab-file ${WORKING_DIR}/megatron/bpe_4w_pcl/vocab \ --merge-file gpt2-merges.txt \ --data-impl mmap \ --split 949,50,1 \ --distributed-backend nccl \ --lr 0.00005 \ --lr-decay-style cosine \ --min-lr 1.0e-6 \ --weight-decay 1e-2 \ --clip-grad 1.0 \ --warmup .01 \ --log-interval 100 \ --save-interval 10000 \ --eval-interval 500 \ --eval-iters 200 \ --attention-dropout 0.1 \ --hidden-dropout 0.1 \ --seed 1234 \ --finetune \ --DDP-impl "torch"\ --checkpoint-activations \ --distribute-checkpointed-activations \ --fp16-lm-cross-entropy \ --use-cpu-initialization \ --make-vocab-size-divisible-by 1 \ --fp16 \ pangu/app/chat/train/000077500000000000000000000000001430334424000150015ustar00rootroot00000000000000pangu/app/chat/train/__init__.py000066400000000000000000000001221430334424000171050ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/8/16 # @Author: qing pangu/app/chat/train/training_main.py000066400000000000000000000064601430334424000202000ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/6/1 # @Author: qing import os import sys import torch sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))) from megatron import get_args from megatron import print_rank_0 from megatron import get_timers from megatron import get_tokenizer from megatron import mpu from megatron.utils import get_ltor_masks_and_position_ids from megatron.utils import reduce_losses from app.chat.datadefine.dialog_dataset import DialogDataset from method.prompttune.model.gpt2_model_pt import GPT2ModelPT from method.prompttune.tasks.prompt_tune import prompt_tune_train from method.prompttune.paras.custom_arguments import add_custom_args def model_provider(): """Build the model.""" print_rank_0('building PT model ...') model = GPT2ModelPT(num_tokentypes=0, parallel_output=True) return model def get_batch(data_iterator): """Generate a batch""" args = get_args() tokenizer = get_tokenizer() # Items and their type. keys = ['input_ids', 'label_ids', 'context_lengths', 'loss_mask'] datatype = torch.int64 # Broadcast data. if data_iterator is not None: data = next(data_iterator) else: data = None data_b = mpu.broadcast_data(keys, data, datatype) # Unpack. tokens_ = data_b['input_ids'].long() target = data_b['label_ids'].long() context_lengths = data_b['context_lengths'].long() loss_mask = data_b['loss_mask'].float() context_lengths[context_lengths>=args.seq_length] = args.seq_length-1 tokens = tokens_ labels = target # Get the masks and postition ids. attention_mask, _, position_ids = get_ltor_masks_and_position_ids( tokens, tokenizer.eod, args.reset_position_ids, args.reset_attention_mask, args.eod_mask_loss) loss_mask[tokens == tokenizer.pad_id] = 0.0 return tokens, labels, target, loss_mask, attention_mask, position_ids, context_lengths def forward_step(data_iterator, model, parallel_output=None): """Forward step.""" timers = get_timers() timers('batch generator').start() tokens, labels, target, loss_mask, attention_mask, position_ids, context_lengths = get_batch(data_iterator) timers('batch generator').stop() loss_ = model(tokens, position_ids, attention_mask, labels=labels, forward_method_parallel_output=parallel_output) loss = torch.sum(loss_*loss_mask)/loss_mask.sum() reduced_loss = reduce_losses([loss]) preds = None return loss, {'lm loss': reduced_loss[0]}, target, preds def train_valid_test_datasets_provider(train_val_test_num_samples): """Build train, valid, and test datasets.""" args = get_args() tokenizer = get_tokenizer() print_rank_0('> building train, validation, and test datasets for pangu ...') train_ds = DialogDataset(args, tokenizer) valid_ds = None test_ds = None print_rank_0("> finished creating pangu finetune datasets ...") return train_ds, valid_ds, test_ds if __name__ == "__main__": prompt_tune_train(train_valid_test_datasets_provider, model_provider, forward_step, extra_args_provider=add_custom_args, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'})pangu/com/000077500000000000000000000000001430334424000127435ustar00rootroot00000000000000pangu/com/__init__.py000066400000000000000000000001221430334424000150470ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/12 # @Author: qing pangu/com/config/000077500000000000000000000000001430334424000142105ustar00rootroot00000000000000pangu/com/config/__init__.py000066400000000000000000000001221430334424000163140ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing pangu/com/config/path_config.py000066400000000000000000000011751430334424000170470ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing import os class PathConfig(object): def __init__(self, data_path=None, save_path=None, log_path=None): self.root_path = os.path.dirname(os.path.dirname(os.path.dirname(__file__))) self.data_path = data_path self.save_path = save_path self.log_path = log_path if not data_path: self.data_path = os.path.join(self.root_path, "data/") if not save_path: self.save_path = self.data_path if not log_path: self.log_path = os.path.join(self.root_path, "log/")pangu/com/tokenizer/000077500000000000000000000000001430334424000147555ustar00rootroot00000000000000pangu/com/tokenizer/__init__.py000066400000000000000000000001221430334424000170610ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing pangu/com/tokenizer/tokenization_test.py000066400000000000000000000012361430334424000211060ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/11 # @Author: qing import os from com.config.path_config import PathConfig from megatron.tokenizer.tokenization_jieba import JIEBATokenizer def test_tokenization_jieba(text): path_config = PathConfig() vocab_path = os.path.join(path_config.root_path, "megatron/bpe_4w_pcl/vocab") tokenizer = JIEBATokenizer(vocab_path) input_ids = tokenizer.encode(text) print(f"input str: {text}.") print(f"encode id: {input_ids}.") if __name__ == '__main__': text = "句子一与句子二的关系是：1、矛盾，2、蕴含，3、中立。答案：3" test_tokenization_jieba(text)pangu/com/utils/000077500000000000000000000000001430334424000141035ustar00rootroot00000000000000pangu/com/utils/__init__.py000066400000000000000000000001221430334424000162070ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/24 # @Author: qing pangu/com/utils/mpu_util.py000066400000000000000000000016031430334424000163130ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/24 # @Author: qing import torch from megatron.mpu.initialize import get_model_parallel_group, get_model_parallel_world_size, get_model_parallel_rank def mp_gather(input_): """Gather tensors and concatinate along the last dimension.""" world_size = get_model_parallel_world_size() # Bypass the function if we are using only 1 GPU. if world_size==1: return input_ # Size and dimension. last_dim = input_.dim() - 1 rank = get_model_parallel_rank() tensor_list = [torch.empty_like(input_) for _ in range(world_size)] tensor_list[rank] = input_.clone().detach() torch.distributed.all_gather(tensor_list, input_, group=get_model_parallel_group()) # Note: torch.cat already creates a contiguous tensor. output = torch.cat(tensor_list, dim=last_dim).contiguous() return outputpangu/megatron/000077500000000000000000000000001430334424000140015ustar00rootroot00000000000000pangu/megatron/__init__.py000066400000000000000000000024651430334424000161210ustar00rootroot00000000000000# 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. import torch from .package_info import ( __description__, __contact_names__, __url__, __download_url__, __keywords__, __license__, __package_name__, __version__, ) from .global_vars import get_args from .global_vars import get_tokenizer from .global_vars import get_tensorboard_writer from .global_vars import get_adlr_autoresume from .global_vars import get_timers from .initialize import initialize_megatron 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) pangu/megatron/arguments.py000066400000000000000000000543301430334424000163650ustar00rootroot00000000000000# 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. """Megatron arguments.""" import argparse import os import torch from megatron import fused_kernels def parse_args(extra_args_provider=None, defaults={}, ignore_unknown_args=False): """Parse all arguments.""" parser = argparse.ArgumentParser(description='Megatron-LM Arguments', allow_abbrev=False) # Standard arguments. parser = _add_network_size_args(parser) parser = _add_regularization_args(parser) parser = _add_training_args(parser) parser = _add_initialization_args(parser) parser = _add_learning_rate_args(parser) parser = _add_checkpointing_args(parser) parser = _add_mixed_precision_args(parser) parser = _add_distributed_args(parser) parser = _add_validation_args(parser) parser = _add_data_args(parser) parser = _add_autoresume_args(parser) parser = _add_realm_args(parser) # Custom arguments. if extra_args_provider is not None: parser = extra_args_provider(parser) # Parse. if ignore_unknown_args: args, _ = parser.parse_known_args() else: args = parser.parse_args() # Distributed args. args.rank = int(os.getenv('RANK', '0')) args.world_size = int(os.getenv("WORLD_SIZE", '1')) args.model_parallel_size = min(args.model_parallel_size, args.world_size) if args.rank == 0: print('using world size: {} and model-parallel size: {} '.format( args.world_size, args.model_parallel_size)) # Fp16 loss scaling. args.dynamic_loss_scale = False if args.loss_scale is None: args.dynamic_loss_scale = True # Parameters dtype. args.params_dtype = torch.float if args.fp16: args.params_dtype = torch.half if args.rank == 0: print('using {} for parameters ...'.format(args.params_dtype), flush=True) # Set input defaults. for key in defaults: # For default to be valid, it should not be provided in the # arguments that are passed to the program. We check this by # ensuring the arg is set to None. if getattr(args, key) is not None: if args.rank == 0: print('WARNING: overriding default arguments for {key}:{v} \ with {key}:{v2}'.format(key=key, v=defaults[key], v2=getattr(args, key)), flush=True) else: setattr(args, key, defaults[key]) # Check required arguments. required_args = ['num_layers', 'hidden_size', 'num_attention_heads', 'max_position_embeddings'] for req_arg in required_args: _check_arg_is_not_none(args, req_arg) # Checks. assert args.hidden_size % args.num_attention_heads == 0 if args.seq_length is not None: assert args.max_position_embeddings >= args.seq_length if args.lr is not None: assert args.min_lr <= args.lr if args.save is not None: assert args.save_interval is not None # Parameters sharing does not work with torch DDP. if (args.num_unique_layers is not None) and (args.num_layers is not None): assert args.num_unique_layers <= args.num_layers assert args.num_layers % args.num_unique_layers == 0, \ 'num-layers should be divisible by num-unique-layers.' if args.num_unique_layers < args.num_layers: assert args.DDP_impl == 'local', \ 'torch-DDP does not work with parameters sharing.' # Mixed precision checks. if args.fp16_lm_cross_entropy: assert args.fp16, 'lm cross entropy in fp16 only support in fp16 mode.' # Activation checkpointing. if args.distribute_checkpointed_activations: assert args.checkpoint_activations, \ 'for distribute-checkpointed-activations to work you '\ 'need to enable checkpoint-activations' # load scaled_upper_triang_masked_softmax_fusion kernel if args.scaled_upper_triang_masked_softmax_fusion: fused_kernels.load_scaled_upper_triang_masked_softmax_fusion_kernel() _print_args(args) return args def _print_args(args): """Print arguments.""" if args.rank == 0: print('-------------------- arguments --------------------', flush=True) str_list = [] for arg in vars(args): dots = '.' * (32 - len(arg)) str_list.append(' {} {} {}'.format(arg, dots, getattr(args, arg))) for arg in sorted(str_list, key=lambda x: x.lower()): print(arg, flush=True) print('---------------- end of arguments ----------------', flush=True) def _check_arg_is_not_none(args, arg): assert getattr(args, arg) is not None, '{} argument is None'.format(arg) def _add_network_size_args(parser): group = parser.add_argument_group(title='network size') group.add_argument('--num-layers', type=int, default=None, help='Number of transformer layers.') group.add_argument('--num-unique-layers', type=int, default=None, help='Number of unique transformer layers. ' '`num-layers` should be divisible by this value.') group.add_argument('--param-sharing-style', default='grouped', choices=['grouped', 'spaced'], help='Ordering of the shared parameters. For example, ' 'for a `num-layers`=4 and `--num-unique-layers`=2, ' 'we will have the following ordering for two unique ' 'layers 1 and 2: ' ' grouped: [1, 2, 1, 2] and spaced: [1, 1, 2, 2].') group.add_argument('--hidden-size', type=int, default=None, help='Tansformer hidden size.') group.add_argument('--num-attention-heads', type=int, default=None, help='Number of transformer attention heads.') group.add_argument('--max-position-embeddings', type=int, default=None, help='Maximum number of position embeddings to use. ' 'This is the size of position embedding.') group.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 efficieny reasons.') group.add_argument('--layernorm-epsilon', type=float, default=1e-5, help='Layer norm epsilon.') group.add_argument('--apply-residual-connection-post-layernorm', action='store_true', help='If set, use original BERT residula connection ' 'ordering.') group.add_argument('--openai-gelu', action='store_true', help='Use OpenAIs GeLU implementation. This option' 'should not be used unless for backward compatibility' 'reasons.') group.add_argument('--onnx-safe', type=bool, required=False, help='Use workarounds for known problems with Torch ONNX exporter') return parser def _add_regularization_args(parser): group = parser.add_argument_group(title='regularization') group.add_argument('--attention-dropout', type=float, default=0.1, help='Post attention dropout ptobability.') group.add_argument('--hidden-dropout', type=float, default=0.1, help='Dropout probability for hidden state transformer.') group.add_argument('--weight-decay', type=float, default=0.01, help='Weight decay coefficient for L2 regularization.') group.add_argument('--clip-grad', type=float, default=1.0, help='Gradient clipping based on global L2 norm.') return parser def _add_training_args(parser): group = parser.add_argument_group(title='training') group.add_argument('--batch-size', type=int, default=None, help='Batch size per model instance (local batch size). ' 'Global batch size is local batch size times data ' 'parallel size.') group.add_argument('--checkpoint-activations', action='store_true', help='Checkpoint activation to allow for training ' 'with larger models, sequences, and batch sizes.') group.add_argument('--distribute-checkpointed-activations', action='store_true', help='If set, distribute checkpointed activations ' 'across model parallel group.') group.add_argument('--checkpoint-num-layers', type=int, default=1, help='chunk size (number of layers) for checkpointing.') group.add_argument('--train-iters', type=int, default=None, help='Total number of iterations to train over all ' 'training runs.') group.add_argument('--log-interval', type=int, default=100, help='Report loss and timing interval.') group.add_argument('--exit-interval', type=int, default=None, help='Exit the program after the iteration is divisible ' 'by this value.') group.add_argument('--tensorboard-dir', type=str, default=None, help='Write TensorBoard logs to this directory.') group.add_argument('--scaled-upper-triang-masked-softmax-fusion', action='store_true', help='Enable fusion of query_key_value_scaling ' 'time (upper diagonal) masking, softmax.') group.add_argument('--bias-gelu-fusion', action='store_true', help='Enable bias and gelu fusion.') group.add_argument('--bias-dropout-fusion', action='store_true', help='Enable bias and dropout fusion.') return parser def _add_initialization_args(parser): group = parser.add_argument_group(title='initialization') group.add_argument('--seed', type=int, default=1234, help='Random seed used for python, numpy, ' 'pytorch, and cuda.') group.add_argument('--init-method-std', type=float, default=0.02, help='Standard deviation of the zero mean normal ' 'distribution used for weight initialization.') return parser def _add_learning_rate_args(parser): group = parser.add_argument_group(title='learning rate') group.add_argument('--lr', type=float, default=None, help='Initial learning rate. Depending on decay style ' 'and initial warmup, the learing rate at each ' 'iteration would be different.') group.add_argument('--lr-decay-style', type=str, default='linear', choices=['constant', 'linear', 'cosine', 'exponential'], help='Learning rate decay function.') group.add_argument('--lr-decay-iters', type=int, default=None, help='number of iterations to decay learning rate over,' ' If None defaults to `--train-iters`') group.add_argument('--min-lr', type=float, default=0.0, help='Minumum value for learning rate. The scheduler' 'clip values below this threshold.') group.add_argument('--warmup', type=float, default=0.01, help='Percentage of total iterations to warmup on ' '(.01 = 1 percent of all training iters).') group.add_argument('--override-lr-scheduler', action='store_true', help='Reset the values of the scheduler (learning rate,' 'warmup iterations, minimum learning rate, maximum ' 'number of iterations, and decay style from input ' 'arguments and ignore values from checkpoints. Note' 'that all the above values will be reset.') group.add_argument('--use-checkpoint-lr-scheduler', action='store_true', help='Use checkpoint to set the values of the scheduler ' '(learning rate, warmup iterations, minimum learning ' 'rate, maximum number of iterations, and decay style ' 'from checkpoint and ignore input arguments.') return parser def _add_checkpointing_args(parser): group = parser.add_argument_group(title='checkpointing') group.add_argument('--save', type=str, default=None, help='Output directory to save checkpoints to.') group.add_argument('--save-interval', type=int, default=None, help='Number of iterations between checkpoint saves.') group.add_argument('--no-save-optim', action='store_true', help='Do not save current optimizer.') group.add_argument('--no-save-rng', action='store_true', help='Do not save current rng state.') group.add_argument('--load', type=str, default=None, help='Directory containing a model checkpoint.') group.add_argument('--no-load-optim', action='store_true', help='Do not load optimizer when loading checkpoint.') group.add_argument('--no-load-rng', action='store_true', help='Do not load rng state when loading checkpoint.') group.add_argument('--finetune', action='store_true', help='Load model for finetuning. Do not load optimizer ' 'or rng state from checkpoint and set iteration to 0. ' 'Assumed when loading a release checkpoint.') return parser def _add_mixed_precision_args(parser): group = parser.add_argument_group(title='mixed precision') group.add_argument('--fp16', action='store_true', help='Run model in fp16 mode.') group.add_argument('--apply-query-key-layer-scaling', action='store_true', help='Scale Q * K^T by 1 / layer-number. If this flag ' 'is set, then it will automatically set ' 'attention-softmax-in-fp32 to true') group.add_argument('--attention-softmax-in-fp32', action='store_true', help='Run attention masking and softmax in fp32.') group.add_argument('--fp32-allreduce', action='store_true', help='All-reduce in fp32') group.add_argument('--hysteresis', type=int, default=2, help='hysteresis for dynamic loss scaling') group.add_argument('--loss-scale', type=float, default=None, help='Static loss scaling, positive power of 2 ' 'values can improve fp16 convergence. If None, dynamic' 'loss scaling is used.') group.add_argument('--loss-scale-window', type=float, default=1000, help='Window over which to raise/lower dynamic scale.') group.add_argument('--min-scale', type=float, default=1, help='Minimum loss scale for dynamic loss scale.') group.add_argument('--fp16-lm-cross-entropy', action='store_true', help='Move the cross entropy unreduced loss calculation' 'for lm head to fp16.') return parser def _add_distributed_args(parser): group = parser.add_argument_group(title='mixed precision') group.add_argument('--model-parallel-size', type=int, default=1, help='Size of the model parallel.') group.add_argument('--distributed-backend', default='nccl', choices=['nccl', 'gloo'], help='Which backend to use for distributed training.') group.add_argument('--DDP-impl', default='local', choices=['local', 'torch'], help='which DistributedDataParallel implementation ' 'to use.') group.add_argument('--local_rank', type=int, default=None, help='local rank passed from distributed launcher.') group.add_argument('--lazy-mpu-init', type=bool, required=False, help='If set to True, initialize_megatron() skips DDP initialization' ' and returns function to complete it instead.' 'Also turns on --use-cpu-initialization flag.' 'This is for external DDP manager.' ) group.add_argument('--use-cpu-initialization', action='store_true', help='If set, affine parallel weights initialization uses CPU' ) return parser def _add_validation_args(parser): group = parser.add_argument_group(title='validation') group.add_argument('--eval-iters', type=int, default=100, help='Number of iterations to run for evaluation' 'validation/test for.') group.add_argument('--eval-interval', type=int, default=1000, help='Interval between running evaluation on ' 'validation set.') return parser def _add_data_args(parser): group = parser.add_argument_group(title='data and dataloader') group.add_argument('--data-path', type=str, default=None, help='Path to combined dataset to split.') group.add_argument('--split', type=str, default='969, 30, 1', help='Comma-separated list of proportions for training,' ' validation, and test split. For example the split ' '`90,5,5` will use 90% of data for training, 5% for ' 'validation and 5% for test.') group.add_argument('--vocab-file', type=str, default=None, help='Path to the vocab file.') group.add_argument('--merge-file', type=str, default=None, help='Path to the BPE merge file.') group.add_argument('--seq-length', type=int, default=None, help="Maximum sequence length to process.") group.add_argument('--mask-prob', type=float, default=0.15, help='Probability of replacing a token with mask.') group.add_argument('--short-seq-prob', type=float, default=0.1, help='Probability of producing a short sequence.') group.add_argument('--mmap-warmup', action='store_true', help='Warm up mmap files.') group.add_argument('--num-workers', type=int, default=2, help="Dataloader number of workers.") group.add_argument('--tokenizer-type', type=str, default=None, choices=['BertWordPieceLowerCase', 'BertWordPieceCase', 'GPT2BPETokenizer'], help='What type of tokenizer to use.') group.add_argument('--data-impl', type=str, default='infer', choices=['lazy', 'cached', 'mmap', 'infer'], help='Implementation of indexed datasets.') group.add_argument('--reset-position-ids', action='store_true', help='Reset posistion ids after end-of-document token.') group.add_argument('--reset-attention-mask', action='store_true', help='Reset self attention maske after ' 'end-of-document token.') group.add_argument('--eod-mask-loss', action='store_true', help='Mask loss for the end of document tokens.') return parser def _add_autoresume_args(parser): group = parser.add_argument_group(title='autoresume') group.add_argument('--adlr-autoresume', action='store_true', help='Enable autoresume on adlr cluster.') group.add_argument('--adlr-autoresume-interval', type=int, default=1000, help='Intervals over which check for autoresume' 'termination signal') return parser def _add_realm_args(parser): group = parser.add_argument_group(title='realm') # network size group.add_argument('--ict-head-size', type=int, default=None, help='Size of block embeddings to be used in ICT and REALM (paper default: 128)') # checkpointing group.add_argument('--ict-load', type=str, default=None, help='Directory containing an ICTBertModel checkpoint') group.add_argument('--bert-load', type=str, default=None, help='Directory containing an BertModel checkpoint (needed to start ICT and REALM)') # data group.add_argument('--titles-data-path', type=str, default=None, help='Path to titles dataset used for ICT') group.add_argument('--query-in-block-prob', type=float, default=0.1, help='Probability of keeping query in block for ICT dataset') group.add_argument('--use-one-sent-docs', action='store_true', help='Whether to use one sentence documents in ICT') # training group.add_argument('--report-topk-accuracies', nargs='+', default=[], help="Which top-k accuracies to report (e.g. '1 5 20')") # faiss index group.add_argument('--faiss-use-gpu', action='store_true', help='Whether create the FaissMIPSIndex on GPU') group.add_argument('--block-data-path', type=str, default=None, help='Where to save/load BlockData to/from') # indexer group.add_argument('--indexer-batch-size', type=int, default=128, help='How large of batches to use when doing indexing jobs') group.add_argument('--indexer-log-interval', type=int, default=1000, help='After how many batches should the indexer report progress') return parser pangu/megatron/bpe_4w_pcl/000077500000000000000000000000001430334424000160175ustar00rootroot00000000000000pangu/megatron/bpe_4w_pcl/vocab.model000066400000000000000000032661211430334424000201460ustar00rootroot00000000000000     ▃ ▂           ▁,0 ▁的H0S ▁。Om ▁~ ▁、X ▁了 ▁是YR ▁:c ▁在oj ▁”q ▁“t ▁我|y ▁和o ▁有l ▁也I ▁不´ ▁他5 ▁你I ▁?; ▁! ▁就k& ▁) ▁(= ▁都] ▁.[ ▁-c ▁人O ▁这] ▁中*o ▁上 ▁为 ▁对/W ▁她A ▁等 ▁... ▁与:D  ▁一个3 ▁说 ▁;3 ▁年] ▁要b ▁3X ▁到̸ ▁着h ▁将G ▁一 ▁大  ▁我们G ▁会)!  ▁自己t ▁而 ▁那3 ▁月c ▁或  ▁可以 ▁被i^  ▁没有 ▁还8 ▁多 ▁之 ▁好 ▁】I 子f ▁【+ ▁地2 ▁让 ▁很 人4. ▁后P ▁1` ▁小H ▁并 ▁下 s ▁以 ▁去- ▁但? ▁来[ ▁《z] ▁》s ▁可x ▁又 ▁/) ▁日Қ ▁从 ▁看 ▁能  ▁中国y ▁时t  ▁什么 ▁2W  ▁他们oF ▁新H ▁把b ▁做&l  ▁这个 ▁更  ▁就是X  2;g ▁及  ▁工作 ▁给  ▁公司HE ▁您cI ▁道6a 性 ▁最 ▁三  ▁产品9!  ▁已经r ▁用 ▁5 ▁高 ▁想S ▁4( ▁个  ▁知道  ▁服务~  ▁发展 ▁快|Z  ▁进行 ▁再 ▁6ǚ 者N  ▁企业 ▁得@+ ▁于F  ▁不是 ▁出  ▁如果jV  ▁使用y ▁所R ▁却bF 出o  ▁问题S  ▁现在U ▁其#b ▁前s  3w ▁里$  ▁提供, ▁过Q 于~ ▁10t 到 ▁本}  ▁这样v ▁没 ▁吧  ▁时间7 ▁·3  ▁还是 ▁8 是  ▁时候  ▁外观?  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جلالهجمجمحجمىجميجىجيححجحجيحمحمىحميحىحيخخجخحخمخىخيدذذٰررسولرٰریالزسسجسجحسجىسحسحجسخسخىسخيسرسمسمجسمحسممسهسىسيششجشجيشحشحمشحيشخشرشمشمخشممشهشىشيصصحصححصحيصخصرصلعمصلىصلى الله عليه 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-10.1681 营 -10.1682 ▁深刻 -10.1682 ▁啥 -10.1683 ▁信心 -10.1686 ▁夹 -10.1686 ▁咬 -10.17 创 -10.1702 ▁简 -10.1703 ▁轮 -10.1703 ▁货币 -10.1704 ▁地下 -10.1706 ▁坚定 -10.1707 众 -10.1708 ▁Sm -10.171 ▁一大 -10.1711 轩 -10.1713 冰 -10.1714 ▁一人 -10.1717 ▁观 -10.1717 ▁账号 -10.1719 裤 -10.1721 ▁经理 -10.1721 ▁逐步 -10.1727 ▁舒服 -10.1731 ▁一套 -10.1732 ▁领先 -10.1733 ▁差不多 -10.1733 ▁必然 -10.1735 ▁亲自 -10.1737 槸 -10.1746 冷 -10.175 备 -10.1757 ▁机器人 -10.1757 ▁一时 -10.1761 潮 -10.1783 ▁灯 -10.1785 ▁画面 -10.1788 ▁引发 -10.1788 ▁冲击 -10.179 ▁采访 -10.1792 ▁秘密 -10.1792 ▁通讯 -10.1794 ▁熟 -10.1794 p -10.1795 ▁不锈钢 -10.1795 ▁荣 -10.1807 ▁大大 -10.1811 ▁名称 -10.1814 ▁四川 -10.1815 ▁地球 -10.1826 ▁头发 -10.1827 ▁营业 -10.1827 罪 -10.1829 ▁截至 -10.183 ▁尺寸 -10.1831 ▁隐 -10.1832 ▁法院 -10.1837 ▁歌 -10.1838 ▁如下 -10.1847 ▁两位 -10.1855 ▁商务 -10.1859 ▁股东 -10.186 ▁黑暗 -10.1864 ▁厂商 -10.1865 ▁泰 -10.1868 ▁挡 -10.1874 ▁CO -10.1882 ▁利润 -10.1889 乡 -10.1893 ▁袁 -10.1895 ▁围绕 -10.1895 h -10.1895 ▁医 -10.1896 ▁一项 -10.1898 ▁不由 -10.1902 er -10.1902 ▁乌 -10.1904 ▁丹 -10.1909 ▁公子 -10.191 ▁总体 -10.1914 ▁市民 -10.1915 持 -10.1917 材料 -10.1919 ▁专利 -10.1921 ▁身影 -10.1921 ▁卫 -10.1926 ▁描述 -10.1927 ▁扑 -10.1927 ▁Er -10.1928 直 -10.193 方面 -10.1932 ▁亿美元 -10.1937 ▁留学 -10.1937 ▁停车 -10.1939 ▁内心 -10.1945 ▁一生 -10.1949 ▁替 -10.1953 ▁对外 -10.1957 ▁怀疑 -10.1958 ▁不然 -10.1958 x -10.196 唇 -10.196 棒 -10.1965 ▁护 -10.197 ▁广场 -10.1973 京 -10.1981 ▁殿 -10.1983 ▁胜 -10.1987 ▁遭 -10.199 沙 -10.1991 绝 -10.1996 泰 -10.2 烈 -10.2001 ▁便利 -10.2002 ▁這 -10.2008 ▁再说 -10.2008 ▁冯 -10.2014 ▁浓 -10.2018 像 -10.2021 末 -10.2022 ▁厨房 -10.2024 ▁公益 -10.2025 Y -10.2033 项目 -10.2033 ▁虚 -10.2033 ▁权益 -10.2038 ▁列 -10.2039 ▁环节 -10.204 环境 -10.2041 ▁链接 -10.2043 ▁QQ -10.2044 ▁考核 -10.2047 ▁学历 -10.2047 ▁私服 -10.205 ▁呆 -10.2052 ▁躲 -10.2053 ▁电商 -10.2057 ▁线上 -10.2061 ▁穿着 -10.2067 刚 -10.2069 ▁爆发 -10.2074 ▁铝 -10.2079 ▁监控 -10.208 凤 -10.2083 ▁没事 -10.2086 ▁伸手 -10.2087 ▁电力 -10.2092 ▁好奇 -10.2094 ▁论 -10.2096 ▁照顾 -10.2097 ▁一口气 -10.2097 险 -10.2101 ▁好看 -10.2101 ▁爬 -10.2104 厚 -10.2113 ▁反正 -10.2113 ▁尊重 -10.2115 ▁居 -10.2119 段时间 -10.2122 ▁驱动 -10.2124 金属 -10.2128 ▁暴 -10.2129 ▁室 -10.2129 叔 -10.2132 ▁公园 -10.2132 ▁自信 -10.2142 ▁兵 -10.2142 ▁凤 -10.2144 ▁霍 -10.2148 ▁一身 -10.2149 ▁业绩 -10.2151 ▁服装 -10.2153 藏 -10.2154 ▁狼 -10.2155 ▁高中 -10.2161 ▁of -10.2165 ▁每日 -10.2166 ▁三人 -10.2166 ▁一面 -10.2169 ▁配件 -10.2171 ▁原创 -10.2173 ▁便宜 -10.2181 露 -10.2183 ▁各自 -10.2184 ▁Pr -10.2186 罩 -10.2187 ▁优先 -10.2187 ▁标志 -10.2193 烟 -10.2195 ▁深化 -10.2201 杆 -10.2204 ▁威 -10.2204 ▁帝 -10.2206 ▁骂 -10.2207 ▁一半 -10.2207 ▁混合 -10.2208 ▁铺 -10.221 ▁上下 -10.2211 ▁略 -10.2211 屾 -10.2213 ▁隐私 -10.2215 患 -10.2217 ▁移 -10.2218 ▁静 -10.2219 ▁车型 -10.222 ▁信用 -10.2229 ▁适当 -10.2234 ▁动态 -10.2239 ▁隔 -10.224 ▁非法 -10.224 ▁尝试 -10.2242 ▁弱 -10.2243 ▁浣 -10.2245 ▁迎来 -10.2249 ▁应对 -10.2251 ▁维 -10.2255 ▁喂 -10.2257 ▁添加 -10.226 ▁标题 -10.2268 ▁小小 -10.2272 ▁紧紧 -10.2274 十 -10.229 ▁废 -10.2294 ▁瞧 -10.23 ▁认可 -10.2302 ▁辅助 -10.2304 ▁身份证 -10.2313 ▁分分 -10.2314 ▁累计 -10.2314 季度 -10.2318 ▁转移 -10.2322 ▁手上 -10.2326 ▁范 -10.2327 郎 -10.2328 ▁账户 -10.2334 涛 -10.2336 裂 -10.2339 ▁话题 -10.234 补 -10.234 ▁思维 -10.2341 ▁董事长 -10.2344 ▁青春 -10.2345 ▁竹 -10.2363 ▁信任 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-12.3931 ▁自考 -12.3933 枕 -12.3933 dd -12.3933 咳 -12.3934 方便 -12.3936 ▁姹 -12.3936 訊 -12.3936 hu -12.3937 ▁驻地 -12.3937 ▁CBA -12.3938 ▁新风 -12.394 呮 -12.394 ▁惰 -12.394 ure -12.3941 ▁希尔 -12.3942 ▁升华 -12.3942 ▁烤肉 -12.3943 ╂ -12.3943 ▁帷幕 -12.3943 ▁赞誉 -12.3945 肿瘤 -12.3946 喳 -12.3946 ▁武学 -12.3948 ▁追加 -12.395 ▁比尔 -12.395 緢 -12.3951 巨 -12.3952 ▁第十七 -12.3953 ans -12.3956 ▁皂 -12.3956 ▁洋溢 -12.3957 St -12.3957 ▁文凭 -12.396 ▁大字 -12.3962 ▁从根本 -12.3966 ▁颂 -12.3967 ▁同名 -12.3967 ▁贫血 -12.3968 難 -12.3969 ▁务工 -12.3969 ▁CA -12.397 ▁衡阳 -12.3972 net -12.3972 cr -12.3973 淨 -12.3974 ▁动画片 -12.3977 ▁吸毒 -12.3977 ▁专家学 -12.3977 ▁感官 -12.3978 ▁230 -12.3978 ▁特工 -12.3978 ▁更长 -12.3978 ▁恩怨 -12.3979 ▁更衣 -12.398 ▁兴业 -12.398 ▁防滑 -12.3981 ▁扩充 -12.3982 ▁最能 -12.3982 盯 -12.3983 橙 -12.3983 ▁短路 -12.3984 ▁点火 -12.3984 ▁儒家 -12.3984 梭 -12.3985 tu -12.3989 ▁Linux -12.3989 ▁品德 -12.399 巅 -12.3991 sy -12.3992 ▁钦 -12.3992 ▁不差 -12.3992 ≤ -12.3992 ▁柔声 -12.3993 恃 -12.3993 一亮 -12.3994 ▁狂热 -12.3999 ▁参议 -12.4 ▁化石 -12.4001 鑸 -12.4001 烬 -12.4002 ▁买来 -12.4004 ▁白金 -12.4006 ▁悉尼 -12.4006 眼圈 -12.4006 谎 -12.4006 ▁全国人大常委会 -12.4007 ▁农作 -12.4008 囱 -12.4009 ▁結構 -12.4009 ▁寒意 -12.401 ▁末日 -12.401 ▁老老 -12.4012 ▁乐视 -12.4013 ▁管事 -12.4014 ▁娑 -12.4016 ▁原生 -12.4016 ▁争执 -12.4016 ▁登出 -12.4017 ▁瞳 -12.4017 IT -12.4018 祭 -12.4018 ▁安阳 -12.4019 ▁雪地 -12.4019 ▁纳兰 -12.4019 煦 -12.402 1.0 -12.4022 ▁TA -12.4022 bi -12.4022 RI -12.4023 ▁朋 -12.4024 ▁破门 -12.4024 CT -12.4025 UT -12.4026 ╃ -12.4027 ▁盘算 -12.4028 ▁直营 -12.403 ▁车友 -12.4032 ▁麻痹 -12.4032 ▁白骨 -12.4034 ▁MO -12.4034 淇 -12.4035 ▁山中 -12.4036 ▁离合 -12.4036 ▁皮鞋 -12.4037 ▁CH -12.4039 ▁裘 -12.404 ▁负有 -12.404 刚落 -12.4042 腐败 -12.4044 怔 -12.4044 聞 -12.4045 ▁波斯 -12.4049 ▁iPad -12.4049 ▁Al -12.4049 进步 -12.4049 ▁加成 -12.405 ▁软硬 -12.405 ▁物理学 -12.405 ▁知己 -12.4052 ▁蓝光 -12.4052 ▁音樂 -12.4053 ▁报关 -12.4053 〗 -12.4053 钮 -12.4054 ▁推崇 -12.4056 ▁吃苦耐劳 -12.4056 ▁书架 -12.4057 ▁心智 -12.4058 ▁經過 -12.4058 ▁几只 -12.4058 ▁定下 -12.406 ▁群里 -12.4061 ▁有余 -12.4061 ▁美甲 -12.4066 ▁拖鞋 -12.4066 ▁公车 -12.4067 ▁筆 -12.4067 武帝 -12.4068 ▁破绽 -12.4069 ▁谦虚 -12.407 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-12.5198 ▁用尽 -12.5199 南京市 -12.5199 ▁Cookie -12.5202 ▁靓丽 -12.5202 ▁狠抓 -12.5202 ▁网约 -12.5203 ▁叱 -12.5204 ▁年版 -12.5204 进行 -12.5205 ▁芊 -12.5205 策划 -12.5208 ▁相隔 -12.521 ▁血统 -12.521 ▁浦东 -12.521 慶 -12.5211 釋 -12.5212 涚 -12.5212 ▁电光 -12.5213 ▁各路 -12.5215 纳斯 -12.5216 ▁联军 -12.5217 不掉 -12.5218 ▁贯通 -12.5218 ▁忐忑 -12.5218 ▁飲 -12.522 ▁侵袭 -12.522 ▁炕 -12.522 ▁接听 -12.5221 ▁属地 -12.5221 ▁燥 -12.5222 ▁冲到 -12.5223 粤 -12.5223 ▁201 -12.5223 ▁千秋 -12.5224 ▁嫣 -12.5225 多时 -12.5226 ▁剥离 -12.5227 ▁乳液 -12.5228 江湖 -12.5232 ▁闪避 -12.5233 ▁轻视 -12.5233 ▁初恋 -12.5233 ▁科技部 -12.5234 ▁首轮 -12.5236 ▁瞩目 -12.5237 ▁竞彩 -12.5239 ▁版主 -12.524 ▁PO -12.5241 ▁找人 -12.5243 ▁沃尔沃 -12.5244 ▁汤姆 -12.5244 ▁献给 -12.5245 ▁計 -12.5245 ▁PET -12.5246 ▁IBM -12.5247 ▁紧身 -12.5249 戮 -12.5249 ▁卦 -12.525 ▁惡 -12.5252 ▁石门 -12.5253 ▁乐山 -12.5254 ▁布衣 -12.5254 ▁多想 -12.5255 ▁余下 -12.5255 ▁魔幻 -12.5256 拉扯 -12.5258 饰品 -12.5258 ▁古巴 -12.5261 ▁排骨 -12.5264 ▁瘫痪 -12.5264 ▁多彩 -12.5265 ▁殴打 -12.5266 ▁凹凸 -12.5266 晓明 -12.5267 ▁计数 -12.5267 理解 -12.5267 ▁意見 -12.5267 女儿 -12.5268 ▁亢 -12.527 ▁捡起 -12.527 峥 -12.5271 ▁水肿 -12.5273 ▁同城 -12.5273 而上 -12.5274 ▁推断 -12.5274 ▁嬴 -12.5274 ▁谨防 -12.5275 ▁六七 -12.5275 ▁夙 -12.5276 肽 -12.5276 ▁遗留 -12.5276 ▁底薪 -12.5277 ≥ -12.5277 ▁揩 -12.5279 ▁SM -12.5281 ▁淡化 -12.5282 ID -12.5283 ▁PA -12.5284 ▁反腐 -12.5284 ▁妇产 -12.5284 肋 -12.5286 ▁武当 -12.5287 ▁榆 -12.529 花园 -12.529 ▁内核 -12.529 ▁考评 -12.5291 良好 -12.5291 ▁并列 -12.5291 .95 -12.5292 ▁跟我 -12.5294 針 -12.5295 ▁极乐 -12.5298 ▁發布 -12.5298 ▁三步 -12.53 前进 -12.53 ▁霹雳 -12.53 ▁海淀 -12.53 巫 -12.53 ▁去向 -12.5301 燒 -12.5302 ▁波及 -12.5303 ▁拉丁 -12.5305 ▁虚幻 -12.5305 ▁亚军 -12.5305 ▁肇庆 -12.5306 思路 -12.5309 ▁私营 -12.5311 ▁風險 -12.5311 婕 -12.5312 寄 -12.5313 ▁估价 -12.5315 旀 -12.5315 ▁房中 -12.5315 ▁单手 -12.5315 VE -12.5315 ▁劳累 -12.5316 驗 -12.5316 ▁灵性 -12.5316 ▁中所 -12.5317 ▁卫东 -12.5317 怯 -12.5317 ▁杨浩 -12.5318 ▁辦理 -12.5318 联播 -12.5318 ▁才刚 -12.5318 ▁学徒 -12.532 濋 -12.532 ▁还敢 -12.5321 ▁嚴 -12.5321 ▁史蒂 -12.5322 ▁从小到 -12.5322 ▁处份 -12.5322 ▁吸尘 -12.5323 蒸 -12.5323 ▁覺得 -12.5327 ▁苍蝇 -12.5327 枯 -12.5329 毒药 -12.5329 ▁電視 -12.533 ▁瞿 -12.533 ▁相等 -12.5331 ▁史诗 -12.5333 ▁189 -12.5335 ▁旧金山 -12.5337 ▁可得 -12.534 改造 -12.5342 ▁流下 -12.5343 ▁绒 -12.5344 ▁商报 -12.5346 ▁今朝 -12.5346 ▁七大 -12.5348 ▁2030 -12.5348 ▁疲倦 -12.5349 ▁震天 -12.5349 ▁盔 -12.5351 护航 -12.5353 ▁虔诚 -12.5353 ▁惊吓 -12.5354 ▁中国银行 -12.5355 ▁Ar -12.5357 ▁萧晨 -12.5357 ▁怎麼 -12.5358 ▁波浪 -12.5359 鏡 -12.5359 魰 -12.5359 ▁自查 -12.5361 龙女 -12.5363 ▁飞奔 -12.5363 ▁研發 -12.5364 浸 -12.5365 ▁造船 -12.5366 揭 -12.5366 ▁单调 -12.5368 ▁咪 -12.5369 ▁螃蟹 -12.537 ▁仇人 -12.5371 汶 -12.5372 ▁人要 -12.5372 阅读 -12.5372 ▁短片 -12.5373 ▁儿科 -12.5375 ▁腮 -12.5376 冤 -12.5377 ▁SH -12.538 ▁冰川 -12.538 量化 -12.538 ▁白皮 -12.538 米尔 -12.5381 庤 -12.5381 ▁液位 -12.5381 歆 -12.5384 ▁钾 -12.5384 ▁舍利 -12.5384 ▁空心 -12.5384 ▁港澳台 -12.5385 岋 -12.5386 ▁立委 -12.5386 ▁梭 -12.5387 ▁软软 -12.5388 茅 -12.5389 ▁应邀 -12.539 120 -12.539 ▁李明 -12.5392 ▁驚 -12.5392 ▁土工 -12.5393 ▁宣誓 -12.5394 ▁一文 -12.5395 时许 -12.5398 ▁挂断 -12.5398 ▁听证 -12.5399 丫 -12.54 ▁棱 -12.5401 ▁2.8 -12.5401 ▁圆圆 -12.5403 ▁尼斯 -12.5403 ▁生平 -12.5404 配合 -12.5405 ▁书信 -12.5407 ▁一个劲 -12.5408 ▁超碰 -12.5409 ▁军区 -12.5409 コ -12.5411 ▁居士 -12.5411 ▁热处理 -12.5412 ▁刪除 -12.5413 ▁天界 -12.5414 530 -12.5415 ▁捷克 -12.5416 ide -12.5416 纳河 -12.5417 ▁通州 -12.5418 崄 -12.5419 ▁从头到 -12.542 ▁滞后 -12.5421 ▁悲痛 -12.5421 ▁低低 -12.5422 保障 -12.5422 ▁进水 -12.5423 ▁元神 -12.5424 短信 -12.5426 ▁洽 -12.5426 ▁由衷 -12.5427 饺 -12.5429 ▁弱小 -12.543 ▁慵 -12.5432 ▁切身 -12.5434 本科 -12.5434 ▁西域 -12.5434 ▁坝 -12.5434 ▁增至 -12.5435 ▁懦 -12.5436 ▁罗汉 -12.5436 320 -12.5437 ▁走红 -12.5437 ▁欒 -12.5438 ▁写信 -12.5439 ▁绊 -12.544 ↑ -12.5441 ▁若能 -12.5442 騗 -12.5443 ▁坦白 -12.5444 ▁衙 -12.5445 ▁木马 -12.5446 ▁诚心 -12.5446 ▁启蒙 -12.5447 ▁马蹄 -12.5447 ▁杜承 -12.5448 ▁环保型 -12.5448 ▁盾牌 -12.5449 ▁五万 -12.545 墩 -12.5451 ▁青蛙 -12.5451 铏 -12.5452 ▁接班 -12.5455 溂 -12.5455 ▁瞬息 -12.5456 ▁卷帘 -12.5456 ▁天佑 -12.5457 ▁大清 -12.5458 ▁花儿 -12.5459 ▁退后 -12.5459 ▁咬着 -12.546 ste -12.5462 ▁满身 -12.5462 ▁奉行 -12.5462 ▁同居 -12.5462 ▁喝醉 -12.5463 ▁藥 -12.5464 ence -12.5465 蜒 -12.5466 ▁檢 -12.5466 ▁殿堂 -12.5467 ▁四点 -12.5467 ▁盐酸 -12.5467 ▁保税 -12.5468 ▁轻巧 -12.5469 ▁民政 -12.547 VA -12.5471 ▁洗发 -12.5472 后果 -12.5473 ▁概不 -12.5473 ▁或多 -12.5475 ▁海滨 -12.5476 ▁杉 -12.5477 ▁专车 -12.5482 ▁声势 -12.5482 ▁臉 -12.5483 ▁阿森纳 -12.5483 ▁车内 -12.5483 烷 -12.5483 ▁討論 -12.5484 ▁红颜 -12.5484 ▁丈 -12.5485 56 -12.5487 ▁天雷 -12.5487 ▁环卫 -12.5487 ▁电量 -12.5488 ▁SD -12.5489 ▁岚 -12.5489 ▁音量 -12.549 産 -12.5491 my -12.5491 ▁挑逗 -12.5491 ▁幻觉 -12.5492 ▁卫士 -12.5492 ▁裂纹 -12.5494 ▁农业部 -12.5495 屡 -12.5495 ▁临走 -12.5498 ▁la -12.5498 ▁远超 -12.5499 ▁贬 -12.5499 ▁重叠 -12.5501 霉素 -12.5501 ▁清冷 -12.5501 ▁零售价 -12.5501 ▁欣然 -12.5501 ▁183 -12.5501 ▁车床 -12.5502 ▁中要 -12.5503 按 -12.5503 ▁讀 -12.5505 ▁救出 -12.5506 ▁显露 -12.5508 ▁兴旺 -12.5508 ▁之子 -12.5509 ▁德育 -12.551 ▁通通 -12.551 ▁快到 -12.5511 ▁大马 -12.5512 ▁皖 -12.5514 ▁玩得 -12.5515 塑料 -12.5515 ▁催生 -12.5516 ▁快车 -12.5516 ▁玩儿 -12.5516 ▁建构 -12.5517 ▁几道 -12.5518 ▁林木 -12.5518 ▁NT -12.5518 同于 -12.5519 ns -12.5521 ▁收银 -12.5521 ▁刺痛 -12.5521 ▁补习 -12.5521 ▁特制 -12.5521 ▁煲 -12.5523 ▁护送 -12.5523 ▁变相 -12.5523 维斯 -12.5525 ▁禁毒 -12.5525 ▁担负 -12.5525 冒 -12.5525 ▁含税 -12.5525 ▁刻画 -12.5526 ▁深感 -12.5526 ▁新郎 -12.553 ▁口子 -12.553 ▁摆摆 -12.5531 ▁软化 -12.5532 ▁亲热 -12.5533 ▁确有 -12.5535 ran -12.5536 ▁泰州 -12.5537 84.23 -12.5537 鐵 -12.5538 松松 -12.5538 ▁席位 -12.5538 iz -12.5538 ▁危急 -12.5538 ▁呕 -12.5538 ▁壓 -12.5539 自治县 -12.5539 芊 -12.554 ▁陰 -12.5541 ▁皇城 -12.5542 ▁參考 -12.5544 ▁淫水 -12.5544 ▁尘土 -12.5544 ▁艳丽 -12.5544 茸 -12.5545 ▁平地 -12.5545 仪表 -12.5545 ▁表妹 -12.5545 ▁爱国主义 -12.5546 ▁选秀 -12.5546 ▁十来 -12.5546 ▁法语 -12.5547 ▁漠 -12.5548 mi -12.5548 ▁国美 -12.5549 ▁迷迷 -12.5551 ▁发泡 -12.5552 ▁石榴 -12.5552 日游 -12.5553 咎 -12.5553 ▁冊 -12.5553 晔 -12.5554 ▁除湿 -12.5554 ▁英勇 -12.5555 ▁安全事故 -12.5555 ▁多头 -12.5556 ▁小姨 -12.5557 ▁16774 -12.5558 ▁收下 -12.5559 ▁坍 -12.5559 黨 -12.556 ▁慷慨 -12.556 ▁提款 -12.556 ▁聊城 -12.5561 91 -12.5562 ▁不分 -12.5562 ▁集市 -12.5563 ▁过长 -12.5563 ▁天灾 -12.5567 ▁朴素 -12.5568 天盖 -12.5568 1.5 -12.5568 蚕 -12.5569 ▁时日 -12.5569 ▁厦门市 -12.5569 囩 -12.5571 博物馆 -12.5574 ▁ICP -12.5576 ▁磁盘 -12.5576 ▁匠心 -12.5577 ▁退伍 -12.5577 ▁战火 -12.5578 ▁纯真 -12.5582 ▁甜品 -12.5582 ▁碍 -12.5583 ▁存货 -12.5583 ▁来宾 -12.5584 IL -12.5584 ▁林立 -12.5585 ▁沼泽 -12.5587 ▁流云 -12.5588 舉 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▁撑起 -12.6467 ▁发源 -12.6467 ▁等离子 -12.6468 ▁榫 -12.6469 ▁前排 -12.647 ▁校准 -12.647 ▁日历 -12.647 ▁改變 -12.6471 羹 -12.6471 ▁通体 -12.6472 ▁黑色素 -12.6472 way -12.6473 ▁歌剧 -12.6473 ▁锋芒 -12.6473 适应 -12.6474 ▁非标 -12.6474 ▁彈 -12.6474 貴 -12.6475 ▁低保 -12.6476 ▁化疗 -12.6477 ▁拉链 -12.6477 叙 -12.6478 ▁vi -12.648 微信 -12.648 ▁per -12.6481 ▁婚庆 -12.6482 ▁王林 -12.6482 强大 -12.6482 ▁狀況 -12.6482 ▁足彩 -12.6483 辊 -12.6484 ▁意气 -12.6484 瞰 -12.6485 願 -12.6485 挟 -12.6486 ▁太低 -12.6488 ▁拜师 -12.6488 ▁絕 -12.6488 ▁因地制 -12.6488 ast -12.649 ▁自然科学 -12.6491 ▁提质 -12.6492 少侠 -12.6493 ▁地雷 -12.6493 ▁秀丽 -12.6494 ▁恶毒 -12.6494 ▁惦记 -12.6495 ▁褒 -12.6496 ▁受灾 -12.6497 ▁称得 -12.6498 ▁哪一 -12.6499 ▁大洋 -12.6499 ▁义工 -12.6499 評 -12.6499 ▁正气 -12.65 会员 -12.65 ▁辛辣 -12.65 ▁TD -12.6501 萃 -12.6501 ▁如风 -12.6502 ▁胭脂 -12.6503 ▁---- -12.6504 ▁心力 -12.6506 ▁京剧 -12.6506 正恩 -12.6507 ▁热忱 -12.6507 ▁令狐冲 -12.651 朔 -12.6512 令营 -12.6512 ▁求救 -12.6513 ▁裁量 -12.6514 ▁申博 -12.6514 沖 -12.6515 ▁觉察 -12.6515 ▁结交 -12.6516 ▁暂且 -12.6517 ▁蝉 -12.6518 ▁冒犯 -12.6518 ▁地底 -12.6518 祖国 -12.6519 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▁span -12.8274 ▁学着 -12.8274 ▁夏利 -12.8274 ▁魔气 -12.8274 ▁受让 -12.8275 ▁袁绍 -12.8275 ▁很早 -12.8275 拆 -12.8276 ▁结石 -12.8277 ▁ч -12.8278 MW -12.8279 ▁部門 -12.8279 钼 -12.828 ▁领航 -12.8281 ▁中风 -12.8281 ▁出生地 -12.8282 ▁敦促 -12.8282 ▁露营 -12.8282 ▁290 -12.8283 ▁隔天 -12.8283 视镜 -12.8284 ▁不绝 -12.8285 酰胺 -12.8285 ▁滑稽 -12.8286 ▁变身 -12.8286 ▁黑板 -12.8287 系統 -12.8289 ▁轻叹 -12.8289 ▁阴险 -12.8289 丽莎 -12.8289 禧 -12.829 ▁避难 -12.829 ▁木瓜 -12.829 ▁醒过 -12.8291 ▁容错 -12.8291 ▁輪 -12.8291 当当 -12.8292 解释 -12.8294 7.4 -12.8295 ▁伯格 -12.8295 ▁摇篮 -12.8296 ▁下级 -12.8296 ▁肖像 -12.8297 ▁145 -12.8297 out -12.8298 賞 -12.8299 ▁同质 -12.8299 ▁韶关 -12.8299 uc -12.8301 ▁鼻梁 -12.8303 ▁汶 -12.8303 ▁械 -12.8304 ▁多米 -12.8304 ▁野战 -12.8304 ▁健脾 -12.8308 ▁遮盖 -12.8308 ▁举个 -12.8308 ▁地市 -12.8309 ▁万美 -12.831 ▁台独 -12.831 eta -12.831 ▁km -12.831 峪 -12.8311 偉 -12.8314 lic -12.8314 ▁一脉 -12.8315 ▁巳 -12.8315 ▁着火 -12.8316 ▁洗护 -12.8318 鍕 -12.8318 ▁Internet -12.832 ▁羽翼 -12.832 und -12.8322 .66 -12.8325 ▁悄无 -12.8326 ▁鲨 -12.8327 ▁Su -12.8327 ▁蛙 -12.8327 ▁雷鸣 -12.8328 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▁台前 -13.1886 ▁階段 -13.1886 梅尔 -13.1887 ▁重合 -13.1887 ▁铃铛 -13.1888 ▁手袋 -13.1888 mar -13.1888 ▁二氧化 -13.1888 ▁地税 -13.189 ▁练兵 -13.189 ▁尸骨 -13.1891 ▁龙潭 -13.1891 ▁逆行 -13.1891 ▁機制 -13.1892 ▁古文 -13.1892 ▁再接再 -13.1892 之徒 -13.1893 4.8 -13.1893 ▁众将 -13.1893 湍 -13.1893 ▁經典 -13.1893 ▁危房 -13.1894 国务院 -13.1895 鲤 -13.1896 ▁春暖花 -13.1896 6.2 -13.1898 ▁成員 -13.1899 忿 -13.19 ▁车用 -13.19 跡 -13.19 ▁训练场 -13.19 ▁施政 -13.1901 490 -13.1901 ▁性病 -13.1902 挨 -13.1902 ▁運行 -13.1902 汞 -13.1903 ▁得不偿失 -13.1906 ▁供图 -13.1906 ▁相间 -13.1907 ▁依着 -13.1907 垰 -13.1908 ▁233 -13.1909 ▁假象 -13.1909 辫 -13.1911 ▁現象 -13.1911 ▁思忖 -13.1912 一凡 -13.1912 ▁摩托罗拉 -13.1912 荟 -13.1913 ▁九寨沟 -13.1915 ▁国际象棋 -13.1915 rp -13.1916 ▁三秒 -13.1918 不錯 -13.1918 ▁Office -13.1918 ▁胎盘 -13.1918 ▁走遍 -13.1919 悯 -13.1922 ▁澹台 -13.1923 ▁內部 -13.1923 如山 -13.1926 突出 -13.1926 ▁结肠 -13.1927 ▁Sun -13.1927 ▁云林 -13.1927 ▁萨克 -13.1927 ▁河畔 -13.1928 风寒 -13.1928 俑 -13.1929 悄悄 -13.1929 ▁尘世 -13.1929 ▁198 -13.1929 场次 -13.1929 ▁压铸 -13.1929 余悸 -13.193 段落 -13.193 掖 -13.193 结局 -13.1931 7.2 -13.1931 ▁献身 -13.1931 ▁条理 -13.1933 晚饭 -13.1934 ▁躲藏 -13.1934 ▁TXT -13.1934 ▁城东 -13.1935 凈 -13.1935 kk -13.1935 ▁农副 -13.1936 .70 -13.1937 ▁颓废 -13.1937 飞燕 -13.1937 ▁吱吱 -13.1939 Net -13.194 彝 -13.1941 ▁漂泊 -13.1942 ▁脖 -13.1942 ▁简简 -13.1943 ▁實性 -13.1946 ▁精兵 -13.1949 ▁生硬 -13.1949 GI -13.195 支队 -13.1951 ▁青丝 -13.1951 ▁延庆 -13.1952 ▁崎岖 -13.1953 ▁花旗 -13.1954 ▁腹胀 -13.1955 ▁爱看 -13.1958 ▁诸法 -13.196 ▁见得 -13.196 ▁装订 -13.196 ▁黑山 -13.1961 ▁取景 -13.1961 ▁科举 -13.1963 ▁達到 -13.1965 悠扬 -13.1965 ▁变大 -13.1965 ▁白内 -13.1966 ▁習慣 -13.1968 盂 -13.1968 425 -13.1968 ▁8.2 -13.1968 ▁收容 -13.1971 ▁吸音 -13.1971 455 -13.1972 大桥 -13.1973 ▁恼羞成 -13.1974 ▁除臭 -13.1975 ▁李佳 -13.1975 技術 -13.1975 920 -13.1975 ▁编导 -13.1976 ▁蝗 -13.1976 ▁国库 -13.1976 ▁城西 -13.1977 Ch -13.1977 榴 -13.1977 ▁漂移 -13.1977 貼 -13.198 ▁先给 -13.198 獨 -13.1981 ≡ -13.1981 ▁乌兰 -13.1981 ▁实况 -13.1982 軟 -13.1982 ▁拳脚 -13.1983 ▁绝情 -13.1984 ▁王八 -13.1984 ▁维稳 -13.1986 ▁有损 -13.1986 ors -13.1988 ▁真實 -13.1988 褂 -13.1988 礼品 -13.1989 850 -13.1989 ▁白龙 -13.199 氨 -13.1991 ▁皓月 -13.1991 ▁磨砂 -13.1991 ▁静坐 -13.1992 ▁龙井 -13.1993 ▁下唇 -13.1993 ▁病害 -13.1993 ▁淮北 -13.1995 ▁斗罗 -13.1997 ▁目不转 -13.1998 ▁一男一 -13.1998 ▁小萝莉 -13.1998 ▁一松 -13.1999 ▁球磨 -13.2 ▁麻花 -13.2 ▁哪吒 -13.2001 孫 -13.2002 宀 -13.2003 ▁損失 -13.2003 ▁除草 -13.2003 颍 -13.2005 ▁司马懿 -13.2005 ▁5500 -13.2005 ▁副驾驶 -13.2006 ▁大四 -13.2009 ▁阴雨 -13.2009 ▁云山 -13.201 ▁蛾 -13.2011 ▁功名 -13.2011 ▁酋长 -13.2011 飚 -13.2012 ▁取件 -13.2013 ▁细嫩 -13.2013 ▁龙腾 -13.2013 条不 -13.2014 生气 -13.2015 家电 -13.2015 ▁抿嘴 -13.2016 ▁白俄罗斯 -13.2017 ▁肉食 -13.2017 ▁改口 -13.2018 ▁南亚 -13.2018 ▁勃然 -13.2018 ▁微红 -13.202 ▁陆陆 -13.202 ▁衰减 -13.202 ▁消防车 -13.202 ▁贾跃亭 -13.2021 ▁米勒 -13.2021 隐私 -13.2022 ▁市容 -13.2023 ▁Mb -13.2024 ▁异口同 -13.2024 ▁苏瑾 -13.2025 ral -13.2026 ▁豆类 -13.2026 ▁婉转 -13.2027 ▁癫 -13.2027 ▁醫師 -13.2028 ▁掌教 -13.2028 ▁仁爱 -13.2029 ▁豪杰 -13.2029 ▁娇媚 -13.2029 ▁一塌糊 -13.203 ▁西证 -13.203 ▁结节 -13.2031 一波 -13.2031 ▁花盆 -13.2031 ▁黑水 -13.2032 ▁叶尘 -13.2032 ▁铁甲 -13.2035 区划 -13.2036 ▁惨淡 -13.2037 ▁依序 -13.2037 ▁稳中 -13.2037 ▁You -13.2037 ▁披肩 -13.2038 ▁显赫 -13.2038 践 -13.204 業者 -13.2041 ▁娄底 -13.2041 ▁随笔 -13.2041 ▁懵懂 -13.2043 万两 -13.2043 ▁制式 -13.2044 ▁狗屁 -13.2045 ▁草酸 -13.2047 ▁电影票 -13.2048 詋 -13.2048 ▁昼 -13.2048 ▁情急 -13.2049 ▁整流 -13.2049 TU -13.205 典型 -13.2051 304 -13.2051 拘 -13.2052 ▁六千 -13.2052 ▁氦 -13.2052 ▁松口 -13.2053 ▁意欲 -13.2054 锂 -13.2055 绞 -13.2057 ▁喜讯 -13.2058 ▁拘束 -13.206 ▁左上 -13.206 ▁聆 -13.2062 幸福 -13.2062 ▁进料 -13.2062 ▁造物 -13.2063 330 -13.2063 ▁擂 -13.2065 ▁声乐 -13.2065 外交部 -13.2067 玩具 -13.2067 ▁楹 -13.2068 ▁掣 -13.2073 ▁Fe -13.2074 ▁顽皮 -13.2075 ▁意犹未 -13.2075 ▁红莲 -13.2075 ▁定睛 -13.2076 ▁蒙蒙 -13.2076 ▁银山 -13.2076 ▁3200 -13.2076 米兰 -13.2076 粟 -13.2076 ▁辨析 -13.2076 訓 -13.2077 ▁時尚 -13.2077 光大 -13.2077 box -13.2078 祐 -13.2078 sf -13.2078 ▁助剂 -13.2079 ▁早产 -13.2079 裱 -13.208 浪费 -13.2082 风发 -13.2082 ▁兴隆 -13.2082 118 -13.2083 唆 -13.2083 ▁鲤 -13.2084 竻 -13.2084 ▁病毒感染 -13.2085 908 -13.2086 ▁储气 -13.2086 ▁成婚 -13.2086 牆 -13.2087 sch -13.2087 525 -13.2088 ▁成瘾 -13.2088 ▁半夏 -13.2088 ▁傲天 -13.2089 ▁传令 -13.2089 ▁难求 -13.209 ous -13.2091 ▁接住 -13.2091 屏幕 -13.2091 扈 -13.2092 ▁1954 -13.2093 ▁捕捞 -13.2093 志刚 -13.2093 ▁二零一 -13.2094 ▁2700 -13.2094 ▁肝胆 -13.2094 ▁非农 -13.2094 ▁一肖中 -13.2094 公室 -13.2096 ▁富民 -13.2097 ▁素颜 -13.2098 ▁瞪眼 -13.2099 ▁诵读 -13.21 益匪浅 -13.21 辘 -13.2101 ▁航站 -13.2103 1.1 -13.2103 ▁葫芦岛 -13.2104 ▁易购 -13.2104 ▁张杰 -13.2104 ▁火电 -13.2105 飛 -13.2105 ▁俘 -13.2106 ▁赠与 -13.2107 ▁伯恩 -13.2108 万计 -13.2109 ▁完好无 -13.211 ▁真够 -13.2112 ▁掘 -13.2113 高考 -13.2113 ▁紫气 -13.2114 ▁印第安 -13.2114 ▁萎 -13.2115 rea -13.2116 反射 -13.2117 ▁交了 -13.2117 ▁富家 -13.2119 699 -13.2119 蔷 -13.2119 ▁宜兴 -13.2119 ▁染发 -13.212 ▁南疆 -13.212 sen -13.212 纷纷 -13.2124 ▁闲话 -13.2124 蒙蒙 -13.2126 氰 -13.2127 ▁山间 -13.2128 ▁覆灭 -13.2128 ▁巴勒斯坦 -13.2129 ▁三叔 -13.2129 235 -13.213 独秀 -13.213 ▁威斯 -13.2131 ▁FB -13.2132 ▁偏重 -13.2132 ▁三段 -13.2133 ▁SW -13.2133 ▁少先队 -13.2134 ▁铂金 -13.2134 ▁五颜六 -13.2135 ▁全封闭 -13.2135 ▁炮火 -13.2135 ▁帝国主义 -13.2137 老四 -13.2137 ▁169 -13.2137 ▁脱手 -13.2137 喜事 -13.2138 ▁城下 -13.2138 ▁埋藏 -13.2139 而无 -13.214 党史 -13.214 men -13.2142 ▁沦落 -13.2142 ▁扎堆 -13.2142 ▁相接 -13.2142 ▁散装 -13.2143 ▁嚦 -13.2143 rad -13.2144 ▁陈云 -13.2144 ▁义气 -13.2144 ▁闯荡 -13.2145 ▁之功 -13.2145 ▁哗啦 -13.2147 ▁寄来 -13.2147 ▁云阳 -13.2148 老虎 -13.2148 ▁果不其 -13.2148 指南 -13.2149 ▁style -13.2151 heng -13.2151 ▁冷却水 -13.2151 ▁无明 -13.2152 桃源 -13.2152 UM -13.2154 视眈 -13.2154 ▁满口 -13.2155 ▁文化名 -13.2155 书店 -13.2155 ▁探亲 -13.2157 忻 -13.2158 ▁MPV -13.2158 ▁弹奏 -13.2159 ▁发狂 -13.2159 ▁萧条 -13.216 tom -13.216 ▁正以 -13.2161 YS -13.2161 ▁鹿晗 -13.2161 女生 -13.2163 ▁读过 -13.2165 ▁美麗 -13.2165 悅 -13.2166 ▁绢 -13.2166 倩倩 -13.2166 ▁RPG -13.2167 670 -13.2167 ▁驻马店 -13.2169 ▁门卫 -13.2169 ▁查尔斯 -13.217 烯 -13.2171 ▁承诺书 -13.2171 配比 -13.2171 ▁议价 -13.2172 ▁太深 -13.2174 ▁密钥 -13.2176 ▁寻宝 -13.2177 装周 -13.2177 ▁has -13.2177 ▁照常 -13.2177 ▁三联 -13.2178 ▁闪躲 -13.2178 ▁读音 -13.2179 亰 -13.2179 ▁硬朗 -13.2179 ▁泡茶 -13.218 par -13.218 白白 -13.218 ▁判别 -13.218 ▁流着 -13.2182 ▁无踪 -13.2182 銅 -13.2183 ▁水压 -13.2183 ▁碧玉 -13.2184 ▁告一 -13.2184 ▁槟榔 -13.2185 推荐 -13.2185 478 -13.2185 昼 -13.2187 ▁翱翔 -13.2187 ▁次第 -13.2188 ▁乔楠 -13.2189 一级 -13.2189 自负 -13.2191 凶恶 -13.2191 6.7 -13.2192 嗲 -13.2193 ▁炒菜 -13.2193 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-13.5075 ▁菱形 -13.5075 ▁居心 -13.5075 ▁ARM -13.5075 ▁佩奇 -13.5076 趙 -13.5076 ▁造影 -13.5077 ▁西昌 -13.5078 ▁崇明 -13.5079 ▁罢免 -13.5079 rin -13.5079 pal -13.5079 ▁长腿 -13.5083 ▁五洲 -13.5084 ▁敲诈 -13.5084 nor -13.5084 一夕 -13.5085 ▁腥味 -13.5086 913 -13.5086 ▁青鸾 -13.5087 狩 -13.5087 ▁谦逊 -13.5087 驯 -13.5087 审查 -13.5088 2.3 -13.5088 襲 -13.5088 ▁闭塞 -13.5089 ▁结转 -13.509 ▁消防队 -13.5091 ▁牌型 -13.5092 ▁贴息 -13.5092 ▁天秤 -13.5093 ▁奥拉 -13.5095 mil -13.5096 ▁第三十六 -13.5096 遢 -13.5096 916 -13.5098 ▁睡觉时 -13.5099 ▁傷害 -13.5099 簯 -13.51 ▁珠光 -13.5101 701 -13.5101 ▁体育用品 -13.5102 滋事 -13.5103 难免 -13.5103 ▁张弛 -13.5103 422 -13.5104 ▁墓葬 -13.5104 ▁倚天 -13.5106 ▁选聘 -13.5106 IQ -13.5108 ▁甩出 -13.5108 357 -13.5108 丰收 -13.5108 865 -13.5109 謀 -13.511 ▁正月初 -13.5111 ▁老僧 -13.5112 ▁兰博基 -13.5112 778 -13.5113 笃 -13.5113 惺 -13.5113 ▁西江 -13.5114 ▁开门见 -13.5115 ▁担负起 -13.5117 ▁炼金 -13.5117 暫 -13.5118 ▁打開 -13.5118 ▁竹叶 -13.5119 BR -13.5119 oth -13.5119 ▁读数 -13.5119 ▁军政 -13.5119 tory -13.5119 醬 -13.5121 ▁反潜 -13.5122 ▁仙王 -13.5124 ▁乔木 -13.5125 ▁墨绿 -13.5125 spec -13.5126 868 -13.5127 ump -13.5129 ▁消泡 -13.513 ▁Wind -13.513 ▁计程 -13.513 ▁施以 -13.513 ▁娇笑 -13.5131 ▁烧饼 -13.5131 ▁抚州 -13.5133 ▁继续加强 -13.5133 ▁姆巴 -13.5133 ▁山峦 -13.5134 ▁龙辰 -13.5134 ▁自欺 -13.5135 惩 -13.5135 ▁易碎 -13.5135 ▁刻录 -13.5135 ▁祝你 -13.5136 ▁血雾 -13.5137 ▁生津 -13.5137 ▁陈奕 -13.5138 ▁LPL -13.5139 萤 -13.514 ▁祝融 -13.5141 ▁绿意 -13.5142 ▁WEB -13.5142 基督 -13.5144 嗟 -13.5145 Mobile -13.5145 ▁单亲 -13.5145 ▁兴办 -13.5146 340 -13.5147 2016 -13.5147 ▁类药 -13.5148 ▁胸襟 -13.5148 999 -13.5148 ▁国务委员 -13.515 ▁根深蒂 -13.5151 噸 -13.5151 ▁胃部 -13.5152 漸 -13.5154 ▁语重心 -13.5154 ▁售中 -13.5154 〝 -13.5155 ▁娇声 -13.5155 ▁家俱 -13.5159 擀 -13.5159 不挂 -13.516 ▁連接 -13.516 ▁進步 -13.5161 ache -13.5161 ▁米粒 -13.5163 吊胆 -13.5164 ▁延吉 -13.5165 ▁洛离 -13.5165 ▁约瑟夫 -13.5166 ▁几杯 -13.5167 ▁刘枫 -13.5167 ▁存管 -13.5167 ▁united -13.517 ▁国产品牌 -13.517 ▁总领事 -13.5171 ▁颠覆性 -13.5172 货币基金 -13.5172 一刻 -13.5172 ▁李昊 -13.5173 aj -13.5173 UC -13.5176 磺 -13.5176 1.9 -13.5177 罗列 -13.5178 ▁指標 -13.518 差异 -13.5181 齐齐 -13.5184 祎 -13.5184 ▁艳阳 -13.5186 徕 -13.5186 ▁红霞 -13.5187 ridge -13.5187 ▁收跌 -13.5187 ▁OPEC -13.5187 ▁嗓门 -13.5188 呷 -13.5188 很 -13.5189 ▁锯齿 -13.5189 ration -13.519 ▁皮炎 -13.5191 完整 -13.5191 步枪 -13.5192 ▁关进 -13.5192 吴 -13.5192 ▁不同寻常 -13.5193 ▁汗毛 -13.5193 呛 -13.5194 核心 -13.5194 ▁法兰西 -13.5194 璜 -13.5195 渡河 -13.5195 plus -13.5196 ▁朝野 -13.5198 ▁汽水 -13.5198 佯 -13.5199 ▁三门峡 -13.5199 ▁莫急 -13.5199 ▁拼凑 -13.5199 ▁脚掌 -13.5199 ▁MSCI -13.52 bao -13.5201 ▁铜川 -13.5201 Service -13.5202 ▁扣留 -13.5202 ▁商標 -13.5203 ▁1933 -13.5203 蜃 -13.5204 杳 -13.5206 ▁龚瑞妮 -13.5207 ▁人山人 -13.5208 ▁水煮 -13.5208 ▁面红耳 -13.5208 ▁包揽 -13.5208 ▁从无到 -13.5209 198 -13.5209 ▁喷气 -13.5209 ▁致谢 -13.521 ▁南朝 -13.5211 划一 -13.5213 ▁發出 -13.5214 ▁目视 -13.5215 ▁翻譯 -13.5216 岫 -13.5217 ▁第五套 -13.5218 ▁夺命 -13.522 ▁闹市 -13.522 建工 -13.522 978 -13.5221 执法 -13.5222 ▁瑶瑶 -13.5223 ▁民情 -13.5223 助理 -13.5224 桉 -13.5227 硬盘 -13.5228 ▁爬虫 -13.5228 ▁德鲁 -13.5229 富贵 -13.5229 势所 -13.523 ▁觀光 -13.523 耶稣 -13.523 ▁卡巴斯基 -13.5231 ▁負擔 -13.5232 ▁卡耐 -13.5234 ries -13.5234 ▁仰面 -13.5234 楦 -13.5236 ▁力帆 -13.5237 Pad -13.5237 侮辱 -13.5238 恍惚 -13.524 ▁拔尖 -13.5242 ▁灰飞烟 -13.5243 117 -13.5244 ▁迷彩 -13.5244 ▁阿姆斯特 -13.5244 闠 -13.5244 ▁幽兰 -13.5244 ▁圆筒 -13.5244 ▁东道主 -13.5248 ▁问鼎 -13.5248 ▁农商 -13.5249 ▁一百六十 -13.525 park -13.525 煩 -13.5251 ▁收費 -13.5252 ▁枇杷 -13.5253 ▁树根 -13.5255 ▁定瓶 -13.5256 ▁阻滞 -13.5257 ▁互鉴 -13.5257 转印 -13.5257 ▁锅底 -13.526 醤 -13.5261 ▁狂飙 -13.5262 ▁趁热 -13.5263 ▁斩首 -13.5263 ▁绵羊 -13.5264 ▁撑开 -13.5264 ▁辍学 -13.5264 ▁陪你 -13.5264 order -13.5265 ▁蜂巢 -13.5265 ▁鹤壁 -13.5265 ▁晚些 -13.5266 878 -13.5267 美食 -13.5267 骋 -13.5268 ▁1927 -13.5269 677 -13.5269 ▁热乎 -13.5269 ▁脉动 -13.527 ▁試驗 -13.527 ▁酣畅 -13.5271 ▁瑞丽 -13.5273 4.6 -13.5273 推行 -13.5275 ▁檀香 -13.5276 言秽 -13.5278 ▁罪孽 -13.5279 ▁丁浩 -13.528 ▁瑞克 -13.5281 ▁FBI -13.5284 工夫 -13.5284 美奂 -13.5285 421 -13.5285 ▁限售 -13.5285 ▁DV -13.5286 讫 -13.5287 ▁速成 -13.5288 公公 -13.5289 ▁Paul -13.529 习气 -13.529 ower -13.529 579 -13.5292 ▁統一 -13.5292 体表 -13.5292 ▁浮料 -13.5295 妙妙 -13.5296 汉语 -13.5296 ▁崇文 -13.5299 ▁独木 -13.5299 ▁发际 -13.53 壒 -13.5302 ▁启闭 -13.5302 遗憾 -13.5302 ▁招工 -13.5302 ▁鬼谷 -13.5303 ▁下厨 -13.5304 ▁离散 -13.5305 ▁常备 -13.5305 动摇 -13.5306 151 -13.5306 相对 -13.5307 ▁bwin -13.5307 冶金 -13.5308 ▁荣登 -13.5308 芥 -13.5308 ▁广进 -13.5308 ▁貂蝉 -13.5308 ▁治水 -13.5311 ┻ -13.5311 ▁10.2 -13.5311 持續 -13.5311 ▁租售 -13.5312 攝 -13.5313 ▁贾诩 -13.5314 ▁家私 -13.5315 ▁送医 -13.5315 信赖 -13.5315 ▁正欲 -13.5317 ▁遇事 -13.5317 ▁一左一 -13.5318 ▁福寿 -13.5318 ▁光驱 -13.5319 ▁盐湖 -13.5319 彌 -13.5319 ▁柱塞 -13.532 ▁大惊小 -13.532 芳芳 -13.532 ▁足球赛 -13.5321 ▁柳絮 -13.5321 牽 -13.5322 609 -13.5323 ▁two -13.5323 ▁南无 -13.5325 ▁泄密 -13.5325 ▁桃红 -13.5326 乜 -13.5327 ▁干净利 -13.5328 ▁烟灰 -13.5329 ▁老丈 -13.5329 257 -13.5329 譙 -13.533 ▁推论 -13.533 欢庆 -13.5331 ▁鼻息 -13.5331 媠 -13.5332 ▁森林狼 -13.5333 race -13.5333 ▁昙花 -13.5334 ▁继母 -13.5335 一番 -13.5336 ▁体弱 -13.5336 痂 -13.5337 ▁邓超 -13.5337 ▁王珉 -13.5338 ▁力挺 -13.5338 ▁脑残 -13.5339 ▁專門 -13.534 ▁一邊 -13.5341 ▁規則 -13.5341 胫 -13.5342 ▁官能 -13.5344 ▁碳素 -13.5345 ▁丫丫 -13.5348 ▁数千万 -13.5348 骐 -13.5349 ▁每时每刻 -13.535 ▁所罗门 -13.535 宽恕 -13.5352 ▁九十九 -13.5355 ▁梳洗 -13.5355 ism -13.5355 ▁佛罗伦萨 -13.5355 ▁入夜 -13.5356 ▁轨制 -13.5356 ▁维基百 -13.5357 ▁黑黑 -13.5358 ▁西伯利亚 -13.5358 ▁Free -13.5358 ▁河马 -13.5359 撷 -13.5359 ▁张掖 -13.5361 east -13.5362 ▁亲卫 -13.5364 ates -13.5364 蟠 -13.5365 ▁认认 -13.5365 凑 -13.5365 ▁相對 -13.5365 ▁乙级 -13.5366 ▁装傻 -13.5366 岁月 -13.5366 緩 -13.5368 见识 -13.5369 ▁小猴 -13.537 2.9 -13.5371 珞 -13.5372 ▁分類 -13.5373 ▁安邦 -13.5374 ▁悻悻 -13.5376 參 -13.5377 ▁肺结核 -13.5377 ▁蒙古人 -13.5377 ▁兴安 -13.5377 ▁创维 -13.5378 ▁几滴 -13.538 519 -13.538 ▁差旅 -13.5381 ▁HDMI -13.5381 作画 -13.5381 ▁紧追 -13.5382 ▁HPV -13.5382 祢 -13.5384 ▁阿凡 -13.5384 ▁喜迎 -13.5384 ▁栀子 -13.5384 ▁韵律 -13.5385 512 -13.5385 ▁面不改 -13.5385 ▁森严 -13.5386 ▁烘托 -13.5386 侯爷 -13.5386 631 -13.5386 ▁查核 -13.5387 雏 -13.5387 ▁鸡尾酒 -13.5389 ▁滤料 -13.539 ▁串口 -13.5391 ▁雪上加 -13.5392 ▁艾玛 -13.5394 lc -13.5394 ▁逐级 -13.5395 屁股 -13.5397 816 -13.5397 宾馆 -13.5398 无惧 -13.5398 ▁肖华 -13.5399 ▁士林 -13.5399 ▁村支 -13.5399 788 -13.54 ▁周维清 -13.54 ▁Ke -13.54 ▁转嫁 -13.54 知识产权 -13.5401 暑假 -13.5404 ▁电荷 -13.5404 ▁艾斯 -13.5404 ▁永定 -13.5404 ▁暴虐 -13.5405 ▁血魔 -13.5407 ▁苏苏 -13.5409 ▁举目 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-13.9931 拏 -13.9933 氓 -13.9933 慘 -13.9938 鳃 -13.9938 菖 -13.9961 垩 -14 檯 -14 耦 -14.0032 膨 -14.0037 筜 -14.0038 砺 -14.0044 蟢 -14.0047 鸠 -14.0053 鍾 -14.006 锻 -14.0064 喅 -14.0064 ゥ -14.0066 苟 -14.0069 廈 -14.0073 氖 -14.0073 讹 -14.0091 摝 -14.0103 穢 -14.0103 厤 -14.0114 镕 -14.0114 怠 -14.0119 襪 -14.0129 寜 -14.0136 窇 -14.014 婀 -14.0142 剎 -14.0147 扒 -14.0157 噫 -14.0162 聽 -14.0168 懼 -14.0173 涣 -14.0175 啋 -14.0175 闱 -14.0184 蚠 -14.0188 稗 -14.0203 溏 -14.0205 儓 -14.0225 曘 -14.0225 飄 -14.0227 荧 -14.0231 犮 -14.0232 偗 -14.0243 苸 -14.0254 馗 -14.026 羔 -14.0284 琥 -14.0285 钂 -14.0287 懠 -14.0293 罵 -14.03 绛 -14.0307 啮 -14.0309 摱 -14.0318 幹 -14.0318 眳 -14.032 蓑 -14.0324 孳 -14.0335 γ -14.0342 蹊 -14.036 玳 -14.036 珪 -14.0364 颔 -14.0367 婼 -14.0369 睶 -14.0375 湳 -14.0378 崱 -14.0382 儆 -14.0391 檻 -14.0398 綱 -14.0398 檸 -14.0404 ┛ -14.0418 珅 -14.042 檄 -14.0425 鑿 -14.0431 楸 -14.0436 蚜 -14.0456 畲 -14.0458 睙 -14.0463 僭 -14.0465 ┒ -14.0476 湃 -14.0478 悥 -14.0487 ず -14.0492 嵎 -14.0492 崉 -14.0496 巩 -14.0498 碁 -14.0499 坥 -14.051 噹 -14.0515 簽 -14.0519 忖 -14.0524 蚥 -14.0539 羍 -14.0542 墽 -14.0544 膻 -14.0551 畀 -14.0553 癜 -14.0555 俠 -14.0562 渿 -14.0564 棯 -14.0571 傍 -14.0572 噶 -14.0579 铄 -14.0583 遼 -14.0589 憳 -14.0592 蕲 -14.0596 厠 -14.0601 虁 -14.0601 鸩 -14.0601 纨 -14.0603 韕 -14.0603 鸷 -14.0603 惕 -14.0605 嗪 -14.0605 銈 -14.0605 蝗 -14.0609 葡 -14.0611 髦 -14.063 姤 -14.064 鉛 -14.064 螭 -14.0649 岘 -14.0653 懶 -14.0656 頃 -14.0669 羧 -14.0674 燧 -14.0676 莪 -14.0695 暂 -14.0696 繇 -14.0697 跶 -14.0697 龜 -14.0699 褝 -14.0704 赅 -14.0706 嘢 -14.0708 擇 -14.071 о -14.0711 颢 -14.0736 郃 -14.0738 徹 -14.0754 臘 -14.0768 镭 -14.0771 Е -14.0771 誊 -14.0775 壆 -14.0778 酆 -14.078 犸 -14.0787 嚐 -14.0794 柩 -14.0803 砭 -14.0803 蝴 -14.0812 騼 -14.0812 慣 -14.0815 杵 -14.0815 灬 -14.0829 邨 -14.0829 颁 -14.0832 篪 -14.0833 偧 -14.0838 痚 -14.0843 執 -14.0849 祜 -14.085 蘊 -14.0854 屍 -14.0857 谚 -14.0861 诳 -14.0868 褚 -14.0882 ア -14.0885 綜 -14.0894 稞 -14.0897 蚑 -14.0901 洺 -14.0904 邙 -14.0908 厱 -14.0913 葫 -14.092 讧 -14.0925 伓 -14.0927 皎 -14.0927 既 -14.0941 頬 -14.0941 昉 -14.0948 碚 -14.0951 樊 -14.0954 櫎 -14.0955 苾 -14.096 窸 -14.0984 蜘 -14.0986 刽 -14.0991 剳 -14.0991 瓤 -14.0991 槛 -14.0991 庛 -14.0996 塩 -14.1005 蹦 -14.1008 寳 -14.1024 ▉ -14.1026 ト -14.1026 販 -14.1038 棂 -14.1043 斡 -14.1048 蚱 -14.1053 ┿ -14.1055 黜 -14.1062 ゼ -14.1067 惛 -14.1072 澍 -14.1079 骈 -14.1091 釘 -14.1098 薅 -14.1108 遜 -14.1108 亹 -14.111 儹 -14.1112 钏 -14.1115 驷 -14.1117 蛐 -14.1136 嫆 -14.1141 腭 -14.1144 諷 -14.1144 誼 -14.1158 紐 -14.116 奧 -14.1162 摔 -14.1163 聒 -14.1163 ˋ -14.1165 嬌 -14.1165 谡 -14.1168 蜇 -14.117 姙 -14.1172 鴨 -14.1175 ∧ -14.1189 鈴 -14.1199 妧 -14.1201 純 -14.1202 巖 -14.1209 骶 -14.1216 呋 -14.1221 彟 -14.1221 炙 -14.1224 枬 -14.1226 緬 -14.1226 恫 -14.1228 魍 -14.1228 倶 -14.1231 暎 -14.125 醺 -14.1252 仝 -14.1252 韹 -14.1262 ∕ -14.1272 囿 -14.1274 艷 -14.1279 冯 -14.1286 喹 -14.1294 晠 -14.1301 镔 -14.1321 р -14.133 琍 -14.133 渚 -14.1331 蓐 -14.134 ∏ -14.1343 谯 -14.1343 砙 -14.1345 湟 -14.1362 颛 -14.1365 唁 -14.1367 棦 -14.1372 舛 -14.1372 釀 -14.1377 哗 -14.1379 鸔 -14.1382 硩 -14.1385 舸 -14.1392 幺 -14.1396 诮 -14.1397 軴 -14.1397 侑 -14.1409 殣 -14.1409 輯 -14.1416 賊 -14.1429 潍 -14.1434 誦 -14.1436 靼 -14.1454 錧 -14.1456 賜 -14.1461 尯 -14.1481 痨 -14.1481 卮 -14.1484 リ -14.1494 呤 -14.1494 嚩 -14.1499 褟 -14.1501 璭 -14.1508 瘉 -14.1521 杲 -14.1523 墮 -14.1526 苣 -14.1533 鐍 -14.1533 辗 -14.1536 蚩 -14.1536 堅 -14.1537 贮 -14.1539 艌 -14.1546 芫 -14.1546 砒 -14.1553 窩 -14.1561 魃 -14.1566 憶 -14.1568 賠 -14.1569 濠 -14.1572 ゃ -14.1584 é -14.1586 懑 -14.1589 歙 -14.1589 愍 -14.1591 纥 -14.1599 熄 -14.1604 拺 -14.1614 喱 -14.1624 帿 -14.1624 忏 -14.1636 滂 -14.1642 簸 -14.1644 阇 -14.1647 ˇ -14.1657 卂 -14.1667 鸪 -14.1669 躺 -14.1673 ╔ -14.168 埚 -14.169 僮 -14.1695 耒 -14.1697 鍡 -14.17 浏 -14.1713 鉔 -14.1713 寵 -14.173 饴 -14.1743 藍 -14.1754 崕 -14.1758 徊 -14.1758 鲖 -14.1758 駁 -14.1764 旖 -14.1769 郅 -14.1776 囵 -14.1782 堀 -14.1782 毘 -14.1799 谘 -14.1802 霰 -14.1805 崏 -14.1815 縍 -14.1817 奄 -14.182 疥 -14.1823 垙 -14.1825 苡 -14.1828 疡 -14.1833 蚹 -14.1833 ̇ -14.1835 炁 -14.1835 锑 -14.1835 旸 -14.1838 晳 -14.1841 緷 -14.1843 脐 -14.1846 叴 -14.1848 婠 -14.1861 魉 -14.1874 灑 -14.1892 墉 -14.1895 縹 -14.1895 汜 -14.1903 俎 -14.1908 衅 -14.1912 彴 -14.1913 寔 -14.1916 甆 -14.1921 竊 -14.1924 簌 -14.1924 ∮ -14.1926 锗 -14.1929 溿 -14.1931 毊 -14.1939 エ -14.1947 妊 -14.1947 廁 -14.195 洹 -14.1952 葚 -14.1955 騷 -14.196 埮 -14.1963 萒 -14.1989 蘿 -14.1991 イ -14.1999 鵬 -14.2002 涮 -14.2002 ˉ -14.201 黏 -14.2011 ╰ -14.2015 迄 -14.2015 宑 -14.2023 粲 -14.2025 釣 -14.2025 鬩 -14.2031 亓 -14.2033 绚 -14.2044 姽 -14.2044 阖 -14.2045 咛 -14.2056 惫 -14.206 ̈ -14.2062 ㄢ -14.2062 砲 -14.2062 鞠 -14.2069 躲 -14.2071 匝 -14.2072 ハ -14.2081 叮 -14.2085 ニ -14.2086 禱 -14.2091 蛀 -14.2096 遑 -14.2097 鑜 -14.2102 崟 -14.2107 暝 -14.2107 撮 -14.2115 砥 -14.2118 蔺 -14.2123 镊 -14.2131 瞅 -14.2137 螟 -14.2142 呂 -14.2144 捺 -14.2153 埕 -14.2168 衙 -14.2174 咭 -14.2182 笆 -14.2182 胚 -14.2183 跎 -14.2187 纠 -14.2188 儳 -14.2195 爞 -14.2195 綑 -14.2206 媾 -14.2208 礽 -14.2208 暈 -14.2211 拭 -14.2212 胱 -14.2214 蹙 -14.2215 禩 -14.2246 禍 -14.2254 醐 -14.2254 ╜ -14.2257 鲢 -14.2262 傣 -14.2269 垵 -14.2273 萋 -14.2278 潸 -14.2281 韓 -14.2284 潑 -14.2286 ∈ -14.2292 嫍 -14.2292 ö -14.2294 僖 -14.2297 顛 -14.2302 勸 -14.2308 牡 -14.2312 嘈 -14.2313 趺 -14.2316 刎 -14.2319 鎳 -14.2321 韣 -14.2321 锰 -14.2322 榉 -14.2329 叻 -14.2338 甙 -14.2338 濃 -14.2347 熔 -14.2348 欤 -14.2351 葺 -14.2351 苻 -14.2359 睐 -14.2361 馳 -14.2365 溾 -14.237 锾 -14.2373 и -14.2384 佶 -14.2384 旳 -14.2386 悄 -14.2391 蒋 -14.2394 襕 -14.2395 每 -14.241 揆 -14.2414 帺 -14.2419 砆 -14.2419 汾 -14.2427 巹 -14.2427 蒼 -14.2427 赡 -14.243 旎 -14.2433 嘯 -14.2436 喋 -14.2438 裾 -14.2438 偛 -14.2446 蹟 -14.2446 陼 -14.2446 蠟 -14.246 揵 -14.2466 娩 -14.247 镐 -14.2472 蔼 -14.2477 搬 -14.2477 浐 -14.2479 乞 -14.2484 挹 -14.2485 頌 -14.2488 氅 -14.249 呵 -14.2497 饋 -14.2499 籠 -14.2504 椰 -14.2512 帚 -14.2515 馔 -14.2518 侈 -14.2521 嘻 -14.2527 犟 -14.2529 馒 -14.2543 漊 -14.2543 ╯ -14.2546 诌 -14.2548 嫑 -14.2551 贽 -14.2554 濫 -14.2562 豈 -14.2584 瓨 -14.2587 桠 -14.2601 殓 -14.2604 荪 -14.2604 疳 -14.2612 羯 -14.2613 攫 -14.2615 菸 -14.2618 釼 -14.2621 枒 -14.2635 潬 -14.2637 畼 -14.2651 碣 -14.2654 橏 -14.2657 逍 -14.2661 憬 -14.2662 彧 -14.2664 鐡 -14.2665 帹 -14.2674 氐 -14.2674 偵 -14.2682 雌 -14.2687 懲 -14.2688 睜 -14.2688 嗥 -14.2691 髰 -14.2691 糾 -14.2693 拈 -14.2694 吆 -14.2696 僑 -14.2696 卟 -14.2696 敮 -14.2699 梾 -14.2699 鄧 -14.2702 鬱 -14.2716 闩 -14.2719 亜 -14.2733 弰 -14.2736 炪 -14.2738 喌 -14.2744 掴 -14.2744 鈣 -14.2747 矜 -14.2753 谄 -14.2758 惞 -14.2764 蔛 -14.2767 叢 -14.2786 仞 -14.2788 姢 -14.2789 憉 -14.2792 ║ -14.2795 瘔 -14.2806 椈 -14.2812 繊 -14.2812 铖 -14.2815 涸 -14.2847 燚 -14.2855 苁 -14.2858 吭 -14.2858 泅 -14.286 た -14.2872 猗 -14.2875 裘 -14.2875 ∞ -14.288 圜 -14.2886 桵 -14.2892 嫃 -14.2895 孰 -14.2895 惁 -14.2903 掼 -14.2909 麂 -14.2912 燦 -14.2918 ∫ -14.2923 槁 -14.2932 铣 -14.2933 莖 -14.2935 ク -14.2955 蘇 -14.2962 喧 -14.2971 尋 -14.2977 睷 -14.2987 捱 -14.299 嗏 -14.2993 俚 -14.3001 肓 -14.3019 巻 -14.3022 蕤 -14.3022 し -14.3024 茴 -14.3029 岿 -14.3033 ∥ -14.3036 斂 -14.3039 蔣 -14.3042 鉆 -14.3048 觥 -14.3054 埡 -14.3056 轟 -14.3059 籲 -14.3062 尻 -14.3068 オ -14.3071 陜 -14.308 輻 -14.3083 篁 -14.3086 陰 -14.309 澘 -14.3091 軒 -14.31 惚 -14.3103 崲 -14.3103 瓕 -14.3115 阗 -14.3115 畹 -14.3121 哦 -14.3123 貉 -14.3124 岬 -14.3132 嬰 -14.3135 圻 -14.3135 踒 -14.3138 縺 -14.3141 嫭 -14.3147 佥 -14.3159 砣 -14.3162 撐 -14.3167 溉 -14.3173 ッ -14.3182 崃 -14.3191 薨 -14.3197 臬 -14.3209 鸯 -14.3215 绉 -14.3227 蘍 -14.3233 カ -14.3236 篙 -14.3247 芾 -14.325 値 -14.3253 咤 -14.3255 笄 -14.3271 闽 -14.3279 瑰 -14.3282 崌 -14.3283 菟 -14.3286 媯 -14.3292 虢 -14.3292 嗜 -14.3297 噦 -14.3307 斫 -14.3325 畢 -14.3328 狒 -14.3328 絨 -14.3328 诡 -14.3333 堃 -14.3349 鹹 -14.3349 丼 -14.3355 铬 -14.3358 珥 -14.3376 繋 -14.3376 緥 -14.3379 熵 -14.3385 淸 -14.3388 隸 -14.3391 緝 -14.3394 諎 -14.3397 桡 -14.3403 鍨 -14.3409 ぜ -14.3412 玠 -14.3418 祿 -14.3421 ǒ -14.343 滯 -14.3433 膑 -14.3433 崧 -14.3445 茓 -14.3445 澗 -14.3454 ナ -14.346 徆 -14.3463 咋 -14.3475 蜻 -14.3479 儫 -14.3482 伢 -14.3485 賬 -14.3488 粸 -14.3488 珒 -14.35 蔥 -14.3503 ロ -14.3506 纏 -14.3506 蚵 -14.3506 枂 -14.3521 説 -14.3524 啉 -14.3527 坷 -14.353 蚤 -14.3536 ❀ -14.3552 廂 -14.3552 赟 -14.3552 鰯 -14.3555 瞾 -14.3558 弁 -14.3561 邰 -14.3561 嶼 -14.3579 芨 -14.3579 殁 -14.3586 戊 -14.359 亶 -14.3592 彥 -14.3604 蒡 -14.3604 雎 -14.3619 驚 -14.3622 鲍 -14.3622 誯 -14.3622 眙 -14.3629 叱 -14.3634 齆 -14.3644 茏 -14.3647 獾 -14.3653 サ -14.3656 掬 -14.3671 鷹 -14.3672 珷 -14.3675 喑 -14.3678 餓 -14.3678 棝 -14.3681 韋 -14.3681 湧 -14.369 兖 -14.3696 蜗 -14.3709 兌 -14.3709 秣 -14.3709 馮 -14.3718 簻 -14.3734 繍 -14.3734 紩 -14.3746 倆 -14.3753 荑 -14.3753 凄 -14.377 猥 -14.3771 皺 -14.3771 盃 -14.3771 寑 -14.3781 淦 -14.3781 舂 -14.3781 櫓 -14.3784 ˊ -14.379 ゚ -14.38 枞 -14.3806 魈 -14.3806 粛 -14.3822 茭 -14.3828 佗 -14.383 遐 -14.3834 暱 -14.3837 鍥 -14.3837 棗 -14.384 刕 -14.3844 鍿 -14.3844 孀 -14.385 凇 -14.3869 塬 -14.3869 镍 -14.3873 箟 -14.3875 瓮 -14.3882 跖 -14.3885 鎿 -14.3888 鄏 -14.39 憤 -14.3907 叿 -14.3913 酊 -14.392 蹴 -14.3929 レ -14.3932 臑 -14.3939 鵝 -14.3939 寷 -14.3951 彞 -14.3951 ╟ -14.3955 湊 -14.3955 崛 -14.3963 窠 -14.3964 笟 -14.3967 櫥 -14.397 炕 -14.3976 蘆 -14.3983 鋅 -14.3983 箰 -14.3999 蹭 -14.4002  -14.4002 礊 -14.4006 萻 -14.4031 衄 -14.4038 猊 -14.4044 毚 -14.4054 瑀 -14.4054 碜 -14.4063 蠹 -14.4063 銜 -14.4067 鬡 -14.4067 剝 -14.4076 绺 -14.4076 ˹ -14.4083 缢 -14.4083 緒 -14.4088 + -14.4106 愘 -14.4109 鶴 -14.4111 叺 -14.4115 霹 -14.4115 這 -14.4117 仺 -14.4118 镛 -14.4122 烩 -14.4127 迂 -14.4128 旃 -14.4131 蓼 -14.4131 诅 -14.4141 墐 -14.4144 鳶 -14.4151 ┐ -14.416 痼 -14.416 懗 -14.4164 棷 -14.4167 缰 -14.4173 笳 -14.4183 箜 -14.4183 吒 -14.4191 誇 -14.42 龔 -14.4206 粐 -14.4213 浒 -14.4218 彭 -14.4223 谔 -14.4226 鲫 -14.4229 懇 -14.4229 癒 -14.4229 啾 -14.423 箸 -14.4232 雓 -14.4236 鮛 -14.4242 泯 -14.4253 ě -14.4258 殦 -14.4262 綈 -14.4262 钿 -14.4272 鲷 -14.4275 甾 -14.4288 臢 -14.4295 檮 -14.4314 赁 -14.4331 嚯 -14.4331 掋 -14.4334 フ -14.4341 琵 -14.4341 笇 -14.4344 缮 -14.4347 蝈 -14.4354 匮 -14.4354 禺 -14.4357 畿 -14.436 囔 -14.4372 垃 -14.4374 悌 -14.4374 焖 -14.4374 な -14.4377 悱 -14.4377 乭 -14.4381 犍 -14.4381 ╮ -14.4387 諦 -14.4387 讥 -14.4408 唛 -14.4414 祕 -14.4414 挣 -14.4415 貞 -14.4421 媱 -14.4427 н -14.4437 紆 -14.4451 逞 -14.4453 耋 -14.4454 菓 -14.4457 £ -14.4464 悼 -14.4472 締 -14.4484 俅 -14.4491 蹑 -14.4494 肛 -14.4497 痪 -14.4504 ァ -14.4504 噼 -14.4511 嵬 -14.4511 啒 -14.4525 闖 -14.4525 遒 -14.4528 陟 -14.4531 儨 -14.4538 粎 -14.4538 ケ -14.4548 峒 -14.4548 テ -14.4552 泱 -14.4558 忒 -14.4569 崭 -14.4596 暹 -14.463 橄 -14.4637 搐 -14.467 屉 -14.4673 邓 -14.4694 阮 -14.4726 採 -14.4738 蜓 -14.476 谩 -14.4816 窿 -14.4924 豺 -14.4945 殡 -14.4946 婪 -14.4983 仍 -14.5014 玻 -14.5046 鱿 -14.5169 熨 -14.5196 掃 -14.5222 遵 -14.5236 郜 -14.5281 嫖 -14.5292 肪 -14.5303 劾 -14.5324 螨 -14.5366 拯 -14.5415 預 -14.5438 鳄 -14.5502 惰 -14.5547 廢 -14.5589 踐 -14.5599 瘠 -14.5625 衮 -14.5635 堕 -14.5635 钨 -14.5658 紹 -14.5666 芎 -14.567 铂 -14.5674 龈 -14.5678 茁 -14.5712 矸 -14.5777 蠕 -14.5786 荀 -14.588 纜 -14.588 忤 -14.5892 唬 -14.6026 浜 -14.6047 噩 -14.606 黔 -14.6066 嫦 -14.6123 榈 -14.6131 咖 -14.6139 髅 -14.6151 镶 -14.6212 奂 -14.6233 戬 -14.6237 莅 -14.627 毋 -14.6286 圾 -14.6286 敝 -14.6302 罂 -14.6319 徇 -14.6367 沒 -14.6384 纫 -14.6428 柑 -14.6434 咄 -14.6461 骼 -14.6489 楹 -14.6514 嚅 -14.6522 佼 -14.6535 刪 -14.6539 甬 -14.6543 荤 -14.6573 藓 -14.658 甥 -14.659 聂 -14.661 狈 -14.6613 郸 -14.6718 阎 -14.6777 寬 -14.6803 珮 -14.6816 斟 -14.6816 饲 -14.6839 瘪 -14.6844 掰 -14.6846 惋 -14.69 锺 -14.6946 蟆 -14.6947 椹 -14.6951 骜 -14.6964 秸 -14.7003 貢 -14.706 惦 -14.7099 菏 -14.7169 稔 -14.7173 蛔 -14.72 榛 -14.7262 颓 -14.7267 扪 -14.7279 沭 -14.7288 腋 -14.7357 橇 -14.7395 噴 -14.7398 閭 -14.7403 俞 -14.7463 咧 -14.7466 棣 -14.7559 鹦 -14.7568 妪 -14.7577 蹿 -14.7658 懵 -14.7663 嬴 -14.7682 赂 -14.7724 哺 -14.7742 枸 -14.7743 拷 -14.7743 訾 -14.7818 嗫 -14.7869 涓 -14.789 薦 -14.7933 覃 -14.7935 憧 -14.7973 詹 -14.7978 儡 -14.8035 娄 -14.8189 稣 -14.8312 烘 -14.8314 刨 -14.8318 嘀 -14.8319 蜈 -14.8377 炅 -14.841 橡 -14.8418 蜕 -14.8445 鹑 -14.8466 谆 -14.8511 诧 -14.8526 诽 -14.8541 纾 -14.8677 另 -14.8698 漉 -14.8709 卤 -14.876 餮 -14.8766 狰 -14.8833 亳 -14.891 阱 -14.8932 阆 -14.8958 殘 -14.901 甄 -14.9069 硼 -14.9085 鍍 -14.9091 毂 -14.9165 潛 -14.9195 挎 -14.9277 叵 -14.9305 耽 -14.9405 荥 -14.9481 鑰 -14.9529 汲 -14.9586 螳 -14.9649 阐 -14.9729 濡 -14.9776 鸵 -14.9796 奚 -14.981 奘 -14.9819 牺 -14.9882 脅 -14.9899 谲 -14.9928 搂 -14.9993 芭 -15.0017 匍 -15.0033 樺 -15.0053 競 -15.0077 挝 -15.0117 诿 -15.0119 葩 -15.0233 咨 -15.0276 櫧 -15.0343 夙 -15.0362 獠 -15.0368 泞 -15.0378 倜 -15.0427 粼 -15.0494 翌 -15.0511 阈 -15.0537 氫 -15.0561 擞 -15.0597 烙 -15.0599 殉 -15.0618 冗 -15.0618 偻 -15.0636 啕 -15.0698 氦 -15.0726 隨 -15.0732 訶 -15.0818 叩 -15.0834 螂 -15.0843 礎 -15.0874 迢 -15.0964 娉 -15.1011 廖 -15.1077 捡 -15.1103 鑾 -15.1217 鼬 -15.123 踉 -15.1251 遨 -15.1304 眨 -15.1392 翟 -15.1399 镝 -15.1413 狞 -15.1516 搡 -15.1532 缭 -15.1583 骆 -15.1599 跄 -15.1683 刁 -15.1705 嵋 -15.1787 倭 -15.1825 ō -15.1839 跺 -15.1839 喽 -15.1882 催 -15.1889 靳 -15.1926 纰 -15.1962 烏 -15.2188 碘 -15.2233 聆 -15.23 蹶 -15.2324 嗡 -15.2345 臾 -15.2353 鄱 -15.2376 傥 -15.239 佃 -15.2428 幄 -15.251 蛰 -15.2626 憑 -15.2627 矫 -15.2657 咂 -15.2673 咙 -15.2798 疙 -15.2798 淆 -15.286 莠 -15.2891 窕 -15.2962 呜 -15.2964 颯 -15.3004 龌 -15.3025 躇 -15.3064 蘋 -15.3072 祛 -15.3132 伉 -15.3152 咫 -15.32 苜 -15.3264 臀 -15.3306 謗 -15.3321 骰 -15.3481 飨 -15.3489 铈 -15.3599 薛 -15.3616 倨 -15.3702 啧 -15.3762 嬉 -15.3762 脍 -15.3778 昵 -15.3788 尴 -15.3795 帼 -15.3872 蚓 -15.3881 邏 -15.3941 饪 -15.4045 镳 -15.4062 菠 -15.4079 擅 -15.4088 磋 -15.4168 啬 -15.4169 詮 -15.4248 á -15.4296 锲 -15.4302 錘 -15.4332 瘩 -15.4338 锛 -15.4341 劉 -15.4387 崔 -15.4406 擺 -15.4424 娠 -15.4437 褛 -15.4437 娛 -15.4483 撩 -15.4536 豌 -15.4538 抛 -15.4554 蓿 -15.4667 搪 -15.4744 訟 -15.4789 猬 -15.4807 丢 -15.482 騎 -15.484 袁 -15.4898 潺 -15.4931 澧 -15.4956 裴 -15.4975 绔 -15.5015 蓟 -15.5024 誹 -15.5037 栾 -15.5084 撕 -15.5183 扳 -15.5203 奠 -15.5233 昙 -15.5233 蝙 -15.5251 狡 -15.5253 雳 -15.534 烹 -15.5415 猝 -15.5466 搔 -15.5497 褒 -15.5506 胰 -15.5507 賃 -15.5531 癸 -15.5621 捋 -15.5652 卻 -15.5715 赳 -15.5726 吳 -15.5736 缅 -15.5779 糯 -15.5789 怦 -15.5829 娓 -15.5939 疸 -15.606 佔 -15.608 龊 -15.6115 唔 -15.6256 俯 -15.6289 瀏 -15.63 扼 -15.6344 滇 -15.6424 啥 -15.6459 遺 -15.6595 镓 -15.6722 驸 -15.6748 汴 -15.6757 恍 -15.6862 溧 -15.6874 í -15.6898 抿 -15.6945 皈 -15.6957 滄 -15.6993 痉 -15.7223 绷 -15.7445 拴 -15.7445 邹 -15.7559 儋 -15.7669 绯 -15.7802 汩 -15.7822 唏 -15.7828 廿 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-16.5749 俨 -16.5749 旯 -16.5879 邢 -16.5952 徉 -16.5965 岖 -16.6081 賺 -16.6159 / -16.6219 迥 -16.6649 掳 -16.6775 拧 -16.6839 爰 -16.6936 豁 -16.7033 啊 -16.7065 诬 -16.7636 矗 -16.7638 閬 -16.7638 臧 -16.7708 縊 -16.7778 撻 -16.8099 浣 -16.8172 沮 -16.8209 瞿 -16.8432 閾 -16.8583 腌 -16.8775 “ -16.8814 诠 -16.8814 曝 -16.8851 剃 -16.8853 篑 -16.9092 雖 -16.9293 嗤 -16.9839 濮 -17.0236 漩 -17.0326 鐫 -17.0649 ú -17.1031 缥 -17.1129 龚 -17.1684 岂 -17.1841 蜊 -17.1924 捶 -17.2383 咩 -17.2609 扛 -17.2839 誰 -17.2898 佟 -17.3135 蹇 -17.3256 。 -17.3691 姹 -17.4014 蛻 -17.4014 婁 -17.4146 敕 -17.4279 姘 -17.5883 唉 -17.5962 撬 -17.6042 昨 -17.6123 à -17.6204 ī -17.6369 囤 -17.6706 嗚 -17.6793 踩 -17.6793 扔 -17.688 幌 -17.7145 椋 -17.7235 篡 -17.7235 α -17.8694 《 -17.8906 撅 -17.9122 贰 -17.9232 、 -17.9569 晾 -17.9684 胯 -18.0157 ? -18.1588 闫 -18.2934 噗 -18.3095 偌 -18.3259 с -18.4121 搴 -18.4303 硒 -18.4303 т -18.4303 拄 -18.4488 у -18.4488 喔 -18.4869 嗨 -18.4869 琚 -18.5065 麾 -18.5265 : -18.5469 嘤 -18.5469 舐 -18.5891 掀 -18.6108 钪 -18.6108 μ -18.6557 妳 -18.679 窒 -18.7028 伫 -18.7522 嚦 -18.7522 揩 -18.7522 瀹 -18.7522 逮 -18.7778 抉 -18.7778 ò -18.8042 【 -18.8042 堝 -18.8042 拚 -18.8312 舀 -18.859 卞 -18.8875 壬 -18.9169 π -18.9785 硷 -18.9785 ( -18.9785 ” -19.0108 δ -19.0786 · -19.0786 唾 -19.0786 隋 -19.1142 [ -19.1143 偈 -19.1143 钕 -19.1898 踊 -19.2298 繃 -19.2298 啜 -19.3604 甩 -19.408 羟 -19.458 镌 -19.458 ч -19.5106 慵 -19.5106 拎 -19.5106 揪 -19.5662 妗 -19.5662 绠 -19.5662 ǎ -19.5662 甯 -19.625 舔 -19.7542 欒 -19.8256 嶄 -19.8256 銆 -19.9025 亟 -19.9859 捅 -19.9859 迸 -19.9859 澹 -20.0768 " -20.0768 俺 -20.0768 氤 -20.0768 β -20.1768 捂 -20.1768 ц -20.1768 「 -20.1768 х -20.2879 】 -20.2879 倏 -20.2879 畬 -20.2879 嫡 -20.4129 憋 -20.4129 掺 -20.4129 タ -20.5558 ; -20.5558 蹲 -20.5558 漕 -20.7224 ス -20.7224 ] -20.9224 》 -20.9224 诋 -20.9224 蛊 -20.9225 诟 -20.9225 ф -20.9225 撂 -21.1725 甭 -21.1725 臃 -21.1725 □ -21.1725 怂 -21.1725 戦 -21.1725 绗 -21.1725 — -21.5059 ★ -21.5059 噢 -21.5059 捎 -21.5059 闇 -21.5059 ～ -21.5059 徳 -21.5059 摁 -22.006 満 -22.006 滀 -22.006 註 -22.006 陛 -22.006 陨 -22.006 * -22.006 > -22.006 ’ -22.006 『 -22.006 剉 -22.006 э -22.006 蒹 -22.9768 讣 -22.9769 谶 -22.977 戕 -22.9771 痱 -22.9772 郓 -22.9773 囫 -22.9774 螓 -22.9775 蓖 -22.9776 渑 -22.9777 铩 -22.9778 囟 -22.9779 傩 -22.978 玔 -22.9781 钁 -22.9782 踰 -22.9783 鹧 -22.9784 抻 -22.9785 悭 -22.9786 盱 -22.9787 龅 -22.9788 柞 -22.9789 颞 -22.979 耄 -22.9791 🌕 -22.9792 齙 -22.9793 赭 -22.9794 瘈 -22.9795 摈 -22.9796 莜 -22.9797 犄 -22.9798 坩 -22.9799 欸 -22.98 蒯 -22.9801 缱 -22.9802 牝 -22.9803 揾 -22.9804 醍 -22.9805 襁 -22.9806 馊 -22.9807 〜 -22.9808 雒 -22.9809 铡 -22.981 辎 -22.9811 儗 -22.9812 蟋 -22.9813 偍 -22.9814 郫 -22.9815 噘 -22.9816 氙 -22.9817 蹉 -22.9818 箬 -22.9819 赝 -22.982 睚 -22.9821 濛 -22.9822 噯 -22.9823 怙 -22.9824 シ -22.9825 嗳 -22.9826 蹩 -22.9827 魑 -22.9828 麋 -22.9829 弸 -22.983 鲶 -22.9831 梖 -22.9832 孱 -22.9833 铤 -22.9834 醴 -22.9835 盥 -22.9836 睥 -22.9837 垝 -22.9838 啫 -22.9839 邝 -22.984 孬 -22.9841 醡 -22.9842 郧 -22.9843 邛 -22.9844 梽 -22.9845 薙 -22.9846 鹌 -22.9847 阚 -22.9848 愠 -22.9849 瘯 -22.985 氩 -22.9851 拋 -22.9852 犒 -22.9853 蝌 -22.9854 鄞 -22.9855 嗬 -22.9856 丟 -22.9857 袈 -22.9858 汨 -22.9859 綦 -22.986 煅 -22.9861 佝 -22.9862 嵊 -22.9863 珐 -22.9864 黉 -22.9865 跛 -22.9866 樋 -22.9867 沏 -22.9868 孪 -22.9869 柊 -22.987 齎 -22.9871 寤 -22.9872 拮 -22.9873 牦 -22.9874 嬁 -22.9875 キ -22.9876 鹈 -22.9877 龇 -22.9878 矇 -22.9879 涿 -22.988 玷 -22.9881 崆 -22.9882 枇 -22.9883 枳 -22.9884 怘 -22.9885 扦 -22.9886 哐 -22.9887 桁 -22.9888 蒐 -22.9889 剽 -22.989 胴 -22.9891 髋 -22.9892 閰 -22.9893 搧 -22.9894 掞 -22.9895 砝 -22.9896 砼 -22.9897 踮 -22.9898 诙 -22.9899 酗 -22.99 崂 -22.9901 喺 -22.9902 蚯 -22.9903 讴 -22.9904 阉 -22.9905 腼 -22.9906 蔔 -22.9907 漯 -22.9908 畠 -22.9909 撵 -22.991 鞑 -22.9911 馄 -22.9912 痤 -22.9913 喎 -22.9914 噱 -22.9915 觊 -22.9916 钣 -22.9917 佘 -22.9918 豢 -22.9919 遛 -22.992 婺 -22.9921 氶 -22.9922 俾 -22.9923 麝 -22.9924 槦 -22.9925 荨 -22.9926 鐤 -22.9927 滁 -22.9928 蚝 -22.9929 侏 -22.993 蝼 -22.9931 橈 -22.9932 螾 -22.9933 瞠 -22.9934 跆 -22.9935 抨 -22.9936 蹂 -22.9937 冮 -22.9938 惆 -22.9939 藐 -22.994 邬 -22.9941 蟑 -22.9942 踌 -22.9943 涳 -22.9944 涢 -22.9945 缜 -22.9946 碉 -22.9947 缂 -22.9948 琊 -22.9949 瘙 -22.995 綍 -22.9951 屌 -22.9952 踱 -22.9953 猕 -22.9954 █ -22.9955 啀 -22.9956 肮 -22.9957 摒 -22.9958 骻 -22.9959 蜿 -22.996 楋 -22.9961 洖 -22.9962 猖 -22.9963 蜷 -22.9964 飓 -22.9965 罹 -22.9966 炰 -22.9967 邂 -22.9968 瞭 -22.9969 裇 -22.997 忚 -22.9971 跻 -22.9972 椭 -22.9973 痊 -22.9974 螃 -22.9975 婄 -22.9976 哽 -22.9977 侀 -22.9978 遴 -22.9979 酝 -22.998 莆 -22.9981 鎭 -22.9982 瞟 -22.9983 搀 -22.9984 忐 -22.9985 胭 -22.9986 彷 -22.9987 婊 -22.9988 惬 -22.9989 粽 -22.999 呦 -22.9991 冨 -22.9992 繬 -22.9993 懊 -22.9994 侥 -22.9995 囨 -22.9996 泸 -22.9997 徘 -22.9998 簨 -22.9999 濇 -23 撳 -23.0001 ョ -23.0002 闃 -23.0003 邯 -23.0004 欑 -23.0005 狙 -23.0006 熺 -23.0007 灞 -23.0008 踹 -23.0009 夌 -23.001 ‧ -23.0011 煽 -23.0012 咔 -23.0013 暧 -23.0014 匈 -23.0015 籗 -23.0016 彲 -23.0017 犱 -23.0018 褪 -23.0019 傝 -23.002 擄 -23.0021 剁 -23.0022 庡 -23.0023 嗅 -23.0024 咆 -23.0025 緱 -23.0026 栧 -23.0027 椂 -23.0028 湴 -23.0029 嬫 -23.003 蓦 -23.0031 氬 -23.0032 骷 -23.0033 鑷 -23.0034 黯 -23.0035 丨 -23.0036 孵 -23.0037 佷 -23.0038 撸 -23.0039 ぇ -23.004 倘 -23.0041 鍘 -23.0042 闂 -23.0043 夊 -23.0044 胳 -23.0045 缁 -23.0046 潵 -23.0047 傚 -23.0048 滃 -23.0049 趴 -23.005 ′ -23.0051 傗 -23.0052 粬 -23.0053 岃 -23.0054 垜 -23.0055 鏉 -23.0056 哎 -23.0057 屼 -23.0058 竴 -23.0059 鏄 -23.006 钬 -23.0061 屽 -23.0062 鈥 -23.0063 镥 -23.0064 铽 -23.0065 铕 -23.0066 钆 -23.0067 嗯 -23.0068 殑 -23.0069 铒 -23.007 钐 -23.0071 弑 -23.0072 ~ -23.0072 ° -23.0072 × -23.0072 ― -23.0072 ‘ -23.0072 → -23.0072 ♀ -23.0072 お -23.0072 ヤ -23.0072 ワ -23.0072 佹 -23.0072 勭 -23.0072 咱 -23.0072 堟 -23.0072 紝 -23.0072 臺 -23.0072 貔 -23.0072 鍓 -23.0072 鍙 -23.0072 鐨 -23.0072 镧 -23.0072 镨 -23.0072 呃 -23.0072 」 -23.0072 哂 -23.0072 pangu/megatron/checkpointing.py000066400000000000000000000247451430334424000172140ustar00rootroot00000000000000# 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. """Input/output checkpointing.""" import os import random import sys import numpy as np import torch from torch.nn.parallel import DistributedDataParallel as torchDDP from megatron import mpu, get_args from megatron import get_args from megatron import print_rank_0 def check_checkpoint_args(checkpoint_args): """Ensure fixed arguments for a model are the same for the input arguments and the one retreived frm checkpoint.""" args = get_args() def _compare(arg_name): checkpoint_value = getattr(checkpoint_args, arg_name) args_value = getattr(args, arg_name) error_message = '{} value from checkpoint ({}) is not equal to the ' \ 'input argument value ({}).'.format( arg_name, checkpoint_value, args_value) assert checkpoint_value == args_value, error_message _compare('num_layers') _compare('hidden_size') _compare('num_attention_heads') _compare('max_position_embeddings') _compare('make_vocab_size_divisible_by') _compare('padded_vocab_size') _compare('tokenizer_type') _compare('model_parallel_size') def ensure_directory_exists(filename): """Build filename's path if it does not already exists.""" dirname = os.path.dirname(filename) if not os.path.exists(dirname): os.makedirs(dirname) def get_checkpoint_name(checkpoints_path, iteration, release=False, mp_rank=None): """A unified checkpoint name.""" if release: directory = 'release' else: directory = 'iter_{:07d}'.format(iteration) return os.path.join(checkpoints_path, directory, 'mp_rank_{:02d}'.format( mpu.get_model_parallel_rank() if mp_rank is None else mp_rank), 'model_optim_rng.pt') def get_checkpoint_tracker_filename(checkpoints_path): """Tracker file rescords the latest chckpoint during training to restart from.""" return os.path.join(checkpoints_path, 'latest_checkpointed_iteration.txt') def save_checkpoint(iteration, model, optimizer, lr_scheduler): """Save a model checkpoint.""" args = get_args() # Only rank zero of the data parallel writes to the disk. if isinstance(model, torchDDP): model = model.module if mpu.get_data_parallel_rank() == 0: # Arguments, iteration, and model. state_dict = {} state_dict['args'] = args state_dict['iteration'] = iteration state_dict['model'] = model.state_dict_for_save_checkpoint() # Optimizer stuff. if not args.no_save_optim: if optimizer is not None: state_dict['optimizer'] = optimizer.state_dict() if lr_scheduler is not None: state_dict['lr_scheduler'] = lr_scheduler.state_dict() # RNG states. if not args.no_save_rng: state_dict['random_rng_state'] = random.getstate() state_dict['np_rng_state'] = np.random.get_state() state_dict['torch_rng_state'] = torch.get_rng_state() state_dict['cuda_rng_state'] = torch.cuda.get_rng_state() state_dict['rng_tracker_states'] \ = mpu.get_cuda_rng_tracker().get_states() # Save. checkpoint_name = get_checkpoint_name(args.save, iteration) print('global rank {} is saving checkpoint at iteration {:7d} to {}'. format(torch.distributed.get_rank(), iteration, checkpoint_name)) ensure_directory_exists(checkpoint_name) torch.save(state_dict, checkpoint_name) print(' successfully saved {}'.format(checkpoint_name)) # Wait so everyone is done (necessary) torch.distributed.barrier() # And update the latest iteration if torch.distributed.get_rank() == 0: tracker_filename = get_checkpoint_tracker_filename(args.save) with open(tracker_filename, 'w') as f: f.write(str(iteration)) # Wait so everyone is done (not necessary) torch.distributed.barrier() def load_checkpoint(model, optimizer, lr_scheduler, load_arg='load'): """Load a model checkpoint and return the iteration.""" args = get_args() load_dir = getattr(args, load_arg) if isinstance(model, torchDDP): model = model.module # Read the tracker file and set the iteration. tracker_filename = get_checkpoint_tracker_filename(load_dir) # If no tracker file, return iretation zero. if not os.path.isfile(tracker_filename): print_rank_0('WARNING: could not find the metadata file {} '.format( tracker_filename)) print_rank_0(' will not load any checkpoints and will start from ' 'random') return 0 # Otherwise, read the tracker file and either set the iteration or # mark it as a release checkpoint. iteration = 0 release = False with open(tracker_filename, 'r') as f: metastring = f.read().strip() try: iteration = int(metastring) except ValueError: release = metastring == 'release' if not release: print_rank_0('ERROR: Invalid metadata file {}. Exiting'.format( tracker_filename)) sys.exit() assert iteration > 0 or release, 'error parsing metadata file {}'.format( tracker_filename) # Checkpoint. checkpoint_name = get_checkpoint_name(load_dir, iteration, release) if mpu.get_data_parallel_rank() == 0: print('global rank {} is loading checkpoint {}'.format( torch.distributed.get_rank(), checkpoint_name)) # Load the checkpoint. try: state_dict = torch.load(checkpoint_name, map_location='cpu') except ModuleNotFoundError: # For backward compatibility. print_rank_0(' > deserializing using the old code structure ...') sys.modules['fp16.loss_scaler'] = sys.modules[ 'megatron.fp16.loss_scaler'] state_dict = torch.load(checkpoint_name, map_location='cpu') sys.modules.pop('fp16.loss_scaler', None) except BaseException: print_rank_0('could not load the checkpoint') sys.exit() # Set iteration. if args.finetune or release: iteration = 0 else: try: iteration = state_dict['iteration'] except KeyError: try: # Backward compatible with older checkpoints iteration = state_dict['total_iters'] except KeyError: print_rank_0('A metadata file exists but unable to load ' 'iteration from checkpoint {}, exiting'.format( checkpoint_name)) sys.exit() # Check arguments. if 'args' in state_dict: checkpoint_args = state_dict['args'] check_checkpoint_args(checkpoint_args) else: print_rank_0('could not find arguments in the checkpoint ...') # Model. model.load_state_dict(state_dict['model']) # Optimizer. if not release and not args.finetune and not args.no_load_optim: try: if optimizer is not None: optimizer.load_state_dict(state_dict['optimizer']) if lr_scheduler is not None: lr_scheduler.load_state_dict(state_dict['lr_scheduler']) except KeyError: print_rank_0('Unable to load optimizer from checkpoint {}. ' 'Specify --no-load-optim or --finetune to prevent ' 'attempting to load the optimizer state, ' 'exiting ...'.format(checkpoint_name)) sys.exit() # rng states. if not release and not args.finetune and not args.no_load_rng: try: random.setstate(state_dict['random_rng_state']) np.random.set_state(state_dict['np_rng_state']) torch.set_rng_state(state_dict['torch_rng_state']) torch.cuda.set_rng_state(state_dict['cuda_rng_state']) mpu.get_cuda_rng_tracker().set_states( state_dict['rng_tracker_states']) except KeyError: print_rank_0('Unable to load optimizer from checkpoint {}. ' 'Specify --no-load-rng or --finetune to prevent ' 'attempting to load the optimizer state, ' 'exiting ...'.format(checkpoint_name)) sys.exit() torch.distributed.barrier() if mpu.get_data_parallel_rank() == 0: print(' successfully loaded {}'.format(checkpoint_name)) return iteration def load_ict_checkpoint(model, only_query_model=False, only_block_model=False, from_realm_chkpt=False): """selectively load ICT models for indexing/retrieving from ICT or REALM checkpoints""" args = get_args() if isinstance(model, torchDDP): model = model.module load_path = args.load if from_realm_chkpt else args.ict_load tracker_filename = get_checkpoint_tracker_filename(load_path) with open(tracker_filename, 'r') as f: iteration = int(f.read().strip()) # assert iteration > 0 checkpoint_name = get_checkpoint_name(load_path, iteration, False) if mpu.get_data_parallel_rank() == 0: print('global rank {} is loading checkpoint {}'.format( torch.distributed.get_rank(), checkpoint_name)) state_dict = torch.load(checkpoint_name, map_location='cpu') ict_state_dict = state_dict['model'] if from_realm_chkpt and mpu.get_data_parallel_rank() == 0: print(" loading ICT state dict from REALM", flush=True) ict_state_dict = ict_state_dict['retriever']['ict_model'] if only_query_model: ict_state_dict.pop('context_model') if only_block_model: ict_state_dict.pop('question_model') model.load_state_dict(ict_state_dict) torch.distributed.barrier() if mpu.get_data_parallel_rank() == 0: print(' successfully loaded {}'.format(checkpoint_name)) return model pangu/megatron/data/000077500000000000000000000000001430334424000147125ustar00rootroot00000000000000pangu/megatron/data/Makefile000066400000000000000000000004271430334424000163550ustar00rootroot00000000000000CXXFLAGS += -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 $@ pangu/megatron/data/__init__.py000066400000000000000000000000361430334424000170220ustar00rootroot00000000000000from . import indexed_dataset pangu/megatron/data/bert_dataset.py000066400000000000000000000226111430334424000177270ustar00rootroot00000000000000# 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. """BERT Style dataset.""" import os import time import numpy as np import torch from torch.utils.data import Dataset from megatron import get_tokenizer, get_args from megatron import print_rank_0 from megatron import mpu from megatron.data.dataset_utils import get_a_and_b_segments from megatron.data.dataset_utils import truncate_segments from megatron.data.dataset_utils import create_tokens_and_tokentypes from megatron.data.dataset_utils import pad_and_convert_to_numpy from megatron.data.dataset_utils import create_masked_lm_predictions class BertDataset(Dataset): def __init__(self, name, indexed_dataset, data_prefix, num_epochs, max_num_samples, masked_lm_prob, max_seq_length, short_seq_prob, seed): # Params to store. self.name = name self.seed = seed self.masked_lm_prob = masked_lm_prob self.max_seq_length = max_seq_length # Dataset. self.indexed_dataset = indexed_dataset # Build the samples mapping. self.samples_mapping = get_samples_mapping_(self.indexed_dataset, data_prefix, num_epochs, max_num_samples, self.max_seq_length, short_seq_prob, self.seed, self.name) # Vocab stuff. tokenizer = get_tokenizer() self.vocab_id_list = list(tokenizer.inv_vocab.keys()) self.vocab_id_to_token_dict = tokenizer.inv_vocab self.cls_id = tokenizer.cls self.sep_id = tokenizer.sep self.mask_id = tokenizer.mask self.pad_id = tokenizer.pad def __len__(self): return self.samples_mapping.shape[0] def __getitem__(self, idx): start_idx, end_idx, seq_length = self.samples_mapping[idx] sample = [self.indexed_dataset[i] for i in range(start_idx, end_idx)] # Note that this rng state should be numpy and not python since # python randint is inclusive whereas the numpy one is exclusive. np_rng = np.random.RandomState(seed=(self.seed + idx)) return build_training_sample(sample, seq_length, self.max_seq_length, # needed for padding self.vocab_id_list, self.vocab_id_to_token_dict, self.cls_id, self.sep_id, self.mask_id, self.pad_id, self.masked_lm_prob, np_rng) def get_samples_mapping_(indexed_dataset, data_prefix, num_epochs, max_num_samples, max_seq_length, short_seq_prob, seed, name): if not num_epochs: if not max_num_samples: raise ValueError("Need to specify either max_num_samples " "or num_epochs") num_epochs = np.iinfo(np.int32).max - 1 if not max_num_samples: max_num_samples = np.iinfo(np.int64).max - 1 # Filename of the index mapping indexmap_filename = data_prefix indexmap_filename += '_{}_indexmap'.format(name) if num_epochs != (np.iinfo(np.int32).max - 1): indexmap_filename += '_{}ep'.format(num_epochs) if max_num_samples != (np.iinfo(np.int64).max - 1): indexmap_filename += '_{}mns'.format(max_num_samples) indexmap_filename += '_{}msl'.format(max_seq_length) indexmap_filename += '_{:0.2f}ssp'.format(short_seq_prob) indexmap_filename += '_{}s'.format(seed) indexmap_filename += '.npy' # Build the indexed mapping if not exist. if torch.distributed.get_rank() == 0 and \ not os.path.isfile(indexmap_filename): print(' > WARNING: could not find index map file {}, building ' 'the indices on rank 0 ...'.format(indexmap_filename)) # Make sure the types match the helpers input types. assert indexed_dataset.doc_idx.dtype == np.int64 assert indexed_dataset.sizes.dtype == np.int32 # Build samples mapping verbose = torch.distributed.get_rank() == 0 start_time = time.time() print_rank_0(' > building sapmles index mapping for {} ...'.format( name)) # First compile and then import. from megatron.data.dataset_utils import compile_helper compile_helper() from megatron.data import helpers samples_mapping = helpers.build_mapping( indexed_dataset.doc_idx, indexed_dataset.sizes, num_epochs, max_num_samples, max_seq_length - 3, # account for added tokens short_seq_prob, seed, verbose) print_rank_0(' > done building sapmles index maping') np.save(indexmap_filename, samples_mapping, allow_pickle=True) print_rank_0(' > saved the index mapping in {}'.format( indexmap_filename)) # Make sure all the ranks have built the mapping print_rank_0(' > elasped time to build and save samples mapping ' '(seconds): {:4f}'.format( time.time() - start_time)) # This should be a barrier but nccl barrier assumes # device_index=rank which is not the case for model # parallel case counts = torch.cuda.LongTensor([1]) torch.distributed.all_reduce(counts, group=mpu.get_data_parallel_group()) assert counts[0].item() == torch.distributed.get_world_size( group=mpu.get_data_parallel_group()) # Load indexed dataset. print_rank_0(' > loading indexed mapping from {}'.format( indexmap_filename)) start_time = time.time() samples_mapping = np.load(indexmap_filename, allow_pickle=True, mmap_mode='r') print_rank_0(' loaded indexed file in {:3.3f} seconds'.format( time.time() - start_time)) print_rank_0(' total number of samples: {}'.format( samples_mapping.shape[0])) return samples_mapping def build_training_sample(sample, target_seq_length, max_seq_length, vocab_id_list, vocab_id_to_token_dict, cls_id, sep_id, mask_id, pad_id, masked_lm_prob, np_rng): """Biuld training sample. Arguments: sample: A list of sentences in which each sentence is a list token ids. target_seq_length: Desired sequence length. max_seq_length: Maximum length of the sequence. All values are padded to this length. vocab_id_list: List of vocabulary ids. Used to pick a random id. vocab_id_to_token_dict: A dictionary from vocab ids to text tokens. cls_id: Start of example id. sep_id: Separator id. mask_id: Mask token id. pad_id: Padding token id. masked_lm_prob: Probability to mask tokens. np_rng: Random number genenrator. Note that this rng state should be numpy and not python since python randint is inclusive for the opper bound whereas the numpy one is exclusive. """ # We assume that we have at least two sentences in the sample assert len(sample) > 1 assert target_seq_length <= max_seq_length # Divide sample into two segments (A and B). tokens_a, tokens_b, is_next_random = get_a_and_b_segments(sample, np_rng) # Truncate to `target_sequence_length`. max_num_tokens = target_seq_length truncated = truncate_segments(tokens_a, tokens_b, len(tokens_a), len(tokens_b), max_num_tokens, np_rng) # Build tokens and toketypes. tokens, tokentypes = create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id) # Masking. max_predictions_per_seq = masked_lm_prob * max_num_tokens (tokens, masked_positions, masked_labels, _) = create_masked_lm_predictions( tokens, vocab_id_list, vocab_id_to_token_dict, masked_lm_prob, cls_id, sep_id, mask_id, max_predictions_per_seq, np_rng) # Padding. tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np \ = pad_and_convert_to_numpy(tokens, tokentypes, masked_positions, masked_labels, pad_id, max_seq_length) train_sample = { 'text': tokens_np, 'types': tokentypes_np, 'labels': labels_np, 'is_random': int(is_next_random), 'loss_mask': loss_mask_np, 'padding_mask': padding_mask_np, 'truncated': int(truncated)} return train_sample pangu/megatron/data/dataset_utils.py000066400000000000000000000437641430334424000201470ustar00rootroot00000000000000# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors, and NVIDIA. # # 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. # Most of the code here has been copied from: # https://github.com/google-research/albert/blob/master/create_pretraining_data.py # with some modifications. import time import collections import numpy as np from megatron import get_args, print_rank_0 from megatron.data.indexed_dataset import make_dataset as make_indexed_dataset DSET_TYPE_STD = 'standard_bert' DSET_TYPE_ICT = 'ict' DSET_TYPES = [DSET_TYPE_ICT, DSET_TYPE_STD] def compile_helper(): """Compile helper function ar runtime. Make sure this is invoked on a single process.""" import os import subprocess path = os.path.abspath(os.path.dirname(__file__)) ret = subprocess.run(['make', '-C', path]) if ret.returncode != 0: print("Making C++ dataset helpers module failed, exiting.") import sys sys.exit(1) def get_a_and_b_segments(sample, np_rng): """Divide sample into a and b segments.""" # Number of sentences in the sample. n_sentences = len(sample) # Make sure we always have two sentences. assert n_sentences > 1, 'make sure each sample has at least two sentences.' # First part: # `a_end` is how many sentences go into the `A`. a_end = 1 if n_sentences >= 3: # Note that randin in numpy is exclusive. a_end = np_rng.randint(1, n_sentences) tokens_a = [] for j in range(a_end): tokens_a.extend(sample[j]) # Second part: tokens_b = [] for j in range(a_end, n_sentences): tokens_b.extend(sample[j]) # Random next: is_next_random = False if np_rng.random() < 0.5: is_next_random = True tokens_a, tokens_b = tokens_b, tokens_a return tokens_a, tokens_b, is_next_random def truncate_segments(tokens_a, tokens_b, len_a, len_b, max_num_tokens, np_rng): """Truncates a pair of sequences to a maximum sequence length.""" #print(len_a, len_b, max_num_tokens) assert len_a > 0 assert len_b > 0 if len_a + len_b <= max_num_tokens: return False while len_a + len_b > max_num_tokens: if len_a > len_b: len_a -= 1 tokens = tokens_a else: len_b -= 1 tokens = tokens_b if np_rng.random() < 0.5: del tokens[0] else: tokens.pop() return True def create_tokens_and_tokentypes(tokens_a, tokens_b, cls_id, sep_id): """Merge segments A and B, add [CLS] and [SEP] and build tokentypes.""" tokens = [] tokentypes = [] # [CLS]. tokens.append(cls_id) tokentypes.append(0) # Segment A. for token in tokens_a: tokens.append(token) tokentypes.append(0) # [SEP]. tokens.append(sep_id) tokentypes.append(0) # Segment B. for token in tokens_b: tokens.append(token) tokentypes.append(1) # [SEP]. tokens.append(sep_id) tokentypes.append(1) return tokens, tokentypes MaskedLmInstance = collections.namedtuple("MaskedLmInstance", ["index", "label"]) def is_start_piece(piece): """Check if the current word piece is the starting piece (BERT).""" # When a word has been split into # WordPieces, the first token does not have any marker and any subsequence # tokens are prefixed with ##. So whenever we see the ## token, we # append it to the previous set of word indexes. return not piece.startswith("##") def create_masked_lm_predictions(tokens, vocab_id_list, vocab_id_to_token_dict, masked_lm_prob, cls_id, sep_id, mask_id, max_predictions_per_seq, np_rng, max_ngrams=3, do_whole_word_mask=True, favor_longer_ngram=False, do_permutation=False): """Creates the predictions for the masked LM objective. Note: Tokens here are vocab ids and not text tokens.""" cand_indexes = [] # Note(mingdachen): We create a list for recording if the piece is # the starting piece of current token, where 1 means true, so that # on-the-fly whole word masking is possible. token_boundary = [0] * len(tokens) for (i, token) in enumerate(tokens): if token == cls_id or token == sep_id: token_boundary[i] = 1 continue # Whole Word Masking means that if we mask all of the wordpieces # corresponding to an original word. # # Note that Whole Word Masking does *not* change the training code # at all -- we still predict each WordPiece independently, softmaxed # over the entire vocabulary. if (do_whole_word_mask and len(cand_indexes) >= 1 and not is_start_piece(vocab_id_to_token_dict[token])): cand_indexes[-1].append(i) else: cand_indexes.append([i]) if is_start_piece(vocab_id_to_token_dict[token]): token_boundary[i] = 1 output_tokens = list(tokens) masked_lm_positions = [] masked_lm_labels = [] if masked_lm_prob == 0: return (output_tokens, masked_lm_positions, masked_lm_labels, token_boundary) num_to_predict = min(max_predictions_per_seq, max(1, int(round(len(tokens) * masked_lm_prob)))) # Note(mingdachen): # By default, we set the probilities to favor shorter ngram sequences. ngrams = np.arange(1, max_ngrams + 1, dtype=np.int64) pvals = 1. / np.arange(1, max_ngrams + 1) pvals /= pvals.sum(keepdims=True) if favor_longer_ngram: pvals = pvals[::-1] ngram_indexes = [] for idx in range(len(cand_indexes)): ngram_index = [] for n in ngrams: ngram_index.append(cand_indexes[idx:idx + n]) ngram_indexes.append(ngram_index) np_rng.shuffle(ngram_indexes) masked_lms = [] covered_indexes = set() for cand_index_set in ngram_indexes: if len(masked_lms) >= num_to_predict: break if not cand_index_set: continue # Note(mingdachen): # Skip current piece if they are covered in lm masking or previous ngrams. for index_set in cand_index_set[0]: for index in index_set: if index in covered_indexes: continue n = np_rng.choice(ngrams[:len(cand_index_set)], p=pvals[:len(cand_index_set)] / pvals[:len(cand_index_set)].sum(keepdims=True)) index_set = sum(cand_index_set[n - 1], []) n -= 1 # Note(mingdachen): # Repeatedly looking for a candidate that does not exceed the # maximum number of predictions by trying shorter ngrams. while len(masked_lms) + len(index_set) > num_to_predict: if n == 0: break index_set = sum(cand_index_set[n - 1], []) n -= 1 # If adding a whole-word mask would exceed the maximum number of # predictions, then just skip this candidate. if len(masked_lms) + len(index_set) > num_to_predict: continue is_any_index_covered = False for index in index_set: if index in covered_indexes: is_any_index_covered = True break if is_any_index_covered: continue for index in index_set: covered_indexes.add(index) masked_token = None # 80% of the time, replace with [MASK] if np_rng.random() < 0.8: masked_token = mask_id else: # 10% of the time, keep original if np_rng.random() < 0.5: masked_token = tokens[index] # 10% of the time, replace with random word else: masked_token = vocab_id_list[np_rng.randint(0, len(vocab_id_list))] output_tokens[index] = masked_token masked_lms.append(MaskedLmInstance(index=index, label=tokens[index])) assert len(masked_lms) <= num_to_predict np_rng.shuffle(ngram_indexes) select_indexes = set() if do_permutation: for cand_index_set in ngram_indexes: if len(select_indexes) >= num_to_predict: break if not cand_index_set: continue # Note(mingdachen): # Skip current piece if they are covered in lm masking or previous ngrams. for index_set in cand_index_set[0]: for index in index_set: if index in covered_indexes or index in select_indexes: continue n = np.random.choice(ngrams[:len(cand_index_set)], p=pvals[:len(cand_index_set)] / pvals[:len(cand_index_set)].sum(keepdims=True)) index_set = sum(cand_index_set[n - 1], []) n -= 1 while len(select_indexes) + len(index_set) > num_to_predict: if n == 0: break index_set = sum(cand_index_set[n - 1], []) n -= 1 # If adding a whole-word mask would exceed the maximum number of # predictions, then just skip this candidate. if len(select_indexes) + len(index_set) > num_to_predict: continue is_any_index_covered = False for index in index_set: if index in covered_indexes or index in select_indexes: is_any_index_covered = True break if is_any_index_covered: continue for index in index_set: select_indexes.add(index) assert len(select_indexes) <= num_to_predict select_indexes = sorted(select_indexes) permute_indexes = list(select_indexes) np_rng.shuffle(permute_indexes) orig_token = list(output_tokens) for src_i, tgt_i in zip(select_indexes, permute_indexes): output_tokens[src_i] = orig_token[tgt_i] masked_lms.append(MaskedLmInstance(index=src_i, label=orig_token[src_i])) masked_lms = sorted(masked_lms, key=lambda x: x.index) for p in masked_lms: masked_lm_positions.append(p.index) masked_lm_labels.append(p.label) return (output_tokens, masked_lm_positions, masked_lm_labels, token_boundary) def pad_and_convert_to_numpy(tokens, tokentypes, masked_positions, masked_labels, pad_id, max_seq_length): """Pad sequences and convert them to numpy.""" # Some checks. num_tokens = len(tokens) padding_length = max_seq_length - num_tokens assert padding_length >= 0 assert len(tokentypes) == num_tokens assert len(masked_positions) == len(masked_labels) # Tokens and token types. filler = [pad_id] * padding_length tokens_np = np.array(tokens + filler, dtype=np.int64) tokentypes_np = np.array(tokentypes + filler, dtype=np.int64) # Padding mask. padding_mask_np = np.array([1] * num_tokens + [0] * padding_length, dtype=np.int64) # Lables and loss mask. labels = [-1] * max_seq_length loss_mask = [0] * max_seq_length for i in range(len(masked_positions)): assert masked_positions[i] < num_tokens labels[masked_positions[i]] = masked_labels[i] loss_mask[masked_positions[i]] = 1 labels_np = np.array(labels, dtype=np.int64) loss_mask_np = np.array(loss_mask, dtype=np.int64) return tokens_np, tokentypes_np, labels_np, padding_mask_np, loss_mask_np def build_train_valid_test_datasets(data_prefix, data_impl, splits_string, train_valid_test_num_samples, max_seq_length, masked_lm_prob, short_seq_prob, seed, skip_warmup, dataset_type='standard_bert'): if dataset_type not in DSET_TYPES: raise ValueError("Invalid dataset_type: ", dataset_type) # Indexed dataset. indexed_dataset = get_indexed_dataset_(data_prefix, data_impl, skip_warmup) if dataset_type == DSET_TYPE_ICT: args = get_args() title_dataset = get_indexed_dataset_(args.titles_data_path, data_impl, skip_warmup) # Get start and end indices of train/valid/train into doc-idx # Note that doc-idx is desinged to be num-docs + 1 so we can # easily iterate over it. total_num_of_documents = indexed_dataset.doc_idx.shape[0] - 1 splits = get_train_valid_test_split_(splits_string, total_num_of_documents) # Print stats about the splits. print_rank_0(' > dataset split:') def print_split_stats(name, index): print_rank_0(' {}:'.format(name)) print_rank_0(' document indices in [{}, {}) total of {} ' 'documents'.format(splits[index], splits[index + 1], splits[index + 1] - splits[index])) start_index = indexed_dataset.doc_idx[splits[index]] end_index = indexed_dataset.doc_idx[splits[index + 1]] print_rank_0(' sentence indices in [{}, {}) total of {} ' 'sentences'.format(start_index, end_index, end_index - start_index)) print_split_stats('train', 0) print_split_stats('validation', 1) print_split_stats('test', 2) def build_dataset(index, name): from megatron.data.bert_dataset import BertDataset from megatron.data.ict_dataset import ICTDataset dataset = None if splits[index + 1] > splits[index]: # Get the pointer to the original doc-idx so we can set it later. doc_idx_ptr = indexed_dataset.get_doc_idx() # Slice the doc-idx start_index = splits[index] # Add +1 so we can index into the dataset to get the upper bound. end_index = splits[index + 1] + 1 # New doc_idx view. indexed_dataset.set_doc_idx(doc_idx_ptr[start_index:end_index]) # Build the dataset accordingly. kwargs = dict( name=name, data_prefix=data_prefix, num_epochs=None, max_num_samples=train_valid_test_num_samples[index], max_seq_length=max_seq_length, seed=seed ) if dataset_type == DSET_TYPE_ICT: args = get_args() dataset = ICTDataset( block_dataset=indexed_dataset, title_dataset=title_dataset, query_in_block_prob=args.query_in_block_prob, use_one_sent_docs=args.use_one_sent_docs, **kwargs ) else: dataset = BertDataset( indexed_dataset=indexed_dataset, masked_lm_prob=masked_lm_prob, short_seq_prob=short_seq_prob, **kwargs ) # Set the original pointer so dataset remains the main dataset. indexed_dataset.set_doc_idx(doc_idx_ptr) # Checks. assert indexed_dataset.doc_idx[0] == 0 assert indexed_dataset.doc_idx.shape[0] == \ (total_num_of_documents + 1) return dataset train_dataset = build_dataset(0, 'train') valid_dataset = build_dataset(1, 'valid') test_dataset = build_dataset(2, 'test') return (train_dataset, valid_dataset, test_dataset) def get_indexed_dataset_(data_prefix, data_impl, skip_warmup): print_rank_0(' > building dataset index ...') start_time = time.time() indexed_dataset = make_indexed_dataset(data_prefix, data_impl, skip_warmup) assert indexed_dataset.sizes.shape[0] == indexed_dataset.doc_idx[-1] print_rank_0(' > finished creating indexed dataset in {:4f} ' 'seconds'.format(time.time() - start_time)) print_rank_0(' > indexed dataset stats:') print_rank_0(' number of documents: {}'.format( indexed_dataset.doc_idx.shape[0] - 1)) print_rank_0(' number of sentences: {}'.format( indexed_dataset.sizes.shape[0])) return indexed_dataset def get_train_valid_test_split_(splits_string, size): """ Get dataset splits from comma or '/' separated string list.""" splits = [] 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.) splits = splits[:3] splits_sum = sum(splits) assert splits_sum > 0.0 splits = [split / splits_sum for split in splits] splits_index = [0] for index, split in enumerate(splits): splits_index.append(splits_index[index] + int(round(split * float(size)))) diff = splits_index[-1] - size for index in range(1, len(splits_index)): splits_index[index] -= diff assert len(splits_index) == 4 assert splits_index[-1] == size return splits_index pangu/megatron/data/gpt2_dataset.py000066400000000000000000000320221430334424000176440ustar00rootroot00000000000000# 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. """GPT2 style dataset.""" import os import time import numpy as np import torch from megatron import mpu, print_rank_0 from megatron.data.dataset_utils import get_train_valid_test_split_ from megatron.data.indexed_dataset import make_dataset as make_indexed_dataset def build_train_valid_test_datasets(data_prefix, data_impl, splits_string, train_valid_test_num_samples, seq_length, seed, skip_warmup): """Build train, valid, and test datasets.""" # Indexed dataset. indexed_dataset = get_indexed_dataset_(data_prefix, data_impl, skip_warmup) total_num_of_documents = indexed_dataset.sizes.shape[0] splits = get_train_valid_test_split_(splits_string, total_num_of_documents) # Print stats about the splits. print_rank_0(' > dataset split:') def print_split_stats(name, index): print_rank_0(' {}:'.format(name)) print_rank_0(' document indices in [{}, {}) total of {} ' 'documents'.format(splits[index], splits[index + 1], splits[index + 1] - splits[index])) print_split_stats('train', 0) print_split_stats('validation', 1) print_split_stats('test', 2) def build_dataset(index, name): dataset = None if splits[index + 1] > splits[index]: documents = np.arange(start=splits[index], stop=splits[index + 1], step=1, dtype=np.int32) dataset = GPT2Dataset(name, data_prefix, documents, indexed_dataset, train_valid_test_num_samples[index], seq_length, seed) return dataset train_dataset = build_dataset(0, 'train') valid_dataset = build_dataset(1, 'valid') test_dataset = build_dataset(2, 'test') return (train_dataset, valid_dataset, test_dataset) def get_indexed_dataset_(data_prefix, data_impl, skip_warmup): """Build indexed dataset.""" print_rank_0(' > building dataset index ...') start_time = time.time() indexed_dataset = make_indexed_dataset(data_prefix, data_impl, skip_warmup) print_rank_0(' > finished creating indexed dataset in {:4f} ' 'seconds'.format(time.time() - start_time)) print_rank_0(' number of documents: {}'.format( indexed_dataset.sizes.shape[0])) return indexed_dataset class GPT2Dataset(torch.utils.data.Dataset): def __init__(self, name, data_prefix, documents, indexed_dataset, num_samples, seq_length, seed): self.name = name self.indexed_dataset = indexed_dataset # Checks assert np.min(documents) >= 0 assert np.max(documents) < indexed_dataset.sizes.shape[0] # Build index mappings. self.doc_idx, self.sample_idx, self.shuffle_idx = _build_index_mappings( self.name, data_prefix, documents, self.indexed_dataset.sizes, num_samples, seq_length, seed) def __len__(self): # -1 is due to data structure used to retieve the index: # sample i --> [sample_idx[i], sample_idx[i+1]) return self.sample_idx.shape[0] - 1 def __getitem__(self, idx): # Get the shuffled index. idx = self.shuffle_idx[idx] # Start and end documents and offsets. doc_index_f = self.sample_idx[idx][0] doc_index_l = self.sample_idx[idx + 1][0] offset_f = self.sample_idx[idx][1] offset_l = self.sample_idx[idx + 1][1] # If we are within the same document, just extract the chunk. if doc_index_f == doc_index_l: sample = self.indexed_dataset.get(self.doc_idx[doc_index_f], offset=offset_f, length=offset_l - offset_f + 1) else: # Otherwise, get the rest of the initial document. sample_list = [self.indexed_dataset.get(self.doc_idx[doc_index_f], offset=offset_f)] # Loop over all in between documents and add the entire document. for i in range(doc_index_f + 1, doc_index_l): sample_list.append(self.indexed_dataset.get(self.doc_idx[i])) # And finally add the relevant portion of last document. sample_list.append(self.indexed_dataset.get( self.doc_idx[doc_index_l], length=offset_l + 1)) sample = np.concatenate(sample_list) return {'text': np.array(sample, dtype=np.int64)} def _build_index_mappings(name, data_prefix, documents, sizes, num_samples, seq_length, seed): """Build doc-idx, sample-idx, and shuffle-idx. doc-idx: is an array (ordered) of documents to be used in training. sample-idx: is the start document index and document offset for each training sample. shuffle-idx: maps the sample index into a random index into sample-idx. """ # Number of tokens in each epoch and number of required epochs. tokens_per_epoch = _num_tokens(documents, sizes) num_epochs = _num_epochs(tokens_per_epoch, seq_length, num_samples) # rng state np_rng = np.random.RandomState(seed=seed) # Filename of the index mappings. _filename = data_prefix _filename += '_{}_indexmap'.format(name) _filename += '_{}ns'.format(num_samples) _filename += '_{}sl'.format(seq_length) _filename += '_{}s'.format(seed) doc_idx_filename = _filename + '_doc_idx.npy' sample_idx_filename = _filename + '_sample_idx.npy' shuffle_idx_filename = _filename + '_shuffle_idx.npy' # Build the indexed mapping if not exist. if torch.distributed.get_rank() == 0: if (not os.path.isfile(doc_idx_filename)) or \ (not os.path.isfile(sample_idx_filename)) or \ (not os.path.isfile(shuffle_idx_filename)): print_rank_0(' > WARNING: could not find index map files, building ' 'the indices on rank 0 ...') # doc-idx. start_time = time.time() doc_idx = _build_doc_idx(documents, num_epochs, np_rng) np.save(doc_idx_filename, doc_idx, allow_pickle=True) print_rank_0(' > elasped time to build and save doc-idx mapping ' '(seconds): {:4f}'.format(time.time() - start_time)) # sample-idx. start_time = time.time() # Use C++ implementation for speed. # First compile and then import. from megatron.data.dataset_utils import compile_helper compile_helper() from megatron.data import helpers assert doc_idx.dtype == np.int32 assert sizes.dtype == np.int32 sample_idx = helpers.build_sample_idx(sizes, doc_idx, seq_length, num_epochs, tokens_per_epoch) # sample_idx = _build_sample_idx(sizes, doc_idx, seq_length, # num_epochs, tokens_per_epoch) np.save(sample_idx_filename, sample_idx, allow_pickle=True) print_rank_0(' > elasped time to build and save sample-idx mapping ' '(seconds): {:4f}'.format(time.time() - start_time)) # shuffle-idx. start_time = time.time() # -1 is due to data structure used to retieve the index: # sample i --> [sample_idx[i], sample_idx[i+1]) shuffle_idx = _build_shuffle_idx(sample_idx.shape[0] - 1, np_rng) np.save(shuffle_idx_filename, shuffle_idx, allow_pickle=True) print_rank_0(' > elasped time to build and save shuffle-idx mapping' ' (seconds): {:4f}'.format(time.time() - start_time)) # This should be a barrier but nccl barrier assumes # device_index=rank which is not the case for model # parallel case counts = torch.cuda.LongTensor([1]) torch.distributed.all_reduce(counts, group=mpu.get_data_parallel_group()) assert counts[0].item() == torch.distributed.get_world_size( group=mpu.get_data_parallel_group()) # Load mappings. start_time = time.time() print_rank_0(' > loading doc-idx mapping from {}'.format( doc_idx_filename)) doc_idx = np.load(doc_idx_filename, allow_pickle=True, mmap_mode='r') print_rank_0(' > loading sample-idx mapping from {}'.format( sample_idx_filename)) sample_idx = np.load(sample_idx_filename, allow_pickle=True, mmap_mode='r') print_rank_0(' > loading shuffle-idx mapping from {}'.format( shuffle_idx_filename)) shuffle_idx = np.load(shuffle_idx_filename, allow_pickle=True, mmap_mode='r') print_rank_0(' loaded indexed file in {:3.3f} seconds'.format( time.time() - start_time)) print_rank_0(' total number of samples: {}'.format( sample_idx.shape[0])) print_rank_0(' total number of epochs: {}'.format(num_epochs)) return doc_idx, sample_idx, shuffle_idx def _num_tokens(documents, sizes): """Total number of tokens in the dataset.""" return np.sum(sizes[documents]) def _num_epochs(tokens_per_epoch, seq_length, num_samples): """Based on number of samples and sequence lenght, calculate how many epochs will be needed.""" num_epochs = 0 total_tokens = 0 while True: num_epochs += 1 total_tokens += tokens_per_epoch # -1 is because we need to retrieve seq_length + 1 token each time # but the last token will overlap with the first token of the next # sample except for the last sample. if ((total_tokens - 1) // seq_length) >= num_samples: return num_epochs def _build_doc_idx(documents, num_epochs, np_rng): """Build an array with length = number-of-epochs * number-of-dcuments. Each index is mapped to a corresponding document.""" doc_idx = np.mgrid[0:num_epochs, 0:len(documents)][1] doc_idx[:] = documents doc_idx = doc_idx.reshape(-1) doc_idx = doc_idx.astype(np.int32) np_rng.shuffle(doc_idx) return doc_idx def _build_sample_idx(sizes, doc_idx, seq_length, num_epochs, tokens_per_epoch): """Sample index mapping 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.""" # Total number of samples. For -1 see comments in `_num_epochs`. num_samples = (num_epochs * tokens_per_epoch - 1) // seq_length sample_idx = np.zeros([num_samples + 1, 2], dtype=np.int32) # Index into sample_idx. sample_index = 0 # Index into doc_idx. doc_idx_index = 0 # Begining offset for each document. doc_offset = 0 # Start with first document and no offset. sample_idx[sample_index][0] = doc_idx_index sample_idx[sample_index][1] = doc_offset sample_index += 1 while sample_index <= num_samples: # Start with a fresh sequence. remaining_seq_length = seq_length + 1 while remaining_seq_length != 0: # Get the document length. doc_id = doc_idx[doc_idx_index] 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 begining of the next document. doc_idx_index += 1 doc_offset = 0 # Record the sequence. sample_idx[sample_index][0] = doc_idx_index sample_idx[sample_index][1] = doc_offset sample_index += 1 return sample_idx def _build_shuffle_idx(size, np_rng): """Build the range [0, size) and shuffle.""" dtype_ = np.uint32 if size >= (np.iinfo(np.uint32).max - 1): dtype_ = np.int64 shuffle_idx = np.arange(start=0, stop=size, step=1, dtype=dtype_) np_rng.shuffle(shuffle_idx) return shuffle_idx pangu/megatron/data/helpers.cpp000066400000000000000000000600311430334424000170600ustar00rootroot00000000000000/* 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; py::array build_sample_idx(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; 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; // Begining 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 begining 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 } 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= 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 begining 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= 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 begining 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", &build_sample_idx); } pangu/megatron/data/ict_dataset.py000066400000000000000000000126631430334424000175600ustar00rootroot00000000000000import itertools import random import numpy as np from torch.utils.data import Dataset from megatron import get_tokenizer from megatron import get_args from megatron.data.dataset_utils import get_indexed_dataset_ from megatron.data.realm_dataset_utils import get_block_samples_mapping def get_ict_dataset(use_titles=True, query_in_block_prob=1): """Get a dataset which uses block samples mappings to get ICT/block indexing data (via get_block()) rather than for training, since it is only built with a single epoch sample mapping. """ args = get_args() block_dataset = get_indexed_dataset_(args.data_path, 'mmap', True) titles_dataset = get_indexed_dataset_(args.titles_data_path, 'mmap', True) kwargs = dict( name='full', block_dataset=block_dataset, title_dataset=titles_dataset, data_prefix=args.data_path, num_epochs=1, max_num_samples=None, max_seq_length=args.seq_length, seed=1, query_in_block_prob=query_in_block_prob, use_titles=use_titles, use_one_sent_docs=args.use_one_sent_docs ) dataset = ICTDataset(**kwargs) return dataset class ICTDataset(Dataset): """Dataset containing sentences and their blocks for an inverse cloze task.""" def __init__(self, name, block_dataset, title_dataset, data_prefix, num_epochs, max_num_samples, max_seq_length, query_in_block_prob, seed, use_titles=True, use_one_sent_docs=False): self.name = name self.seed = seed self.max_seq_length = max_seq_length self.query_in_block_prob = query_in_block_prob self.block_dataset = block_dataset self.title_dataset = title_dataset self.rng = random.Random(self.seed) self.use_titles = use_titles self.use_one_sent_docs = use_one_sent_docs self.samples_mapping = get_block_samples_mapping( block_dataset, title_dataset, data_prefix, num_epochs, max_num_samples, max_seq_length, seed, name, use_one_sent_docs) self.tokenizer = get_tokenizer() self.vocab_id_list = list(self.tokenizer.inv_vocab.keys()) self.vocab_id_to_token_list = self.tokenizer.inv_vocab self.cls_id = self.tokenizer.cls self.sep_id = self.tokenizer.sep self.mask_id = self.tokenizer.mask self.pad_id = self.tokenizer.pad def __len__(self): return len(self.samples_mapping) def __getitem__(self, idx): """Get an ICT example of a pseudo-query and the block of text from which it was extracted""" sample_data = self.samples_mapping[idx] start_idx, end_idx, doc_idx, block_idx = sample_data.as_tuple() if self.use_titles: title = self.title_dataset[int(doc_idx)] title_pad_offset = 3 + len(title) else: title = None title_pad_offset = 2 block = [self.block_dataset[i] for i in range(start_idx, end_idx)] assert len(block) > 1 or self.use_one_sent_docs or self.query_in_block_prob == 1 # randint() is inclusive for Python rng rand_sent_idx = self.rng.randint(0, len(block) - 1) # keep the query in the context query_in_block_prob fraction of the time. if self.rng.random() < self.query_in_block_prob: query = block[rand_sent_idx].copy() else: query = block.pop(rand_sent_idx) # still need to truncate because blocks are concluded when # the sentence lengths have exceeded max_seq_length. query = query[:self.max_seq_length - 2] block = list(itertools.chain(*block))[:self.max_seq_length - title_pad_offset] query_tokens, query_pad_mask = self.concat_and_pad_tokens(query) block_tokens, block_pad_mask = self.concat_and_pad_tokens(block, title) block_data = sample_data.as_array() sample = { 'query_tokens': query_tokens, 'query_pad_mask': query_pad_mask, 'block_tokens': block_tokens, 'block_pad_mask': block_pad_mask, 'block_data': block_data, } return sample def get_block(self, start_idx, end_idx, doc_idx): """Get the IDs for an evidence block plus the title of the corresponding document""" block = [self.block_dataset[i] for i in range(start_idx, end_idx)] title = self.title_dataset[int(doc_idx)] block = list(itertools.chain(*block))[:self.max_seq_length - (3 + len(title))] block_tokens, block_pad_mask = self.concat_and_pad_tokens(block, title) return block_tokens, block_pad_mask def get_null_block(self): """Get empty block and title - used in REALM pretraining""" block, title = [], [] block_tokens, block_pad_mask = self.concat_and_pad_tokens(block, title) return block_tokens, block_pad_mask def concat_and_pad_tokens(self, tokens, title=None): """Concat with special tokens and pad sequence to self.max_seq_length""" tokens = list(tokens) if title is None: tokens = [self.cls_id] + tokens + [self.sep_id] else: title = list(title) tokens = [self.cls_id] + title + [self.sep_id] + tokens + [self.sep_id] assert len(tokens) <= self.max_seq_length num_pad = self.max_seq_length - len(tokens) pad_mask = [1] * len(tokens) + [0] * num_pad tokens += [self.pad_id] * num_pad return np.array(tokens), np.array(pad_mask) pangu/megatron/data/indexed_dataset.py000066400000000000000000000441761430334424000204250ustar00rootroot00000000000000# Copyright (c) Facebook, Inc. and its affiliates. # # This source code is licensed under the MIT license found in the # LICENSE file in the root directory of this source tree. # copied from fairseq/fairseq/data/indexed_dataset.py # Removed IndexedRawTextDataset since it relied on Fairseq dictionary # other slight modifications to remove fairseq dependencies # Added document index to index file and made it accessible. # An empty sentence no longer separates documents. from functools import lru_cache import os import shutil import struct from itertools import accumulate import numpy as np import torch from megatron import print_rank_0 def __best_fitting_dtype(vocab_size=None): if vocab_size is not None and vocab_size < 65500: return np.uint16 else: return np.int32 def get_available_dataset_impl(): return ['lazy', 'cached', 'mmap'] def infer_dataset_impl(path): if IndexedDataset.exists(path): with open(index_file_path(path), 'rb') as f: magic = f.read(8) if magic == IndexedDataset._HDR_MAGIC: return 'cached' elif magic == MMapIndexedDataset.Index._HDR_MAGIC[:8]: return 'mmap' else: return None else: print(f"Dataset does not exist: {path}") print("Path should be a basename that both .idx and .bin can be appended to get full filenames.") return None def make_builder(out_file, impl, vocab_size=None): if impl == 'mmap': return MMapIndexedDatasetBuilder(out_file, dtype=__best_fitting_dtype(vocab_size)) else: return IndexedDatasetBuilder(out_file) def make_dataset(path, impl, skip_warmup=False): if not IndexedDataset.exists(path): print(f"Dataset does not exist: {path}") print("Path should be a basename that both .idx and .bin can be appended to get full filenames.") return None if impl == 'infer': impl = infer_dataset_impl(path) if impl == 'lazy' and IndexedDataset.exists(path): return IndexedDataset(path) elif impl == 'cached' and IndexedDataset.exists(path): return IndexedCachedDataset(path) elif impl == 'mmap' and MMapIndexedDataset.exists(path): return MMapIndexedDataset(path, skip_warmup) print(f"Unknown dataset implementation: {impl}") return None def dataset_exists(path, impl): if impl == 'mmap': return MMapIndexedDataset.exists(path) else: return IndexedDataset.exists(path) def read_longs(f, n): a = np.empty(n, dtype=np.int64) f.readinto(a) return a def write_longs(f, a): f.write(np.array(a, dtype=np.int64)) dtypes = { 1: np.uint8, 2: np.int8, 3: np.int16, 4: np.int32, 5: np.int64, 6: np.float, 7: np.double, 8: np.uint16 } def code(dtype): for k in dtypes.keys(): if dtypes[k] == dtype: return k raise ValueError(dtype) def index_file_path(prefix_path): return prefix_path + '.idx' def data_file_path(prefix_path): return prefix_path + '.bin' def create_doc_idx(sizes): doc_idx = [0] for i, s in enumerate(sizes): if s == 0: doc_idx.append(i + 1) return doc_idx import torch.utils.data class IndexedDataset(torch.utils.data.Dataset): """Loader for IndexedDataset""" _HDR_MAGIC = b'TNTIDX\x00\x00' def __init__(self, path): super().__init__() self.path = path self.data_file = None self.read_index(path) def read_index(self, path): with open(index_file_path(path), 'rb') as f: magic = f.read(8) assert magic == self._HDR_MAGIC, ( 'Index file doesn\'t match expected format. ' 'Make sure that --dataset-impl is configured properly.' ) version = f.read(8) assert struct.unpack('= self._len: raise IndexError('index out of range') def __del__(self): if self.data_file: self.data_file.close() # @lru_cache(maxsize=8) def __getitem__(self, idx): if not self.data_file: self.read_data(self.path) if isinstance(idx, int): i = idx self.check_index(i) tensor_size = self.sizes[self.dim_offsets[i]:self.dim_offsets[i + 1]] a = np.empty(tensor_size, dtype=self.dtype) self.data_file.seek(self.data_offsets[i] * self.element_size) self.data_file.readinto(a) return a elif isinstance(idx, slice): start, stop, step = idx.indices(len(self)) if step != 1: raise ValueError("Slices into indexed_dataset must be contiguous") sizes = self.sizes[self.dim_offsets[start]:self.dim_offsets[stop]] size = sum(sizes) a = np.empty(size, dtype=self.dtype) self.data_file.seek(self.data_offsets[start] * self.element_size) self.data_file.readinto(a) offsets = list(accumulate(sizes)) sents = np.split(a, offsets[:-1]) return sents def __len__(self): return self._len def num_tokens(self, index): return self.sizes[index] def size(self, index): return self.sizes[index] @staticmethod def exists(path): return ( os.path.exists(index_file_path(path)) and os.path.exists(data_file_path(path)) ) @property def supports_prefetch(self): return False # avoid prefetching to save memory class IndexedCachedDataset(IndexedDataset): def __init__(self, path): super().__init__(path) self.cache = None self.cache_index = {} @property def supports_prefetch(self): return True def prefetch(self, indices): if all(i in self.cache_index for i in indices): return if not self.data_file: self.read_data(self.path) indices = sorted(set(indices)) total_size = 0 for i in indices: total_size += self.data_offsets[i + 1] - self.data_offsets[i] self.cache = np.empty(total_size, dtype=self.dtype) ptx = 0 self.cache_index.clear() for i in indices: self.cache_index[i] = ptx size = self.data_offsets[i + 1] - self.data_offsets[i] a = self.cache[ptx: ptx + size] self.data_file.seek(self.data_offsets[i] * self.element_size) self.data_file.readinto(a) ptx += size if self.data_file: # close and delete data file after prefetch so we can pickle self.data_file.close() self.data_file = None # @lru_cache(maxsize=8) def __getitem__(self, idx): if isinstance(idx, int): i = idx self.check_index(i) tensor_size = self.sizes[self.dim_offsets[i]:self.dim_offsets[i + 1]] a = np.empty(tensor_size, dtype=self.dtype) ptx = self.cache_index[i] np.copyto(a, self.cache[ptx: ptx + a.size]) return a elif isinstance(idx, slice): # Hack just to make this work, can optimizer later if necessary sents = [] for i in range(*idx.indices(len(self))): sents.append(self[i]) return sents class IndexedDatasetBuilder(object): element_sizes = { np.uint8: 1, np.int8: 1, np.int16: 2, np.int32: 4, np.int64: 8, np.float: 4, np.double: 8 } def __init__(self, out_file, dtype=np.int32): self.out_file = open(out_file, 'wb') self.dtype = dtype self.data_offsets = [0] self.dim_offsets = [0] self.sizes = [] self.element_size = self.element_sizes[self.dtype] self.doc_idx = [0] def add_item(self, tensor): bytes = self.out_file.write(np.array(tensor.numpy(), dtype=self.dtype)) self.data_offsets.append(self.data_offsets[-1] + bytes / self.element_size) for s in tensor.size(): self.sizes.append(s) self.dim_offsets.append(self.dim_offsets[-1] + len(tensor.size())) def end_document(self): self.doc_idx.append(len(self.sizes)) def merge_file_(self, another_file): index = IndexedDataset(another_file) assert index.dtype == self.dtype begin = self.data_offsets[-1] for offset in index.data_offsets[1:]: self.data_offsets.append(begin + offset) self.sizes.extend(index.sizes) begin = self.dim_offsets[-1] for dim_offset in index.dim_offsets[1:]: self.dim_offsets.append(begin + dim_offset) with open(data_file_path(another_file), 'rb') as f: while True: data = f.read(1024) if data: self.out_file.write(data) else: break def finalize(self, index_file): self.out_file.close() index = open(index_file, 'wb') index.write(b'TNTIDX\x00\x00') index.write(struct.pack(' WARNING: could not find index map file {}, building ' 'the indices on rank 0 ...'.format(indexmap_filename)) # Make sure the types match the helpers input types. assert block_dataset.doc_idx.dtype == np.int64 assert block_dataset.sizes.dtype == np.int32 # Build samples mapping verbose = torch.distributed.get_rank() == 0 start_time = time.time() print_rank_0(' > building samples index mapping for {} ...'.format( name)) # compile/bind the C++ helper code from megatron.data.dataset_utils import compile_helper compile_helper() from megatron.data import helpers mapping_array = helpers.build_blocks_mapping( block_dataset.doc_idx, block_dataset.sizes, title_dataset.sizes, num_epochs, max_num_samples, max_seq_length - 3, # account for added tokens seed, verbose, use_one_sent_docs) print_rank_0(' > done building samples index mapping') np.save(indexmap_filename, mapping_array, allow_pickle=True) print_rank_0(' > saved the index mapping in {}'.format( indexmap_filename)) # Make sure all the ranks have built the mapping print_rank_0(' > elapsed time to build and save samples mapping ' '(seconds): {:4f}'.format( time.time() - start_time)) # This should be a barrier but nccl barrier assumes # device_index=rank which is not the case for model # parallel case counts = torch.cuda.LongTensor([1]) torch.distributed.all_reduce(counts, group=mpu.get_data_parallel_group()) assert counts[0].item() == torch.distributed.get_world_size( group=mpu.get_data_parallel_group()) # Load indexed dataset. print_rank_0(' > loading indexed mapping from {}'.format( indexmap_filename)) start_time = time.time() mapping_array = np.load(indexmap_filename, allow_pickle=True, mmap_mode='r') samples_mapping = BlockSamplesMapping(mapping_array) print_rank_0(' loaded indexed file in {:3.3f} seconds'.format( time.time() - start_time)) print_rank_0(' total number of samples: {}'.format( mapping_array.shape[0])) return samples_mapping pangu/megatron/data/realm_index.py000066400000000000000000000211661430334424000175610ustar00rootroot00000000000000import itertools import os import pickle import shutil import numpy as np import torch from megatron import get_args from megatron import mpu def detach(tensor): return tensor.detach().cpu().numpy() class BlockData(object): """Serializable data structure for holding data for blocks -- embeddings and necessary metadata for REALM""" def __init__(self, block_data_path=None, load_from_path=True, rank=None): self.embed_data = dict() self.meta_data = dict() if block_data_path is None: args = get_args() block_data_path = args.block_data_path rank = args.rank self.block_data_path = block_data_path self.rank = rank if load_from_path: self.load_from_file() block_data_name = os.path.splitext(self.block_data_path)[0] self.temp_dir_name = block_data_name + '_tmp' def state(self): return { 'embed_data': self.embed_data, 'meta_data': self.meta_data, } def clear(self): """Clear the embedding data structures to save memory. The metadata ends up getting used, and is also much smaller in dimensionality so it isn't really worth clearing. """ self.embed_data = dict() def load_from_file(self): """Populate members from instance saved to file""" if mpu.is_unitialized() or mpu.get_data_parallel_rank() == 0: print("\n> Unpickling BlockData", flush=True) state_dict = pickle.load(open(self.block_data_path, 'rb')) if mpu.is_unitialized() or mpu.get_data_parallel_rank() == 0: print(">> Finished unpickling BlockData\n", flush=True) self.embed_data = state_dict['embed_data'] self.meta_data = state_dict['meta_data'] def add_block_data(self, block_indices, block_embeds, block_metas, allow_overwrite=False): """Add data for set of blocks :param block_indices: 1D array of unique int ids for the blocks :param block_embeds: 2D array of embeddings of the blocks :param block_metas: 2D array of metadata for the blocks. In the case of REALM this will be [start_idx, end_idx, doc_idx] """ for idx, embed, meta in zip(block_indices, block_embeds, block_metas): if not allow_overwrite and idx in self.embed_data: raise ValueError("Unexpectedly tried to overwrite block data") self.embed_data[idx] = np.float16(embed) self.meta_data[idx] = meta def save_shard(self): """Save the block data that was created this in this process""" if not os.path.isdir(self.temp_dir_name): os.makedirs(self.temp_dir_name, exist_ok=True) # save the data for each shard with open('{}/{}.pkl'.format(self.temp_dir_name, self.rank), 'wb') as data_file: pickle.dump(self.state(), data_file) def merge_shards_and_save(self): """Combine all the shards made using self.save_shard()""" shard_names = os.listdir(self.temp_dir_name) seen_own_shard = False for fname in os.listdir(self.temp_dir_name): shard_rank = int(os.path.splitext(fname)[0]) if shard_rank == self.rank: seen_own_shard = True continue with open('{}/{}'.format(self.temp_dir_name, fname), 'rb') as f: data = pickle.load(f) old_size = len(self.embed_data) shard_size = len(data['embed_data']) # add the shard's data and check to make sure there is no overlap self.embed_data.update(data['embed_data']) self.meta_data.update(data['meta_data']) assert len(self.embed_data) == old_size + shard_size assert seen_own_shard # save the consolidated shards and remove temporary directory with open(self.block_data_path, 'wb') as final_file: pickle.dump(self.state(), final_file) shutil.rmtree(self.temp_dir_name, ignore_errors=True) print("Finished merging {} shards for a total of {} embeds".format( len(shard_names), len(self.embed_data)), flush=True) class FaissMIPSIndex(object): """Wrapper object for a BlockData which similarity search via FAISS under the hood""" def __init__(self, embed_size, block_data=None, use_gpu=False): self.embed_size = embed_size self.block_data = block_data self.use_gpu = use_gpu self.id_map = dict() self.block_mips_index = None self._set_block_index() def _set_block_index(self): """Create a Faiss Flat index with inner product as the metric to search against""" try: import faiss except ImportError: raise Exception("Error: Please install faiss to use FaissMIPSIndex") if mpu.is_unitialized() or mpu.get_data_parallel_rank() == 0: print("\n> Building index", flush=True) self.block_mips_index = faiss.index_factory(self.embed_size, 'Flat', faiss.METRIC_INNER_PRODUCT) if self.use_gpu: # create resources and config for GpuIndex res = faiss.StandardGpuResources() config = faiss.GpuIndexFlatConfig() config.device = torch.cuda.current_device() config.useFloat16 = True self.block_mips_index = faiss.GpuIndexFlat(res, self.block_mips_index, config) if mpu.is_unitialized() or mpu.get_data_parallel_rank() == 0: print(">> Initialized index on GPU {}".format(self.block_mips_index.getDevice()), flush=True) else: # CPU index supports IDs so wrap with IDMap self.block_mips_index = faiss.IndexIDMap(self.block_mips_index) if mpu.is_unitialized() or mpu.get_data_parallel_rank() == 0: print(">> Initialized index on CPU", flush=True) # if we were constructed with a BlockData, then automatically load it when the FAISS structure is built if self.block_data is not None: self.add_block_embed_data(self.block_data) def reset_index(self): """Delete existing index and create anew""" del self.block_mips_index # reset the block data so that _set_block_index will reload it as well if self.block_data is not None: block_data_path = self.block_data.block_data_path del self.block_data self.block_data = BlockData(block_data_path) self._set_block_index() def add_block_embed_data(self, all_block_data): """Add the embedding of each block to the underlying FAISS index""" # this assumes the embed_data is a dict : {int: np.array} block_indices, block_embeds = zip(*all_block_data.embed_data.items()) # the embeddings have to be entered in as float32 even though the math internally is done with float16. block_embeds_arr = np.float32(np.array(block_embeds)) block_indices_arr = np.array(block_indices) # faiss GpuIndex doesn't work with IDMap wrapper so store ids to map back with if self.use_gpu: for i, idx in enumerate(block_indices): self.id_map[i] = idx # we no longer need the embedding data since it's in the index now all_block_data.clear() if self.use_gpu: self.block_mips_index.add(block_embeds_arr) else: self.block_mips_index.add_with_ids(block_embeds_arr, block_indices_arr) if mpu.is_unitialized() or mpu.get_data_parallel_rank() == 0: print(">>> Finished adding block data to index", flush=True) def search_mips_index(self, query_embeds, top_k, reconstruct=True): """Get the top-k blocks by the index distance metric. :param reconstruct: if True: return a [num_queries x k x embed_dim] array of blocks if False: return [num_queries x k] array of distances, and another for indices """ query_embeds = np.float32(detach(query_embeds)) if reconstruct: # get the vectors themselves top_k_block_embeds = self.block_mips_index.search_and_reconstruct(query_embeds, top_k) return top_k_block_embeds else: # get distances and indices of closest vectors distances, block_indices = self.block_mips_index.search(query_embeds, top_k) if self.use_gpu: fresh_indices = np.zeros(block_indices.shape) for i, j in itertools.product(block_indices.shape): fresh_indices[i, j] = self.id_map[block_indices[i, j]] block_indices = fresh_indices return distances, block_indices pangu/megatron/data/samplers.py000066400000000000000000000136471430334424000171250ustar00rootroot00000000000000# 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. """Batch samplers that work with either random or sequential data samplers.""" import torch from torch.utils import data class RandomSampler(data.sampler.Sampler): """Based off of pytorch RandomSampler and DistributedSampler. Essentially a RandomSampler, but this class lets the user set an epoch like DistributedSampler Samples elements randomly. If without replacement, then sample from a shuffled dataset. If with replacement, then user can specify ``num_samples`` to draw. Arguments: data_source (Dataset): dataset to sample from num_samples (int): number of samples to draw, default=len(dataset) replacement (bool): samples are drawn with replacement if ``True``, default=False """ def __init__(self, data_source, replacement=False, num_samples=None): self.data_source = data_source self.replacement = replacement self._num_samples = num_samples self.epoch = -1 if self._num_samples is not None and replacement is False: raise ValueError("With replacement=False, num_samples should not " "be specified, since a random permute will be " "performed.") if not isinstance(self.num_samples, int) or self.num_samples <= 0: raise ValueError("num_samples should be a positive integer " "value, but got num_samples={}".format( self.num_samples)) if not isinstance(self.replacement, bool): raise ValueError("replacement should be a boolean value, but got " "replacement={}".format(self.replacement)) @property def num_samples(self): # dataset size might change at runtime if self._num_samples is None: return len(self.data_source) return self._num_samples def __iter__(self): n = len(self.data_source) g = torch.Generator() if self.epoch >= 0: g.manual_seed(self.epoch) if self.replacement: return iter(torch.randint(high=n, size=(self.num_samples,), dtype=torch.int64, generator=g).tolist()) return iter(torch.randperm(n, generator=g).tolist()) def __len__(self): return self.num_samples def set_epoch(self, epoch): self.epoch = epoch class DistributedBatchSampler(data.sampler.BatchSampler): """Similar to normal implementation of distributed sampler, except implementation is at the batch sampler level, instead of just the sampler level. This allows wrapping of arbitrary data samplers (sequential, random, WeightedRandomSampler, etc.) with this batch sampler. The `interleave` argument specifies how to distribute a batch. A value of True combined with the above random sampler is equivalent to pytorch's torch.utils.data.distributed.DistributedSampler. For the following batch [0,1,2,3,4,5,6,7] and data parallelism of 2 specifying True will result in the following samples for each gpu: GPU0: [0,2,4,6] GPU1: [1,3,5,7] specifying False will result in the following samples: GPU0: [0,1,2,3] GPU1: [4,5,6,7]""" def __init__(self, sampler, batch_size, drop_last, rank=-1, world_size=2, wrap_last=False, interleave=False): super(DistributedBatchSampler, self).__init__(sampler, batch_size, drop_last) if rank == -1: assert False, 'should not be here' rank = torch.distributed.get_rank() self.rank = rank self.world_size = world_size self.sampler.wrap_around = 0 self.wrap_around = 0 self.wrap_last = wrap_last self.start_iter = 0 self.interleave = interleave def __iter__(self): batch = [] i = 0 for idx in self.data_iterator(self.sampler, wrap_around=False): batch.append(idx) if len(batch) == self.batch_size: tbatch = self._batch(batch) if i >= self.start_iter: yield tbatch self.start_iter = 0 i += 1 batch = [] batch_len = len(batch) if batch_len > 0 and not self.drop_last: if self.wrap_last: self.sampler.wrap_around -= (self.batch_size) self.wrap_around += (len(batch)) self.wrap_around %= self.batch_size yield self._batch(batch) if self.wrap_last: self.sampler.wrap_around += self.batch_size def data_iterator(self, _iter, wrap_around=False): """iterates through data and handles wrap around""" for i, idx in enumerate(_iter): if i < self.wrap_around % self.batch_size: continue if wrap_around: self.wrap_around += 1 self.wrap_around %= self.batch_size yield idx def _batch(self, batch): """extracts samples only pertaining to this worker's batch""" if self.interleave: return batch[self.rank:self.batch_size:self.world_size] start = self.rank * self.batch_size // self.world_size end = (self.rank + 1) * self.batch_size // self.world_size return batch[start:end] pangu/megatron/data/test/000077500000000000000000000000001430334424000156715ustar00rootroot00000000000000pangu/megatron/data/test/test_indexed_dataset.py000066400000000000000000000110131430334424000224230ustar00rootroot00000000000000# This file isn't really a formal automated test, it's just a place to # put some code used during development and manual testing of # indexed_dataset. from megatron.data import indexed_dataset from megatron.tokenizer import build_tokenizer import argparse import os import sys import torch script_dir = os.path.dirname(os.path.realpath(__file__)) sys.path.append(os.path.join(script_dir, "../../../")) def test_indexed_dataset(args): ds = indexed_dataset.make_dataset(args.data, args.dataset_impl) tokenizer = build_tokenizer(args) print(len(ds.doc_idx)) print(len(ds)) print(ds.doc_idx[-1]) if ds.supports_prefetch: # just prefetch the whole thing in test (so assume it is small) ds.prefetch(range(len(ds))) if args.count > len(ds.doc_idx) - 1: args.count = len(ds.doc_idx) - 1 for i in range(args.count): start = ds.doc_idx[i] end = ds.doc_idx[i + 1] ids = ds[start:end] print(f"Document {i}:") print("--------------") for s in ids: assert len(s) > 0 l = s.data.tolist() text = tokenizer.detokenize(l) print(text) print("---") def test_indexed_dataset_get(args): ds = indexed_dataset.make_dataset(args.data, args.dataset_impl) tokenizer = build_tokenizer(args) size = ds.sizes[0] print(f"size: {size}") full = ds.get(0) print(full) # print(tokenizer.detokenize(full.data.tolist())) print("---") end = ds.get(0, offset=size - 10) print(end) # print(tokenizer.detokenize(end.data.tolist())) start = ds.get(0, length=10) print(start) # print(tokenizer.detokenize(start.data.tolist())) part = ds.get(0, offset=2, length=8) print(part) # print(tokenizer.detokenize(part.data.tolist())) # def test_albert_dataset(args): # # tokenizer = FullBertTokenizer(args.vocab, do_lower_case=True) # # idataset = indexed_dataset.make_dataset(args.data, args.dataset_impl) # # ds = AlbertDataset(idataset, tokenizer) # ds = AlbertDataset.from_paths(args.vocab, args.data, args.dataset_impl, # args.epochs, args.max_num_samples, # args.masked_lm_prob, args.seq_length, # args.short_seq_prob, args.seed) # truncated = 0 # total = 0 # for i, s in enumerate(ds): # ids = s['text'] # tokens = ds.tokenizer.convert_ids_to_tokens(ids) # print(tokens) # if i >= args.count-1: # exit() def main(): parser = argparse.ArgumentParser() parser.add_argument('--data', type=str, help='prefix to data files') parser.add_argument('--dataset-impl', type=str, default='infer', choices=['lazy', 'cached', 'mmap', 'infer']) parser.add_argument('--count', type=int, default=10, help='Number of samples/documents to print') group = parser.add_argument_group(title='tokenizer') group.add_argument('--tokenizer-type', type=str, required=True, choices=['BertWordPieceLowerCase', 'GPT2BPETokenizer'], help='What type of tokenizer to use.') group.add_argument('--vocab-file', type=str, default=None, help='Path to the vocab file') group.add_argument('--merge-file', type=str, default=None, help='Path to the BPE merge file (if necessary).') parser.add_argument('--epochs', type=int, default=5, help='Number of epochs to plan for') parser.add_argument('--max-num-samples', type=int, default=None, help='Maximum number of samples to plan for') parser.add_argument('--masked-lm-prob', type=float, default=0.15, help='probability of masking tokens') parser.add_argument('--seq-length', type=int, default=512, help='maximum sequence length') parser.add_argument('--short-seq-prob', type=float, default=0.1, help='probability of creating a short sequence') parser.add_argument('--seed', type=int, default=1234, help='random seed') args = parser.parse_args() args.rank = 0 args.make_vocab_size_divisible_by = 128 args.model_parallel_size = 1 if args.dataset_impl == "infer": args.dataset_impl = indexed_dataset.infer_dataset_impl(args.data) # test_albert_dataset(args) test_indexed_dataset_get(args) if __name__ == "__main__": main() pangu/megatron/data/test/test_preprocess_data.sh000066400000000000000000000003611430334424000224420ustar00rootroot00000000000000#!/bin/bash IMPL=cached python ../preprocess_data.py \ --input test_samples.json \ --vocab vocab.txt \ --dataset-impl ${IMPL} \ --output-prefix test_samples_${IMPL} \ --workers 1 \ --log-interval 2 pangu/megatron/deprecated_data_utils/000077500000000000000000000000001430334424000203125ustar00rootroot00000000000000pangu/megatron/deprecated_data_utils/__init__.py000066400000000000000000000134631430334424000224320ustar00rootroot00000000000000# 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. """utils for creating datasets""" import os import math import torch from .samplers import DistributedBatchSampler from .datasets import json_dataset, csv_dataset, split_ds, ConcatDataset, SplitDataset, bert_sentencepair_dataset, GPT2Dataset from .lazy_loader import exists_lazy, make_lazy, lazy_array_loader from .tokenization import Tokenization, CommandToken, Tokenizer, CharacterLevelTokenizer, BertWordPieceTokenizer, GPT2BPETokenizer, make_tokenizer from . import corpora TRAIN_DATA = 0 VAL_DATA = 1 TEST_DATA = 2 def should_split(split): """ given split proportions checks if should split Examples: >>> should_split([10,0,0]) False >>> should_split([1,.1,.2]) True """ return max(split) / sum(split) != 1. def get_ext(path): """gets path extension""" return os.path.splitext(path)[1] def get_dataset(path, **kwargs): """gets dataset object based on keyword args and file at `path`""" if supported_corpus(path): return corpora.NAMED_CORPORA[path](**kwargs) ext = get_ext(path) if '.json' in ext: text = json_dataset(path, **kwargs) elif ext in ['.csv', '.tsv']: text = csv_dataset(path, **kwargs) else: raise NotImplementedError('data file type %s is not supported' % (ext)) return text def supported_corpus(corpus_name): """checks if corpus name is defined in `corpora.py`""" return corpus_name in corpora.NAMED_CORPORA def make_dataset(path, seq_length, text_key, label_key, lazy=False, process_fn=None, split=[1.], delim=',', loose=False, binarize_sent=False, drop_unlabeled=False, tokenizer=None, tokenizer_type='CharacterLevelTokenizer', tokenizer_model_path=None, vocab_size=None, model_type='bpe', pad_token=0, character_converage=1.0, non_binary_cols=None, parallel_group=None, **kwargs): """function to create datasets+tokenizers for common options""" if isinstance(process_fn, str): process_fn = eval(process_fn) if non_binary_cols is not None: # multilabel dataset support (only for csvs) label_key = non_binary_cols def get_dataset_from_path(path_): if lazy: # get lazily loaded dataset named_corpora = False if supported_corpus(path_): named_corpora = True name = path_ path_ = corpora.NAMED_CORPORA[path_].PATH if torch.distributed.get_rank() == 0 and not exists_lazy(path_, data_type='data'): # create cached version of dataset for lazy loading if it doesn't exist text = get_dataset(name if named_corpora else path_, text_key=text_key, label_key=label_key, binarize_sent=binarize_sent, delim=delim, drop_unlabeled=drop_unlabeled, loose_json=loose) make_lazy(path_, text.X, data_type='data') # This should be a barrier but nccl barrier assumes # device_index=rank which is not the case for model # parallel case counts = torch.cuda.LongTensor([1]) torch.distributed.all_reduce(counts, group=parallel_group) assert counts[0].item() == torch.distributed.get_world_size( group=parallel_group) text = lazy_array_loader(path_, data_type='data', map_fn=process_fn) else: # get dataset text = get_dataset(path_, text_key=text_key, label_key=label_key, binarize_sent=binarize_sent, delim=delim, drop_unlabeled=drop_unlabeled, loose_json=loose, preprocess_fn=process_fn) return text # get one or multiple datasets and concatenate if isinstance(path, str): path = [path] datasets = [get_dataset_from_path(p) for p in path] if len(datasets) == 1: ds = datasets[0] else: ds = ConcatDataset(datasets) # make tokenizer for dataset if tokenizer is None: tokenizer = make_tokenizer(tokenizer_type, ds, tokenizer_model_path, vocab_size, model_type, pad_token, character_converage, **kwargs) ds_type = '' if 'ds_type' in kwargs: ds_type = kwargs['ds_type'] ds.SetTokenizer(tokenizer) # Split dataset into train/val/test (and wrap bert dataset) if should_split(split): ds = split_ds(ds, split) if 'bert' in ds_type.lower(): presplit_sentences = kwargs['presplit_sentences'] if 'presplit_sentences' in kwargs else False dstype = bert_sentencepair_dataset ds = [dstype(d, max_seq_len=seq_length, presplit_sentences=presplit_sentences) if d is not None else None for d in ds] elif ds_type.lower() == 'gpt2': ds = [GPT2Dataset(d, max_seq_len=seq_length) if d is not None else None for d in ds] else: if 'bert' in ds_type.lower(): presplit_sentences = kwargs['presplit_sentences'] if 'presplit_sentences' in kwargs else False dstype = bert_sentencepair_dataset ds = dstype(ds, max_seq_len=seq_length, presplit_sentences=presplit_sentences) elif ds_type.lower() == 'gpt2': ds = GPT2Dataset(ds, max_seq_len=seq_length) return ds, tokenizer pangu/megatron/deprecated_data_utils/configure_data.py000066400000000000000000000220131430334424000236340ustar00rootroot00000000000000# 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. """parses arguments and preps data loader""" import copy import torch from megatron import data_utils from megatron import mpu class DataConfig: def __init__(self, defaults={}): super(DataConfig, self).__init__() self.defaults = defaults def apply(self, args): if torch.distributed.get_rank() == 0: print('configuring data') self.apply_defaults(args) return make_loaders(args) def set_defaults(self, **kwargs): for k, v in kwargs.items(): self.defaults[k] = v def apply_defaults(self, args): for k, v in self.defaults.items(): k = k.replace('-', '_') if not hasattr(args, k): setattr(args, k, v) def make_data_loader(dataset, batch_size, args): shuffle = args.shuffle if shuffle: sampler = data_utils.samplers.RandomSampler( dataset, replacement=True, num_samples=batch_size * args.train_iters) else: sampler = torch.utils.data.SequentialSampler(dataset) world_size = torch.distributed.get_world_size( group=mpu.get_data_parallel_group()) rank = torch.distributed.get_rank(group=mpu.get_data_parallel_group()) distributed = world_size > 1 drop_last = distributed if distributed: batch_sampler = data_utils.samplers.DistributedBatchSampler(sampler, batch_size, drop_last, rank, world_size) else: batch_sampler = torch.utils.data.BatchSampler(sampler, batch_size, drop_last) data_loader = torch.utils.data.DataLoader(dataset, batch_sampler=batch_sampler, num_workers=args.num_workers, pin_memory=True) return data_loader def make_tfrecord_loaders(args): """Load train/val/test dataset from shuffled TFRecords""" import data_utils.tf_dl data_set_args = {'batch_size': args.batch_size, 'max_seq_len': args.seq_length, 'max_preds_per_seq': args.max_preds_per_seq, 'train': True, 'num_workers': max(args.num_workers, 1), 'seed': args.seed + args.rank + 1, 'threaded_dl': args.num_workers > 0 } train = data_utils.tf_dl.TFRecordDataLoader(args.train_data, **data_set_args) data_set_args['train'] = False if args.eval_seq_length is not None: data_set_args['max_seq_len'] = args.eval_seq_length if args.eval_max_preds_per_seq is not None: data_set_args['max_preds_per_seq'] = args.eval_max_preds_per_seq valid = None if args.valid_data is not None: valid = data_utils.tf_dl.TFRecordDataLoader(args.valid_data, **data_set_args) test = None if args.test_data is not None: test = data_utils.tf_dl.TFRecordDataLoader(args.test_data, **data_set_args) tokenizer = data_utils.make_tokenizer(args.tokenizer_type, train, args.tokenizer_path, args.vocab_size, args.tokenizer_model_type, cache_dir=args.cache_dir) return (train, valid, test), tokenizer def make_loaders(args): """makes training/val/test""" if args.data_loader == 'tfrecords': return make_tfrecord_loaders(args) world_size = torch.distributed.get_world_size( group=mpu.get_data_parallel_group()) batch_size = args.batch_size * world_size eval_batch_size = batch_size if args.eval_batch_size is not None: eval_batch_size = args.eval_batch_size * world_size seq_length = args.seq_length if seq_length < 0: seq_length = seq_length * world_size eval_seq_length = args.eval_seq_length if eval_seq_length is not None and eval_seq_length < 0: eval_seq_length = eval_seq_length * world_size split = get_split(args) if args.data_path is not None: args.train_data = args.data_path data_set_args = { 'path': args.train_data, 'seq_length': seq_length, 'lazy': args.data_loader == 'lazy', 'delim': args.delim, 'text_key': args.text_key, 'label_key': 'label', 'non_binary_cols': None, 'ds_type': args.data_set_type, 'split': split, 'loose': args.loose_json, 'tokenizer_type': args.tokenizer_type, 'tokenizer_model_path': args.tokenizer_path, 'vocab_size': args.vocab_size, 'model_type': args.tokenizer_model_type, 'cache_dir': args.cache_dir, 'max_preds_per_seq': args.max_preds_per_seq, 'presplit_sentences': args.presplit_sentences, 'parallel_group': mpu.get_data_parallel_group()} eval_set_args = copy.copy(data_set_args) eval_set_args['split'] = [1.] # if optional eval args were set then replace their # equivalent values in the arg dict if eval_seq_length: eval_set_args['seq_length'] = eval_seq_length if args.eval_max_preds_per_seq: eval_set_args['max_preds_per_seq'] = args.eval_max_preds_per_seq if args.eval_text_key is not None: eval_set_args['text_key'] = args.eval_text_key # make datasets splits and tokenizer train = None valid = None test = None if args.train_data is not None: train, tokenizer = data_utils.make_dataset(**data_set_args) if data_utils.should_split(split): train, valid, test = train eval_set_args['tokenizer'] = tokenizer # make training and val dataset if necessary if valid is None and args.valid_data is not None: eval_set_args['path'] = args.valid_data valid, tokenizer = data_utils.make_dataset(**eval_set_args) eval_set_args['tokenizer'] = tokenizer if test is None and args.test_data is not None: eval_set_args['path'] = args.test_data test, tokenizer = data_utils.make_dataset(**eval_set_args) # wrap datasets with data loader if train is not None and args.batch_size > 0: train = make_data_loader(train, batch_size, args) args.do_train = True else: args.do_train = False eval_batch_size = eval_batch_size if eval_batch_size != 0 else batch_size if valid is not None: valid = make_data_loader(valid, eval_batch_size, args) args.do_valid = True else: args.do_valid = False if test is not None: test = make_data_loader(test, eval_batch_size, args) args.do_test = True else: args.do_test = False return (train, valid, test), tokenizer def get_split(args): """ Get dataset splits from comma separated string list """ splits = [] if args.split.find(',') != -1: splits = [float(s) for s in args.split.split(',')] elif args.split.find('/') != -1: splits = [float(s) for s in args.split.split('/')] else: splits = [float(args.split)] split_total = sum(splits) if split_total < 1.: splits.append(1 - split_total) while len(splits) < 3: splits.append(0.) splits = splits[:3] if args.valid_data is not None: splits[1] = 0. if args.test_data is not None: splits[2] = 0. final_sum = sum(splits) return [s / final_sum for s in splits] def configure_data(): """add cmdline flags for configuring datasets""" # These are options that are used by data_utils, but are either # deprecated or not meant to be exposed to the command line user. # These options are intneded to be set in code by specific scripts. defaults = { 'world_size': 1, 'rank': -1, 'persist_state': 0, 'lazy': False, 'transpose': False, 'data_set_type': 'supervised', 'seq_length': 256, 'eval_seq_length': 256, 'samples_per_shard': 100 } return DataConfig(defaults=defaults) pangu/megatron/deprecated_data_utils/corpora.py000066400000000000000000000036341430334424000223370ustar00rootroot00000000000000# 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. """several datasets with preset arguments""" from .datasets import json_dataset, csv_dataset import os class wikipedia(json_dataset): """ dataset for wikipedia with arguments configured for convenience command line usage: `--train-data wikipedia` """ PATH = 'data/wikipedia/wikidump_lines.json' assert_str = "make sure to set PATH for wikipedia data_utils/corpora.py" def __init__(self, **kwargs): assert os.path.exists(wikipedia.PATH), \ wikipedia.assert_str if not kwargs: kwargs = {} kwargs['text_key'] = 'text' kwargs['loose_json'] = True super(wikipedia, self).__init__(wikipedia.PATH, **kwargs) class webtext(json_dataset): """ dataset for webtext with arguments configured for convenience command line usage: `--train-data webtext` """ PATH = 'data/webtext/data.json' assert_str = "make sure to set PATH for webtext data_utils/corpora.py" def __init__(self, **kwargs): assert os.path.exists(webtext.PATH), \ webtext.assert_str if not kwargs: kwargs = {} kwargs['text_key'] = 'text' kwargs['loose_json'] = True super(webtext, self).__init__(webtext.PATH, **kwargs) NAMED_CORPORA = { 'wikipedia': wikipedia, 'webtext': webtext, } pangu/megatron/deprecated_data_utils/datasets.py000066400000000000000000000777001430334424000225070ustar00rootroot00000000000000# 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. """dataset objects for jsons, csvs, and BERT datasets""" import os import time from operator import itemgetter from bisect import bisect_right import json import csv import math import random from itertools import accumulate from torch.utils import data import pandas as pd import numpy as np import nltk from nltk import tokenize from .lazy_loader import lazy_array_loader, exists_lazy, make_lazy from .tokenization import Tokenization class ConcatDataset(data.Dataset): """ Dataset to concatenate multiple datasets. Purpose: useful to assemble different existing datasets, possibly large-scale datasets as the concatenation operation is done in an on-the-fly manner. Arguments: datasets (sequence): List of datasets to be concatenated. """ @staticmethod def cumsum(sequence): r, s = [], 0 for e in sequence: l = len(e) r.append(l + s) s += l return r def __init__(self, datasets, **kwargs): super(ConcatDataset, self).__init__() assert len(datasets) > 0, 'datasets should not be an empty iterable' self.datasets = list(datasets) self.is_lazy = sum([isinstance(ds, lazy_array_loader) for ds in self.datasets]) == len(self.datasets) self.cumulative_sizes = self.cumsum(self.datasets) self._X = None self._Y = None self._lens = None def SetTokenizer(self, tokenizer): for ds in self.datasets: ds.SetTokenizer(tokenizer) def GetTokenizer(self): return self.datasets[0].GetTokenizer() def __len__(self): return self.cumulative_sizes[-1] def __getitem__(self, idx): dataset_idx = bisect_right(self.cumulative_sizes, idx) if dataset_idx == 0: sample_idx = idx else: sample_idx = idx - self.cumulative_sizes[dataset_idx - 1] return self.datasets[dataset_idx][sample_idx] @property def lens(self): if self._lens is None: self._lens = [] if self.is_lazy: for data in self.datasets: self._lens.extend(data.lens) else: for data in self.datasets: self._lens.extend([len(d['text']) if isinstance( d, dict) else len(d) for d in data]) return self._lens @property def X(self): if self._X is None: self._X = [] for data in self.datasets: self._X.extend(data.X) return self._X @property def Y(self): if self._Y is None: self._Y = [] for data in self.datasets: self._Y.extend(list(data.Y)) self._Y = np.array(self._Y) return self._Y @property def cummulative_sizes(self): warnings.warn("cummulative_sizes attribute is renamed to " "cumulative_sizes", DeprecationWarning, stacklevel=2) return self.cumulative_sizes class SplitDataset(data.Dataset): """ Dataset wrapper to access a subset of another dataset. Purpose: useful to index into existing datasets, possibly large-scale datasets as the subindexing operation is done in an on-the-fly manner. Arguments: ds (Dataset or array-like): List of datasets to be subindexed split_inds (1D array-like): List of indices part of subset """ def __init__(self, ds, split_inds, **kwargs): self.split_inds = list(split_inds) self.wrapped_data = ds self.is_lazy = isinstance(ds, lazy_array_loader) or (hasattr(ds, 'is_lazy') and ds.is_lazy) if self.is_lazy: self.lens = itemgetter(*self.split_inds)(list(self.wrapped_data.lens)) self._X = None self._Y = None def __len__(self): return len(self.split_inds) def __getitem__(self, index): return self.wrapped_data[self.split_inds[index]] def SetTokenizer(self, tokenizer): self.wrapped_data.SetTokenizer(tokenizer) def GetTokenizer(self): return self.wrapped_data.GetTokenizer() @property def X(self): if self._X is None: self._X = itemgetter(*self.split_inds)(self.wrapped_data.X) return self._X @property def Y(self): if self._Y is None: self._Y = np.array(itemgetter(*self.split_inds)(self.wrapped_data.Y)) return self._Y def __iter__(self): for idx in self.split_inds: yield self.wrapped_data[idx] def split_ds(ds, split=[.8, .2, .0], shuffle=True): """ Split a dataset into subsets given proportions of how much to allocate per split. If a split is 0% returns None for that split. Purpose: Useful for creating train/val/test splits Arguments: ds (Dataset or array-like): Data to be split. split (1D array-like): proportions to split `ds`. `sum(splits) != 0` shuffle (boolean): Randomly split dataset. Default: True """ split_sum = sum(split) if split_sum == 0: raise Exception('Split cannot sum to 0.') split = np.array(split) split /= split_sum ds_len = len(ds) inds = np.arange(ds_len) if shuffle: np.random.shuffle(inds) start_idx = 0 residual_idx = 0 rtn_ds = [None] * len(split) for i, f in enumerate(split): if f != 0: proportion = ds_len * split[i] residual_idx += proportion % 1 split_ = int(int(proportion) + residual_idx) split_inds = inds[start_idx:start_idx + max(split_, 1)] rtn_ds[i] = SplitDataset(ds, split_inds) start_idx += split_ residual_idx %= 1 return rtn_ds class csv_dataset(data.Dataset): """ Class for loading datasets from csv files. Purpose: Useful for loading data for unsupervised modeling or transfer tasks Arguments: path (str): Path to csv file with dataset. tokenizer (data_utils.Tokenizer): Tokenizer to use when processing text. Default: None preprocess_fn (callable): Callable that process a string into desired format. delim (str): delimiter for csv. Default: ',' binarize_sent (bool): binarize label values to 0 or 1 if they\'re on a different scale. Default: False drop_unlabeled (bool): drop rows with unlabelled values. Always fills remaining empty columns with -1 (regardless if rows are dropped based on value) Default: False text_key (str): key to get text from csv. Default: 'sentence' label_key (str): key to get label from json dictionary. Default: 'label' Attributes: X (list): all strings from the csv file Y (np.ndarray): labels to train with """ def __init__(self, path, tokenizer=None, preprocess_fn=None, delim=',', binarize_sent=False, drop_unlabeled=False, text_key='sentence', label_key='label', **kwargs): self.is_lazy = False self.preprocess_fn = preprocess_fn self.SetTokenizer(tokenizer) self.path = path self.delim = delim self.text_key = text_key self.label_key = label_key self.drop_unlabeled = drop_unlabeled if '.tsv' in self.path: self.delim = '\t' self.X = [] self.Y = [] try: cols = [text_key] if isinstance(label_key, list): cols += label_key else: cols += [label_key] data = pd.read_csv(self.path, sep=self.delim, usecols=cols, encoding='latin-1') except BaseException: data = pd.read_csv(self.path, sep=self.delim, usecols=[text_key], encoding='latin-1') data = data.dropna(axis=0) self.X = data[text_key].values.tolist() try: self.Y = data[label_key].values except Exception as e: self.Y = np.ones(len(self.X)) * -1 if binarize_sent: self.Y = binarize_labels(self.Y, hard=binarize_sent) def SetTokenizer(self, tokenizer): if tokenizer is None: self.using_tokenizer = False if not hasattr(self, '_tokenizer'): self._tokenizer = tokenizer else: self.using_tokenizer = True self._tokenizer = tokenizer def GetTokenizer(self): return self._tokenizer @property def tokenizer(self): if self.using_tokenizer: return self._tokenizer return None def __len__(self): return len(self.X) def __getitem__(self, index): """process+tokenize string and return string,label,and stringlen""" x = self.X[index] if self.tokenizer is not None: x = self.tokenizer.EncodeAsIds(x, self.preprocess_fn) elif self.preprocess_fn is not None: x = self.preprocess_fn(x) y = self.Y[index] if isinstance(y, str): if self.tokenizer is not None: y = self.tokenizer.EncodeAsIds(y, self.preprocess_fn) elif self.preprocess_fn is not None: y = self.preprocess_fn(y) return {'text': x, 'length': len(x), 'label': y} def write(self, writer_gen=None, path=None, skip_header=False): """ given a generator of metrics for each of the data points X_i, write the metrics, text, and labels to a csv file """ if path is None: path = self.path + '.results' print('generating csv at ' + path) with open(path, 'w') as csvfile: c = csv.writer(csvfile, delimiter=self.delim) if writer_gen is not None: # if first item of generator is a header of what the metrics mean then # write header to csv file if not skip_header: header = (self.label_key,) + tuple(next(writer_gen)) + (self.text_key,) c.writerow(header) for i, row in enumerate(writer_gen): row = (self.Y[i],) + tuple(row) + (self.X[i],) c.writerow(row) else: c.writerow([self.label_key, self.text_key]) for row in zip(self.Y, self.X): c.writerow(row) class json_dataset(data.Dataset): """ Class for loading datasets from a json dump. Purpose: Useful for loading data for unsupervised modeling or transfer tasks Arguments: path (str): path to json file with dataset. tokenizer (data_utils.Tokenizer): Tokenizer to use when processing text. Default: None preprocess_fn (callable): callable function that process a string into desired format. Takes string, maxlen=None, encode=None as arguments. Default: process_str text_key (str): key to get text from json dictionary. Default: 'sentence' label_key (str): key to get label from json dictionary. Default: 'label' Attributes: all_strs (list): list of all strings from the dataset all_labels (list): list of all labels from the dataset (if they have it) """ def __init__(self, path, tokenizer=None, preprocess_fn=None, binarize_sent=False, text_key='sentence', label_key='label', loose_json=False, **kwargs): self.is_lazy = False self.preprocess_fn = preprocess_fn self.path = path self.SetTokenizer(tokenizer) self.X = [] self.Y = [] self.text_key = text_key self.label_key = label_key self.loose_json = loose_json for j in self.load_json_stream(self.path): s = j[text_key] self.X.append(s) self.Y.append(j[label_key]) if binarize_sent: self.Y = binarize_labels(self.Y, hard=binarize_sent) def SetTokenizer(self, tokenizer): if tokenizer is None: self.using_tokenizer = False if not hasattr(self, '_tokenizer'): self._tokenizer = tokenizer else: self.using_tokenizer = True self._tokenizer = tokenizer def GetTokenizer(self): return self._tokenizer @property def tokenizer(self): if self.using_tokenizer: return self._tokenizer return None def __getitem__(self, index): """gets the index'th string from the dataset""" x = self.X[index] if self.tokenizer is not None: x = self.tokenizer.EncodeAsIds(x, self.preprocess_fn) elif self.preprocess_fn is not None: x = self.preprocess_fn(x) y = self.Y[index] if isinstance(y, str): if self.tokenizer is not None: y = self.tokenizer.EncodeAsIds(y, self.preprocess_fn) elif self.preprocess_fn is not None: y = self.preprocess_fn(y) return {'text': x, 'length': len(x), 'label': y} def __len__(self): return len(self.X) def write(self, writer_gen=None, path=None, skip_header=False): """ given a generator of metrics for each of the data points X_i, write the metrics, text, and labels to a json file """ if path is None: path = self.path + '.results' jsons = [] if writer_gen is not None: # if first item of generator is a header of what the metrics mean then # write header to csv file def gen_helper(): keys = {} keys[0] = self.label_key if not skip_header: for idx, k in enumerate(tuple(next(writer_gen))): keys[idx + 1] = k for i, row in enumerate(writer_gen): if i == 0 and skip_header: for idx, _ in enumerate(row): keys[idx + 1] = 'metric_%d' % (idx,) j = {} for idx, v in enumerate((self.Y[i],) + tuple(row)): k = keys[idx] j[k] = v yield j else: def gen_helper(): for y in self.Y: j = {} j[self.label_key] = y yield j def out_stream(): for i, j in enumerate(gen_helper()): j[self.text_key] = self.X[i] yield j self.save_json_stream(path, out_stream()) def save_json_stream(self, save_path, json_stream): if self.loose_json: with open(save_path, 'w') as f: for i, j in enumerate(json_stream): write_string = '' if i != 0: write_string = '\n' write_string += json.dumps(j) f.write(write_string) else: jsons = [j for j in json_stream] json.dump(jsons, open(save_path, 'w'), separators=(',', ':')) def load_json_stream(self, load_path): if not self.loose_json: jsons = json.load(open(load_path, 'r')) generator = iter(jsons) else: def gen_helper(): with open(load_path, 'r') as f: for row in f: yield json.loads(row) generator = gen_helper() for j in generator: if self.label_key not in j: j[self.label_key] = -1 yield j class GPT2Dataset(data.Dataset): def __init__(self, ds, max_seq_len=1024, num_samples=None, weighted=True, sample_across_doc=True, random_across_doc_sampling=True, bias_for_single_doc=False, sentence_start=False, **kwargs): self.ds = ds self.ds_len = len(self.ds) self.num_samples = num_samples if num_samples is None: self.num_samples = 1000 * self.ds_len self.max_seq_len = max_seq_len self.tokenizer = self.ds.GetTokenizer() self.ds.SetTokenizer(None) self.weighted = weighted self.sample_across_doc = sample_across_doc self.random_across_doc_sampling = random_across_doc_sampling self.bias_for_single_doc = bias_for_single_doc self.sentence_start = sentence_start self.init_weighting() def init_weighting(self): if self.weighted: if hasattr(self.ds, 'is_lazy') and self.ds.is_lazy: lens = np.array(self.ds.lens) else: lens = np.array([len(d['text']) if isinstance(d, dict) else len(d) for d in self.ds]) self.total_len = np.sum(lens) self.weighting = list(accumulate(lens)) else: self.weighting = None def get_weighted_samples(self, np_rng): if self.weighting is not None: idx = np_rng.randint(self.total_len) return bisect_right(self.weighting, idx) else: return np_rng.randint(self.ds_len) def __len__(self): return self.num_samples def __getitem__(self, idx): # init rng rng = random.Random(idx) rng = np.random.RandomState(seed=[rng.randint(0, 2**32 - 1) for _ in range(16)]) # get possibly weighted random index from dataset data_idx = self.get_weighted_samples(rng) # data_idx = rng.choice(self.ds_len, p=self.weighting) tokens = self.getidx(data_idx) # truncate or pad tokens num_tokens = len(tokens) if self.bias_for_single_doc: tokens_to_strip = num_tokens - self.max_seq_len - 1 else: tokens_to_strip = num_tokens - 1 if tokens_to_strip > 0: strip_left_tokens = rng.randint(tokens_to_strip + 1) tokens = tokens[strip_left_tokens:] if self.sentence_start: token_copy = list(tokens) not_done = True while (len(token_copy) > 0) and not_done: tok = token_copy.pop(0) if self.contains_sentence_end(tok): tokens = token_copy not_done = False strip_right_rokens = len(tokens) - self.max_seq_len - 1 if strip_right_rokens > 0: tokens = tokens[:-strip_right_rokens] if self.sample_across_doc: while (len(tokens) < (self.max_seq_len + 1)): if self.random_across_doc_sampling: data_idx = self.get_weighted_samples(rng) else: data_idx = (data_idx + 1) % self.ds_len tokens += self.getidx(data_idx) tokens = tokens[:(self.max_seq_len + 1)] tokens = self.pad_seq(tokens) return {'text': np.array(tokens), } def getidx(self, data_idx): data = self.ds[data_idx] if isinstance(data, dict): data = data['text'] # tokenize tokenization = self.tokenizer.EncodeAsIds(data) tokenization.append(self.tokenizer.get_command('eos')) tokens = tokenization.tokenization return tokens def pad_seq(self, seq): total_tokens = self.max_seq_len + 1 num_pad_tokens = max(0, total_tokens - len(seq)) seq += [self.tokenizer.get_command('pad').Id] * (num_pad_tokens) return seq def contains_sentence_end(self, tok): tok = self.tokenizer.IdToToken(tok) if '.' in tok: return True if '?' in tok: return True if '!' in tok: return True return False class bert_sentencepair_dataset(data.Dataset): """ Dataset containing sentencepairs for BERT training. Each index corresponds to a randomly generated sentence pair. Arguments: ds (Dataset or array-like): data corpus to use for training max_seq_len (int): maximum sequence length to use for a sentence pair mask_lm_prob (float): proportion of tokens to mask for masked LM max_preds_per_seq (int): Maximum number of masked tokens per sentence pair. Default: math.ceil(max_seq_len*mask_lm_prob/10)*10 short_seq_prob (float): Proportion of sentence pairs purposefully shorter than max_seq_len dataset_size (int): number of random sentencepairs in the dataset. Default: len(ds)*(len(ds)-1) """ def __init__(self, ds, max_seq_len=512, mask_lm_prob=.15, max_preds_per_seq=None, short_seq_prob=.01, dataset_size=None, presplit_sentences=False, weighted=True, **kwargs): self.ds = ds self.ds_len = len(self.ds) self.tokenizer = self.ds.GetTokenizer() self.vocab_words = list(self.tokenizer.text_token_vocab.values()) self.ds.SetTokenizer(None) self.max_seq_len = max_seq_len self.mask_lm_prob = mask_lm_prob if max_preds_per_seq is None: max_preds_per_seq = math.ceil(max_seq_len * mask_lm_prob / 10) * 10 self.max_preds_per_seq = max_preds_per_seq self.short_seq_prob = short_seq_prob self.dataset_size = dataset_size if self.dataset_size is None: self.dataset_size = self.ds_len * (self.ds_len - 1) self.presplit_sentences = presplit_sentences if not self.presplit_sentences: nltk.download('punkt', download_dir="./nltk") self.weighted = weighted self.get_weighting() def get_weighting(self): if self.weighted: if hasattr(self.ds, 'is_lazy') and self.ds.is_lazy: lens = np.array(self.ds.lens) else: lens = np.array([len(d['text']) if isinstance(d, dict) else len(d) for d in self.ds]) self.total_len = np.sum(lens) self.weighting = list(accumulate(lens)) else: self.weighting = None def get_weighted_samples(self, np_rng): if self.weighting is not None: idx = np_rng.randint(self.total_len) return bisect_right(self.weighting, idx) else: return np_rng.randint(self.ds_len) def __len__(self): return self.dataset_size def __getitem__(self, idx): # get rng state corresponding to index (allows deterministic random pair) rng = random.Random(idx) np_rng = np.random.RandomState(seed=[rng.randint(0, 2**32 - 1) for _ in range(16)]) # get seq length target_seq_length = self.max_seq_len short_seq = False if rng.random() < self.short_seq_prob: target_seq_length = rng.randint(2, target_seq_length) short_seq = True # get sentence pair and label is_random_next = None lena = 0 lenb = 0 while (is_random_next is None) or (lena < 1) or (lenb < 1): tokensa, tokensb, is_random_next = self.create_random_sentencepair( target_seq_length, rng, np_rng) lena = len(tokensa[0]) lenb = len(tokensb[0]) # truncate sentence pair to max_seq_len tokensa, tokensb = self.truncate_seq_pair(tokensa, tokensb, self.max_seq_len, rng) # join sentence pair, mask, and pad tokens, mask, mask_labels, pad_mask = self.create_masked_lm_predictions( tokensa, tokensb, self.mask_lm_prob, self.max_preds_per_seq, self.vocab_words, rng) sample = { 'text': np.array( tokens[0]), 'types': np.array( tokens[1]), 'is_random': int(is_random_next), 'mask': np.array(mask), 'mask_labels': np.array(mask_labels), 'pad_mask': np.array(pad_mask)} return sample def sentence_split(self, document): """split document into sentences""" lines = document.split('\n') if self.presplit_sentences: return [line for line in lines if line] rtn = [] for line in lines: if line != '': rtn.extend(tokenize.sent_tokenize(line)) return rtn def sentence_tokenize(self, sent, sentence_num=0, beginning=False, ending=False): """tokenize sentence and get token types""" tokens = self.tokenizer.EncodeAsIds(sent).tokenization str_type = 'str' + str(sentence_num) token_types = [self.tokenizer.get_type(str_type).Id] * len(tokens) return tokens, token_types def get_doc(self, idx): """gets text of document corresponding to idx""" rtn = self.ds[idx] if isinstance(rtn, dict): rtn = rtn['text'] return rtn def create_random_sentencepair(self, target_seq_length, rng, np_rng): """ fetches a random sentencepair corresponding to rng state similar to https://github.com/google-research/bert/blob/master/create_pretraining_data.py#L248-L294 """ is_random_next = None curr_strs = [] curr_str_types = [] curr_len = 0 while curr_len < 1: curr_len = 0 doc_a = None while doc_a is None: if self.weighted: # doc_a_idx = np_rng.choice(self.ds_len, p=self.weighting) doc_a_idx = self.get_weighted_samples(np_rng) else: doc_a_idx = rng.randint(0, self.ds_len - 1) doc_a = self.sentence_split(self.get_doc(doc_a_idx)) if not doc_a: doc_a = None random_start_a = rng.randint(0, len(doc_a) - 1) while random_start_a < len(doc_a): sentence = doc_a[random_start_a] sentence, sentence_types = self.sentence_tokenize( sentence, 0, random_start_a == 0, random_start_a == len(doc_a)) curr_strs.append(sentence) curr_str_types.append(sentence_types) curr_len += len(sentence) if random_start_a == len(doc_a) - 1 or curr_len >= target_seq_length: break random_start_a = (random_start_a + 1) if curr_strs: num_a = 1 if len(curr_strs) >= 2: num_a = rng.randint(0, len(curr_strs)) tokens_a = [] token_types_a = [] for j in range(num_a): tokens_a.extend(curr_strs[j]) token_types_a.extend(curr_str_types[j]) tokens_b = [] token_types_b = [] is_random_next = False if len(curr_strs) == 1 or rng.random() < 0.5: is_random_next = True target_b_length = target_seq_length - len(tokens_a) b_len = 0 while b_len < 1: doc_b = None while doc_b is None: doc_b_idx = rng.randint(0, self.ds_len - 2) doc_b_idx += int(doc_b_idx >= doc_a_idx) doc_b = self.sentence_split(self.get_doc(doc_b_idx)) if not doc_b: doc_b = None random_start_b = rng.randint(0, len(doc_b) - 1) while random_start_b < len(doc_b): sentence_b = doc_b[random_start_b] new_b_tokens, new_b_types = self.sentence_tokenize( sentence_b, 1, random_start_b == 0, random_start_b == len(doc_b)) b_len += len(new_b_tokens) tokens_b.extend(new_b_tokens) token_types_b.extend(new_b_types) if len(tokens_b) >= target_b_length: break random_start_b = (random_start_b + 1) else: is_random_next = False for j in range(num_a, len(curr_strs)): tokens_b.extend(curr_strs[j]) token_types_b.extend(curr_str_types[j]) return (tokens_a, token_types_a), (tokens_b, token_types_b), is_random_next def truncate_seq_pair(self, a, b, max_seq_len, rng): """ Truncate sequence pair according to original BERT implementation: https://github.com/google-research/bert/blob/master/create_pretraining_data.py#L391 """ tokens_a, token_types_a = a tokens_b, token_types_b = b max_num_tokens = self.calc_seq_len(max_seq_len) # max_num_tokens = max_seq_len - 3 while True: len_a = len(tokens_a) len_b = len(tokens_b) total_length = len_a + len_b if total_length <= max_num_tokens: break if len(tokens_a) > len(tokens_b): trunc_tokens = tokens_a trunc_types = token_types_a else: trunc_tokens = tokens_b trunc_types = token_types_b assert len(trunc_tokens) >= 1 if rng.random() < 0.5: trunc_tokens.pop(0) trunc_types.pop(0) else: trunc_tokens.pop() trunc_types.pop() return (tokens_a, token_types_a), (tokens_b, token_types_b) def calc_seq_len(self, max_seq_len): return max_seq_len - 3 def mask_token(self, idx, tokens, types, vocab_words, rng): """ helper function to mask `idx` token from `tokens` according to section 3.3.1 of https://arxiv.org/pdf/1810.04805.pdf """ label = tokens[idx] if rng.random() < 0.8: new_label = self.tokenizer.get_command('MASK').Id else: if rng.random() < 0.5: new_label = label else: new_label = rng.choice(vocab_words) tokens[idx] = new_label return label def pad_seq(self, seq): """helper function to pad sequence pair""" num_pad = max(0, self.max_seq_len - len(seq)) pad_mask = [0] * len(seq) + [1] * num_pad seq += [self.tokenizer.get_command('pad').Id] * num_pad return seq, pad_mask def concat_tokens(self, tokens_a, token_types_a, tokens_b, token_types_b): tokens = [self.tokenizer.get_command('ENC').Id] + tokens_a + [self.tokenizer.get_command( 'sep').Id] + tokens_b + [self.tokenizer.get_command('sep').Id] token_types = [token_types_a[0]] + token_types_a + \ [token_types_a[0]] + token_types_b + [token_types_b[0]] return tokens, token_types def create_masked_lm_predictions(self, a, b, mask_lm_prob, max_preds_per_seq, vocab_words, rng): """ Mask sequence pair for BERT training according to: https://github.com/google-research/bert/blob/master/create_pretraining_data.py#L338 """ tokens_a, token_types_a = a tokens_b, token_types_b = b tokens, token_types = self.concat_tokens(tokens_a, token_types_a, tokens_b, token_types_b) len_a = len(tokens_a) len_b = len(tokens_b) cand_indices = [idx + 1 for idx in range(len_a)] + [idx + 2 + len_a for idx in range(len_b)] rng.shuffle(cand_indices) output_tokens, pad_mask = self.pad_seq(list(tokens)) output_types, _ = self.pad_seq(list(token_types)) num_to_predict = min(max_preds_per_seq, max(1, int(round(len(tokens) * mask_lm_prob)))) mask = [0] * len(output_tokens) mask_labels = [-1] * len(output_tokens) for idx in sorted(cand_indices[:num_to_predict]): mask[idx] = 1 label = self.mask_token(idx, output_tokens, output_types, vocab_words, rng) mask_labels[idx] = label return (output_tokens, output_types), mask, mask_labels, pad_mask pangu/megatron/deprecated_data_utils/file_utils.py000066400000000000000000000203721430334424000230270ustar00rootroot00000000000000# This file is provided as is from: # https://github.com/huggingface/pytorch-pretrained-BERT # Please refer to their repository for copyright. """ Utilities for working with the local dataset cache. This file is adapted from the AllenNLP library at https://github.com/allenai/allennlp Copyright by the AllenNLP authors. """ from __future__ import (absolute_import, division, print_function, unicode_literals) import json import logging import os import shutil import tempfile from functools import wraps from hashlib import sha256 import sys from io import open import boto3 import requests from botocore.exceptions import ClientError from tqdm import tqdm try: from urllib.parse import urlparse except ImportError: from urlparse import urlparse try: from pathlib import Path PYTORCH_PRETRAINED_BERT_CACHE = Path(os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', Path.home() / '.pytorch_pretrained_bert')) except (AttributeError, ImportError): PYTORCH_PRETRAINED_BERT_CACHE = os.getenv('PYTORCH_PRETRAINED_BERT_CACHE', os.path.join(os.path.expanduser("~"), '.pytorch_pretrained_bert')) logger = logging.getLogger(__name__) # pylint: disable=invalid-name def url_to_filename(url, etag=None): """ Convert `url` into a hashed filename in a repeatable way. If `etag` is specified, append its hash to the url's, delimited by a period. """ url_bytes = url.encode('utf-8') url_hash = sha256(url_bytes) filename = url_hash.hexdigest() if etag: etag_bytes = etag.encode('utf-8') etag_hash = sha256(etag_bytes) filename += '.' + etag_hash.hexdigest() return filename def filename_to_url(filename, cache_dir=None): """ Return the url and etag (which may be ``None``) stored for `filename`. Raise ``EnvironmentError`` if `filename` or its stored metadata do not exist. """ if cache_dir is None: cache_dir = PYTORCH_PRETRAINED_BERT_CACHE if sys.version_info[0] == 3 and isinstance(cache_dir, Path): cache_dir = str(cache_dir) cache_path = os.path.join(cache_dir, filename) if not os.path.exists(cache_path): raise EnvironmentError("file {} not found".format(cache_path)) meta_path = cache_path + '.json' if not os.path.exists(meta_path): raise EnvironmentError("file {} not found".format(meta_path)) with open(meta_path, encoding="utf-8") as meta_file: metadata = json.load(meta_file) url = metadata['url'] etag = metadata['etag'] return url, etag def cached_path(url_or_filename, cache_dir=None): """ Given something that might be a URL (or might be a local path), determine which. If it's a URL, download the file and cache it, and return the path to the cached file. If it's already a local path, make sure the file exists and then return the path. """ if cache_dir is None: cache_dir = PYTORCH_PRETRAINED_BERT_CACHE if sys.version_info[0] == 3 and isinstance(url_or_filename, Path): url_or_filename = str(url_or_filename) if sys.version_info[0] == 3 and isinstance(cache_dir, Path): cache_dir = str(cache_dir) parsed = urlparse(url_or_filename) if parsed.scheme in ('http', 'https', 's3'): # URL, so get it from the cache (downloading if necessary) return get_from_cache(url_or_filename, cache_dir) elif os.path.exists(url_or_filename): # File, and it exists. return url_or_filename elif parsed.scheme == '': # File, but it doesn't exist. raise EnvironmentError("file {} not found".format(url_or_filename)) else: # Something unknown raise ValueError("unable to parse {} as a URL or as a local path".format(url_or_filename)) def split_s3_path(url): """Split a full s3 path into the bucket name and path.""" parsed = urlparse(url) if not parsed.netloc or not parsed.path: raise ValueError("bad s3 path {}".format(url)) bucket_name = parsed.netloc s3_path = parsed.path # Remove '/' at beginning of path. if s3_path.startswith("/"): s3_path = s3_path[1:] return bucket_name, s3_path def s3_request(func): """ Wrapper function for s3 requests in order to create more helpful error messages. """ @wraps(func) def wrapper(url, *args, **kwargs): try: return func(url, *args, **kwargs) except ClientError as exc: if int(exc.response["Error"]["Code"]) == 404: raise EnvironmentError("file {} not found".format(url)) else: raise return wrapper @s3_request def s3_etag(url): """Check ETag on S3 object.""" s3_resource = boto3.resource("s3") bucket_name, s3_path = split_s3_path(url) s3_object = s3_resource.Object(bucket_name, s3_path) return s3_object.e_tag @s3_request def s3_get(url, temp_file): """Pull a file directly from S3.""" s3_resource = boto3.resource("s3") bucket_name, s3_path = split_s3_path(url) s3_resource.Bucket(bucket_name).download_fileobj(s3_path, temp_file) def http_get(url, temp_file): req = requests.get(url, stream=True) content_length = req.headers.get('Content-Length') total = int(content_length) if content_length is not None else None progress = tqdm(unit="B", total=total) for chunk in req.iter_content(chunk_size=1024): if chunk: # filter out keep-alive new chunks progress.update(len(chunk)) temp_file.write(chunk) progress.close() def get_from_cache(url, cache_dir=None): """ Given a URL, look for the corresponding dataset in the local cache. If it's not there, download it. Then return the path to the cached file. """ if cache_dir is None: cache_dir = PYTORCH_PRETRAINED_BERT_CACHE if sys.version_info[0] == 3 and isinstance(cache_dir, Path): cache_dir = str(cache_dir) if not os.path.exists(cache_dir): os.makedirs(cache_dir) # Get eTag to add to filename, if it exists. if url.startswith("s3://"): etag = s3_etag(url) else: response = requests.head(url, allow_redirects=True) if response.status_code != 200: raise IOError("HEAD request failed for url {} with status code {}" .format(url, response.status_code)) etag = response.headers.get("ETag") filename = url_to_filename(url, etag) # get cache path to put the file cache_path = os.path.join(cache_dir, filename) if not os.path.exists(cache_path): # Download to temporary file, then copy to cache dir once finished. # Otherwise you get corrupt cache entries if the download gets interrupted. with tempfile.NamedTemporaryFile() as temp_file: logger.info("%s not found in cache, downloading to %s", url, temp_file.name) # GET file object if url.startswith("s3://"): s3_get(url, temp_file) else: http_get(url, temp_file) # we are copying the file before closing it, so flush to avoid truncation temp_file.flush() # shutil.copyfileobj() starts at the current position, so go to the start temp_file.seek(0) logger.info("copying %s to cache at %s", temp_file.name, cache_path) with open(cache_path, 'wb') as cache_file: shutil.copyfileobj(temp_file, cache_file) logger.info("creating metadata file for %s", cache_path) meta = {'url': url, 'etag': etag} meta_path = cache_path + '.json' with open(meta_path, 'w', encoding="utf-8") as meta_file: json.dump(meta, meta_file) logger.info("removing temp file %s", temp_file.name) return cache_path def read_set_from_file(filename): ''' Extract a de-duped collection (set) of text from a file. Expected file format is one item per line. ''' collection = set() with open(filename, 'r', encoding='utf-8') as file_: for line in file_: collection.add(line.rstrip()) return collection def get_file_extension(path, dot=True, lower=True): ext = os.path.splitext(path)[1] ext = ext if dot else ext[1:] return ext.lower() if lower else ext pangu/megatron/deprecated_data_utils/lazy_loader.py000066400000000000000000000154321430334424000231760ustar00rootroot00000000000000# 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. """utils for loading text from disk""" import os import mmap import pickle as pkl import time from itertools import accumulate import torch from torch.multiprocessing import Lock def get_lazy_path(path): """ Gets directory path where lazy files are stored. """ return os.path.splitext(path)[0] + '.lazy' def exists_lazy(path, data_type='data'): """ Check if we've already made a lazy version of this file for the `data_type` field. """ if not os.path.exists(get_lazy_path(path)): return False contents = os.listdir(get_lazy_path(path)) if data_type not in contents: return False if data_type + '.len.pkl' not in contents: return False return True def make_lazy(path, strs, data_type='data'): """ Make lazy version of `data_type` field of the file. Byte offsets corresponding to data indices are stored in a `.len.pkl` data file. """ lazypath = get_lazy_path(path) if not os.path.exists(lazypath): os.makedirs(lazypath) datapath = os.path.join(lazypath, data_type) lenpath = os.path.join(lazypath, data_type + '.len.pkl') if not torch.distributed.is_initialized() or torch.distributed.get_rank() == 0: with open(datapath, 'wb') as f: str_lens = [] str_cnt = 0 for s in strs: if isinstance(s, dict): s = s['text'] encoded = s.encode('utf-8') f.write(encoded) str_cnt = len(encoded) str_lens.append(str_cnt) pkl.dump(str_lens, open(lenpath, 'wb')) else: while not os.path.exists(lenpath): time.sleep(1) def split_strings(strings, start, chr_lens): """ Split strings based on string lengths and given start. """ return [strings[i - start:j - start] for i, j in zip([start] + chr_lens[:-1], chr_lens)] class ProcessorTokenizer: """ callable class that runs a preprocessing, as well as tokenization step, on input text. """ def __init__(self, tokenizer, process_fn=None): self.tokenizer = tokenizer self.process_fn = process_fn def __call__(self, string): if self.tokenizer is not None: string = self.tokenizer(string, process_fn=self.process_fn) elif self.process_fn is not None: string = self.process_fn(string) return string class lazy_array_loader(object): """ Arguments: path: path to directory where array entries are concatenated into one big string file and the .len file are located data_type (str): Some datsets have multiple fields that are stored in different paths. `data_type` specifies which of these fields to load in this class mem_map (boolean): Specifies whether to memory map file `path` map_fn (callable): Fetched strings are passed through map_fn before being returned. Example of lazy loader directory structure: file.json file.lazy/ data_type1 data_type1.len.pkl data_type2 data_type2.len.pkl """ def __init__(self, path, data_type='data', mem_map=False, map_fn=None): lazypath = get_lazy_path(path) datapath = os.path.join(lazypath, data_type) # get file where array entries are concatenated into one big string self._file = open(datapath, 'rb', buffering=0) self.file = self._file # memory map file if necessary self.mem_map = mem_map if self.mem_map: self.file = mmap.mmap(self.file.fileno(), 0, prot=mmap.PROT_READ) lenpath = os.path.join(lazypath, data_type + '.len.pkl') self.lens = pkl.load(open(lenpath, 'rb')) self.ends = list(accumulate(self.lens)) self.dumb_ends = list(self.ends) self.read_lock = Lock() self.process_fn = map_fn self.map_fn = map_fn self._tokenizer = None def SetTokenizer(self, tokenizer): """ logic to set and remove (set to None) tokenizer. combines preprocessing/tokenization into one callable. """ if tokenizer is None: if not hasattr(self, '_tokenizer'): self._tokenizer = tokenizer else: self._tokenizer = tokenizer self.map_fn = ProcessorTokenizer(tokenizer, self.process_fn) def GetTokenizer(self): return self._tokenizer def __getitem__(self, index): """ read file and splice strings based on string ending array `self.ends` """ if not isinstance(index, slice): if index == 0: start = 0 else: start = self.ends[index - 1] end = self.ends[index] rtn = self.file_read(start, end) if self.map_fn is not None: return self.map_fn(rtn) else: # if slice, fetch strings with 1 diskread and then splice in memory chr_lens = self.ends[index] if index.start == 0 or index.start is None: start = 0 else: start = self.ends[index.start - 1] stop = chr_lens[-1] strings = self.file_read(start, stop) rtn = split_strings(strings, start, chr_lens) if self.map_fn is not None: return self.map_fn([s for s in rtn]) return rtn def __len__(self): return len(self.ends) def file_read(self, start=0, end=None): """read specified portion of file""" # atomic reads to avoid race conditions with multiprocess dataloader self.read_lock.acquire() # seek to start of file read self.file.seek(start) # read to end of file if no end point provided if end is None: rtn = self.file.read() # else read amount needed to reach end point else: rtn = self.file.read(end - start) self.read_lock.release() # TODO: @raulp figure out mem map byte string bug # if mem map'd need to decode byte string to string rtn = rtn.decode('utf-8', 'ignore') # rtn = str(rtn) if self.mem_map: rtn = rtn.decode('unicode_escape') return rtn pangu/megatron/deprecated_data_utils/samplers.py000066400000000000000000000132661430334424000225220ustar00rootroot00000000000000# 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. """batch samplers that work with either random or sequential data samplers""" import math import os import sys import torch from torch.utils import data import numpy as np class RandomSampler(data.sampler.Sampler): r""" Based off of pytorch RandomSampler and DistributedSampler. Essentially a RandomSampler, but this class lets the user set an epoch like DistributedSampler Samples elements randomly. If without replacement, then sample from a shuffled dataset. If with replacement, then user can specify ``num_samples`` to draw. Arguments: data_source (Dataset): dataset to sample from num_samples (int): number of samples to draw, default=len(dataset) replacement (bool): samples are drawn with replacement if ``True``, default=False """ def __init__(self, data_source, replacement=False, num_samples=None): self.data_source = data_source self.replacement = replacement self._num_samples = num_samples self.epoch = -1 if self._num_samples is not None and replacement is False: raise ValueError("With replacement=False, num_samples should not be specified, " "since a random permute will be performed.") if not isinstance(self.num_samples, int) or self.num_samples <= 0: raise ValueError("num_samples should be a positive integer " "value, but got num_samples={}".format(self.num_samples)) if not isinstance(self.replacement, bool): raise ValueError("replacement should be a boolean value, but got " "replacement={}".format(self.replacement)) @property def num_samples(self): # dataset size might change at runtime if self._num_samples is None: return len(self.data_source) return self._num_samples def __iter__(self): n = len(self.data_source) g = torch.Generator() if self.epoch >= 0: g.manual_seed(self.epoch) if self.replacement: return iter(torch.randint(high=n, size=(self.num_samples,), dtype=torch.int64, generator=g).tolist()) return iter(torch.randperm(n, generator=g).tolist()) def __len__(self): return self.num_samples def set_epoch(self, epoch): self.epoch = epoch class DistributedBatchSampler(data.sampler.BatchSampler): """ similar to normal implementation of distributed sampler, except implementation is at the batch sampler level, instead of just the sampler level. This allows wrapping of arbitrary data samplers (sequential, random, WeightedRandomSampler, etc.) with this batch sampler. """ def __init__(self, sampler, batch_size, drop_last, rank=-1, world_size=2, wrap_last=False): super(DistributedBatchSampler, self).__init__(sampler, batch_size, drop_last) if rank == -1: assert False, 'should not be here' rank = torch.distributed.get_rank() self.rank = rank self.world_size = world_size self.sampler.wrap_around = 0 self.wrap_around = 0 self.wrap_last = wrap_last self.start_iter = 0 def __iter__(self): batch = [] last_batch = None i = 0 for idx in self.data_iterator(self.sampler, wrap_around=False): batch.append(idx) if len(batch) == self.batch_size: tbatch = self._batch(batch) if i >= self.start_iter: yield tbatch self.start_iter = 0 i += 1 last_batch = np.array(list(tbatch)) batch = [] batch_len = len(batch) if batch_len > 0 and not self.drop_last: if self.wrap_last: self.sampler.wrap_around -= (self.batch_size) self.wrap_around += (len(batch)) self.wrap_around %= self.batch_size if isinstance(self.sampler, TransposedSampler): for i, idx in enumerate(self.data_iterator(self.sampler, wrap_around=True)): if i == 0: continue batch.append(idx) new_batch_len = len(batch) if len(batch) == self.batch_size: break yield self._batch(batch) if self.wrap_last: self.sampler.wrap_around += self.batch_size def data_iterator(self, _iter, wrap_around=False): """iterates through data and handles wrap around""" for i, idx in enumerate(_iter): if i < self.wrap_around % self.batch_size: continue if wrap_around: self.wrap_around += 1 self.wrap_around %= self.batch_size yield idx def _batch(self, batch): """extracts samples only pertaining to this worker's batch""" start = self.rank * self.batch_size // self.world_size end = (self.rank + 1) * self.batch_size // self.world_size return batch[start:end] pangu/megatron/deprecated_data_utils/scripts/000077500000000000000000000000001430334424000220015ustar00rootroot00000000000000pangu/megatron/deprecated_data_utils/scripts/presplit_sentences_json.py000066400000000000000000000012671430334424000273230ustar00rootroot00000000000000""" Usage: python scripts/presplit_sentences_json.py """ import sys import json import nltk nltk.download('punkt') input_file = sys.argv[1] output_file = sys.argv[2] line_seperator = "\n" with open(input_file, 'r') as ifile: with open(output_file, "w") as ofile: for doc in ifile.readlines(): parsed = json.loads(doc) sent_list = [] for line in parsed['text'].split('\n'): if line != '\n': sent_list.extend(nltk.tokenize.sent_tokenize(line)) parsed['text'] = line_seperator.join(sent_list) ofile.write(json.dumps(parsed) + '\n') pangu/megatron/deprecated_data_utils/scripts/split_gpt2_json.py000066400000000000000000000103401430334424000254710ustar00rootroot00000000000000# 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. """ Takes a corpora of files (specified by `--input_files`) with json data separated by newlines (loose json). Splits data into train.json, val.json, test.json files under `output_dir`. Note: This code has the potential to override files with the names train.json, val.json, test.json in `--output_dir`. """ import os import argparse import math import random parser = argparse.ArgumentParser('resplit loose json data into train/val/test') parser.add_argument('--input_files', nargs='+', required=True, help='whitespace separated list of input data files') parser.add_argument('--output_dir', required=True, help='output directory where to put files') parser.add_argument('--test_percent', type=float, nargs='+', default=[0.05, 0], help='percentage of available data to use for val/test dataset') args = parser.parse_args() def get_lines(filepath): lines = [] with open(filepath, 'r') as f: for i, l in enumerate(f.readlines()): l = l.strip() lines.append(l) return lines def get_splits(lines, line_counts): all_lines = [] line_idx = [] file_mappings = [] for i, l in enumerate(lines): all_lines.extend(l) line_idx.extend(list(range(len(l)))) file_mappings.extend([i] * len(l)) indices = list(range(len(all_lines))) random.shuffle(indices) all_lines = [all_lines[idx] for idx in indices] line_idx = [line_idx[idx] for idx in indices] file_mappings = [file_mappings[idx] for idx in indices] splits = [] mappings = [] start = 0 for end in line_counts: end += start splits.append(all_lines[start:end]) mappings.append(format_mappings(line_idx[start:end], file_mappings[start:end])) start = end return splits, mappings def format_mappings(line_idx, file_mappings): lines = [] for m, l in zip(file_mappings, line_idx): lines.append(str(m).strip() + '\t' + str(l).strip()) return lines def get_filepaths(filepaths, output_dir): paths = [] train_path = 'train.json' dev_path = 'dev.json' test_path = 'test.json' paths.append(os.path.join(output_dir, train_path)) paths.append(os.path.join(output_dir, dev_path)) paths.append(os.path.join(output_dir, test_path)) return paths def write_files(lines, mappings, filepaths): for l, m, path in zip(lines, mappings, filepaths): write_file(l, path) write_mapping_file(m, path) def write_file(lines, path): print('Writing:', path) with open(path, 'w') as f: for l in lines: f.write(l + '\n') def write_mapping_file(m, path): path = path + '.map' m = [get_mapping_header()] + m write_file(m, path) def get_mapping_header(): return 'file\tline #' if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) lines = [] for filepath in args.input_files: _lines = get_lines(filepath) lines.append(_lines) # calculate number of lines to use for each line_counts = [len(l) for l in lines] total_lines = sum(line_counts) dev_percent = args.test_percent[0] dev_lines = math.ceil(dev_percent * total_lines) test_percent = 0 if len(args.test_percent) == 2: test_percent = args.test_percent[1] test_lines = math.ceil(test_percent * total_lines) train_lines = total_lines - (test_lines + dev_lines) normed_lines = [train_lines, dev_lines, test_lines] normed_lines = [int(l) for l in normed_lines] splits, mappings = get_splits(lines, normed_lines) filepaths = get_filepaths(args.input_files, args.output_dir) print('Writing output to:', filepaths) write_files(splits, mappings, filepaths) pangu/megatron/deprecated_data_utils/scripts/split_json.py000066400000000000000000000071561430334424000245500ustar00rootroot00000000000000""" Takes a corpora of files (specified by `--input_files`) with json data separated by newlines (loose json). Splits data into train.json, val.json, test.json files under `output_dir`. Note: This code has the potential to override files with the names train.json, val.json, test.json in `--output_dir`. """ import os import argparse import math import random parser = argparse.ArgumentParser('resplit loose json data into train/val/test') parser.add_argument('--input_files', nargs='+', required=True, help='whitespace separated list of input data files') parser.add_argument('--output_dir', required=True, help='output directory where to put files') parser.add_argument('--test_percent', type=float, nargs='+', default=[0.05, 0], help='percentage of available data to use for val/test dataset') args = parser.parse_args() def get_lines(filepath): lines = [] with open(filepath, 'r') as f: for i, l in enumerate(f.readlines()): l = l.strip() lines.append(l) return lines def get_splits(lines, line_counts): all_lines = [] line_idx = [] file_mappings = [] for i, l in enumerate(lines): all_lines.extend(l) line_idx.extend(list(range(len(l)))) file_mappings.extend([i] * len(l)) indices = list(range(len(all_lines))) random.shuffle(indices) all_lines = [all_lines[idx] for idx in indices] line_idx = [line_idx[idx] for idx in indices] file_mappings = [file_mappings[idx] for idx in indices] splits = [] mappings = [] start = 0 for end in line_counts: end += start splits.append(all_lines[start:end]) mappings.append(format_mappings(line_idx[start:end], file_mappings[start:end])) start = end return splits, mappings def format_mappings(line_idx, file_mappings): lines = [] for m, l in zip(file_mappings, line_idx): lines.append(str(m).strip() + '\t' + str(l).strip()) return lines def get_filepaths(filepaths, output_dir): paths = [] train_path = 'train.json' dev_path = 'dev.json' test_path = 'test.json' paths.append(os.path.join(output_dir, train_path)) paths.append(os.path.join(output_dir, dev_path)) paths.append(os.path.join(output_dir, test_path)) return paths def write_files(lines, mappings, filepaths): for l, m, path in zip(lines, mappings, filepaths): write_file(l, path) write_mapping_file(m, path) def write_file(lines, path): print('Writing:', path) with open(path, 'w') as f: for l in lines: f.write(l + '\n') def write_mapping_file(m, path): path = path + '.map' m = [get_mapping_header()] + m write_file(m, path) def get_mapping_header(): return 'file\tline #' if not os.path.exists(args.output_dir): os.makedirs(args.output_dir) lines = [] for filepath in args.input_files: _lines = get_lines(filepath) lines.append(_lines) # calculate number of lines to use for each line_counts = [len(l) for l in lines] total_lines = sum(line_counts) dev_percent = args.test_percent[0] dev_lines = math.ceil(dev_percent * total_lines) test_percent = 0 if len(args.test_percent) == 2: test_percent = args.test_percent[1] test_lines = math.ceil(test_percent * total_lines) train_lines = total_lines - (test_lines + dev_lines) normed_lines = [train_lines, dev_lines, test_lines] normed_lines = [int(l) for l in normed_lines] splits, mappings = get_splits(lines, normed_lines) filepaths = get_filepaths(args.input_files, args.output_dir) print('Writing output to:', filepaths) write_files(splits, mappings, filepaths) pangu/megatron/deprecated_data_utils/tf_dl.py000066400000000000000000000126021430334424000217550ustar00rootroot00000000000000# 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. """PyTorch DataLoader for TFRecords""" import numpy as np import torch import queue import threading import tensorflow as tf tf.enable_eager_execution() class TFRecordDataLoader(object): def __init__(self, records, batch_size, max_seq_len, max_preds_per_seq, train, num_workers=2, seed=1, threaded_dl=False): assert max_preds_per_seq is not None, "--max-preds-per-seq MUST BE SPECIFIED when using tfrecords" tf.set_random_seed(seed) if isinstance(records, str): records = [records] self.record_converter = Record2Example({"input_ids": tf.FixedLenFeature([max_seq_len], tf.int64), "input_mask": tf.FixedLenFeature([max_seq_len], tf.int64), "segment_ids": tf.FixedLenFeature([max_seq_len], tf.int64), "masked_lm_positions": tf.FixedLenFeature([max_preds_per_seq], tf.int64), "masked_lm_ids": tf.FixedLenFeature([max_preds_per_seq], tf.int64), "masked_lm_weights": tf.FixedLenFeature([max_preds_per_seq], tf.float32), "next_sentence_labels": tf.FixedLenFeature([1], tf.int64)}) # Instantiate dataset according to original BERT implementation if train: self.dataset = tf.data.Dataset.from_tensor_slices(tf.constant(records)) self.dataset = self.dataset.repeat() self.dataset = self.dataset.shuffle(buffer_size=len(records)) # use sloppy tfrecord dataset self.dataset = self.dataset.apply( tf.contrib.data.parallel_interleave( tf.data.TFRecordDataset, sloppy=train, cycle_length=min(num_workers, len(records)))) self.dataset = self.dataset.shuffle(buffer_size=100) else: self.dataset = tf.data.TFRecordDataset(records) self.dataset = self.dataset.repeat() # Instantiate dataloader (do not drop remainder for eval) loader_args = {'batch_size': batch_size, 'num_parallel_batches': num_workers, 'drop_remainder': train} self.dataloader = self.dataset.apply( tf.contrib.data.map_and_batch( self.record_converter, **loader_args)) self.threaded_dl = threaded_dl self.num_workers = num_workers def __iter__(self): if self.threaded_dl: data_iter = iter(MultiprocessLoader(self.dataloader, self.num_workers)) for item in data_iter: yield item else: data_iter = iter(self.dataloader) for item in data_iter: yield convert_tf_example_to_torch_tensors(item) class Record2Example(object): def __init__(self, feature_map): self.feature_map = feature_map def __call__(self, record): """Decodes a BERT TF record to a TF example.""" example = tf.parse_single_example(record, self.feature_map) for k, v in list(example.items()): if v.dtype == tf.int64: example[k] = tf.to_int32(v) return example def convert_tf_example_to_torch_tensors(example): item = {k: (v.numpy()) for k, v in example.items()} mask = np.zeros_like(item['input_ids']) mask_labels = np.ones_like(item['input_ids']) * -1 for b, row in enumerate(item['masked_lm_positions'].astype(int)): for i, idx in enumerate(row): if item['masked_lm_weights'][b, i] != 0: mask[b, idx] = 1 mask_labels[b, idx] = item['masked_lm_ids'][b, i] output = {'text': item['input_ids'], 'types': item['segment_ids'], 'is_random': item['next_sentence_labels'], 'pad_mask': 1 - item['input_mask'], 'mask': mask, 'mask_labels': mask_labels} return {k: torch.from_numpy(v) for k, v in output.items()} class MultiprocessLoader(object): def __init__(self, dataloader, num_workers=2): self.dl = dataloader self.queue_size = 2 * num_workers def __iter__(self): output_queue = queue.Queue(self.queue_size) output_thread = threading.Thread(target=_multiproc_iter, args=(self.dl, output_queue)) output_thread.daemon = True output_thread.start() while output_thread.is_alive(): yield output_queue.get(block=True) else: print(RuntimeError('TF record data loader thread exited unexpectedly')) def _multiproc_iter(dl, output_queue): data_iter = iter(dl) for item in data_iter: tensors = convert_tf_example_to_torch_tensors(item) output_queue.put(tensors, block=True) pangu/megatron/deprecated_data_utils/tokenization.py000066400000000000000000001061371430334424000234120ustar00rootroot00000000000000# 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. """Utilities for using and training tokenizers (char, wordpiece, sentencepiece)""" from collections import namedtuple import random import os import csv import torch import nltk from nltk import tokenize as nltk_tokenize import sentencepiece as spm from .wordpiece import BertTokenizer, PRETRAINED_VOCAB_ARCHIVE_MAP from .tokenization_gpt2 import GPT2Tokenizer import regex as re def make_tokenizer(tokenizer_type, corpus, model_path=None, vocab_size=None, model_type='bpe', pad_token=0, character_coverage=1.0, command_tokens=None, type_tokens=None, **kwargs): """ Helper function to instantiate a tokenizer given common combinations of options. """ tokenizer_class = tokenizer_type if isinstance(tokenizer_class, str): tokenizer_class = eval(tokenizer_class) if tokenizer_class is BertWordPieceTokenizer: return BertWordPieceTokenizer(model_type, **kwargs) elif tokenizer_class is GPT2BPETokenizer: return GPT2BPETokenizer(**kwargs) text_tokenizer = tokenizer_class(corpus=corpus, vocab_size=vocab_size, model_path=model_path, model_type=model_type, pad_token=pad_token, character_coverage=character_coverage) return Tokenizer(text_tokenizer, command_tokens, type_tokens) class Tokenization(object): """ Tokenization object to hold tokenization, (processed text),and original text. Can hold tokenization as Ids or tokens. It also holds command tokens (pad, unk, etc.) for the tokenization. This allows functions to pad/operate on tokenizations without having access to the full tokenizer, just the tokenization. Several standard array operations are implemented (insert, append, extend). """ def __init__(self, tokenization, text=None, original_text=None, command_tokens=None, asIds=True): self.tokenization = tokenization self.text = text if self.text is None: self.text = self.tokenization self.original_text = original_text if self.original_text is None: self.original_text = self.text self.command_tokens = command_tokens self.asIds = asIds self.parse_command_tokens() def set_command_tokens(self, command_tokens): self.command_tokens = command_tokens return self.parse_command_tokens() def parse_command_tokens(self): if self.command_tokens is None: return for command_token in self.command_tokens: if self.asIds: setattr(self, command_token.name, command_token.Id) else: setattr(self, command_token.name, command_token.token) def __getitem__(self, index): return self.tokenization[index] def __len__(self): return len(self.tokenization) def insert(self, idx, other): if isinstance(other, (CommandToken, TypeToken)): self.tokenization.insert(idx, other.Id) if idx == 0: self.text = other.token + self.text self.original_text = other.token + self.original_text elif idx == len(self.tokenization) - 1: self.text += other.token self.original_text += other.token elif isinstance(other, Tokenization): self.tokenization = self.tokenization[:idx] + \ other.tokenization + self.tokenization[idx:] else: self.tokenization = self.tokenization[:idx] + \ other.tokenization + self.tokenization[idx:] def append(self, other): if isinstance(other, (CommandToken, TypeToken)): self.tokenization.append(other.Id) self.text += other.token self.original_text += other.token elif isinstance(other, Tokenization): self.tokenization.extend(other.tokenization) self.text += other.text self.original_text += other.original_text else: self.tokenization.append(other) return self def extend(self, other): if isinstance(other, (CommandToken, TypeToken)): self.tokenization.append(other.Id) self.text += other.token self.original_text += other.token elif isinstance(other, list) and isinstance(other[0], (CommandToken, TypeToken)): self.tokenization.extend([o.Id for o in other]) self.text += [o.token for o in other] self.original_text += [o.token for o in other] elif isinstance(other, Tokenization): self.tokenization.extend(other.tokenization) self.text += other.text self.original_text += other.original_text else: self.tokenization.extend(other) return self """define some default command tokens for the tokenizer to use""" token_format = "<{0}>" COMMAND_TUPLE = namedtuple('CommandToken', ('name', 'token', 'Id')) def prep_command_tokens(tokenlist, token_format=token_format): return [CommandToken(tok[0], token_format.format(tok[0]), tok[1]) for tok in tokenlist] class CommandToken(object): def __init__(self, name, token, Id): self.name = name self.token = token self.Id = Id def __str__(self): return str(COMMAND_TUPLE(self.name, self.token, self.Id)) DEFAULT_COMMAND_TOKENS = [ ('pad', 0), ('eos', 1), ('bos', 2), ('unk', 3), ('sep', 4), ('L2R', 5), ('ENC', 6), ('MASK', 7), ] DEFAULT_COMMAND_TOKENS = prep_command_tokens(DEFAULT_COMMAND_TOKENS) """define some default type tokens for bert training""" TYPE_TUPLE = namedtuple('TypeToken', ('name', 'token', 'Id')) def prep_type_tokens(tokenlist, token_format=token_format): return [TypeToken(tok[0], token_format.format(tok[0]), tok[1]) for tok in tokenlist] class TypeToken(object): def __init__(self, name, token, Id): self.name = name self.token = token self.Id = Id def __str__(self): return str(TYPE_TUPLE(self.name, self.token, self.Id)) DEFAULT_TYPE_TOKENS = [ ('function', 0), ('command', 1), ('str0', 2), ('str1', 3), ('str2', 4), ('embedding0', 5), ('embedding1', 6), ('embedding2', 7), ('arg0', 8), ('arg1', 9), ('arg2', 10), ] DEFAULT_TYPE_TOKENS = prep_type_tokens(DEFAULT_TYPE_TOKENS) class Tokenizer(object): """ Tokenizer object that handles text tokenization, command tokens, and type tokens. Command tokens and text tokens are stored together in one mapping of size `len(text_tokenizer)+len(command_tokens)`. Command tokens are stored as first `len(command_tokens)` tokens. Token idx is stored at `idx+len(command_tokens)`. Token types are stored in a separate mapping of size `len(type_tokens)`. """ def __init__(self, text_tokenizer, command_tokens=None, type_tokens=None): # set text tokenizer self.text_tokenizer = text_tokenizer if not hasattr(self, 'num_text_tokens'): self.num_text_tokens = len(self.text_tokenizer) # set command tokens if command_tokens is None: command_tokens = DEFAULT_COMMAND_TOKENS self._command_tokens = command_tokens self.command_name_map = {tok.name: tok for tok in self._command_tokens} self.command_token_map = {tok.token: tok for tok in self._command_tokens} self.command_id_map = {tok.Id: tok for tok in self._command_tokens} if not hasattr(self, 'num_command_tokens'): self.num_command_tokens = len(self._command_tokens) if not hasattr(self, 'num_tokens'): self.num_tokens = self.num_command_tokens + self.num_text_tokens # set type tokens if type_tokens is None: type_tokens = DEFAULT_TYPE_TOKENS self.type_tokens = type_tokens self.type_name_map = {tok.name: tok for tok in self.type_tokens} self.type_token_map = {tok.token: tok for tok in self.type_tokens} self.type_id_map = {tok.Id: tok for tok in self.type_tokens} if not hasattr(self, 'num_type_tokens'): self.num_type_tokens = len(self.type_tokens) # parse tokens and vocabs from tokenizer self._tokens = list(self.command_token_map.keys()) + list(self.text_tokenizer.tokens) self._vocab = {t: Id for Id, t in self.command_id_map.items()} self._vocab.update({t: Id + self.num_command_tokens for t, Id in self.text_tokenizer.vocab.items()}) self._text_tokens = list(self.text_tokenizer.tokens) self._text_token_vocab = { t: Id + self.num_command_tokens for t, Id in self.text_tokenizer.vocab.items()} self._command_token_tokens = list(self.command_token_map.keys()) self._command_token_vocab = {t: Id for Id, t in self.command_id_map.items()} self._token_types = list(self.type_token_map.keys()) self._token_type_vocab = {t: Id for Id, t in self.type_id_map.items()} def __call__(self, text, process_fn=None): """run preprocessing and encode text as Ids""" return self.EncodeAsIds(text, process_fn=process_fn) def __len__(self): """total number of tokens""" return self.num_tokens def get_command(self, name): """get command token corresponding to `name`""" return self.command_name_map[name] def get_type(self, name): """get type token corresponding to `name`""" return self.type_name_map[name] @property def tokens(self): """list (or iterable) of all tokens for tokenizer""" return self._tokens @property def vocab(self): """dictionary mapping tokens to ids for tokenizer""" return self._vocab @property def token_types(self): """list (or iterable) of all token types for tokenizer""" return self._token_types @property def token_type_vocab(self): """dictionary mapping token types to ids for tokenizer""" return self._token_type_vocab @property def command_tokens(self): """list (or iterable) of all command tokens for tokenizer""" return self._command_token_tokens @property def command_token_vocab(self): """dictionary mapping command tokens to ids for tokenizer""" return self._command_token_vocab @property def text_tokens(self): """list (or iterable) of text tokens for text tokenizer""" return self._text_tokens @property def text_token_vocab(self): """dictionary mapping text tokens to ids for text tokenizer""" return self._text_token_vocab def EncodeAsIds(self, text, process_fn=None): """ encode text using text tokenizer and shift Id values for command tokens """ tokenization = self.text_tokenizer.EncodeAsIds(text, process_fn=process_fn) tokenization.tokenization = [t + self.num_command_tokens for t in tokenization.tokenization] tokenization.set_command_tokens(self._command_tokens) return tokenization def EncodeAsTokens(self, text, process_fn=None): """ encode text as tokens using text tokenizer """ tokenization = self.text_tokenizer.EncodeAsTokens(text, process_fn=process_fn) tokenization.set_command_tokens(self._command_tokens) return tokenization def IdToToken(self, Id, type_token=False): """convert Id to token accounting for command and type tokens""" if isinstance(Id, (TypeToken, CommandToken)): return Id.token if type_token: return self.type_id_map[Id].token if Id < self.num_command_tokens: return self.command_id_map[Id].token return self.text_tokenizer.IdToToken(Id - self.num_command_tokens) def TokenToId(self, token, type_token=False): """convert token to Id accounting for command and type tokens""" if isinstance(token, (TypeToken, CommandToken)): return token.Id if type_token: return self.type_token_map[token].Id if token in self.command_token_map: return self.command_token_map[token].Id return self.text_tokenizer.TokenToId(token) + self.num_command_tokens def DecodeIds(self, Ids, type_token=False): """ convert Ids to tokens accounting for command and type tokens, tokens are joined and returned as a string. """ if type_token: return ' '.join(Id.token if isinstance(Id, TypeToken) else self.type_id_map[Id].token for Id in Ids) rtn_strs = [] current_str = [] if isinstance(Ids, Tokenization): Ids = Ids.tokenization for Id in Ids: if isinstance(Id, CommandToken): rtn_strs.append(self.text_tokenizer.DecodeIds(current_str)) current_str = [] rtn_strs.append(t.token) elif Id < self.num_command_tokens: rtn_strs.append(self.text_tokenizer.DecodeIds(current_str)) current_str = [] rtn_strs.append(self.command_id_map[Id].token) else: current_str.append(Id - self.num_command_tokens) if current_str != []: rtn_strs.append(self.text_tokenizer.DecodeIds(current_str)) return ' '.join(rtn_strs) def DecodeTokens(self, Tokens, type_token=False): """ convert tokens to a string accounting for command and type tokens. """ if type_token: return ' '.join(t.token if isinstance(t, TypeToken) else t for t in Tokens) rtn_strs = [] current_str = [] if isinstance(Tokens, Tokenization): Tokens = Tokens.tokenization for t in Tokens: if isinstance(t, CommandToken): rtn_strs.append(self.text_tokenizer.DecodeTokens(current_str)) current_str = [] rtn_strs.append(t.token) elif t in self.command_token_map: rtn_strs.append(self.text_tokenizer.DecodeTokens(current_str)) current_str = [] rtn_strs.append(t) else: current_str.append(t) if current_str != []: rtn_strs.append(self.text_tokenizer.DecodeTokens(current_str)) return ' '.join(rtn_strs) class TextTokenizer(object): """ Interface for text tokenizer """ def __init__(self): if not hasattr(self, 'num_text_tokens'): self.num_text_tokens = 0 if not hasattr(self, 'num_tokens'): self.num_tokens = self.num_text_tokens def __call__(self, text, process_fn=None): return self.EncodeAsIds(text, process_fn) def __len__(self): return self.num_text_tokens @property def tokens(self): """list (or iterable) of text tokens for text tokenizer""" raise NotImplementedError('TextTokenizer tokens property not implemented') @property def vocab(self): """dictionary mapping tokens to ids""" raise NotImplementedError('TextTokenizer vocab property not implemented') @staticmethod def exists(model_path): """check if the filepath for a text tokenizer exists""" raise NotImplementedError('TextTokenizer exists method not implemented') def Train(self, corpus): """train a tokenizer on a data corpus and save model for future use""" raise NotImplementedError('TextTokenizer Train not implemented') def EncodeAsIds(self, text, process_fn=None): """ Preprocess text and encode as ids. Return a tokenization object with original text, processed text, and id tokenization. """ raise NotImplementedError('TextTokenizer EncodeAsIds not implemented') def EncodeAsTokens(self, text, process_fn=None): """ Preprocess text and encode as tokens. Return a tokenization object with original text, processed text, and token tokenization. """ raise NotImplementedError('TextTokenizer EncodeAsTokens not implemented') def IdToToken(self, Id): """Convert an Id to Token. Reverse lookup of self.vocab""" raise NotImplementedError('TextTokenizer IdToToken not implemented') def TokenToId(self, token): """Convert a Token to Id. Lookup of self.vocab""" raise NotImplementedError('TextTokenizer TokenToId not implemented') def DecodeIds(self, Ids): """Convert a list or tokenization object of Ids to a text string""" raise NotImplementedError('TextTokenizer DecodeIds not implemented') def DecodeTokens(self, Tokens): """Convert a list or tokenization object of tokens to a text string""" raise NotImplementedError('TextTokenizer DecodeTokens not implemented') class CharacterLevelTokenizer(TextTokenizer): """ Text tokenizer for ASCII-256 Character Level Tokenization. """ def __init__(self, **kwargs): self.num_text_tokens = 256 super(CharacterLevelTokenizer, self).__init__() self._tokens = [self.IdToToken(Id) for Id in range(self.num_text_tokens)] self._vocab = {t: i for i, t in enumerate(self._tokens)} def __len__(self): return 256 @staticmethod def exists(model_path): return True def Train(self, corpus): pass @property def tokens(self): return self._tokens @property def vocab(self): return self._vocab def EncodeAsIds(self, text, process_fn=None): """convert text to ascii 256 Ids""" processed_text = text if process_fn is not None: processed_text = process_fn(processed_text) processed_text = str(processed_text) tokens = [self.TokenToId(c) for c in processed_text] return Tokenization(tokens, processed_text, text) def EncodeAsTokens(self, text, process_fn=None): """convert text to ascii 256 characters""" processed_text = text if process_fn is not None: processed_text = process_fn(processed_text) processed_text = str(processed_text) tokens = [c for c in processed_text] return Tokenization(tokens, processed_text, text, asIds=False) def IdToToken(self, Id): """ascii index to character""" return chr(Id) def TokenToId(self, token): """ascii character to index""" return ord(token) def DecodeIds(self, Ids): """converts ascii ids to tokens before joining them into text""" if isinstance(Ids, Tokenization): Ids = Ids.tokenization return ''.join([self.IdToToken(tok) for tok in Ids]) def DecodeTokens(self, Tokens): """just concatenates ascii tokens into text""" if isinstance(Tokens, Tokenization): Tokens = Tokens.tokenization return ''.join(Tokens) MAX_SENTENCEPIECE_SENTENCES = 100000000 def get_corpus_freq(dataset, filepath, filetype='tsv'): """ Take corpus, split it into sentences, and extract word frequencies. Write frequencies to `filepath` as a tsv. Only write the first MAX_SENTENCEPIECE_SENTENCES most common words to the file. """ nltk.download('punkt', download_dir="./nltk") if filetype == 'tsv': delimiter = '\t' else: delimiter = ',' print("compute corpus frequency\n", flush=True) total_sentence_count = 0 maxlen = 0 freqs = {} for entry in dataset: if isinstance(entry, dict): entry = entry['text'] lines = entry.strip().split('\n') for line in lines: sentences = nltk_tokenize.sent_tokenize(line) total_sentence_count += len(sentences) for sentence in sentences: maxlen = max(len(line), maxlen) for word in sentence.split(): if word not in freqs: freqs[word] = 0 freqs[word] += 1 print("length of freqs before truncating " + str(len(freqs)), flush=True) print("file path for freq " + str(filepath), flush=True) freqs_sorted = {} counter = 0 for word, count in sorted(freqs.items(), key=lambda x: x[1], reverse=True): if counter >= MAX_SENTENCEPIECE_SENTENCES: break counter += 1 freqs_sorted[word] = count print("length of freqs after trancating " + str(len(freqs_sorted)), flush=True) with open(filepath, 'w') as f: writer = csv.writer(f, delimiter=delimiter) for k, v in freqs_sorted.items(): writer.writerow([str(k), str(v)]) return total_sentence_count, maxlen class SentencePieceTokenizer(TextTokenizer): """Trains and uses sentencepiece for text tokenization""" def __init__(self, model_type='bpe', vocab_size=None, corpus=None, model_path=None, character_coverage=1.0, **kwargs): self.character_coverage = character_coverage self.model_type = model_type.lower() self.spm_model = model_path self.num_text_tokens = vocab_size make_train = not SentencePieceTokenizer.exists(self.spm_model) if make_train: assert corpus is not None and self.num_text_tokens is not None self.Train(corpus, self.num_text_tokens) self._tokens = [] self._vocab = {} self.load_spm_model() super(SentencePieceTokenizer, self).__init__() def __len__(self): return self.num_text_tokens @property def tokens(self): return self._tokens @property def vocab(self): return self._vocab @staticmethod def exists(model_path): if model_path is None: return False # check if path exists dne = not os.path.exists(model_path) # check if path.model exists if dne and not model_path.endswith('.model'): dne = not os.path.exists(model_path + '.model') return not dne def load_spm_model(self): """load sentencepiece model and parse vocab""" if not os.path.exists(self.spm_model) and not self.spm_model.endswith('.model'): self.spm_model = self.spm_model + '.model' self.sp = spm.SentencePieceProcessor() self.sp.Load(self.spm_model) self.vocab_size = self.num_text_tokens = len(self.sp) self._tokens = [self.IdToToken(t) for t in range(self.vocab_size)] self._vocab = {t: i for i, t in enumerate(self._tokens)} def Train(self, corpus, num_text_tokens): """train sentencepiece model on corpus using word frequencies""" self.num_text_tokens = num_text_tokens use_model_path = self.spm_model random_hash = str(random.randint(0, 2147483647)) if use_model_path is None: use_model_path = random_hash if use_model_path.endswith('.model'): use_model_path = use_model_path[:use_model_path.rfind('.model')] input_path = use_model_path + '.tsv.' + random_hash line_count, maxlenline = get_corpus_freq(corpus, input_path) line_count = min(line_count, MAX_SENTENCEPIECE_SENTENCES) print('line count used as input_sentence_size ', line_count, flush=True) print('training sentencepiece model', flush=True) train_string = '--input={file_path} --model_prefix={model_prefix} --vocab_size={vocab_size}' \ + ' --model_type={model_type} --character_coverage={character_coverage} ' \ + '--input_sentence_size={input_sentence_size} ' \ + '--input_format=tsv' train_string = train_string.format(file_path=input_path, model_prefix=use_model_path, vocab_size=num_text_tokens, model_type=self.model_type, character_coverage=self.character_coverage, input_sentence_size=int(line_count)) # , #)#, print("calling spm.SentencePieceTrainer.Train(%s)" % (train_string), flush=True) spm.SentencePieceTrainer.Train(train_string) os.remove(input_path) self.spm_model = use_model_path + '.model' print('sentencepiece model written to ' + self.spm_model, flush=True) def EncodeAsIds(self, text, process_fn=None): """convert text to sentencepiece Ids""" processed_text = text if process_fn is not None: processed_text = process_fn(processed_text) tokens = self.sp.EncodeAsIds(processed_text) return Tokenization(tokens, processed_text, text) def EncodeAsTokens(self, text, process_fn=None): """convert text to sentencepiece tokens""" processed_text = text if process_fn is not None: processed_text = process_fn(processed_text) tokens = self.sp.EncodeAsTokens(processed_text) return Tokenization(tokens, processed_text, text, asIds=False) def IdToToken(self, Id): """convert Id to sentencpiece token""" return self.sp.IdToPiece(Id) def TokenToId(self, token): """convert sentencpiece token to Id""" return self.sp.PieceToId(token) def DecodeIds(self, Ids): """converts ids to a text string""" if isinstance(Ids, Tokenization): Ids = Ids.tokenization return self.sp.DecodeIds(Ids) def DecodeTokens(self, Tokens): """converts sentencepiece tokens to a text string""" if isinstance(Tokens, Tokenization): Tokens = Tokens.tokenization return self.sp.DecodeTokens(Tokens) class BertWordPieceTokenizer(Tokenizer): """ Loads a pretrained WordPiece tokenizer from `cache_dir` for tokenization in BERT training. Default to bert-large-uncased tokenizer. """ def __init__(self, tokenizer_model_type=None, cache_dir=None, **kwargs): # default to bert-large-uncased tokenizer if tokenizer_model_type not in PRETRAINED_VOCAB_ARCHIVE_MAP: tokenizer_model_type = 'bert-large-uncased' if torch.distributed.get_rank() == 0: print( 'loading BertWordPieceTokenizer (', tokenizer_model_type, ') from cache_dir ', cache_dir) do_lower_case = not ('-cased' in tokenizer_model_type or 'chinese' in tokenizer_model_type) self.text_tokenizer = BertTokenizer.from_pretrained( tokenizer_model_type, do_lower_case=do_lower_case, cache_dir=cache_dir) if torch.distributed.get_rank() == 0: print('loaded', tokenizer_model_type) # disable max len warnings by increasing max len self.text_tokenizer.max_len = int(1e12) # set command tokens from wordpiece tokenizer values self.num_command_tokens = 5 self.num_tokens = len(self.text_tokenizer.vocab) self.num_text_tokens = self.num_tokens - 5 self.num_type_tokens = 2 self._command_tokens = [ CommandToken('pad', '[PAD]', self.text_tokenizer.vocab['[PAD]']), CommandToken('ENC', '[CLS]', self.text_tokenizer.vocab['[CLS]']), CommandToken('MASK', '[MASK]', self.text_tokenizer.vocab['[MASK]']), CommandToken('unk', '[UNK]', self.text_tokenizer.vocab['[UNK]']), CommandToken('sep', '[SEP]', self.text_tokenizer.vocab['[SEP]']), ] self.command_name_map = {tok.name: tok for tok in self._command_tokens} self.command_token_map = {tok.token: tok for tok in self._command_tokens} self.command_id_map = {tok.Id: tok for tok in self._command_tokens} # set type tokens self.type_tokens = [ TypeToken('str0', '', 0), TypeToken('str1', '', 1), ] self.type_name_map = {tok.name: tok for tok in self.type_tokens} self.type_token_map = {tok.token: tok for tok in self.type_tokens} self.type_id_map = {tok.Id: tok for tok in self.type_tokens} # parse tokens and vocabs from tokenizer self._tokens = list(self.text_tokenizer.vocab.keys()) self._vocab = {k: v for k, v in self.text_tokenizer.vocab.items()} self._text_tokens = list(self._tokens) self._text_token_vocab = {k: v for k, v in self.text_tokenizer.vocab.items()} self._command_token_tokens = list(self.command_token_map.keys()) self._command_token_vocab = {t: Id for Id, t in self.command_id_map.items()} self._token_types = list(self.type_token_map.keys()) self._token_type_vocab = {t: Id for Id, t in self.type_id_map.items()} def EncodeAsIds(self, text, process_fn=None): """convert text to wordpiece Ids""" processed_text = text if process_fn is not None: processed_text = process_fn(processed_text) tokens = self.text_tokenizer.tokenize(processed_text) Ids = self.text_tokenizer.convert_tokens_to_ids(tokens) return Tokenization(Ids, processed_text, text) def EncodeAsTokens(self, text, process_fn=None): """convert wordpiece token to Id""" processed_text = text if process_fn is not None: processed_text = process_fn(processed_text) tokens = self.text_tokenizer.tokenize(processed_text) return Tokenization(tokens, processed_text, text, asIds=False) def IdToToken(self, Id, type_token=False): """convert Id to sentencpiece token""" if isinstance(Id, (TypeToken, CommandToken)): return Id.token if type_token: return self.type_id_map[Id].token return self.text_tokenizer.ids_to_tokens[Id] def TokenToId(self, token, type_token=False): """convert sentencpiece token to Id""" if isinstance(token, (TypeToken, CommandToken)): return token.Id if type_token: return self.type_token_map[token].Id return self.text_tokenizer.vocab[token] def DecodeIds(self, Ids, type_token=False): """converts ids to wordpiece tokens and joins them as a text string""" if type_token: return ' '.join(Id.token if isinstance(Id, TypeToken) else self.type_id_map[Id].token for Id in Ids) if isinstance(Ids, Tokenization): Ids = Ids.tokenization Tokens = [] for Id in Ids: Tokens.append(self.text_tokenizer.ids_to_tokens[Id] if Id != -1 else '-1') Tokens = self.text_tokenizer.convert_ids_to_tokens(Ids) return ' '.join(Tokens) def DecodeTokens(self, Tokens, type_token=False): """converts wordpiece tokens to a text string""" if type_token: return ' '.join(t.token if isinstance(t, TypeToken) else t for t in Tokens) if isinstance(Tokens, Tokenization): Tokens = Tokens.tokenization return ' '.join(Tokens) class GPT2BPETokenizer(Tokenizer): def __init__(self, cache_dir=None, **kwargs): self.text_tokenizer = GPT2Tokenizer.from_pretrained('gpt2', cache_dir=cache_dir) # disable max len warnings by increasing max len self.text_tokenizer.max_len = int(1e12) self.num_command_tokens = 2 self.num_tokens = len(self.text_tokenizer.encoder) self.num_text_tokens = self.num_tokens - 1 self.num_type_tokens = 2 self._command_tokens = [ CommandToken('pad', '<|endoftext|>', self.text_tokenizer.encoder['<|endoftext|>']), CommandToken('eos', '<|endoftext|>', self.text_tokenizer.encoder['<|endoftext|>']), ] self.command_name_map = {tok.name: tok for tok in self._command_tokens} self.command_token_map = {tok.token: tok for tok in self._command_tokens} self.command_id_map = {tok.Id: tok for tok in self._command_tokens} self.type_tokens = [ TypeToken('str0', '', 0), TypeToken('str1', '', 1), ] self.type_name_map = {tok.name: tok for tok in self.type_tokens} self.type_token_map = {tok.token: tok for tok in self.type_tokens} self.type_id_map = {tok.Id: tok for tok in self.type_tokens} self._tokens = list(self.text_tokenizer.encoder.keys()) self._vocab = {k: v for k, v in self.text_tokenizer.encoder.items()} self._text_tokens = list(self._tokens) self._text_token_vocab = {k: v for k, v in self.text_tokenizer.encoder.items()} self._command_token_tokens = list(self.command_token_map.keys()) self._command_token_vocab = {t: Id for Id, t in self.command_id_map.items()} self._token_types = list(self.type_token_map.keys()) self._token_type_vocab = {t: Id for Id, t in self.type_id_map.items()} def EncodeAsIds(self, text, process_fn=None): processed_text = text if process_fn is not None: processed_text = process_fn(processed_text) Ids = self.text_tokenizer.encode(processed_text) # return Tokenization(Ids, processed_text, text) tokenization = Tokenization(Ids, processed_text, text) tokenization.set_command_tokens(self._command_tokens) return tokenization def EncodeAsTokens(self, text, process_fn=None): processed_text = text if process_fn is not None: processed_text = process_fn(processed_text) tokens = [] for token in re.findall(self.text_tokenizer.pat, processed_text): token = ''.join(self.text_tokenizer.bye_encoder[b] for b in token.encode('utf-8')) tokens.extend(bpe_token for bpe_token in self.text_tokenizer.bpe(token).split(' ')) tokenization = Tokenization(tokens, processed_text, text, asIds=False) tokenization.set_command_tokens(self._command_tokens) return tokenization # return Tokenization(tokens, processed_text, text, asIds=False) def IdToToken(self, Id, type_token=False): if isinstance(Id, (TypeToken, CommandToken)): return Id.token if type_token: return self.type_id_map[Id].token return self.text_tokenizer.decoder[Id] def TokenToId(self, token, type_token=False): if isinstance(token, (TypeToken, CommandToken)): return token.Id if type_token: return self.type_token_map[token].Id return self.text_tokenizer.encoder[token] def DecodeIds(self, Ids, type_token=False): if type_token: return ' '.join(Id.token if isinstance(Id, TypeToken) else self.type_id_map[Id].token for Id in Ids) if isinstance(Ids, Tokenization): Ids = Ids.tokenization return self.text_tokenizer.decode(Ids) def DecodeTokens(self, Tokens, type_token=False): if type_token: return ' '.join(t.token if isinstance(t, TypeToken) else t for t in Tokens) if isinstance(Tokens, Tokenization): Tokens = Tokens.tokenization return self.text_tokenizer.decode([self.TokenToId(tok) for tok in Tokens]) pangu/megatron/deprecated_data_utils/tokenization_gpt2.py000066400000000000000000000322051430334424000243400ustar00rootroot00000000000000# coding=utf-8 # 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 try: from functools import lru_cache except ImportError: # Just a dummy decorator to get the checks to run on python2 # because honestly I don't want to support a byte-level unicode BPE # tokenizer on python 2 right now. def lru_cache(): return lambda func: func from .file_utils import cached_path 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 signficant 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: 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 wont 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)))) self.cache = {} # 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)) def bpe(self, token): if token in self.cache: return self.cache[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) self.cache[token] = 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(u'#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) + u'\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 + u'\n') index += 1 return vocab_file, merge_file, special_tokens_file pangu/megatron/deprecated_data_utils/wordpiece.py000066400000000000000000000364221430334424000226540ustar00rootroot00000000000000# coding=utf-8 # Copyright 2018 The Google AI Language 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. Provided as is from https://github.com/huggingface/pytorch-pretrained-BERT/blob/master/pytorch_pretrained_bert/tokenization.py""" from __future__ import absolute_import, division, print_function, unicode_literals import collections import logging import os import unicodedata from io import open from .file_utils import cached_path logger = logging.getLogger(__name__) PRETRAINED_VOCAB_ARCHIVE_MAP = { 'bert-base-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt", 'bert-large-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-uncased-vocab.txt", 'bert-base-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-cased-vocab.txt", 'bert-large-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-large-cased-vocab.txt", 'bert-base-multilingual-uncased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-uncased-vocab.txt", 'bert-base-multilingual-cased': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-multilingual-cased-vocab.txt", 'bert-base-chinese': "https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-chinese-vocab.txt", } PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP = { 'bert-base-uncased': 512, 'bert-large-uncased': 512, 'bert-base-cased': 512, 'bert-large-cased': 512, 'bert-base-multilingual-uncased': 512, 'bert-base-multilingual-cased': 512, 'bert-base-chinese': 512, } VOCAB_NAME = 'vocab.txt' def load_vocab(vocab_file): """Loads a vocabulary file into a dictionary.""" vocab = collections.OrderedDict() index = 0 with open(vocab_file, "r", encoding="utf-8") as reader: while True: token = reader.readline() if not token: break token = token.strip() vocab[token] = index index += 1 return vocab def whitespace_tokenize(text): """Runs basic whitespace cleaning and splitting on a piece of text.""" text = text.strip() if not text: return [] tokens = text.split() return tokens class BertTokenizer(object): """Runs end-to-end tokenization: punctuation splitting + wordpiece""" def __init__(self, vocab_file, do_lower_case=True, max_len=None, do_basic_tokenize=True, never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")): """Constructs a BertTokenizer. Args: vocab_file: Path to a one-wordpiece-per-line vocabulary file do_lower_case: Whether to lower case the input Only has an effect when do_wordpiece_only=False do_basic_tokenize: Whether to do basic tokenization before wordpiece. max_len: An artificial maximum length to truncate tokenized sequences to; Effective maximum length is always the minimum of this value (if specified) and the underlying BERT model's sequence length. never_split: List of tokens which will never be split during tokenization. Only has an effect when do_wordpiece_only=False """ if not os.path.isfile(vocab_file): raise ValueError( "Can't find a vocabulary file at path '{}'. To load the vocabulary from a Google pretrained " "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`".format(vocab_file)) self.vocab = load_vocab(vocab_file) self.ids_to_tokens = collections.OrderedDict( [(ids, tok) for tok, ids in self.vocab.items()]) self.do_basic_tokenize = do_basic_tokenize if do_basic_tokenize: self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case, never_split=never_split) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) self.max_len = max_len if max_len is not None else int(1e12) def tokenize(self, text): if self.do_basic_tokenize: split_tokens = [] for token in self.basic_tokenizer.tokenize(text): for sub_token in self.wordpiece_tokenizer.tokenize(token): split_tokens.append(sub_token) else: split_tokens = self.wordpiece_tokenizer.tokenize(text) return split_tokens def convert_tokens_to_ids(self, tokens): """Converts a sequence of tokens into ids using the vocab.""" ids = [] for token in tokens: ids.append(self.vocab[token]) if len(ids) > self.max_len: logger.warning( "Token indices sequence length is longer than the specified maximum " " sequence length for this BERT model ({} > {}). Running this" " sequence through BERT will result in indexing errors".format( len(ids), self.max_len) ) return ids def convert_ids_to_tokens(self, ids): """Converts a sequence of ids in wordpiece tokens using the vocab.""" tokens = [] for i in ids: tokens.append(self.ids_to_tokens[i]) return tokens @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] else: vocab_file = pretrained_model_name_or_path if os.path.isdir(vocab_file): vocab_file = os.path.join(vocab_file, VOCAB_NAME) # redirect to the cache, if necessary try: resolved_vocab_file = cached_path(vocab_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 any file " "associated to this path or url.".format( pretrained_model_name_or_path, ', '.join(PRETRAINED_VOCAB_ARCHIVE_MAP.keys()), vocab_file)) return None if resolved_vocab_file == vocab_file: logger.info("loading vocabulary file {}".format(vocab_file)) else: logger.info("loading vocabulary file {} from cache at {}".format( vocab_file, resolved_vocab_file)) if pretrained_model_name_or_path in PRETRAINED_VOCAB_POSITIONAL_EMBEDDINGS_SIZE_MAP: # if we're using a pretrained model, ensure the tokenizer wont 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. tokenizer = cls(resolved_vocab_file, *inputs, **kwargs) return tokenizer class BasicTokenizer(object): """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" def __init__(self, do_lower_case=True, never_split=("[UNK]", "[SEP]", "[PAD]", "[CLS]", "[MASK]")): """Constructs a BasicTokenizer. Args: do_lower_case: Whether to lower case the input. """ self.do_lower_case = do_lower_case self.never_split = never_split def tokenize(self, text): """Tokenizes a piece of text.""" text = self._clean_text(text) # This was added on November 1st, 2018 for the multilingual and Chinese # models. This is also applied to the English models now, but it doesn't # matter since the English models were not trained on any Chinese data # and generally don't have any Chinese data in them (there are Chinese # characters in the vocabulary because Wikipedia does have some Chinese # words in the English Wikipedia.). text = self._tokenize_chinese_chars(text) orig_tokens = whitespace_tokenize(text) split_tokens = [] for token in orig_tokens: if self.do_lower_case and token not in self.never_split: token = token.lower() token = self._run_strip_accents(token) split_tokens.extend(self._run_split_on_punc(token)) output_tokens = whitespace_tokenize(" ".join(split_tokens)) return output_tokens def _run_strip_accents(self, text): """Strips accents from a piece of text.""" text = unicodedata.normalize("NFD", text) output = [] for char in text: cat = unicodedata.category(char) if cat == "Mn": continue output.append(char) return "".join(output) def _run_split_on_punc(self, text): """Splits punctuation on a piece of text.""" if text in self.never_split: return [text] chars = list(text) i = 0 start_new_word = True output = [] while i < len(chars): char = chars[i] if _is_punctuation(char): output.append([char]) start_new_word = True else: if start_new_word: output.append([]) start_new_word = False output[-1].append(char) i += 1 return ["".join(x) for x in output] def _tokenize_chinese_chars(self, text): """Adds whitespace around any CJK character.""" output = [] for char in text: cp = ord(char) if self._is_chinese_char(cp): output.append(" ") output.append(char) output.append(" ") else: output.append(char) return "".join(output) def _is_chinese_char(self, cp): """Checks whether CP is the codepoint of a CJK character.""" # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) # # Note that the CJK Unicode block is NOT all Japanese and Korean characters, # despite its name. The modern Korean Hangul alphabet is a different block, # as is Japanese Hiragana and Katakana. Those alphabets are used to write # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ((cp >= 0x4E00 and cp <= 0x9FFF) or # (cp >= 0x3400 and cp <= 0x4DBF) or # (cp >= 0x20000 and cp <= 0x2A6DF) or # (cp >= 0x2A700 and cp <= 0x2B73F) or # (cp >= 0x2B740 and cp <= 0x2B81F) or # (cp >= 0x2B820 and cp <= 0x2CEAF) or (cp >= 0xF900 and cp <= 0xFAFF) or # (cp >= 0x2F800 and cp <= 0x2FA1F)): # return True return False def _clean_text(self, text): """Performs invalid character removal and whitespace cleanup on text.""" output = [] for char in text: cp = ord(char) if cp == 0 or cp == 0xfffd or _is_control(char): continue if _is_whitespace(char): output.append(" ") else: output.append(char) return "".join(output) class WordpieceTokenizer(object): """Runs WordPiece tokenization.""" def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=100): self.vocab = vocab self.unk_token = unk_token self.max_input_chars_per_word = max_input_chars_per_word def tokenize(self, text): """Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the given vocabulary. For example: input = "unaffable" output = ["un", "##aff", "##able"] Args: text: A single token or whitespace separated tokens. This should have already been passed through `BasicTokenizer`. Returns: A list of wordpiece tokens. """ output_tokens = [] for token in whitespace_tokenize(text): chars = list(token) if len(chars) > self.max_input_chars_per_word: output_tokens.append(self.unk_token) continue is_bad = False start = 0 sub_tokens = [] while start < len(chars): end = len(chars) cur_substr = None while start < end: substr = "".join(chars[start:end]) if start > 0: substr = "##" + substr if substr in self.vocab: cur_substr = substr break end -= 1 if cur_substr is None: is_bad = True break sub_tokens.append(cur_substr) start = end if is_bad: output_tokens.append(self.unk_token) else: output_tokens.extend(sub_tokens) return output_tokens def _is_whitespace(char): """Checks whether `chars` is a whitespace character.""" # \t, \n, and \r are technically contorl characters but we treat them # as whitespace since they are generally considered as such. if char == " " or char == "\t" or char == "\n" or char == "\r": return True cat = unicodedata.category(char) if cat == "Zs": return True return False def _is_control(char): """Checks whether `chars` is a control character.""" # These are technically control characters but we count them as whitespace # characters. if char == "\t" or char == "\n" or char == "\r": return False cat = unicodedata.category(char) if cat.startswith("C"): return True return False def _is_punctuation(char): """Checks whether `chars` is a punctuation character.""" cp = ord(char) # We treat all non-letter/number ASCII as punctuation. # Characters such as "^", "$", and "`" are not in the Unicode # Punctuation class but we treat them as punctuation anyways, for # consistency. if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): return True cat = unicodedata.category(char) if cat.startswith("P"): return True return False pangu/megatron/fp16/000077500000000000000000000000001430334424000145555ustar00rootroot00000000000000pangu/megatron/fp16/__init__.py000066400000000000000000000016551430334424000166750ustar00rootroot00000000000000# 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. from .fp16util import ( BN_convert_float, network_to_half, prep_param_lists, model_grads_to_master_grads, master_params_to_model_params, tofp16, to_python_float, clip_grad_norm, convert_module, convert_network, FP16Model, ) from .fp16 import * from .loss_scaler import * pangu/megatron/fp16/fp16.py000066400000000000000000000770041430334424000157130ustar00rootroot00000000000000# 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. """Stable version of apex FP16 Optimizer""" import torch from torch import nn from torch.autograd import Variable from torch.nn.parameter import Parameter from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors from .loss_scaler import DynamicLossScaler, LossScaler from .fp16util import model_grads_to_master_grads, master_params_to_model_params, clip_grad_norm from apex.multi_tensor_apply import multi_tensor_applier import amp_C from megatron.module import MegatronModule FLOAT_TYPES = (torch.FloatTensor, torch.cuda.FloatTensor) HALF_TYPES = (torch.HalfTensor, torch.cuda.HalfTensor) def conversion_helper(val, conversion): """Apply conversion to val. Recursively apply conversion if `val` is a nested tuple/list structure.""" if not isinstance(val, (tuple, list)): return conversion(val) rtn = [conversion_helper(v, conversion) for v in val] if isinstance(val, tuple): rtn = tuple(rtn) return rtn def fp32_to_fp16(val): """Convert fp32 `val` to fp16""" def half_conversion(val): val_typecheck = val if isinstance(val_typecheck, (Parameter, Variable)): val_typecheck = val.data if isinstance(val_typecheck, FLOAT_TYPES): val = val.half() return val return conversion_helper(val, half_conversion) def fp16_to_fp32(val): """Convert fp16 `val` to fp32""" def float_conversion(val): val_typecheck = val if isinstance(val_typecheck, (Parameter, Variable)): val_typecheck = val.data if isinstance(val_typecheck, HALF_TYPES): val = val.float() return val return conversion_helper(val, float_conversion) class FP16_Module(MegatronModule): def __init__(self, module): super(FP16_Module, self).__init__() self.add_module('module', module.half()) def forward(self, *inputs, **kwargs): return fp16_to_fp32(self.module(*(fp32_to_fp16(inputs)), **kwargs)) def state_dict(self, destination=None, prefix='', keep_vars=False): return self.module.state_dict(destination, prefix, keep_vars) def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): return self.module.state_dict_for_save_checkpoint(destination, prefix, keep_vars) def load_state_dict(self, state_dict, strict=True): self.module.load_state_dict(state_dict, strict=strict) # TODO: Update overflow check + downscale to use Carl's fused kernel. class FP16_Optimizer(object): """ :class:`FP16_Optimizer` is designed to wrap an existing PyTorch optimizer, and manage static or dynamic loss scaling and master weights in a manner transparent to the user. For standard use, only two lines must be changed: creating the :class:`FP16_Optimizer` instance, and changing the call to ``backward``. Example:: model = torch.nn.Linear(D_in, D_out).cuda().half() optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) # Name the FP16_Optimizer instance to replace the existing optimizer # (recommended but not required): optimizer = FP16_Optimizer(optimizer, static_loss_scale = 128.0) ... # loss.backward() becomes: optimizer.backward(loss) ... Example with dynamic loss scaling:: ... optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True) # optional arg to control dynamic loss scaling behavior # dynamic_loss_args={'scale_window' : 500}) # Usually, dynamic_loss_args is not necessary. Args: init_optimizer (torch.optim.optimizer): Existing optimizer created with the parameters to optimize. Internally, :class:`FP16_Optimizer` replaces the passed optimizer's fp16 parameters, if any, with fp32 master parameters copied from the original ones. :class:`FP16_Optimizer` also stores references to the original fp16 parameters, and updates these fp16 parameters from the master fp32 copy at the end of each :attr:`step`. static_loss_scale (float, optional, default=1.0): Loss scale used internally to scale gradients computed by the model. Any fp16 gradients will be copied to fp32, then downscaled before being applied to the fp32 master params, so ``static_loss_scale`` should not affect learning rate. dynamic_loss_scale (bool, optional, default=False): Use dynamic loss scaling. If True, this will override any ``static_loss_scale`` option. dynamic_loss_args (dict, optional, default=None): Dict of kwargs that will be forwarded to the internal :class:`DynamicLossScaler` instance's constructor. Keys of this dict must match kwargs accepted by :class:`DynamicLossScaler`'s constructor. If ``dynamic_loss_args`` is unspecified, :class:`DynamicLossScaler`'s defaults will be used. verbose (bool, optional, default=True): By default, FP16_Optimizer's constructor prints out the parameters and parameter groups it is ingesting, as a sanity check. If this becomes annoying (e.g. for large models), it can be disabled by passing ``verbose=False``. ``verbose=False`` will not disable printing when the loss scale is readjusted during dynamic loss scaling. ``init_optimizer`` is expected to have been constructed in the ordinary way. It is recommended (although not required) that the newly constructed :class:`FP16_Optimizer` instance be named to replace ``init_optimizer``, for two reasons: First, it means that references to the same name later in the file will not have to change. Second, :class:`FP16_Optimizer` reserves the right (as an implementation detail) to modify ``init_optimizer``. If you do choose a unique name for the new :class:`FP16_Optimizer` instance, you should only work with this new instance, because the preexisting optimizer might no longer behave as expected. ``init_optimizer`` may be any Pytorch optimizer. It may contain a mixture of fp16 and fp32 parameters organized into any number of ``param_groups`` with different hyperparameters. The :class:`FP16_Optimizer` constructor will ingest these ``param_groups`` and remember them. Calls to :: loss.backward() must be replaced with :: optimizer.backward(loss) because :class:`FP16_Optimizer` requires ownership of the backward pass to implement loss scaling and copies to master gradients. .. note:: Loss scaling, either static or dynamic, is orthogonal to learning rate, because gradients are downscaled before being applied. This means that adjusting the loss scale, or using dynamic loss scaling, should not require retuning the learning rate or any other hyperparameters. **Advanced options** **Closures**: :class:`FP16_Optimizer` can wrap a Pytorch optimizer that receives a closure. See docstring for :attr:`step`. **Gradient clipping**: Use :attr:`clip_master_grads`. **Multiple losses**: If your model accumulates gradients from multiple losses, this can be made more efficient by supplying ``update_master_grads=False`` to :attr:`backward`. See docstring for :attr:`backward`. **Manually adjusting loss scale**: The current loss scale can be retrieved or set via :: print(optimizer.loss_scale) optimizer.loss_scale = new_loss_scale For static loss scaling, manually adjusting the loss scale over time is a reasonable thing to do. During later epochs, gradients may become smaller, and a higher loss scale may be required, analogous to scheduling the learning rate. Dynamic loss scaling is more subtle (see :class:`DynamicLossScaler`) and in this case, manually adjusting the loss scale is not recommended. **Multi_GPU training**: If the wrapped ``init_optimizer`` was created from a model wrapped in Pytorch DistributedDataParallel or Apex DistributedDataParallel, :class:`FP16_Optimizer` should still work as intended. """ def __init__(self, init_optimizer, static_loss_scale=1.0, dynamic_loss_scale=False, dynamic_loss_args=None, verbose=False): if not torch.cuda.is_available: raise SystemError("Cannot use fp16 without CUDA.") self.verbose = verbose self.optimizer = init_optimizer # init_state_dict sets up an alternative way to cast per-param state tensors. # Stashing here in case https://github.com/pytorch/pytorch/issues/7733 makes it necessary. # init_state_dict = init_optimizer.state_dict() self.fp16_groups = [] self.fp32_from_fp16_groups = [] self.fp32_from_fp32_groups = [] for i, param_group in enumerate(self.optimizer.param_groups): self.maybe_print("FP16_Optimizer processing param group {}:".format(i)) fp16_params_this_group = [] fp32_params_this_group = [] fp32_from_fp16_params_this_group = [] for i, param in enumerate(param_group['params']): if param.requires_grad: if param.type() == 'torch.cuda.HalfTensor': self.maybe_print("FP16_Optimizer received torch.cuda.HalfTensor with {}" .format(param.size())) fp16_params_this_group.append(param) master_param = param.detach().clone().float() master_param.requires_grad = True # Copythe model parallel flag. master_param.model_parallel = param.model_parallel param_group['params'][i] = master_param fp32_from_fp16_params_this_group.append(master_param) # Reset existing state dict key to the new master param. # We still need to recast per-param state tensors, if any, to FP32. if param in self.optimizer.state: self.optimizer.state[master_param] = self.optimizer.state.pop(param) elif param.type() == 'torch.cuda.FloatTensor': self.maybe_print("FP16_Optimizer received torch.cuda.FloatTensor with {}" .format(param.size())) fp32_params_this_group.append(param) param_group['params'][i] = param else: raise TypeError("Wrapped parameters must be either " "torch.cuda.FloatTensor or torch.cuda.HalfTensor. " "Received {}".format(param.type())) self.fp16_groups.append(fp16_params_this_group) self.fp32_from_fp16_groups.append(fp32_from_fp16_params_this_group) self.fp32_from_fp32_groups.append(fp32_params_this_group) # Leverage state_dict() and load_state_dict() to recast preexisting per-param state tensors self.optimizer.load_state_dict(self.optimizer.state_dict()) # alternative way to cast per-param state tensors: # self.optimizer.load_state_dict(init_state_dict) if dynamic_loss_scale: self.dynamic_loss_scale = True if dynamic_loss_args is not None: self.loss_scaler = DynamicLossScaler(**dynamic_loss_args) else: self.loss_scaler = DynamicLossScaler() else: self.dynamic_loss_scale = False self.loss_scaler = LossScaler(static_loss_scale) self.overflow = False self.first_closure_call_this_step = True self.clip_grad_norm = clip_grad_norm def maybe_print(self, msg): if self.verbose: print(msg) def __getstate__(self): raise RuntimeError("FP16_Optimizer should be serialized using state_dict().") def __setstate__(self, state): raise RuntimeError("FP16_Optimizer should be deserialized using load_state_dict().") def zero_grad(self, set_grads_to_None=False): """ Zero fp32 and fp16 parameter grads. """ # In principle, only the .grad attributes of the model params need to be zeroed, # because gradients are copied into the FP32 master params. However, we zero # all gradients owned by the optimizer, just to be safe: for group in self.optimizer.param_groups: for p in group['params']: if set_grads_to_None: p.grad = None else: if p.grad is not None: p.grad.detach_() p.grad.zero_() # Zero fp16 gradients owned by the model: for fp16_group in self.fp16_groups: for param in fp16_group: if set_grads_to_None: param.grad = None else: if param.grad is not None: param.grad.detach_() # as in torch.optim.optimizer.zero_grad() param.grad.zero_() def _check_overflow(self): params = [] for group in self.fp16_groups: for param in group: params.append(param) for group in self.fp32_from_fp32_groups: for param in group: params.append(param) self.overflow = self.loss_scaler.has_overflow(params) def _update_scale(self, has_overflow=False): self.loss_scaler.update_scale(has_overflow) def _master_params_to_model_params(self): for fp16_group, fp32_from_fp16_group in zip(self.fp16_groups, self.fp32_from_fp16_groups): master_params_to_model_params(fp16_group, fp32_from_fp16_group) def _model_params_to_master_params(self): for fp16_group, fp32_from_fp16_group in zip(self.fp16_groups, self.fp32_from_fp16_groups): master_params_to_model_params(fp32_from_fp16_group, fp16_group) # To consider: Integrate distributed with this wrapper by registering a hook on each variable # that does the overflow check, gradient copy + downscale, and fp32 # allreduce in a different stream. def _model_grads_to_master_grads(self): for fp16_group, fp32_from_fp16_group in zip(self.fp16_groups, self.fp32_from_fp16_groups): model_grads_to_master_grads(fp16_group, fp32_from_fp16_group) def _downscale_master(self): if self.loss_scale != 1.0: for group in self.optimizer.param_groups: grads = [p.grad for p in group['params'] if p.grad is not None] _overflow_buf = torch.cuda.IntTensor([0]) multi_tensor_applier(amp_C.multi_tensor_scale, _overflow_buf, [grads, grads], 1./self.loss_scale) def clip_master_grads(self, max_norm, norm_type=2): """ Clips fp32 master gradients via ``torch.nn.utils.clip_grad_norm``. Args: max_norm (float or int): max norm of the gradients norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for infinity norm. Returns: Total norm of the current fp32 gradients (viewed as a single vector). .. warning:: Returns -1 if the most recently computed fp16 gradients overflowed (that is, if ``self.overflow`` is ``True``). """ if not self.overflow: fp32_params = [] for param_group in self.optimizer.param_groups: for param in param_group['params']: fp32_params.append(param) return self.clip_grad_norm(fp32_params, max_norm, norm_type) else: return -1 def state_dict(self): """ Returns a dict containing the current state of this :class:`FP16_Optimizer` instance. This dict contains attributes of :class:`FP16_Optimizer`, as well as the state_dict of the contained Pytorch optimizer. Example:: checkpoint = {} checkpoint['model'] = model.state_dict() checkpoint['optimizer'] = optimizer.state_dict() torch.save(checkpoint, "saved.pth") """ state_dict = {} state_dict['loss_scaler'] = self.loss_scaler state_dict['dynamic_loss_scale'] = self.dynamic_loss_scale state_dict['overflow'] = self.overflow state_dict['first_closure_call_this_step'] = self.first_closure_call_this_step state_dict['optimizer_state_dict'] = self.optimizer.state_dict() state_dict['fp32_from_fp16'] = self.fp32_from_fp16_groups return state_dict def load_state_dict(self, state_dict): """ Loads a state_dict created by an earlier call to state_dict(). If ``fp16_optimizer_instance`` was constructed from some ``init_optimizer``, whose parameters in turn came from ``model``, it is expected that the user will call ``model.load_state_dict()`` before ``fp16_optimizer_instance.load_state_dict()`` is called. Example:: model = torch.nn.Linear(D_in, D_out).cuda().half() optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) optimizer = FP16_Optimizer(optimizer, static_loss_scale = 128.0) ... checkpoint = torch.load("saved.pth") model.load_state_dict(checkpoint['model']) optimizer.load_state_dict(checkpoint['optimizer']) """ # I think it should actually be ok to reload the optimizer before the model. self.loss_scaler = state_dict['loss_scaler'] self.dynamic_loss_scale = state_dict['dynamic_loss_scale'] self.overflow = state_dict['overflow'] self.first_closure_call_this_step = state_dict['first_closure_call_this_step'] self.optimizer.load_state_dict(state_dict['optimizer_state_dict']) # At this point, the optimizer's references to the model's fp32 parameters are up to date. # The optimizer's hyperparameters and internal buffers are also up to date. # However, the fp32 master copies of the model's fp16 params stored by the optimizer are still # out of date. There are two options. # 1: Refresh the master params from the model's fp16 params. # This requires less storage but incurs precision loss. # 2: Save and restore the fp32 master copies separately. # We choose option 2. # # Pytorch Optimizer.load_state_dict casts saved buffers (e.g. momentum) to the type and device # of their associated parameters, because it's possible those buffers might not exist yet in # the current optimizer instance. In our case, as long as the current FP16_Optimizer has been # constructed in the same way as the one whose state_dict we are loading, the same master params # are guaranteed to exist, so we can just copy_() from the saved master params. for current_group, saved_group in zip( self.fp32_from_fp16_groups, state_dict['fp32_from_fp16']): for current, saved in zip(current_group, saved_group): current.data.copy_(saved.data) def step(self, closure=None): # could add clip option. """ If no closure is supplied, :attr:`step` should be called after ``fp16_optimizer_obj.backward(loss)``. :attr:`step` updates the fp32 master copy of parameters using the optimizer supplied to :class:`FP16_Optimizer`'s constructor, then copies the updated fp32 params into the fp16 params originally referenced by :class:`FP16_Optimizer`'s constructor, so the user may immediately run another forward pass using their model. If a closure is supplied, :attr:`step` may be called without a prior call to :attr:`backward(loss)`. This control flow is identical to `ordinary Pytorch optimizer use`_ with closures. However, the user should take care that any ``loss.backward()`` call within the closure has been replaced by ``fp16_optimizer_obj.backward(loss)``. Args: closure (optional): Closure that will be supplied to the underlying optimizer originally passed to :class:`FP16_Optimizer`'s constructor. closure should call :attr:`zero_grad()` on the :class:`FP16_Optimizer` object, compute the loss, call :attr:`backward(loss)`, and return the loss. Example with closure:: # optimizer is assumed to be an FP16_Optimizer object, previously constructed from an # existing pytorch optimizer. for input, target in dataset: def closure(): optimizer.zero_grad() output = model(input) loss = loss_fn(output, target) # loss.backward() becomes: optimizer.backward(loss) return loss optimizer.step(closure) .. warning:: Currently, calling :attr:`step` with a closure is not compatible with dynamic loss scaling. .. _`ordinary Pytorch optimizer use`: http://pytorch.org/docs/master/optim.html#optimizer-step-closure """ scale = self.loss_scaler.loss_scale self._update_scale(self.overflow) if self.overflow: self.maybe_print("OVERFLOW! Skipping step. Attempted loss scale: {}, reducing to {}" .format(scale, self.loss_scale)) return if closure is not None: retval = self._step_with_closure(closure) else: retval = self.optimizer.step() self._master_params_to_model_params() return retval def _step_with_closure(self, closure): def wrapped_closure(): # helpful for debugging # print("Calling wrapped_closure, first_closure_call_this_step = {}" # .format(self.first_closure_call_this_step)) if self.first_closure_call_this_step: # We expect that the fp16 params are initially fresh on entering self.step(), # so _master_params_to_model_params() is unnecessary the first time wrapped_closure() # is called within self.optimizer.step(). self.first_closure_call_this_step = False else: # If self.optimizer.step() internally calls wrapped_closure more than once, # it may update the fp32 params after each call. However, self.optimizer # doesn't know about the fp16 params at all. If the fp32 params get updated, # we can't rely on self.optimizer to refresh the fp16 params. We need # to handle that manually: self._master_params_to_model_params() # Our API expects the user to give us ownership of the backward() call by # replacing all calls to loss.backward() with optimizer.backward(loss). # This requirement holds whether or not the call to backward() is made within a closure. # If the user is properly calling optimizer.backward(loss) within "closure," # calling closure() here will give the fp32 master params fresh gradients # for the optimizer to play with, so all wrapped_closure needs to do is call # closure() and return the loss. temp_loss = closure() while(self.overflow): scale = self.loss_scaler.loss_scale self._update_scale(self.overflow) self.maybe_print("OVERFLOW within closure! Skipping step. Attempted loss scale: {}, " "reducing to {}".format(scale, self.loss_scale)) temp_loss = closure() return temp_loss retval = self.optimizer.step(wrapped_closure) self.first_closure_call_this_step = True return retval def backward(self, loss, update_master_grads=True, retain_graph=False): """ :attr:`backward` performs the following conceptual steps: 1. fp32_loss = loss.float() (see first Note below) 2. scaled_loss = fp32_loss*loss_scale 3. scaled_loss.backward(), which accumulates scaled gradients into the ``.grad`` attributes of the model's leaves (which may be fp16, fp32, or a mixture, depending how your model was defined). 4. fp16 grads are then copied to the master params' ``.grad`` attributes (see second Note), which are guaranteed to be fp32. 5. Finally, master grads are divided by loss_scale. In this way, after :attr:`backward`, the master params have fresh gradients, and :attr:`step` may be called. .. note:: :attr:`backward` internally converts the loss to fp32 before applying the loss scale. This provides some additional safety against overflow if the user has supplied an fp16 loss value. However, for maximum overflow safety, the user should compute the loss criterion (MSE, cross entropy, etc) in fp32 before supplying it to :attr:`backward`. .. warning:: The gradients found in a model's leaves after the call to :attr:`backward` should not be regarded as valid in general, because it's possible they have been scaled (and in the case of dynamic loss scaling, the scale factor may change over time). If the user wants to inspect gradients after a call to :attr:`backward`, only the master gradients should be regarded as valid. These can be retrieved via :attr:`inspect_master_grad_data()`. Args: loss: The loss output by the user's model. loss may be either float or half (but see first Note above). update_master_grads (bool, optional, default=True): Option to copy fp16 grads to fp32 grads on this call. By setting this to False, the user can delay the copy, which is useful to eliminate redundant fp16->fp32 grad copies if :attr:`backward` is being called on multiple losses in one iteration. If set to False, the user becomes responsible for calling :attr:`update_master_grads` before calling :attr:`step`. retain_graph (bool, optional, default=False): Forwards the usual ``retain_graph=True`` option to the internal call to ``loss.backward``. If ``retain_graph`` is being used to accumulate gradient values from multiple backward passes before calling ``optimizer.step``, passing ``update_master_grads=False`` is also recommended (see Example below). Example:: # Ordinary operation: optimizer.backward(loss) # Naive operation with multiple losses (technically valid, but less efficient): # fp32 grads will be correct after the second call, but # the first call incurs an unnecessary fp16->fp32 grad copy. optimizer.backward(loss1) optimizer.backward(loss2) # More efficient way to handle multiple losses: # The fp16->fp32 grad copy is delayed until fp16 grads from all # losses have been accumulated. optimizer.backward(loss1, update_master_grads=False) optimizer.backward(loss2, update_master_grads=False) optimizer.update_master_grads() """ # To consider: try multiple backward passes using retain_grad=True to find # a loss scale that works. After you find a loss scale that works, do a final dummy # backward pass with retain_graph=False to tear down the graph. Doing this would avoid # discarding the iteration, but probably wouldn't improve overall efficiency. self.loss_scaler.backward(loss.float(), retain_graph=retain_graph) if update_master_grads: self.update_master_grads() def update_master_grads(self): """ Copy the ``.grad`` attribute from stored references to fp16 parameters to the ``.grad`` attribute of the fp32 master parameters that are directly updated by the optimizer. :attr:`update_master_grads` only needs to be called if ``fp16_optimizer_obj.backward`` was called with ``update_master_grads=False``. """ if self.dynamic_loss_scale: self._check_overflow() if self.overflow: return self._model_grads_to_master_grads() self._downscale_master() def inspect_master_grad_data(self): """ When running with :class:`FP16_Optimizer`, ``.grad`` attributes of a model's fp16 leaves should not be regarded as truthful, because they might be scaled. After a call to :attr:`fp16_optimizer_obj.backward(loss)`, if no overflow was encountered, the fp32 master params' ``.grad`` attributes will contain valid gradients properly divided by the loss scale. However, because :class:`FP16_Optimizer` flattens some parameters, accessing them may be nonintuitive. :attr:`inspect_master_grad_data` allows those gradients to be viewed with shapes corresponding to their associated model leaves. Returns: List of lists (one list for each parameter group). The list for each parameter group is a list of the ``.grad.data`` attributes of the fp32 master params belonging to that group. """ if self.overflow: print("Warning: calling FP16_Optimizer.inspect_master_grad_data while in an overflow state. " "Gradients are currently invalid (may be inf, nan, or stale). Returning None.") return None else: # The optimizer owns only references to master params. master_grads_data = [] for param_group in self.optimizer.param_groups: master_grads_this_group = [] for param in param_group['params']: if param.grad is not None: master_grads_this_group.append(param.grad.data) else: master_grads_this_group.append(None) master_grads_data.append(master_grads_this_group) return master_grads_data # Promote loss scale so it can be retrieved or set via "fp16_optimizer_instance.loss_scale" def _get_loss_scale(self): return self.loss_scaler.loss_scale def _set_loss_scale(self, value): self.loss_scaler.cur_scale = value loss_scale = property(_get_loss_scale, _set_loss_scale) # Promote state so it can be retrieved or set via "fp16_optimizer_instance.state" def _get_state(self): return self.optimizer.state def _set_state(self, value): self.optimizer.state = value state = property(_get_state, _set_state) # Promote param_groups so it can be retrieved or set via "fp16_optimizer_instance.param_groups" # (for example, to adjust the learning rate) def _get_param_groups(self): return self.optimizer.param_groups def _set_param_groups(self, value): self.optimizer.param_groups = value param_groups = property(_get_param_groups, _set_param_groups) pangu/megatron/fp16/fp16util.py000066400000000000000000000176631430334424000166160ustar00rootroot00000000000000# 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. import torch import torch.nn as nn from torch.autograd import Variable from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors from apex.multi_tensor_apply import multi_tensor_applier import amp_C from megatron import mpu class tofp16(nn.Module): """ Utility module that implements:: def forward(self, input): return input.half() """ def __init__(self): super(tofp16, self).__init__() def forward(self, input): return input.half() def BN_convert_float(module): """ Utility function for network_to_half(). Retained for legacy purposes. """ if isinstance(module, torch.nn.modules.batchnorm._BatchNorm) and module.affine is True: module.float() for child in module.children(): BN_convert_float(child) return module def network_to_half(network): """ Convert model to half precision in a batchnorm-safe way. Retained for legacy purposes. It is recommended to use FP16Model. """ return nn.Sequential(tofp16(), BN_convert_float(network.half())) def convert_module(module, dtype): """ Converts a module's immediate parameters and buffers to dtype. """ for param in module.parameters(recurse=False): if param is not None: if param.data.dtype.is_floating_point: param.data = param.data.to(dtype=dtype) if param._grad is not None and param._grad.data.dtype.is_floating_point: param._grad.data = param._grad.data.to(dtype=dtype) for buf in module.buffers(recurse=False): if buf is not None and buf.data.dtype.is_floating_point: buf.data = buf.data.to(dtype=dtype) def convert_network(network, dtype): """ Converts a network's parameters and buffers to dtype. """ for module in network.modules(): if isinstance(module, torch.nn.modules.batchnorm._BatchNorm) and module.affine is True: continue convert_module(module, dtype) return network class FP16Model(nn.Module): """ Convert model to half precision in a batchnorm-safe way. """ def __init__(self, network): super(FP16Model, self).__init__() self.network = convert_network(network, dtype=torch.half) def forward(self, *inputs): inputs = tuple(t.half() for t in inputs) return self.network(*inputs) def backwards_debug_hook(grad): raise RuntimeError("master_params recieved a gradient in the backward pass!") def prep_param_lists(model, flat_master=False): """ Creates a list of FP32 master parameters for a given model, as in `Training Neural Networks with Mixed Precision: Real Examples`_. Args: model (torch.nn.Module): Existing Pytorch model flat_master (bool, optional, default=False): Flatten the master parameters into a single tensor, as a performance optimization. Returns: A tuple (``model_params``, ``master_params``). ``model_params`` is a list of the model's parameters for later use with :func:`model_grads_to_master_grads` and :func:`master_params_to_model_params`. ``master_params`` is a list of FP32 master gradients. If ``flat_master=True``, ``master_params`` will be a list with one element. Example:: model_params, master_params = prep_param_lists(model) .. warning:: Currently, if ``flat_master=True``, all the model's parameters must be the same type. If the model has parameters of different types, use ``flat_master=False``, or use :class:`FP16_Optimizer`. .. _`Training Neural Networks with Mixed Precision: Real Examples`: http://on-demand.gputechconf.com/gtc/2018/video/S81012/ """ model_params = [param for param in model.parameters() if param.requires_grad] if flat_master: # Give the user some more useful error messages try: # flatten_dense_tensors returns a contiguous flat array. # http://pytorch.org/docs/master/_modules/torch/_utils.html master_params = _flatten_dense_tensors([param.data for param in model_params]).float() except BaseException: print("Error in prep_param_lists: model may contain a mixture of parameters " "of different types. Use flat_master=False, or use F16_Optimizer.") raise master_params = torch.nn.Parameter(master_params) master_params.requires_grad = True # master_params.register_hook(backwards_debug_hook) if master_params.grad is None: master_params.grad = master_params.new(*master_params.size()) return model_params, [master_params] else: master_params = [param.clone().float().detach() for param in model_params] for param in master_params: param.requires_grad = True return model_params, master_params def model_grads_to_master_grads(model_params, master_params, flat_master=False): """ Copy model gradients to master gradients. Args: model_params: List of model parameters created by :func:`prep_param_lists`. master_params: List of FP32 master parameters created by :func:`prep_param_lists`. If ``master_params`` was created with ``flat_master=True``, ``flat_master=True`` should also be supplied to :func:`model_grads_to_master_grads`. """ if flat_master: # The flattening may incur one more deep copy than is necessary. master_params[0].grad.data.copy_( _flatten_dense_tensors([p.grad.data for p in model_params])) else: for model, master in zip(model_params, master_params): if model.grad is not None: if master.grad is None: master.grad = Variable(master.data.new(*master.data.size())) else: master.grad = None model_grads = [p.grad for p in model_params if p.grad is not None] master_grads = [p.grad for p in master_params if p.grad is not None] _overflow_buf = torch.cuda.IntTensor([0]) multi_tensor_applier(amp_C.multi_tensor_scale, _overflow_buf, [model_grads, master_grads], 1.0) def master_params_to_model_params(model_params, master_params, flat_master=False): """ Copy master parameters to model parameters. Args: model_params: List of model parameters created by :func:`prep_param_lists`. master_params: List of FP32 master parameters created by :func:`prep_param_lists`. If ``master_params`` was created with ``flat_master=True``, ``flat_master=True`` should also be supplied to :func:`master_params_to_model_params`. """ if flat_master: for model, master in zip(model_params, _unflatten_dense_tensors(master_params[0].data, model_params)): model.data.copy_(master) else: for model, master in zip(model_params, master_params): model.data.copy_(master.data) # Backward compatibility fixes def to_python_float(t): if hasattr(t, 'item'): return t.item() else: return t[0] TORCH_MAJOR = int(torch.__version__.split('.')[0]) TORCH_MINOR = int(torch.__version__.split('.')[1]) clip_grad_norm = mpu.clip_grad_norm # elif TORCH_MAJOR == 0 and TORCH_MINOR <= 4: # clip_grad_norm = torch.nn.utils.clip_grad_norm # else: # clip_grad_norm = torch.nn.utils.clip_grad_norm_ pangu/megatron/fp16/loss_scaler.py000066400000000000000000000240101430334424000174350ustar00rootroot00000000000000# 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. import torch from apex.multi_tensor_apply import multi_tensor_applier import amp_C from megatron import mpu # item() is a recent addition, so this helps with backward compatibility. def to_python_float(t): if hasattr(t, 'item'): return t.item() else: return t[0] class LossScaler: """ Class that manages a static loss scale. This class is intended to interact with :class:`FP16_Optimizer`, and should not be directly manipulated by the user. Use of :class:`LossScaler` is enabled via the ``static_loss_scale`` argument to :class:`FP16_Optimizer`'s constructor. Args: scale (float, optional, default=1.0): The loss scale. """ def __init__(self, scale=1): self.cur_scale = scale # `params` is a list / generator of torch.Variable def has_overflow(self, params): return False # `x` is a torch.Tensor def _has_inf_or_nan(x): return False def update_scale(self, overflow): pass @property def loss_scale(self): return self.cur_scale def scale_gradient(self, module, grad_in, grad_out): _overflow_buf = torch.cuda.IntTensor([0]) multi_tensor_applier(amp_C.multi_tensor_scale, _overflow_buf, [grad_in, grad_in], self.loss_scale) return grad_in def backward(self, loss, retain_graph=False): scaled_loss = loss * self.loss_scale scaled_loss.backward(retain_graph=retain_graph) class DynamicLossScaler: """ Class that manages dynamic loss scaling. It is recommended to use :class:`DynamicLossScaler` indirectly, by supplying ``dynamic_loss_scale=True`` to the constructor of :class:`FP16_Optimizer`. However, it's important to understand how :class:`DynamicLossScaler` operates, because the default options can be changed using the the ``dynamic_loss_args`` argument to :class:`FP16_Optimizer`'s constructor. Loss scaling is designed to combat the problem of underflowing gradients encountered at long times when training fp16 networks. Dynamic loss scaling begins by attempting a very high loss scale. Ironically, this may result in OVERflowing gradients. If overflowing gradients are encountered, :class:`DynamicLossScaler` informs :class:`FP16_Optimizer` that an overflow has occurred. :class:`FP16_Optimizer` then skips the update step for this particular iteration/minibatch, and :class:`DynamicLossScaler` adjusts the loss scale to a lower value. If a certain number of iterations occur without overflowing gradients detected, :class:`DynamicLossScaler` increases the loss scale once more. In this way :class:`DynamicLossScaler` attempts to "ride the edge" of always using the highest loss scale possible without incurring overflow. Args: init_scale (float, optional, default=2**32): Initial loss scale attempted by :class:`DynamicLossScaler.` scale_factor (float, optional, default=2.0): Factor used when adjusting the loss scale. If an overflow is encountered, the loss scale is readjusted to loss scale/``scale_factor``. If ``scale_window`` consecutive iterations take place without an overflow, the loss scale is readjusted to loss_scale*``scale_factor``. scale_window (int, optional, default=1000): Number of consecutive iterations without an overflow to wait before increasing the loss scale. """ def __init__(self, init_scale=2**32, scale_factor=2., scale_window=1000, min_scale=1, delayed_shift=1, consecutive_hysteresis=False): self.cur_scale = init_scale self.cur_iter = 0 self.last_overflow_iter = -1 self.scale_factor = scale_factor self.scale_window = scale_window self.min_scale = min_scale self.delayed_shift = delayed_shift self.cur_hysteresis = delayed_shift self.consecutive_hysteresis = consecutive_hysteresis # `params` is a list / generator of torch.Variable def has_overflow_serial(self, params): for p in params: if p.grad is not None and DynamicLossScaler._has_inf_or_nan(p.grad.data): return True return False def has_overflow(self, params): overflow = self.has_overflow_serial(params) # Since each model parallel GPU carries only part of the model, # make sure overflow flag is synced across all the model parallel GPUs overflow_gpu = torch.cuda.ByteTensor([overflow]) torch.distributed.all_reduce(overflow_gpu, op=torch.distributed.ReduceOp.MAX, group=mpu.get_model_parallel_group()) overflow = overflow_gpu[0].item() return bool(overflow) # `x` is a torch.Tensor 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 # `overflow` is boolean indicating whether the gradient overflowed def update_scale(self, overflow): if not hasattr(self, 'min_scale'): self.min_scale = 1 if not hasattr(self, 'delayed_shift'): self.delayed_shift = 1 if not hasattr(self, 'cur_hysteresis'): self.cur_hysteresis = 1 if not hasattr(self, 'consecutive_hysteresis'): self.consecutive_hysteresis = True if overflow: # self.cur_scale /= self.scale_factor if self.delayed_shift == 1 or self.cur_hysteresis == 1: self.cur_scale = max(self.cur_scale / self.scale_factor, self.min_scale) else: self.cur_hysteresis -= 1 self.last_overflow_iter = self.cur_iter else: if self.consecutive_hysteresis: self.cur_hysteresis = self.delayed_shift if (self.cur_iter - self.last_overflow_iter) % self.scale_window == 0: if not self.consecutive_hysteresis: self.cur_hysteresis = self.delayed_shift self.cur_scale *= self.scale_factor self.cur_iter += 1 @property def loss_scale(self): return self.cur_scale def scale_gradient(self, module, grad_in, grad_out): _overflow_buf = torch.cuda.IntTensor([0]) multi_tensor_applier(amp_C.multi_tensor_scale, _overflow_buf, [grad_in, grad_in], self.loss_scale) return grad_in def backward(self, loss, retain_graph=False): scaled_loss = loss * self.loss_scale scaled_loss.backward(retain_graph=retain_graph) ############################################################## # Example usage below here -- assuming it's in a separate file ############################################################## """ TO-DO separate out into an example. if __name__ == "__main__": import torch from torch.autograd import Variable from dynamic_loss_scaler import DynamicLossScaler # N is batch size; D_in is input dimension; # H is hidden dimension; D_out is output dimension. N, D_in, H, D_out = 64, 1000, 100, 10 # Create random Tensors to hold inputs and outputs, and wrap them in Variables. x = Variable(torch.randn(N, D_in), requires_grad=False) y = Variable(torch.randn(N, D_out), requires_grad=False) w1 = Variable(torch.randn(D_in, H), requires_grad=True) w2 = Variable(torch.randn(H, D_out), requires_grad=True) parameters = [w1, w2] learning_rate = 1e-6 optimizer = torch.optim.SGD(parameters, lr=learning_rate) loss_scaler = DynamicLossScaler() for t in range(500): y_pred = x.mm(w1).clamp(min=0).mm(w2) loss = (y_pred - y).pow(2).sum() * loss_scaler.loss_scale print('Iter {} loss scale: {}'.format(t, loss_scaler.loss_scale)) print('Iter {} scaled loss: {}'.format(t, loss.data[0])) print('Iter {} unscaled loss: {}'.format(t, loss.data[0] / loss_scaler.loss_scale)) # Run backprop optimizer.zero_grad() loss.backward() # Check for overflow has_overflow = DynamicLossScaler.has_overflow(parameters) # If no overflow, unscale grad and update as usual if not has_overflow: for param in parameters: param.grad.data.mul_(1. / loss_scaler.loss_scale) optimizer.step() # Otherwise, don't do anything -- ie, skip iteration else: print('OVERFLOW!') # Update loss scale for next iteration loss_scaler.update_scale(has_overflow) """ pangu/megatron/fused_kernels/000077500000000000000000000000001430334424000166325ustar00rootroot00000000000000pangu/megatron/fused_kernels/__init__.py000066400000000000000000000043541430334424000207510ustar00rootroot00000000000000# 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. import pathlib import subprocess from torch.utils import cpp_extension def load_scaled_upper_triang_masked_softmax_fusion_kernel(): def get_cuda_bare_metal_version(cuda_dir): raw_output = subprocess.check_output([cuda_dir + "/bin/nvcc", "-V"], universal_newlines=True) output = raw_output.split() release_idx = output.index("release") + 1 release = output[release_idx].split(".") bare_metal_major = release[0] bare_metal_minor = release[1][0] return raw_output, bare_metal_major, bare_metal_minor # Check, if CUDA11 is installed for compute capability 8.0 cc_flag = [] _, bare_metal_major, _ = get_cuda_bare_metal_version(cpp_extension.CUDA_HOME) if int(bare_metal_major) >= 11: cc_flag.append('-gencode') cc_flag.append('arch=compute_80,code=sm_80') srcpath = pathlib.Path(__file__).parent.absolute() scaled_upper_triang_masked_softmax_cuda = cpp_extension.load( name='scaled_upper_triang_masked_softmax_cuda', sources=[srcpath / 'scaled_upper_triang_masked_softmax.cpp', srcpath / 'scaled_upper_triang_masked_softmax_cuda.cu'], extra_cflags=['-O3',], extra_cuda_cflags=['-O3', '-gencode', 'arch=compute_70,code=sm_70', '-U__CUDA_NO_HALF_OPERATORS__', '-U__CUDA_NO_HALF_CONVERSIONS__', '--expt-relaxed-constexpr', '--expt-extended-lambda', '--use_fast_math'] + cc_flag, verbose=True) pangu/megatron/fused_kernels/scaled_upper_triang_masked_softmax.cpp000066400000000000000000000044241430334424000264410ustar00rootroot00000000000000/* 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. */ #include #include #include namespace multihead_attn { namespace fused_softmax { namespace scaled_upper_triang_masked_softmax { torch::Tensor fwd_cuda( torch::Tensor const& input, float scale_factor); torch::Tensor bwd_cuda( torch::Tensor const& output_grads, torch::Tensor const& softmax_results, float scale_factor); torch::Tensor fwd(torch::Tensor const& input, float scale_factor) { AT_ASSERTM(input.dim() == 3, "expected 3D tensor"); AT_ASSERTM(input.scalar_type() == at::ScalarType::Half, "Only HALF is supported"); return fwd_cuda(input, scale_factor); } torch::Tensor bwd( torch::Tensor const& output_grads, torch::Tensor const& softmax_results, float scale_factor) { AT_ASSERTM(output_grads.dim() == 3, "expected 3D tensor"); AT_ASSERTM(softmax_results.dim() == 3, "expected 3D tensor"); AT_ASSERTM(output_grads.scalar_type() == at::ScalarType::Half, "Only HALF is supported"); AT_ASSERTM(softmax_results.scalar_type() == at::ScalarType::Half, "Only HALF is supported"); return bwd_cuda(output_grads, softmax_results, scale_factor); } } // end namespace scaled_upper_triang_masked_softmax } // end namespace fused_softmax } // end namespace multihead_attn PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) { m.def("forward", &multihead_attn::fused_softmax::scaled_upper_triang_masked_softmax::fwd, "Self Multihead Attention scaled, time masked softmax -- Forward."); m.def("backward", &multihead_attn::fused_softmax::scaled_upper_triang_masked_softmax::bwd, "Self Multihead Attention scaled, time masked softmax -- Backward."); } pangu/megatron/fused_kernels/scaled_upper_triang_masked_softmax.h000066400000000000000000000460721430334424000261130ustar00rootroot00000000000000/* 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. */ #pragma once #include #include #include #include #include #include #include namespace { int log2_ceil(int value) { int log2_value = 0; while ((1 << log2_value) < value) ++log2_value; return log2_value; } template struct Add { __device__ __forceinline__ T operator()(T a, T b) const { return a + b; } }; template struct Max { __device__ __forceinline__ T operator()(T a, T b) const { return a < b ? b : a; } }; template __device__ __forceinline__ T WARP_SHFL_XOR_NATIVE(T value, int laneMask, int width = warpSize, unsigned int mask = 0xffffffff) { #if CUDA_VERSION >= 9000 return __shfl_xor_sync(mask, value, laneMask, width); #else return __shfl_xor(value, laneMask, width); #endif } template class ReduceOp> __device__ __forceinline__ void warp_reduce(acc_t* sum) { ReduceOp r; #pragma unroll for (int offset = WARP_SIZE / 2; offset > 0; offset /= 2) { #pragma unroll for (int i = 0; i < WARP_BATCH; ++i) { acc_t b = WARP_SHFL_XOR_NATIVE(sum[i], offset, WARP_SIZE); sum[i] = r(sum[i], b); } } } /* * Extended softmax (from native aten pytorch) with following additional features * 1) input scaling * 2) Implicit time (diagonal masking) */ template __global__ void scaled_upper_triang_masked_softmax_warp_forward( output_t *dst, const input_t *src, const acc_t scale, int batch_size, int stride, int element_count) { // WARP_SIZE and WARP_BATCH must match the return values batches_per_warp and // warp_size of method warp_softmax_forward_kernel. constexpr int next_power_of_two = 1 << log2_elements; constexpr int WARP_SIZE = (next_power_of_two < C10_WARP_SIZE) ? next_power_of_two : C10_WARP_SIZE; constexpr int WARP_ITERATIONS = next_power_of_two / WARP_SIZE; constexpr int WARP_BATCH = (next_power_of_two <= 128) ? 2 : 1; int first_batch = (blockDim.y * blockIdx.y + threadIdx.y) * gridDim.x * WARP_BATCH + blockIdx.x; int local_seq = blockIdx.x + 1; int warp_iteration_limit = (local_seq + WARP_SIZE - 1)/WARP_SIZE; // batch_size might not be a multiple of WARP_BATCH. Check how // many batches have to computed within this WARP. int local_batches = batch_size - first_batch; if (local_batches > WARP_BATCH) local_batches = WARP_BATCH; // there might be multiple batches per warp. compute the index within the batch int local_idx = threadIdx.x; src += first_batch * stride + local_idx; dst += first_batch * stride + local_idx; // load data from global memory acc_t elements[WARP_BATCH][WARP_ITERATIONS]; #pragma unroll for (int i = 0; i < WARP_BATCH; ++i) { int batch_element_count = (i >= local_batches) ? 0 : local_seq; #pragma unroll for (int it = 0; it < WARP_ITERATIONS; ++it) { int element_index = local_idx + it * WARP_SIZE; if (element_index < batch_element_count) { elements[i][it] = (acc_t)src[i*element_count*stride+it*WARP_SIZE] * scale; } else { elements[i][it] = -std::numeric_limits::infinity(); } } } // compute max_value acc_t max_value[WARP_BATCH]; #pragma unroll for (int i = 0; i < WARP_BATCH; ++i) { max_value[i] = elements[i][0]; #pragma unroll for (int it = 1; it < WARP_ITERATIONS; ++it) { max_value[i] = (max_value[i] > elements[i][it]) ? max_value[i] : elements[i][it]; } } warp_reduce(max_value); acc_t sum[WARP_BATCH] { 0.0f }; #pragma unroll for (int i = 0; i < WARP_BATCH; ++i) { #pragma unroll for (int it = 0; it < WARP_ITERATIONS; ++it) { if (it < warp_iteration_limit) { elements[i][it] = std::exp((elements[i][it] - max_value[i])); sum[i] += elements[i][it]; } } } warp_reduce(sum); // store result #pragma unroll for (int i = 0; i < WARP_BATCH; ++i) { if (i >= local_batches) break; #pragma unroll for (int it = 0; it < WARP_ITERATIONS; ++it) { int element_index = local_idx + it * WARP_SIZE; if (element_index < local_seq) { dst[i*element_count*stride+it*WARP_SIZE] = (output_t)(elements[i][it] / sum[i]); } else if (element_index < element_count) { dst[i*element_count*stride+it*WARP_SIZE] = 0; } else { break; } } } } template __global__ void scaled_upper_triang_masked_softmax_warp_backward( output_t *gradInput, input_t *grad, const input_t *output, acc_t scale, int batch_size, int stride, int element_count) { // WARP_SIZE and WARP_BATCH must match the return values batches_per_warp and // warp_size of method warp_softmax_backward_kernel. constexpr int next_power_of_two = 1 << log2_elements; constexpr int WARP_SIZE = (next_power_of_two < C10_WARP_SIZE) ? next_power_of_two : C10_WARP_SIZE; constexpr int WARP_ITERATIONS = next_power_of_two / WARP_SIZE; constexpr int WARP_BATCH = (next_power_of_two <= 128) ? 2 : 1; int first_batch = (blockDim.y * blockIdx.y + threadIdx.y) * gridDim.x * WARP_BATCH + blockIdx.x; int local_seq = blockIdx.x + 1; // batch_size might not be a multiple of WARP_BATCH. Check how // many batches have to computed within this WARP. int local_batches = batch_size - first_batch; if (local_batches > WARP_BATCH) local_batches = WARP_BATCH; // there might be multiple batches per warp. compute the index within the batch int local_idx = threadIdx.x; // the first element to process by the current thread int thread_offset = first_batch * stride + local_idx; grad += thread_offset; output += thread_offset; gradInput += thread_offset; // load data from global memory acc_t grad_reg[WARP_BATCH][WARP_ITERATIONS] { 0.0f }; acc_t output_reg[WARP_BATCH][WARP_ITERATIONS]; #pragma unroll for (int i = 0; i < WARP_BATCH; ++i) { int batch_element_count = (i >= local_batches) ? 0 : local_seq; #pragma unroll for (int it = 0; it < WARP_ITERATIONS; ++it) { int element_index = local_idx + it * WARP_SIZE; if (element_index < batch_element_count) { output_reg[i][it] = output[i*element_count*stride+it*WARP_SIZE]; } else { output_reg[i][it] = acc_t(0); } } #pragma unroll for (int it = 0; it < WARP_ITERATIONS; ++it) { int element_index = local_idx + it * WARP_SIZE; if (element_index < batch_element_count) { grad_reg[i][it] = (acc_t)grad[i*element_count*stride+it*WARP_SIZE] * output_reg[i][it]; } else { grad_reg[i][it] = acc_t(0); } } } acc_t sum[WARP_BATCH]; #pragma unroll for (int i = 0; i < WARP_BATCH; ++i) { sum[i] = grad_reg[i][0]; #pragma unroll for (int it = 1; it < WARP_ITERATIONS; ++it) { sum[i] += grad_reg[i][it]; } } warp_reduce(sum); // store result #pragma unroll for (int i = 0; i < WARP_BATCH; ++i) { if (i >= local_batches) break; #pragma unroll for (int it = 0; it < WARP_ITERATIONS; ++it) { int element_index = local_idx + it * WARP_SIZE; if (element_index < element_count) { // compute gradients gradInput[i*element_count*stride+it*WARP_SIZE] = (output_t)(scale * (grad_reg[i][it] - output_reg[i][it] * sum[i])); } } } } } // end of anonymous namespace template void dispatch_scaled_upper_triang_masked_softmax_forward( output_t *dst, const input_t *src, const input_t scale, int softmax_elements, int softmax_elements_stride, int attn_batches) { TORCH_INTERNAL_ASSERT(softmax_elements >= 0 && softmax_elements <= 2048 ); if (softmax_elements == 0) { return; } else { int log2_elements = log2_ceil(softmax_elements); const int next_power_of_two = 1 << log2_elements; int seq_len = softmax_elements; int batch_count = attn_batches * seq_len; // This value must match the WARP_SIZE constexpr value computed inside softmax_warp_forward. int warp_size = (next_power_of_two < C10_WARP_SIZE) ? next_power_of_two : C10_WARP_SIZE; // This value must match the WARP_BATCH constexpr value computed inside softmax_warp_forward. int batches_per_warp = (next_power_of_two <= 128) ? 2 : 1; // use 128 threads per block to maximimize gpu utilization constexpr int threads_per_block = 128; int warps_per_block = (threads_per_block / warp_size); int batches_per_block = warps_per_block * batches_per_warp; TORCH_INTERNAL_ASSERT(attn_batches % batches_per_block == 0); int blocks_per_seq = attn_batches / batches_per_block; dim3 blocks(seq_len, blocks_per_seq, 1); dim3 threads(warp_size, warps_per_block, 1); // Launch code would be more elegant if C++ supported FOR CONSTEXPR switch (log2_elements) { case 0: // 1 scaled_upper_triang_masked_softmax_warp_forward <<>>(dst, src, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 1: // 2 scaled_upper_triang_masked_softmax_warp_forward <<>>(dst, src, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 2: // 4 scaled_upper_triang_masked_softmax_warp_forward <<>>(dst, src, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 3: // 8 scaled_upper_triang_masked_softmax_warp_forward <<>>(dst, src, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 4: // 16 scaled_upper_triang_masked_softmax_warp_forward <<>>(dst, src, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 5: // 32 scaled_upper_triang_masked_softmax_warp_forward <<>>(dst, src, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 6: // 64 scaled_upper_triang_masked_softmax_warp_forward <<>>(dst, src, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 7: // 128 scaled_upper_triang_masked_softmax_warp_forward <<>>(dst, src, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 8: // 256 scaled_upper_triang_masked_softmax_warp_forward <<>>(dst, src, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 9: // 512 scaled_upper_triang_masked_softmax_warp_forward <<>>(dst, src, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 10: // 1024 scaled_upper_triang_masked_softmax_warp_forward <<>>(dst, src, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 11: // 2048 scaled_upper_triang_masked_softmax_warp_forward <<>>(dst, src, scale, batch_count, softmax_elements_stride, softmax_elements); break; default: break; } } } template void dispatch_scaled_upper_triang_masked_softmax_backward( output_t *grad_input, input_t *grad, const input_t *output, const acc_t scale, int softmax_elements, int softmax_elements_stride, int attn_batches) { TORCH_INTERNAL_ASSERT( softmax_elements >= 0 && softmax_elements <= 2048 ); if (softmax_elements == 0) { return; } else { int log2_elements = log2_ceil(softmax_elements); const int next_power_of_two = 1 << log2_elements; int seq_len = softmax_elements; int batch_count = attn_batches * seq_len; // This value must match the WARP_SIZE constexpr value computed inside softmax_warp_backward. int warp_size = (next_power_of_two < C10_WARP_SIZE) ? next_power_of_two : C10_WARP_SIZE; // This value must match the WARP_BATCH constexpr value computed inside softmax_warp_backward. int batches_per_warp = (next_power_of_two <= 128) ? 2 : 1; // use 128 threads per block to maximimize gpu utilization constexpr int threads_per_block = 128; int warps_per_block = (threads_per_block / warp_size); int batches_per_block = warps_per_block * batches_per_warp; TORCH_INTERNAL_ASSERT(attn_batches % batches_per_block == 0); int blocks_per_seq = attn_batches / batches_per_block; dim3 blocks(seq_len, blocks_per_seq, 1); dim3 threads(warp_size, warps_per_block, 1); // Launch code would be more elegant if C++ supported FOR CONSTEXPR switch (log2_elements) { case 0: // 1 scaled_upper_triang_masked_softmax_warp_backward <<>>(grad_input, grad, output, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 1: // 2 scaled_upper_triang_masked_softmax_warp_backward <<>>(grad_input, grad, output, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 2: // 4 scaled_upper_triang_masked_softmax_warp_backward <<>>(grad_input, grad, output, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 3: // 8 scaled_upper_triang_masked_softmax_warp_backward <<>>(grad_input, grad, output, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 4: // 16 scaled_upper_triang_masked_softmax_warp_backward <<>>(grad_input, grad, output, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 5: // 32 scaled_upper_triang_masked_softmax_warp_backward <<>>(grad_input, grad, output, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 6: // 64 scaled_upper_triang_masked_softmax_warp_backward <<>>(grad_input, grad, output, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 7: // 128 scaled_upper_triang_masked_softmax_warp_backward <<>>(grad_input, grad, output, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 8: // 256 scaled_upper_triang_masked_softmax_warp_backward <<>>(grad_input, grad, output, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 9: // 512 scaled_upper_triang_masked_softmax_warp_backward <<>>(grad_input, grad, output, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 10: // 1024 scaled_upper_triang_masked_softmax_warp_backward <<>>(grad_input, grad, output, scale, batch_count, softmax_elements_stride, softmax_elements); break; case 11: // 2048 scaled_upper_triang_masked_softmax_warp_backward <<>>(grad_input, grad, output, scale, batch_count, softmax_elements_stride, softmax_elements); break; default: break; } } } pangu/megatron/fused_kernels/scaled_upper_triang_masked_softmax_cuda.cu000066400000000000000000000054661430334424000272710ustar00rootroot00000000000000/* 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. */ #include #include #include #include #include #include "THC/THC.h" #include #include #include "scaled_upper_triang_masked_softmax.h" namespace multihead_attn { namespace fused_softmax { namespace scaled_upper_triang_masked_softmax { torch::Tensor fwd_cuda( torch::Tensor const& input, float scale_factor) { // input is a 3d tensor with dimensions [attn_batches, seq_len, seq_len] const int attn_batches = input.size(0); const int seq_len = input.size(1); TORCH_INTERNAL_ASSERT(seq_len <= 2048); // Output auto act_options = input.options().requires_grad(false); torch::Tensor softmax_results = torch::empty({attn_batches, seq_len, seq_len}, act_options); // Softmax Intermediate Result Ptr void* input_ptr = static_cast(input.data_ptr()); void* softmax_results_ptr = static_cast(softmax_results.data_ptr()); dispatch_scaled_upper_triang_masked_softmax_forward( reinterpret_cast(softmax_results_ptr), reinterpret_cast(input_ptr), scale_factor, seq_len, seq_len, attn_batches); return softmax_results; } torch::Tensor bwd_cuda( torch::Tensor const& output_grads_, torch::Tensor const& softmax_results_, float scale_factor) { auto output_grads = output_grads_.contiguous(); auto softmax_results = softmax_results_.contiguous(); //output grads is a 3d tensor with dimensions [attn_batches, seq_len, seq_len] const int attn_batches = output_grads.size(0); const int seq_len = output_grads.size(1); TORCH_INTERNAL_ASSERT(output_grads.size(1) == output_grads.size(2)); void* output_grads_ptr = static_cast(output_grads.data_ptr()); //Softmax Grad dispatch_scaled_upper_triang_masked_softmax_backward( reinterpret_cast(output_grads_ptr), reinterpret_cast(output_grads_ptr), reinterpret_cast(softmax_results.data_ptr()), scale_factor, seq_len, seq_len, attn_batches); //backward pass is completely in-place return output_grads; } } } } pangu/megatron/global_vars.py000066400000000000000000000162111430334424000166470ustar00rootroot00000000000000# 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. """Megatron global variables.""" import os import sys import time import torch from megatron.tokenizer import build_tokenizer from .arguments import parse_args _GLOBAL_ARGS = None _GLOBAL_TOKENIZER = None _GLOBAL_TENSORBOARD_WRITER = None _GLOBAL_ADLR_AUTORESUME = None _GLOBAL_TIMERS = None def get_args(): """Return arguments.""" _ensure_var_is_initialized(_GLOBAL_ARGS, 'args') return _GLOBAL_ARGS def get_tokenizer(): """Return tokenizer.""" _ensure_var_is_initialized(_GLOBAL_TOKENIZER, 'tokenizer') return _GLOBAL_TOKENIZER def get_tensorboard_writer(): """Return tensorboard writer. It can be None so no need to check if it is initialized.""" return _GLOBAL_TENSORBOARD_WRITER def get_adlr_autoresume(): """ADLR autoresume object. It can be None so no need to check if it is initialized.""" return _GLOBAL_ADLR_AUTORESUME def get_timers(): """Return timers.""" _ensure_var_is_initialized(_GLOBAL_TIMERS, 'timers') return _GLOBAL_TIMERS def set_global_variables(extra_args_provider=None, args_defaults={}, ignore_unknown_args=False): """Set args, tokenizer, tensorboard-writer, adlr-autoresume, and timers.""" args = _parse_args(extra_args_provider=extra_args_provider, defaults=args_defaults, ignore_unknown_args=ignore_unknown_args) _ = _build_tokenizer(args) _set_tensorboard_writer(args) _set_adlr_autoresume(args) _set_timers() def _parse_args(extra_args_provider=None, defaults={}, ignore_unknown_args=False): """Parse entire arguments.""" global _GLOBAL_ARGS _ensure_var_is_not_initialized(_GLOBAL_ARGS, 'args') _GLOBAL_ARGS = parse_args(extra_args_provider=extra_args_provider, defaults=defaults, ignore_unknown_args=ignore_unknown_args) return _GLOBAL_ARGS def _build_tokenizer(args): """Initialize tokenizer.""" global _GLOBAL_TOKENIZER _ensure_var_is_not_initialized(_GLOBAL_TOKENIZER, 'tokenizer') _GLOBAL_TOKENIZER = build_tokenizer(args) return _GLOBAL_TOKENIZER def rebuild_tokenizer(args): global _GLOBAL_TOKENIZER _GLOBAL_TOKENIZER = None return _build_tokenizer(args) def _set_tensorboard_writer(args): """Set tensorboard writer.""" global _GLOBAL_TENSORBOARD_WRITER _ensure_var_is_not_initialized(_GLOBAL_TENSORBOARD_WRITER, 'tensorboard writer') if hasattr(args, 'tensorboard_dir') and \ args.tensorboard_dir and args.rank == 0: try: from torch.utils.tensorboard import SummaryWriter print('> setting tensorboard ...') _GLOBAL_TENSORBOARD_WRITER = SummaryWriter( log_dir=args.tensorboard_dir) except ModuleNotFoundError: print('WARNING: TensorBoard writing requested but is not ' 'available (are you using PyTorch 1.1.0 or later?), ' 'no TensorBoard logs will be written.', flush=True) def _set_adlr_autoresume(args): """Initialize ADLR autoresume.""" global _GLOBAL_ADLR_AUTORESUME _ensure_var_is_not_initialized(_GLOBAL_ADLR_AUTORESUME, 'adlr autoresume') if args.adlr_autoresume: if args.rank == 0: print('enabling autoresume ...', flush=True) sys.path.append(os.environ.get('SUBMIT_SCRIPTS', '.')) try: from userlib.auto_resume import AutoResume except BaseException: print('ADLR autoresume is not available, exiting ...') sys.exit() _GLOBAL_ADLR_AUTORESUME = AutoResume def _set_timers(): """Initialize timers.""" global _GLOBAL_TIMERS _ensure_var_is_not_initialized(_GLOBAL_TIMERS, 'timers') _GLOBAL_TIMERS = Timers() def _ensure_var_is_initialized(var, name): """Make sure the input variable is not None.""" assert var is not None, '{} is not initialized.'.format(name) def _ensure_var_is_not_initialized(var, name): """Make sure the input variable is not None.""" assert var is None, '{} is already initialized.'.format(name) class _Timer: """Timer.""" def __init__(self, name): self.name_ = name self.elapsed_ = 0.0 self.started_ = False self.start_time = time.time() def start(self): """Start the timer.""" assert not self.started_, 'timer has already been started' torch.cuda.synchronize() self.start_time = time.time() self.started_ = True def stop(self): """Stop the timer.""" assert self.started_, 'timer is not started' torch.cuda.synchronize() self.elapsed_ += (time.time() - self.start_time) self.started_ = False def reset(self): """Reset timer.""" self.elapsed_ = 0.0 self.started_ = False def elapsed(self, reset=True): """Calculate the elapsed time.""" started_ = self.started_ # If the timing in progress, end it first. if self.started_: self.stop() # Get the elapsed time. elapsed_ = self.elapsed_ # Reset the elapsed time if reset: self.reset() # If timing was in progress, set it back. if started_: self.start() return elapsed_ class Timers: """Group of timers.""" def __init__(self): self.timers = {} def __call__(self, name): if name not in self.timers: self.timers[name] = _Timer(name) return self.timers[name] def write(self, names, writer, iteration, normalizer=1.0, reset=False): """Write timers to a tensorboard writer""" # currently when using add_scalars, # torch.utils.add_scalars makes each timer its own run, which # polutes the runs list, so we just add each as a scalar assert normalizer > 0.0 for name in names: value = self.timers[name].elapsed(reset=reset) / normalizer writer.add_scalar(name + '_time', value, iteration) def log(self, names, normalizer=1.0, reset=True): """Log a group of timers.""" assert normalizer > 0.0 string = 'time (ms)' for name in names: elapsed_time = self.timers[name].elapsed( reset=reset) * 1000.0 / normalizer string += ' | {}: {:.2f}'.format(name, elapsed_time) if torch.distributed.is_initialized(): if torch.distributed.get_rank() == 0: print(string, flush=True) else: print(string, flush=True) pangu/megatron/indexer.py000066400000000000000000000077731430334424000160270ustar00rootroot00000000000000import torch import torch.distributed as dist from megatron import get_args from megatron import mpu from megatron.checkpointing import load_ict_checkpoint from megatron.data.ict_dataset import get_ict_dataset from megatron.data.realm_dataset_utils import get_one_epoch_dataloader from megatron.data.realm_index import detach, BlockData from megatron.data.realm_dataset_utils import get_ict_batch from megatron.model.realm_model import general_ict_model_provider from megatron.training import get_model class IndexBuilder(object): """Object for taking one pass over a dataset and creating a BlockData of its embeddings""" def __init__(self): args = get_args() self.model = None self.dataloader = None self.block_data = None # need to know whether we're using a REALM checkpoint (args.load) or ICT checkpoint assert not (args.load and args.ict_load) self.using_realm_chkpt = args.ict_load is None self.log_interval = args.indexer_log_interval self.batch_size = args.indexer_batch_size self.load_attributes() self.is_main_builder = mpu.get_data_parallel_rank() == 0 self.num_total_builders = mpu.get_data_parallel_world_size() self.iteration = self.total_processed = 0 def load_attributes(self): """Load the necessary attributes: model, dataloader and empty BlockData""" model = get_model(lambda: general_ict_model_provider(only_block_model=True)) self.model = load_ict_checkpoint(model, only_block_model=True, from_realm_chkpt=self.using_realm_chkpt) self.model.eval() self.dataset = get_ict_dataset() self.dataloader = iter(get_one_epoch_dataloader(self.dataset, self.batch_size)) self.block_data = BlockData(load_from_path=False) def track_and_report_progress(self, batch_size): """Utility function for tracking progress""" self.iteration += 1 self.total_processed += batch_size * self.num_total_builders if self.is_main_builder and self.iteration % self.log_interval == 0: print('Batch {:10d} | Total {:10d}'.format(self.iteration, self.total_processed), flush=True) def build_and_save_index(self): """Goes through one epoch of the dataloader and adds all data to this instance's BlockData. The copy of BlockData is saved as a shard, which when run in a distributed setting will be consolidated by the rank 0 process and saved as a final pickled BlockData. """ while True: try: # batch also has query_tokens and query_pad_data _, _, block_tokens, block_pad_mask, block_sample_data = get_ict_batch(self.dataloader) except (StopIteration, IndexError): break unwrapped_model = self.model while not hasattr(unwrapped_model, 'embed_block'): unwrapped_model = unwrapped_model.module # detach, separate fields and add to BlockData block_logits = detach(unwrapped_model.embed_block(block_tokens, block_pad_mask)) detached_data = detach(block_sample_data) # block_sample_data is a 2D array [batch x 4] # with columns [start_idx, end_idx, doc_idx, block_idx] same as class BlockSampleData block_indices = detached_data[:, 3] block_metas = detached_data[:, :3] self.block_data.add_block_data(block_indices, block_logits, block_metas) self.track_and_report_progress(batch_size=block_tokens.shape[0]) # This process signals to finalize its shard and then synchronize with the other processes self.block_data.save_shard() torch.distributed.barrier() del self.model # rank 0 process builds the final copy if self.is_main_builder: self.block_data.merge_shards_and_save() # make sure that every single piece of data was embedded assert len(self.block_data.embed_data) == len(self.dataset) self.block_data.clear() pangu/megatron/initialize.py000066400000000000000000000131601430334424000165150ustar00rootroot00000000000000# 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. """Megatron initialization.""" import random import os import numpy as np import torch from megatron import get_adlr_autoresume from megatron import get_args from megatron import get_tensorboard_writer from megatron import mpu from megatron.global_vars import set_global_variables from megatron.mpu import set_model_parallel_rank, set_model_parallel_world_size def initialize_megatron(extra_args_provider=None, args_defaults={}, ignore_unknown_args=False, allow_no_cuda=False): """Set global variables, initialize distributed, and set autoresume and random seeds. `allow_no_cuda` should not be set unless using megatron for cpu only data processing. In general this arg should not be set unless you know what you are doing. Returns a function to finalize distributed env initialization (optionally, only when args.lazy_mpu_init == True) """ if not allow_no_cuda: # Make sure cuda is available. assert torch.cuda.is_available(), 'Megatron requires CUDA.' # Parse args, build tokenizer, and set adlr-autoresume, # tensorboard-writer, and timers. set_global_variables(extra_args_provider=extra_args_provider, args_defaults=args_defaults, ignore_unknown_args=ignore_unknown_args) # torch.distributed initialization def finish_mpu_init(): args = get_args() # Pytorch distributed. _initialize_distributed() # Random seeds for reproducibility. if args.rank == 0: print('> setting random seeds to {} ...'.format(args.seed)) _set_random_seed(args.seed) args = get_args() if args.lazy_mpu_init: args.use_cpu_initialization=True # delayed initialization of DDP-related stuff # We only set basic DDP globals set_model_parallel_world_size(args.model_parallel_size) # and return function for external DDP manager to call when it has DDP initialized set_model_parallel_rank(args.rank) return finish_mpu_init else: # Megatron's MPU is the master. Complete initialization right away. finish_mpu_init() # Initialize memory buffers. _initialize_mem_buffs() # Autoresume. _init_autoresume() # Write arguments to tensorboard. _write_args_to_tensorboard() # No continuation function return None def _initialize_distributed(): """Initialize torch.distributed and mpu.""" args = get_args() device_count = torch.cuda.device_count() if torch.distributed.is_initialized(): if args.rank == 0: print('torch distributed is already initialized, ' 'skipping initialization ...', flush=True) args.rank = torch.distributed.get_rank() args.world_size = torch.distributed.get_world_size() else: if args.rank == 0: print('> initializing torch distributed ...', flush=True) # Manually set the device ids. if device_count > 0: device = args.rank % device_count if args.local_rank is not None: assert args.local_rank == device, \ 'expected local-rank to be the same as rank % device-count.' else: args.local_rank = device torch.cuda.set_device(device) # Call the init process init_method = 'tcp://' master_ip = os.getenv('MASTER_ADDR', 'localhost') master_port = os.getenv('MASTER_PORT', '6000') init_method += master_ip + ':' + master_port torch.distributed.init_process_group( backend=args.distributed_backend, world_size=args.world_size, rank=args.rank, init_method=init_method) # Set the model-parallel / data-parallel communicators. if device_count > 0: mpu.initialize_model_parallel(args.model_parallel_size) def _init_autoresume(): """Set autoresume start time.""" autoresume = get_adlr_autoresume() if autoresume: torch.distributed.barrier() autoresume.init() torch.distributed.barrier() def _set_random_seed(seed): """Set random seed for reproducability.""" if seed is not None and seed > 0: random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) if torch.cuda.device_count() > 0: mpu.model_parallel_cuda_manual_seed(seed) else: raise ValueError('Seed ({}) should be a positive integer.'.format(seed)) def _write_args_to_tensorboard(): """Write arguments to tensorboard.""" args = get_args() writer = get_tensorboard_writer() if writer: for arg in vars(args): writer.add_text(arg, str(getattr(args, arg))) def _initialize_mem_buffs(): """Initialize manually allocated static memory.""" args = get_args() # Initialize memory for checkpointed activations. if args.distribute_checkpointed_activations: mpu.init_checkpointed_activations_memory_buffer() pangu/megatron/learning_rates.py000066400000000000000000000115031430334424000173500ustar00rootroot00000000000000# 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. """Learning rate decay functions.""" import math from megatron 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): # 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 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_rank_0('> learning rate decay style: {}'.format(self.decay_style)) 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: 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) pangu/megatron/memory.py000066400000000000000000000116631430334424000156720ustar00rootroot00000000000000# 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. import torch # A dictionary of all the memory buffers allocated. _MEM_BUFFS = dict() def allocate_mem_buff(name, numel, dtype, track_usage): """Allocate a memory buffer.""" assert name not in _MEM_BUFFS, \ 'memory buffer {} already allocated.'.format(name) _MEM_BUFFS[name] = MemoryBuffer(name, numel, dtype, track_usage) return _MEM_BUFFS[name] def get_mem_buff(name): """Get the memory buffer.""" return _MEM_BUFFS[name] class MemoryBuffer: """Contiguous memory buffer. Allocate a contiguous memory of type `dtype` and size `numel`. It is used to reduce memory fragmentation. Usage: After the allocation, the `_start` index is set tot the first index of the memory. A memory chunk starting from `_start` index can be `allocated` for an input tensor, with the elements of the tensor being coppied. The buffer can be reused by resetting the `_start` index. """ def __init__(self, name, numel, dtype, track_usage): if torch.distributed.get_rank() == 0: element_size = torch.tensor([], dtype=dtype).element_size() print('> building the {} memory buffer with {} num elements ' 'and {} dtype ({:.1f} MB)...'.format( name, numel, dtype, numel*element_size/1024/1024), flush=True) self.name = name self.numel = numel self.dtype = dtype self.data = torch.empty(self.numel, dtype=self.dtype, device=torch.cuda.current_device(), requires_grad=False) # Index tracking the start of the free memory. self._start = 0 # Values used for tracking usage. self.track_usage = track_usage if self.track_usage: self.in_use_value = 0.0 self.total_value = 0.0 def reset(self): """Reset the buffer start index to the beginning of the buffer.""" self._start = 0 def is_in_use(self): """Whether the current buffer hold on to any memory.""" return self._start > 0 def numel_in_use(self): """Return number of elements in use.""" return self._start def add(self, tensor): """Allocate a chunk of memory from the buffer to tensor and copy the values.""" assert tensor.dtype == self.dtype, \ 'Input tensor type {} different from buffer type {}'.format( tensor.dtype, self.dtype) # Number of elements of the input tensor. tensor_numel = torch.numel(tensor) new_start = self._start + tensor_numel assert new_start <= self.numel, \ 'Not enough memory left in the buffer ({} > {})'.format( tensor_numel, self.numel - self._start) # New tensor is a view into the memory. new_tensor = self.data[self._start:new_start] self._start = new_start new_tensor = new_tensor.view(tensor.shape) new_tensor.copy_(tensor) # Return a pointer to the new tensor. return new_tensor def get_data(self): """Return the data currently in use.""" if self.track_usage: self.in_use_value += float(self._start) self.total_value += float(self.numel) return self.data[:self._start] def print_average_usage(self): """Print memory usage average over time. We would like this value to be as high as possible.""" assert self.track_usage, 'You need to enable track usage.' if torch.distributed.get_rank() == 0: print(' > usage of {} memory buffer: {:.2f} %'.format( self.name, self.in_use_value * 100.0 / self.total_value), flush=True) class RingMemBuffer: """A ring of memory buffers.""" def __init__(self, name, num_buffers, numel, dtype, track_usage): self.num_buffers = num_buffers self.buffers = [ allocate_mem_buff(name+' {}'.format(i), numel, dtype, track_usage) for i in range(num_buffers)] self._index = -1 def get_next_buffer(self): self._index += 1 self._index = self._index % self.num_buffers buff = self.buffers[self._index] assert not buff.is_in_use(), 'buffer is already in use.' return buff pangu/megatron/model/000077500000000000000000000000001430334424000151015ustar00rootroot00000000000000pangu/megatron/model/__init__.py000066400000000000000000000015421430334424000172140ustar00rootroot00000000000000# 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. from .distributed import * from .bert_model import BertModel from .realm_model import ICTBertModel from .gpt2_model import GPT2Model from .utils import get_params_for_weight_decay_optimization from .language_model import get_language_model pangu/megatron/model/bert_model.py000066400000000000000000000177331430334424000176020ustar00rootroot00000000000000# 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. """BERT model.""" import torch from megatron import get_args from megatron import mpu from megatron.model.language_model import parallel_lm_logits from megatron.model.language_model import get_language_model from megatron.model.transformer import LayerNorm from megatron.model.utils import openai_gelu, erf_gelu from megatron.model.utils import get_linear_layer from megatron.model.utils import init_method_normal from megatron.model.utils import scaled_init_method_normal from megatron.module import MegatronModule def bert_attention_mask_func(attention_scores, attention_mask): attention_scores = attention_scores + attention_mask return attention_scores def bert_extended_attention_mask(attention_mask, dtype): # We create a 3D attention mask from a 2D tensor mask. # [b, 1, s] attention_mask_b1s = attention_mask.unsqueeze(1) # [b, s, 1] attention_mask_bs1 = attention_mask.unsqueeze(2) # [b, s, s] attention_mask_bss = attention_mask_b1s * attention_mask_bs1 # [b, 1, s, s] extended_attention_mask = attention_mask_bss.unsqueeze(1) # Since attention_mask is 1.0 for positions we want to attend and 0.0 # for masked positions, this operation will create a tensor which is # 0.0 for positions we want to attend and -10000.0 for masked positions. # Since we are adding it to the raw scores before the softmax, this is # effectively the same as removing these entirely. # fp16 compatibility extended_attention_mask = extended_attention_mask.to(dtype=dtype) extended_attention_mask = (1.0 - extended_attention_mask) * -10000.0 return extended_attention_mask def bert_position_ids(token_ids): # Create position ids seq_length = token_ids.size(1) position_ids = torch.arange(seq_length, dtype=torch.long, device=token_ids.device) position_ids = position_ids.unsqueeze(0).expand_as(token_ids) return position_ids class BertLMHead(MegatronModule): """Masked LM head for Bert Arguments: mpu_vocab_size: model parallel size of vocabulary. hidden_size: hidden size init_method: init method for weight initialization layernorm_epsilon: tolerance for layer norm divisions parallel_output: whether output logits being distributed or not. """ def __init__(self, mpu_vocab_size, hidden_size, init_method, layernorm_epsilon, parallel_output): super(BertLMHead, self).__init__() args = get_args() self.bias = torch.nn.Parameter(torch.zeros(mpu_vocab_size)) self.bias.model_parallel = True self.bias.partition_dim = 0 self.bias.stride = 1 self.parallel_output = parallel_output self.dense = get_linear_layer(hidden_size, hidden_size, init_method) self.layernorm = LayerNorm(hidden_size, eps=layernorm_epsilon) self.gelu = torch.nn.functional.gelu if args.openai_gelu: self.gelu = openai_gelu elif args.onnx_safe: self.gelu = erf_gelu def forward(self, hidden_states, word_embeddings_weight): hidden_states = self.dense(hidden_states) hidden_states = self.gelu(hidden_states) hidden_states = self.layernorm(hidden_states) output = parallel_lm_logits(hidden_states, word_embeddings_weight, self.parallel_output, bias=self.bias) return output class BertModel(MegatronModule): """Bert Language model.""" def __init__(self, num_tokentypes=2, add_binary_head=True, parallel_output=True): super(BertModel, self).__init__() args = get_args() self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy self.add_binary_head = add_binary_head self.parallel_output = parallel_output init_method = init_method_normal(args.init_method_std) scaled_init_method = scaled_init_method_normal(args.init_method_std, args.num_layers) self.language_model, self._language_model_key = get_language_model( attention_mask_func=bert_attention_mask_func, num_tokentypes=num_tokentypes, add_pooler=self.add_binary_head, init_method=init_method, scaled_init_method=scaled_init_method) self.lm_head = BertLMHead( self.language_model.embedding.word_embeddings.weight.size(0), args.hidden_size, init_method, args.layernorm_epsilon, parallel_output) self._lm_head_key = 'lm_head' if self.add_binary_head: self.binary_head = get_linear_layer(args.hidden_size, 2, init_method) self._binary_head_key = 'binary_head' def forward(self, input_ids, attention_mask, tokentype_ids=None, lm_labels=None): extended_attention_mask = bert_extended_attention_mask( attention_mask, next(self.language_model.parameters()).dtype) position_ids = bert_position_ids(input_ids) if self.add_binary_head: lm_output, pooled_output = self.language_model( input_ids, position_ids, extended_attention_mask, tokentype_ids=tokentype_ids) else: lm_output = self.language_model( input_ids, position_ids, extended_attention_mask, tokentype_ids=tokentype_ids) # Output. lm_logits = self.lm_head( lm_output, self.language_model.embedding.word_embeddings.weight) binary_logits = None if self.add_binary_head: binary_logits = self.binary_head(pooled_output) if lm_labels is None: return lm_logits, binary_logits else: if self.fp16_lm_cross_entropy: assert lm_logits.dtype == torch.half lm_loss = mpu.vocab_parallel_cross_entropy(lm_logits, lm_labels) else: lm_loss = mpu.vocab_parallel_cross_entropy(lm_logits.float(), lm_labels) return lm_loss, binary_logits def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): """For easy load when model is combined with other heads, add an extra key.""" state_dict_ = {} state_dict_[self._language_model_key] \ = self.language_model.state_dict_for_save_checkpoint( destination, prefix, keep_vars) state_dict_[self._lm_head_key] \ = self.lm_head.state_dict_for_save_checkpoint( destination, prefix, keep_vars) if self.add_binary_head: state_dict_[self._binary_head_key] \ = self.binary_head.state_dict(destination, prefix, keep_vars) return state_dict_ def load_state_dict(self, state_dict, strict=True): """Customized load.""" self.language_model.load_state_dict( state_dict[self._language_model_key], strict=strict) self.lm_head.load_state_dict( state_dict[self._lm_head_key], strict=strict) if self.add_binary_head: self.binary_head.load_state_dict( state_dict[self._binary_head_key], strict=strict) pangu/megatron/model/classification.py000066400000000000000000000101311430334424000204420ustar00rootroot00000000000000# 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. """Classification model.""" import torch from megatron import get_args, print_rank_0 from megatron.model.bert_model import bert_attention_mask_func, bert_extended_attention_mask, bert_position_ids from megatron.model.language_model import get_language_model from megatron.model.utils import get_linear_layer from megatron.model.utils import init_method_normal from megatron.model.utils import scaled_init_method_normal from megatron.module import MegatronModule class Classification(MegatronModule): def __init__(self, num_classes, num_tokentypes=2): super(Classification, self).__init__() args = get_args() self.num_classes = num_classes init_method = init_method_normal(args.init_method_std) self.language_model, self._language_model_key = get_language_model( attention_mask_func=bert_attention_mask_func, num_tokentypes=num_tokentypes, add_pooler=True, init_method=init_method, scaled_init_method=scaled_init_method_normal(args.init_method_std, args.num_layers)) # Multi-choice head. self.classification_dropout = torch.nn.Dropout(args.hidden_dropout) self.classification_head = get_linear_layer(args.hidden_size, self.num_classes, init_method) self._classification_head_key = 'classification_head' def forward(self, input_ids, attention_mask, tokentype_ids): extended_attention_mask = bert_extended_attention_mask( attention_mask, next(self.language_model.parameters()).dtype) position_ids = bert_position_ids(input_ids) _, pooled_output = self.language_model(input_ids, position_ids, extended_attention_mask, tokentype_ids=tokentype_ids) # Output. classification_output = self.classification_dropout(pooled_output) classification_logits = self.classification_head(classification_output) # Reshape back to separate choices. classification_logits = classification_logits.view(-1, self.num_classes) return classification_logits def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): """For easy load when model is combined with other heads, add an extra key.""" state_dict_ = {} state_dict_[self._language_model_key] \ = self.language_model.state_dict_for_save_checkpoint( destination, prefix, keep_vars) state_dict_[self._classification_head_key] \ = self.classification_head.state_dict( destination, prefix, keep_vars) return state_dict_ def load_state_dict(self, state_dict, strict=True): """Customized load.""" self.language_model.load_state_dict( state_dict[self._language_model_key], strict=strict) if self._classification_head_key in state_dict: self.classification_head.load_state_dict( state_dict[self._classification_head_key], strict=strict) else: print_rank_0('***WARNING*** could not find {} in the checkpoint, ' 'initializing to random'.format( self._classification_head_key)) pangu/megatron/model/distributed.py000066400000000000000000000116571430334424000200070ustar00rootroot00000000000000# 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. import torch from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors import torch.distributed as dist from torch.nn.modules import Module from torch.autograd import Variable from megatron import mpu from megatron.module import MegatronModule class DistributedDataParallel(MegatronModule): def __init__(self, module): super(DistributedDataParallel, self).__init__() self.warn_on_half = True if dist._backend == dist.dist_backend.GLOO else False self.module = module self.data_parallel_group = mpu.get_data_parallel_group() def allreduce_params(reduce_after=True, no_scale=False, fp32_allreduce=False): if(self.needs_reduction): self.needs_reduction = False buckets = {} for name, param in self.module.named_parameters(): if param.requires_grad and param.grad is not None: tp = (param.data.type()) if tp not in buckets: buckets[tp] = [] buckets[tp].append(param) if self.warn_on_half: if torch.cuda.HalfTensor in buckets: print("WARNING: gloo dist backend for half parameters may be extremely slow." + " It is recommended to use the NCCL backend in this case.") self.warn_on_half = False for tp in buckets: bucket = buckets[tp] grads = [param.grad.data for param in bucket] coalesced = _flatten_dense_tensors(grads) if fp32_allreduce: coalesced = coalesced.float() if not no_scale and not reduce_after: coalesced /= dist.get_world_size(group=self.data_parallel_group) dist.all_reduce(coalesced, group=self.data_parallel_group) torch.cuda.synchronize() if not no_scale and reduce_after: coalesced /= dist.get_world_size(group=self.data_parallel_group) for buf, synced in zip(grads, _unflatten_dense_tensors(coalesced, grads)): buf.copy_(synced) self.hook_handles = [] self.hooks = [] for param in list(self.module.parameters()): def allreduce_hook(*unused): Variable._execution_engine.queue_callback(allreduce_params) # handle = param.register_hook(allreduce_hook) # self.hooks.append(allreduce_hook) # self.hook_handles.append(handle) self.allreduce_params = allreduce_params def forward(self, *inputs, **kwargs): self.needs_reduction = True return self.module(*inputs, **kwargs) def state_dict(self, destination=None, prefix='', keep_vars=False): #[h.remove() for h in self.hook_handles] sd = self.module.state_dict(destination, prefix, keep_vars) # for handle, hook in zip(self.hook_handles, self.hooks): # d = handle.hooks_dict_ref() # d[handle.id] = hook return sd def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): return self.module.state_dict_for_save_checkpoint(destination, prefix, keep_vars) def load_state_dict(self, state_dict, strict=True): self.module.load_state_dict(state_dict, strict=strict) ''' def _sync_buffers(self): buffers = list(self.module._all_buffers()) if len(buffers) > 0: # cross-node buffer sync flat_buffers = _flatten_dense_tensors(buffers) dist.broadcast(flat_buffers, 0) for buf, synced in zip(buffers, _unflatten_dense_tensors(flat_buffers, buffers)): buf.copy_(synced) def train(self, mode=True): # Clear NCCL communicator and CUDA event cache of the default group ID, # These cache will be recreated at the later call. This is currently a # work-around for a potential NCCL deadlock. if dist._backend == dist.dist_backend.NCCL: dist._clear_group_cache() super(DistributedDataParallel, self).train(mode) self.module.train(mode) ''' pangu/megatron/model/fused_bias_gelu.py000066400000000000000000000040531430334424000205750ustar00rootroot00000000000000# 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. import torch torch._C._jit_set_profiling_mode(False) torch._C._jit_set_profiling_executor(False) torch._C._jit_override_can_fuse_on_cpu(True) torch._C._jit_override_can_fuse_on_gpu(True) ###### BIAS GELU FUSION/ NO AUTOGRAD ################ # 1/sqrt(2*pi)-> 0.3989423 # 1/sqrt(2) -> 0.70710678 # sqrt(2/pi) -> 0.79788456 # this function is tanh approximation of gelu # actual gelu is: # x * 0.5 * (1.0 + torch.erf(x * 0.70710678)) @torch.jit.script def bias_gelu(bias, y): x = bias + y return x * 0.5 * (1.0 + torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x))) # gradient of tanh approximation of gelu # gradient of actual gelu is: # 0.5 * (1. + torch.erf(x * 0.70710678)) + 0.3989423 * x * torch.exp(-0.5 * x * x) @torch.jit.script def bias_gelu_back(g, bias, y): x = bias + y tanh_out = torch.tanh(0.79788456 * x * (1 + 0.044715 * x * x)) # sqrt(2/pi) * 3 * 0.044715 -> 0.1070322243 ff = 0.5 * x * ((1 - tanh_out * tanh_out) * (0.79788456 + 0.1070322243 * x * x)) + 0.5 * (1 + tanh_out) return ff*g class GeLUFunction(torch.autograd.Function): @staticmethod # bias is an optional argument def forward(ctx, input, bias): ctx.save_for_backward(input, bias) return bias_gelu(bias, input) @staticmethod def backward(ctx, grad_output): input, bias = ctx.saved_tensors tmp = bias_gelu_back(grad_output, bias, input) return tmp, tmp bias_gelu_impl = GeLUFunction.apply pangu/megatron/model/fused_softmax.py000066400000000000000000000073241430334424000203300ustar00rootroot00000000000000# 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. import torch class ScaledUpperTriangMaskedSoftmax(torch.autograd.Function) : """ Fused operation which performs following three operations in sequence 1. Scale the tensor. 2. Apply upper triangular mask (typically used in gpt models). 3. Perform softmax. """ @staticmethod def forward(ctx, inputs, scale): import scaled_upper_triang_masked_softmax_cuda scale_t = torch.tensor([scale]) softmax_results = \ scaled_upper_triang_masked_softmax_cuda.forward(inputs, scale_t[0]) ctx.save_for_backward(softmax_results, scale_t) return softmax_results @staticmethod def backward(ctx, output_grads): import scaled_upper_triang_masked_softmax_cuda softmax_results, scale_t = ctx.saved_tensors input_grads = \ scaled_upper_triang_masked_softmax_cuda.backward(output_grads, softmax_results, scale_t[0]) return input_grads, None class FusedScaleMaskSoftmax(torch.nn.Module): """ fused operation: scaling + mask + softmax Arguments: input_in_fp16: flag to indicate if input in fp16 data format. upper_triang_mask: if true, apply upper triangular masking. (used in gpt family networks) mask_func: mask function to be applied. softmax_in_fp32: if true, softmax in performed at fp32 precision. scale: scaling factor used in input tensor scaling. """ def __init__(self, input_in_fp16, upper_triang_mask, mask_func, softmax_in_fp32, scale): super(FusedScaleMaskSoftmax, self).__init__() self.input_in_fp16 = input_in_fp16 self.upper_triang_mask = upper_triang_mask self.mask_func = mask_func self.softmax_in_fp32 = softmax_in_fp32 self.scale = scale assert self.scale is None or softmax_in_fp32, \ 'softmax should be in fp32 when scaled' def forward(self, input, mask): # [b, np, s, s] data_size = input.size() assert input.dim() == 4 # invoke custom kernel for implicit uuper triangular masking if self.input_in_fp16 and self.upper_triang_mask and \ data_size[-1] <= 2048 and input.size()[2] == input.size()[3]: input = input.view(-1, data_size[2], data_size[3]) scale = self.scale if self.scale is not None else 1.0 probs = ScaledUpperTriangMaskedSoftmax.apply(input, scale) probs = probs.view(*data_size) else: if self.input_in_fp16 and self.softmax_in_fp32: input = input.float() mask_output = self.mask_func(input, mask) if self.scale is not None: mask_output = mask_output * self.scale probs = torch.nn.Softmax(dim=-1)(mask_output) if self.input_in_fp16 and self.softmax_in_fp32: probs = probs.half() return probs pangu/megatron/model/gpt2_model.py000066400000000000000000000122331430334424000175100ustar00rootroot00000000000000# 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. """GPT-2 model.""" import torch from megatron import get_args from megatron import mpu from megatron.module import MegatronModule from .language_model import parallel_lm_logits from .language_model import get_language_model from .utils import init_method_normal from .utils import scaled_init_method_normal def gpt2_attention_mask_func(attention_scores, ltor_mask): attention_scores.masked_fill_(ltor_mask, -10000.0) return attention_scores class GPT2Model(MegatronModule): """GPT-2 Language model.""" def __init__(self, num_tokentypes=0, parallel_output=True, should_get_lm=True): super(GPT2Model, self).__init__() args = get_args() self.parallel_output = parallel_output self.fp16_lm_cross_entropy = args.fp16_lm_cross_entropy if not should_get_lm: return self.language_model, self._language_model_key = get_language_model( attention_mask_func=gpt2_attention_mask_func, num_tokentypes=num_tokentypes, add_pooler=False, init_method=init_method_normal(args.init_method_std), scaled_init_method=scaled_init_method_normal(args.init_method_std, args.num_layers)) def forward(self, input_ids, position_ids, attention_mask, labels=None, tokentype_ids=None, layer_past=None, get_key_value=False, forward_method_parallel_output=None): ############################## debug mp ################################## # import numpy as np # from megatron import print_rank_0 # from megatron import get_tokenizer # print_rank_0(input_ids.shape) # input_ids = [39,2401,17,4448,615,5188,2783,2762,10,2371,5720,11050,3373,24420,1613] # input_ids.extend([6] * (1024 - len(input_ids))) # input_ids = torch.Tensor(np.atleast_2d(input_ids)).long().cuda() # print_rank_0('='*100) # print_rank_0('='*100) # print_rank_0('input id is: ') # tokenizer = get_tokenizer() # for ids in input_ids.cpu().numpy().tolist(): # print_rank_0(ids) # # print_rank_0(tokenizer.convert_ids_to_tokens(ids)) # print_rank_0('*'*50) ################################################################ # Language model. lm_output = self.language_model(input_ids, position_ids, attention_mask, tokentype_ids=tokentype_ids, layer_past=layer_past, get_key_value=get_key_value) ######################### debug mp ####################################### # with torch.no_grad(): # logits = lm_output.cpu().numpy() # index = np.argmax(logits, axis=2) # ids_ = index.tolist() # for ids in ids_: # print_rank_0(ids) # print_rank_0(tokenizer.convert_ids_to_tokens(ids)) ################################################################ if get_key_value: lm_output, presents = lm_output lm_output = torch.add(lm_output,0) # Output. parallel_output = self.parallel_output if forward_method_parallel_output is not None: parallel_output = forward_method_parallel_output output = parallel_lm_logits( lm_output, self.language_model.embedding.word_embeddings.weight, parallel_output) if get_key_value: output = [output, presents] if labels is None: return output else: if self.fp16_lm_cross_entropy: assert output.dtype == torch.half loss = mpu.vocab_parallel_cross_entropy(output, labels) else: loss = mpu.vocab_parallel_cross_entropy(output.float(), labels) return loss def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): state_dict_ = {} state_dict_[self._language_model_key] \ = self.language_model.state_dict_for_save_checkpoint( destination, prefix, keep_vars) return state_dict_ def load_state_dict(self, state_dict, strict=True): """Customized load.""" if self._language_model_key in state_dict: state_dict = state_dict[self._language_model_key] self.language_model.load_state_dict(state_dict, strict=strict) pangu/megatron/model/language_model.py000066400000000000000000000522341430334424000204240ustar00rootroot00000000000000# 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. """Transformer based language model.""" import torch import torch.nn.functional as F from megatron import get_args from megatron import mpu from megatron.module import MegatronModule from megatron.model.transformer import ParallelTransformer from megatron.model.utils import get_linear_layer from megatron.model.utils import init_method_normal, scaled_init_method_normal def parallel_lm_logits(input_, word_embeddings_weight, parallel_output, bias=None): """LM logits using word embedding weights.""" # Parallel logits. input_parallel = mpu.copy_to_model_parallel_region(input_) # Matrix multiply. if bias is None: logits_parallel = F.linear(input_parallel, word_embeddings_weight) else: logits_parallel = F.linear(input_parallel, word_embeddings_weight, bias) # Gather if needed. if parallel_output: return logits_parallel return mpu.gather_from_model_parallel_region(logits_parallel) def get_language_model(attention_mask_func, num_tokentypes, add_pooler, init_method=None, scaled_init_method=None): """Build language model and return along with the key to save.""" args = get_args() if init_method is None: init_method = init_method_normal(args.init_method_std) if scaled_init_method is None: scaled_init_method = scaled_init_method_normal(args.init_method_std, args.num_layers) # Language model. language_model = TransformerLanguageModel( attention_mask_func=attention_mask_func, init_method=init_method, output_layer_init_method=scaled_init_method, num_tokentypes=num_tokentypes, add_pooler=add_pooler) # key used for checkpoints. language_model_key = 'language_model' return language_model, language_model_key class Pooler(MegatronModule): """Pooler layer. Pool hidden states of a specific token (for example start of the sequence) and add a linear transformation followed by a tanh. Arguments: hidden_size: hidden size init_method: weight initialization method for the linear layer. bias is set to zero. """ def __init__(self, hidden_size, init_method): super(Pooler, self).__init__() self.dense = get_linear_layer(hidden_size, hidden_size, init_method) def forward(self, hidden_states, sequence_index=0): # hidden_states: [b, s, h] # sequence_index: index of the token to pool. pooled = hidden_states[:, sequence_index, :] pooled = self.dense(pooled) pooled = torch.tanh(pooled) return pooled class Embedding(MegatronModule): """Language model embeddings. Arguments: hidden_size: hidden size vocab_size: vocabulary size max_sequence_length: maximum size of sequence. This is used for positional embedding embedding_dropout_prob: dropout probability for embeddings init_method: weight initialization method num_tokentypes: size of the token-type embeddings. 0 value will ignore this embedding """ def __init__(self, hidden_size, vocab_size, max_sequence_length, embedding_dropout_prob, init_method, num_tokentypes=0): super(Embedding, self).__init__() self.hidden_size = hidden_size self.init_method = init_method self.num_tokentypes = num_tokentypes self.vocab_size = vocab_size # Word embeddings (parallel). self.word_embeddings = mpu.VocabParallelEmbedding( vocab_size, self.hidden_size, init_method=self.init_method) self._word_embeddings_key = 'word_embeddings' # Position embedding (serial). self.position_embeddings = torch.nn.Embedding( max_sequence_length, self.hidden_size) self._position_embeddings_key = 'position_embeddings' # Initialize the position embeddings. self.init_method(self.position_embeddings.weight) # Token type embedding. # Add this as an optional field that can be added through # method call so we can load a pretrain model without # token types and add them as needed. self._tokentype_embeddings_key = 'tokentype_embeddings' if self.num_tokentypes > 0: self.tokentype_embeddings = torch.nn.Embedding(self.num_tokentypes, self.hidden_size) # Initialize the token-type embeddings. self.init_method(self.tokentype_embeddings.weight) else: self.tokentype_embeddings = None # Embeddings dropout self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob) def add_tokentype_embeddings(self, num_tokentypes): """Add token-type embedding. This function is provided so we can add token-type embeddings in case the pretrained model does not have it. This allows us to load the model normally and then add this embedding. """ if self.tokentype_embeddings is not None: raise Exception('tokentype embeddings is already initialized') if torch.distributed.get_rank() == 0: print('adding embedding for {} tokentypes'.format(num_tokentypes), flush=True) self.num_tokentypes = num_tokentypes self.tokentype_embeddings = torch.nn.Embedding(num_tokentypes, self.hidden_size) # Initialize the token-type embeddings. self.init_method(self.tokentype_embeddings.weight) def forward(self, input_ids, position_ids, tokentype_ids=None): # Embeddings. words_embeddings = self.word_embeddings(input_ids) position_embeddings = self.position_embeddings(position_ids) embeddings = words_embeddings + position_embeddings if tokentype_ids is not None: assert self.tokentype_embeddings is not None embeddings = embeddings + self.tokentype_embeddings(tokentype_ids) else: assert self.tokentype_embeddings is None # Dropout. embeddings = self.embedding_dropout(embeddings) return embeddings def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): """For easy load.""" state_dict_ = {} state_dict_[self._word_embeddings_key] \ = self.word_embeddings.state_dict(destination, prefix, keep_vars) state_dict_[self._position_embeddings_key] \ = self.position_embeddings.state_dict( destination, prefix, keep_vars) if self.num_tokentypes > 0: state_dict_[self._tokentype_embeddings_key] \ = self.tokentype_embeddings.state_dict( destination, prefix, keep_vars) return state_dict_ def load_state_dict(self, state_dict, strict=True): """Customized load.""" # Word embedding. if self._word_embeddings_key in state_dict: state_dict_ = state_dict[self._word_embeddings_key] else: # for backward compatibility. state_dict_ = {} for key in state_dict.keys(): if 'word_embeddings' in key: state_dict_[key.split('word_embeddings.')[1]] \ = state_dict[key] if len(state_dict_["weight"])!=self.vocab_size: # print(f'Warning, word embeddings size mismatch for weight, copying a vocab size with {len(state_dict_["weight"])} ' # f'from checkpoint, the vocab size in current model is {self.vocab_size}.') state_dict_["weight"] = state_dict_["weight"][:self.vocab_size] self.word_embeddings.load_state_dict(state_dict_, strict=strict) # Position embedding. if self._position_embeddings_key in state_dict: state_dict_ = state_dict[self._position_embeddings_key] else: # for backward compatibility. state_dict_ = {} for key in state_dict.keys(): if 'position_embeddings' in key: state_dict_[key.split('position_embeddings.')[1]] \ = state_dict[key] self.position_embeddings.load_state_dict(state_dict_, strict=strict) # Tokentype embedding. if self.num_tokentypes > 0: state_dict_ = {} if self._tokentype_embeddings_key in state_dict: state_dict_ = state_dict[self._tokentype_embeddings_key] else: # for backward compatibility. for key in state_dict.keys(): if 'tokentype_embeddings' in key: state_dict_[key.split('tokentype_embeddings.')[1]] \ = state_dict[key] if len(state_dict_.keys()) > 0: self.tokentype_embeddings.load_state_dict(state_dict_, strict=strict) else: print('***WARNING*** expected tokentype embeddings in the ' 'checkpoint but could not find it', flush=True) class QueryEmbedding(MegatronModule): """Language model embeddings. Arguments: hidden_size: hidden size vocab_size: vocabulary size max_sequence_length: maximum size of sequence. This is used for positional embedding embedding_dropout_prob: dropout probability for embeddings init_method: weight initialization method num_tokentypes: size of the token-type embeddings. 0 value will ignore this embedding """ def __init__(self, hidden_size, vocab_size, max_sequence_length, embedding_dropout_prob, init_method, num_tokentypes=0): super(QueryEmbedding, self).__init__() self.hidden_size = hidden_size self.init_method = init_method self.num_tokentypes = num_tokentypes # Top query position embedding (serial). self.top_query_embeddings = torch.nn.Embedding( max_sequence_length, self.hidden_size) self._top_query_embeddings_key = 'top_query_embeddings' # Initialize the top query position embeddings. self.init_method(self.top_query_embeddings.weight) # Token type embedding. # Add this as an optional field that can be added through # method call so we can load a pretrain model without # token types and add them as needed. self._tokentype_embeddings_key = 'tokentype_embeddings' if self.num_tokentypes > 0: self.tokentype_embeddings = torch.nn.Embedding(self.num_tokentypes, self.hidden_size) # Initialize the token-type embeddings. self.init_method(self.tokentype_embeddings.weight) else: self.tokentype_embeddings = None # Embeddings dropout self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob) def add_tokentype_embeddings(self, num_tokentypes): """Add token-type embedding. This function is provided so we can add token-type embeddings in case the pretrained model does not have it. This allows us to load the model normally and then add this embedding. """ if self.tokentype_embeddings is not None: raise Exception('tokentype embeddings is already initialized') if torch.distributed.get_rank() == 0: print('adding embedding for {} tokentypes'.format(num_tokentypes), flush=True) self.num_tokentypes = num_tokentypes self.tokentype_embeddings = torch.nn.Embedding(num_tokentypes, self.hidden_size) # Initialize the token-type embeddings. self.init_method(self.tokentype_embeddings.weight) def forward(self, position_ids, tokentype_ids=None): # Embeddings. embeddings = self.top_query_embeddings(position_ids) if tokentype_ids is not None: assert self.tokentype_embeddings is not None embeddings = embeddings + self.tokentype_embeddings(tokentype_ids) else: assert self.tokentype_embeddings is None # Dropout. embeddings = self.embedding_dropout(embeddings) return embeddings def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): """For easy load.""" state_dict_ = {} state_dict_[self._top_query_embeddings_key] \ = self.top_query_embeddings.state_dict( destination, prefix, keep_vars) if self.num_tokentypes > 0: state_dict_[self._tokentype_embeddings_key] \ = self.tokentype_embeddings.state_dict( destination, prefix, keep_vars) return state_dict_ def load_state_dict(self, state_dict, strict=True): """Customized load.""" # Position embedding. if self._top_query_embeddings_key in state_dict: state_dict_ = state_dict[self._top_query_embeddings_key] else: # for backward compatibility. state_dict_ = {} for key in state_dict.keys(): if 'top_query_embeddings' in key: state_dict_[key.split('top_query_embeddings.')[1]] \ = state_dict[key] self.top_query_embeddings.load_state_dict(state_dict_, strict=strict) # Tokentype embedding. if self.num_tokentypes > 0: state_dict_ = {} if self._tokentype_embeddings_key in state_dict: state_dict_ = state_dict[self._tokentype_embeddings_key] else: # for backward compatibility. for key in state_dict.keys(): if 'tokentype_embeddings' in key: state_dict_[key.split('tokentype_embeddings.')[1]] \ = state_dict[key] if len(state_dict_.keys()) > 0: self.tokentype_embeddings.load_state_dict(state_dict_, strict=strict) else: print('***WARNING*** expected tokentype embeddings in the ' 'checkpoint but could not find it', flush=True) class TransformerLanguageModel(MegatronModule): """Transformer language model. Arguments: transformer_hparams: transformer hyperparameters attention_mask_func: a function that takes `unmaksed-attention-scores` with size [b, np, s, s] and an `attention-mask` and will apply the masking. The function should return a masked score of the same size [b, np, s, s]. masked-attention-scores = attention_mask_func( unmaksed-attention-scores, attention-mask) vocab_size: vocabulary size max_sequence_length: maximum size of sequence. This is used for positional embedding embedding_dropout_prob: dropout probability for embeddings num_tokentypes: size of the token-type embeddings. 0 value will ignore this embedding """ def __init__(self, attention_mask_func, init_method, output_layer_init_method, num_tokentypes=0, add_pooler=False): super(TransformerLanguageModel, self).__init__() args = get_args() self.hidden_size = args.hidden_size self.num_tokentypes = num_tokentypes self.init_method = init_method self.add_pooler = add_pooler # Embeddings self.embedding = Embedding(self.hidden_size, args.padded_vocab_size, args.max_position_embeddings, args.hidden_dropout, self.init_method, self.num_tokentypes) self._embedding_key = 'embedding' # Query embeddings self.topQueryEmbedding = QueryEmbedding(self.hidden_size, args.padded_vocab_size, args.max_position_embeddings, args.hidden_dropout, self.init_method, self.num_tokentypes) self._topQueryEmbedding_key = 'topQueryEmbedding' # Transformer self.transformer = ParallelTransformer( attention_mask_func, self.init_method, output_layer_init_method) self._transformer_key = 'transformer' # Pooler if self.add_pooler: self.pooler = Pooler(self.hidden_size, self.init_method) self._pooler_key = 'pooler' def forward(self, input_ids, position_ids, attention_mask, tokentype_ids=None, layer_past=None, get_key_value=False, pooling_sequence_index=0): # Embeddings. embedding_output = self.embedding(input_ids, position_ids, tokentype_ids=tokentype_ids) # Query Embeddings. queryEmbedding_out = self.topQueryEmbedding(position_ids, tokentype_ids=tokentype_ids) # Transformer. transformer_output = self.transformer(embedding_output, queryEmbedding_out, attention_mask, layer_past=layer_past, get_key_value=get_key_value) if self.add_pooler: pooled_output = self.pooler(transformer_output, pooling_sequence_index) return transformer_output, pooled_output return transformer_output def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): """For easy load.""" state_dict_ = {} state_dict_[self._embedding_key] \ = self.embedding.state_dict_for_save_checkpoint( destination, prefix, keep_vars) state_dict_[self._topQueryEmbedding_key] \ = self.topQueryEmbedding.state_dict_for_save_checkpoint( destination, prefix, keep_vars) state_dict_[self._transformer_key] \ = self.transformer.state_dict_for_save_checkpoint( destination, prefix, keep_vars) if self.add_pooler: state_dict_[self._pooler_key] \ = self.pooler.state_dict_for_save_checkpoint( destination, prefix, keep_vars) return state_dict_ def load_state_dict(self, state_dict, strict=True): """Customized load.""" # Embedding. if self._embedding_key in state_dict: state_dict_ = state_dict[self._embedding_key] else: # for backward compatibility. state_dict_ = {} for key in state_dict.keys(): if '_embeddings' in key: state_dict_[key] = state_dict[key] self.embedding.load_state_dict(state_dict_, strict=strict) if self._topQueryEmbedding_key in state_dict: state_dict_ = state_dict[self._topQueryEmbedding_key] else: # for backward compatibility. state_dict_ = {} for key in state_dict.keys(): if '_embeddings' in key: state_dict_[key] = state_dict[key] self.topQueryEmbedding.load_state_dict(state_dict_, strict=strict) # Transformer. if self._transformer_key in state_dict: state_dict_ = state_dict[self._transformer_key] else: # for backward compatibility. state_dict_ = {} for key in state_dict.keys(): if 'transformer.' in key: state_dict_[key.split('transformer.')[1]] = state_dict[key] self.transformer.load_state_dict(state_dict_, strict=strict) # Pooler. if self.add_pooler: assert 'pooler' in state_dict, \ 'could not find data for pooler in the checkpoint' self.pooler.load_state_dict(state_dict[self._pooler_key], strict=strict) pangu/megatron/model/multiple_choice.py000066400000000000000000000110421430334424000206160ustar00rootroot00000000000000# 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. """Multiple choice model.""" import torch from megatron import get_args, print_rank_0 from megatron.model.bert_model import bert_attention_mask_func, bert_extended_attention_mask, bert_position_ids from megatron.model.language_model import get_language_model from megatron.model.utils import get_linear_layer from megatron.model.utils import init_method_normal from megatron.model.utils import scaled_init_method_normal from megatron.module import MegatronModule class MultipleChoice(MegatronModule): def __init__(self, num_tokentypes=2): super(MultipleChoice, self).__init__() args = get_args() init_method = init_method_normal(args.init_method_std) self.language_model, self._language_model_key = get_language_model( attention_mask_func=bert_attention_mask_func, num_tokentypes=num_tokentypes, add_pooler=True, init_method=init_method, scaled_init_method=scaled_init_method_normal(args.init_method_std, args.num_layers)) # Multi-choice head. self.multichoice_dropout = torch.nn.Dropout(args.hidden_dropout) self.multichoice_head = get_linear_layer(args.hidden_size, 1, init_method) self._multichoice_head_key = 'multichoice_head' def forward(self, input_ids, attention_mask, tokentype_ids): # [batch, choices, sequence] --> [batch * choices, sequence] --> # transformer --> [batch, choices] --> softmax # Ensure the shape is [batch-size, choices, sequence] assert len(input_ids.shape) == 3 assert len(attention_mask.shape) == 3 assert len(tokentype_ids.shape) == 3 # Reshape and treat choice dimension the same as batch. num_choices = input_ids.shape[1] input_ids = input_ids.view(-1, input_ids.size(-1)) attention_mask = attention_mask.view(-1, attention_mask.size(-1)) tokentype_ids = tokentype_ids.view(-1, tokentype_ids.size(-1)) extended_attention_mask = bert_extended_attention_mask( attention_mask, next(self.language_model.parameters()).dtype) position_ids = bert_position_ids(input_ids) _, pooled_output = self.language_model(input_ids, position_ids, extended_attention_mask, tokentype_ids=tokentype_ids) # Output. multichoice_output = self.multichoice_dropout(pooled_output) multichoice_logits = self.multichoice_head(multichoice_output) # Reshape back to separate choices. multichoice_logits = multichoice_logits.view(-1, num_choices) return multichoice_logits def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): """For easy load when model is combined with other heads, add an extra key.""" state_dict_ = {} state_dict_[self._language_model_key] \ = self.language_model.state_dict_for_save_checkpoint( destination, prefix, keep_vars) state_dict_[self._multichoice_head_key] \ = self.multichoice_head.state_dict( destination, prefix, keep_vars) return state_dict_ def load_state_dict(self, state_dict, strict=True): """Customized load.""" self.language_model.load_state_dict( state_dict[self._language_model_key], strict=strict) if self._multichoice_head_key in state_dict: self.multichoice_head.load_state_dict( state_dict[self._multichoice_head_key], strict=strict) else: print_rank_0('***WARNING*** could not find {} in the checkpoint, ' 'initializing to random'.format( self._multichoice_head_key)) pangu/megatron/model/realm_model.py000066400000000000000000000207361430334424000177430ustar00rootroot00000000000000import os import torch from megatron import get_args, print_rank_0 from megatron.checkpointing import get_checkpoint_tracker_filename, get_checkpoint_name from megatron.model import BertModel from megatron.module import MegatronModule from megatron import mpu from megatron.model.utils import get_linear_layer from megatron.model.utils import init_method_normal from megatron.model.language_model import get_language_model from megatron.model.utils import scaled_init_method_normal from megatron.model.bert_model import bert_attention_mask_func, bert_extended_attention_mask, bert_position_ids def general_ict_model_provider(only_query_model=False, only_block_model=False): """Build the model.""" args = get_args() assert args.ict_head_size is not None, \ "Need to specify --ict-head-size to provide an ICTBertModel" assert args.model_parallel_size == 1, \ "Model parallel size > 1 not supported for ICT" print_rank_0('building ICTBertModel...') # simpler to just keep using 2 tokentypes since the LM we initialize with has 2 tokentypes model = ICTBertModel( ict_head_size=args.ict_head_size, num_tokentypes=2, parallel_output=True, only_query_model=only_query_model, only_block_model=only_block_model) return model class ICTBertModel(MegatronModule): """Bert-based module for Inverse Cloze task.""" def __init__(self, ict_head_size, num_tokentypes=1, parallel_output=True, only_query_model=False, only_block_model=False): super(ICTBertModel, self).__init__() bert_kwargs = dict( ict_head_size=ict_head_size, num_tokentypes=num_tokentypes, parallel_output=parallel_output ) assert not (only_block_model and only_query_model) self.use_block_model = not only_query_model self.use_query_model = not only_block_model if self.use_query_model: # this model embeds (pseudo-)queries - Embed_input in the paper self.query_model = IREncoderBertModel(**bert_kwargs) self._query_key = 'question_model' if self.use_block_model: # this model embeds evidence blocks - Embed_doc in the paper self.block_model = IREncoderBertModel(**bert_kwargs) self._block_key = 'context_model' def forward(self, query_tokens, query_attention_mask, block_tokens, block_attention_mask): """Run a forward pass for each of the models and return the respective embeddings.""" query_logits = self.embed_query(query_tokens, query_attention_mask) block_logits = self.embed_block(block_tokens, block_attention_mask) return query_logits, block_logits def embed_query(self, query_tokens, query_attention_mask): """Embed a batch of tokens using the query model""" if self.use_query_model: query_types = torch.cuda.LongTensor(*query_tokens.shape).fill_(0) query_ict_logits, _ = self.query_model.forward(query_tokens, query_attention_mask, query_types) return query_ict_logits else: raise ValueError("Cannot embed query without query model.") def embed_block(self, block_tokens, block_attention_mask): """Embed a batch of tokens using the block model""" if self.use_block_model: block_types = torch.cuda.LongTensor(*block_tokens.shape).fill_(0) block_ict_logits, _ = self.block_model.forward(block_tokens, block_attention_mask, block_types) return block_ict_logits else: raise ValueError("Cannot embed block without block model.") def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): """Save dict with state dicts of each of the models.""" state_dict_ = {} if self.use_query_model: state_dict_[self._query_key] \ = self.query_model.state_dict_for_save_checkpoint( destination, prefix, keep_vars) if self.use_block_model: state_dict_[self._block_key] \ = self.block_model.state_dict_for_save_checkpoint( destination, prefix, keep_vars) return state_dict_ def load_state_dict(self, state_dict, strict=True): """Load the state dicts of each of the models""" if self.use_query_model: print("Loading ICT query model", flush=True) self.query_model.load_state_dict( state_dict[self._query_key], strict=strict) if self.use_block_model: print("Loading ICT block model", flush=True) self.block_model.load_state_dict( state_dict[self._block_key], strict=strict) def init_state_dict_from_bert(self): """Initialize the state from a pretrained BERT model on iteration zero of ICT pretraining""" args = get_args() tracker_filename = get_checkpoint_tracker_filename(args.bert_load) if not os.path.isfile(tracker_filename): raise FileNotFoundError("Could not find BERT load for ICT") with open(tracker_filename, 'r') as f: iteration = int(f.read().strip()) assert iteration > 0 checkpoint_name = get_checkpoint_name(args.bert_load, iteration, False) if mpu.get_data_parallel_rank() == 0: print('global rank {} is loading checkpoint {}'.format( torch.distributed.get_rank(), checkpoint_name)) try: state_dict = torch.load(checkpoint_name, map_location='cpu') except BaseException: raise ValueError("Could not load checkpoint") # load the LM state dict into each model model_dict = state_dict['model']['language_model'] self.query_model.language_model.load_state_dict(model_dict) self.block_model.language_model.load_state_dict(model_dict) # give each model the same ict_head to begin with as well query_ict_head_state_dict = self.state_dict_for_save_checkpoint()[self._query_key]['ict_head'] self.block_model.ict_head.load_state_dict(query_ict_head_state_dict) class IREncoderBertModel(MegatronModule): """BERT-based encoder for queries or blocks used for learned information retrieval.""" def __init__(self, ict_head_size, num_tokentypes=2, parallel_output=True): super(IREncoderBertModel, self).__init__() args = get_args() self.ict_head_size = ict_head_size self.parallel_output = parallel_output init_method = init_method_normal(args.init_method_std) scaled_init_method = scaled_init_method_normal(args.init_method_std, args.num_layers) self.language_model, self._language_model_key = get_language_model( attention_mask_func=bert_attention_mask_func, num_tokentypes=num_tokentypes, add_pooler=True, init_method=init_method, scaled_init_method=scaled_init_method) self.ict_head = get_linear_layer(args.hidden_size, ict_head_size, init_method) self._ict_head_key = 'ict_head' def forward(self, input_ids, attention_mask, tokentype_ids=None): extended_attention_mask = bert_extended_attention_mask( attention_mask, next(self.language_model.parameters()).dtype) position_ids = bert_position_ids(input_ids) lm_output, pooled_output = self.language_model( input_ids, position_ids, extended_attention_mask, tokentype_ids=tokentype_ids) # Output. ict_logits = self.ict_head(pooled_output) return ict_logits, None def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): """For easy load when model is combined with other heads, add an extra key.""" state_dict_ = {} state_dict_[self._language_model_key] \ = self.language_model.state_dict_for_save_checkpoint( destination, prefix, keep_vars) state_dict_[self._ict_head_key] \ = self.ict_head.state_dict(destination, prefix, keep_vars) return state_dict_ def load_state_dict(self, state_dict, strict=True): """Customized load.""" self.language_model.load_state_dict( state_dict[self._language_model_key], strict=strict) self.ict_head.load_state_dict( state_dict[self._ict_head_key], strict=strict) pangu/megatron/model/transformer.py000066400000000000000000001054061430334424000200230ustar00rootroot00000000000000# 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. """Transformer.""" import math import torch import torch.nn.functional as F from megatron import get_args from megatron import mpu from megatron.mpu import LayerNorm from megatron.module import MegatronModule from megatron.model.fused_softmax import FusedScaleMaskSoftmax from megatron.model.fused_bias_gelu import bias_gelu_impl from megatron.model.utils import openai_gelu, erf_gelu # flags required to enable jit fusion kernels torch._C._jit_set_profiling_mode(False) torch._C._jit_set_profiling_executor(False) torch._C._jit_override_can_fuse_on_cpu(True) torch._C._jit_override_can_fuse_on_gpu(True) """ We use the following notation throughout this file: h: hidden size n: number of attention heads p: number of model parallel partitions np: n/p hp: h/p hn: h/n b: batch size s: sequence length l: number of layers Transformer takes input of size [s, b, h] and returns a tensor of the same size. We use the following arguments: hyperparameters: transformer hyperparameters attention_mask_func: a function that takes `unmaksed-attention-scores` with size [b, np, s, s] and an `attention-mask` and will apply the masking. The function should return a masked score of the same size [b, np, s, s]. masked-attention-scores = attention_mask_func( unmaksed-attention-scores, attention-mask) """ class ParallelMLP(MegatronModule): """MLP. MLP will take the input with h hidden state, project it to 4*h hidden dimension, perform nonlinear transformation, and project the state back into h hidden dimension. At the end, dropout is also applied. """ def __init__(self, init_method, output_layer_init_method): super(ParallelMLP, self).__init__() args = get_args() # Project to 4h. self.dense_h_to_4h = mpu.ColumnParallelLinear( args.hidden_size, 4 * args.hidden_size, gather_output=False, init_method=init_method, skip_bias_add=True) self.bias_gelu_fusion = args.bias_gelu_fusion self.activation_func = F.gelu if args.openai_gelu: self.activation_func = openai_gelu elif args.onnx_safe: self.activation_func = erf_gelu # Project back to h. self.dense_4h_to_h = mpu.RowParallelLinear( 4 * args.hidden_size, args.hidden_size, input_is_parallel=True, init_method=output_layer_init_method, skip_bias_add=True) def forward(self, hidden_states): # [s, b, 4hp] intermediate_parallel, bias_parallel = self.dense_h_to_4h(hidden_states) if self.bias_gelu_fusion: intermediate_parallel = \ bias_gelu_impl(intermediate_parallel, bias_parallel) else: intermediate_parallel = \ self.activation_func(intermediate_parallel + bias_parallel) # [s, b, h] output, output_bias = self.dense_4h_to_h(intermediate_parallel) return output, output_bias class ParallelSelfAttention(MegatronModule): """Parallel self-attention layer abstract class. Self-attention layer takes input with size [b, s, h] and returns output of the same size. """ def __init__(self, attention_mask_func, init_method, output_layer_init_method, layer_number): super(ParallelSelfAttention, self).__init__() args = get_args() self.fp16 = args.fp16 self.attention_mask_func = attention_mask_func self.apply_query_key_layer_scaling = args.apply_query_key_layer_scaling self.attention_softmax_in_fp32 = args.attention_softmax_in_fp32 if self.apply_query_key_layer_scaling: self.attention_softmax_in_fp32 = True self.layer_number = max(1, layer_number) # Per attention head and per partition values. world_size = mpu.get_model_parallel_world_size() self.hidden_size_per_partition = mpu.divide(args.hidden_size, world_size) self.hidden_size_per_attention_head = mpu.divide( args.hidden_size, args.num_attention_heads) self.num_attention_heads_per_partition = mpu.divide( args.num_attention_heads, world_size) # Strided linear layer. self.query = mpu.ColumnParallelLinear( args.hidden_size, args.hidden_size, gather_output=False, init_method=init_method) self.key = mpu.ColumnParallelLinear( args.hidden_size, args.hidden_size, gather_output=False, init_method=init_method) self.value = mpu.ColumnParallelLinear( args.hidden_size, args.hidden_size, gather_output=False, init_method=init_method) coeff = None self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) if self.apply_query_key_layer_scaling: coeff = self.layer_number self.norm_factor *= coeff self.scale_mask_softmax = FusedScaleMaskSoftmax( self.fp16, args.scaled_upper_triang_masked_softmax_fusion, self.attention_mask_func, self.attention_softmax_in_fp32, coeff) # Dropout. Note that for a single iteration, this layer will generate # different outputs on different number of parallel partitions but # on average it should not be partition dependent. self.attention_dropout = torch.nn.Dropout(args.attention_dropout) # Output. self.dense = mpu.RowParallelLinear( args.hidden_size, args.hidden_size, input_is_parallel=True, init_method=output_layer_init_method, skip_bias_add=True) def forward(self, hidden_states, attention_mask, layer_past=None, get_key_value=False): # hidden_states: [s, b, h] # ===================== # Query, Key, and Value # ===================== query_layer, _ = self.query(hidden_states) key_layer, _ = self.key(hidden_states) value_layer, _ = self.value(hidden_states) new_query_layer_shape = query_layer.size()[:-1] + \ (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) query_layer = query_layer.view(*new_query_layer_shape) new_query_layer_shape = key_layer.size()[:-1] + \ (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) key_layer = key_layer.view(*new_query_layer_shape) new_query_layer_shape = value_layer.size()[:-1] + \ (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) value_layer = value_layer.view(*new_query_layer_shape) # ================================== # Adjust key and value for inference # ================================== if layer_past is not None: past_key, past_value = layer_past key_layer = torch.cat((past_key.type_as(key_layer), key_layer), dim=0) value_layer = torch.cat((past_value.type_as(value_layer), value_layer), dim=0) if get_key_value: present = (key_layer, value_layer) # =================================== # Raw attention scores. [b, np, s, s] # =================================== # [b, np, s, s] output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) # [s, b, np, hn] -> [s, b * np, hn] query_layer = query_layer.contiguous().view(output_size[2], output_size[0] * output_size[1], -1) key_layer = key_layer.contiguous().view(output_size[3], output_size[0] * output_size[1], -1) # preallocting result tensor: [b * np, s, s] matmul_result = torch.empty( output_size[0]*output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, device=torch.cuda.current_device()) # Raw attention scores. [b * np, s, s] matmul_result = torch.baddbmm(matmul_result, query_layer.transpose(0, 1), # [b * np, s, hn] key_layer.transpose(0,1).transpose(1, 2), #[b * np, hn, s] beta=0.0, alpha=(1.0/self.norm_factor)) # change view to [b, np, s, s] attention_scores = matmul_result.view(*output_size) # ================================================== # Update attention mask for inference. [b, np, s, s] # ================================================== if get_key_value: with torch.no_grad(): if layer_past is not None: attention_mask = attention_mask[ ..., attention_scores.size(3) - 1, :attention_scores.size(3)].unsqueeze(2) else: attention_mask = attention_mask[ ..., :attention_scores.size(3), :attention_scores.size(3)] # =========================== # Attention probs and dropout # =========================== # attention scores and attention mask [b, np, s, s] attention_probs = self.scale_mask_softmax(attention_scores, attention_mask) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. with mpu.get_cuda_rng_tracker().fork(): attention_probs = self.attention_dropout(attention_probs) # ========================= # Context layer. [s, b, hp] # ========================= # value_layer -> context layer. # [s, b, np, hn] --> [b, np, s, hn] # context layer shape: [b, np, s, hn] output_size = (value_layer.size(1), value_layer.size(2), value_layer.size(0), value_layer.size(3)) # change view [s, b * np, hn] value_layer = value_layer.contiguous().view(output_size[2], output_size[0] * output_size[1], -1) # change view [b * np, s, s] attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) # matmul: [b * np, s, hn] context_layer = torch.bmm(attention_probs, value_layer.transpose(0,1)) # change view [b, np, s, hn] context_layer = context_layer.view(*output_size) # [b, np, s, hn] --> [s, b, np, hn] context_layer = context_layer.permute(2, 0, 1, 3).contiguous() # [s, b, np, hn] --> [s, b, hp] new_context_layer_shape = context_layer.size()[:-2] + \ (self.hidden_size_per_partition,) context_layer = context_layer.view(*new_context_layer_shape) # ================= # Output. [s, b, h] # ================= output, bias = self.dense(context_layer) if get_key_value: output = [output, present] return output, bias class ParallelTopQuerySelfAttention(MegatronModule): """Parallel top query self-attention layer abstract class. Self-attention layer takes input with size [b, s, h] and returns output of the same size. """ def __init__(self, attention_mask_func, init_method, output_layer_init_method, layer_number): super(ParallelTopQuerySelfAttention, self).__init__() args = get_args() self.fp16 = args.fp16 self.attention_mask_func = attention_mask_func self.apply_query_key_layer_scaling = args.apply_query_key_layer_scaling self.attention_softmax_in_fp32 = args.attention_softmax_in_fp32 if self.apply_query_key_layer_scaling: self.attention_softmax_in_fp32 = True self.layer_number = max(1, layer_number) # Per attention head and per partition values. world_size = mpu.get_model_parallel_world_size() self.hidden_size_per_partition = mpu.divide(args.hidden_size, world_size) self.hidden_size_per_attention_head = mpu.divide( args.hidden_size, args.num_attention_heads) self.num_attention_heads_per_partition = mpu.divide( args.num_attention_heads, world_size) self.query = mpu.ColumnParallelLinear( args.hidden_size, args.hidden_size, gather_output=False, init_method=init_method) self.key = mpu.ColumnParallelLinear( args.hidden_size, args.hidden_size, gather_output=False, init_method=init_method) self.value = mpu.ColumnParallelLinear( args.hidden_size, args.hidden_size, gather_output=False, init_method=init_method) coeff = None self.norm_factor = math.sqrt(self.hidden_size_per_attention_head) if self.apply_query_key_layer_scaling: coeff = self.layer_number self.norm_factor *= coeff self.scale_mask_softmax = FusedScaleMaskSoftmax( self.fp16, args.scaled_upper_triang_masked_softmax_fusion, self.attention_mask_func, self.attention_softmax_in_fp32, coeff) # Dropout. Note that for a single iteration, this layer will generate # different outputs on different number of parallel partitions but # on average it should not be partition dependent. self.attention_dropout = torch.nn.Dropout(args.attention_dropout) # Output. self.dense = mpu.RowParallelLinear( args.hidden_size, args.hidden_size, input_is_parallel=True, init_method=output_layer_init_method, skip_bias_add=True) def forward(self, hidden_states, query_hidden_state, attention_mask, layer_past=None, get_key_value=False): # hidden_states: [s, b, h] query_layer, _ = self.query(query_hidden_state) key_layer, _ = self.key(hidden_states) value_layer, _ = self.value(hidden_states) new_query_layer_shape = query_layer.size()[:-1] + \ (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) query_layer = query_layer.view(*new_query_layer_shape) new_query_layer_shape = key_layer.size()[:-1] + \ (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) key_layer = key_layer.view(*new_query_layer_shape) new_query_layer_shape = value_layer.size()[:-1] + \ (self.num_attention_heads_per_partition, self.hidden_size_per_attention_head) value_layer = value_layer.view(*new_query_layer_shape) # ================================== # Adjust key and value for inference # ================================== if layer_past is not None: past_key, past_value = layer_past key_layer = torch.cat((past_key.type_as(key_layer), key_layer), dim=0) value_layer = torch.cat((past_value.type_as(value_layer), value_layer), dim=0) if get_key_value: present = (key_layer, value_layer) # =================================== # Raw attention scores. [b, np, s, s] # =================================== # [b, np, s, s] output_size = (query_layer.size(1), query_layer.size(2), query_layer.size(0), key_layer.size(0)) # [s, b, np, hn] -> [s, b * np, hn] query_layer = query_layer.view(output_size[2], output_size[0] * output_size[1], -1) key_layer = key_layer.contiguous().view(output_size[3], output_size[0] * output_size[1], -1) # preallocting result tensor: [b * np, s, s] matmul_result = torch.empty( output_size[0]*output_size[1], output_size[2], output_size[3], dtype=query_layer.dtype, device=torch.cuda.current_device()) # Raw attention scores. [b * np, s, s] matmul_result = torch.baddbmm(matmul_result, query_layer.transpose(0, 1), # [b * np, s, hn] key_layer.transpose(0,1).transpose(1, 2), #[b * np, hn, s] beta=0.0, alpha=(1.0/self.norm_factor)) # change view to [b, np, s, s] attention_scores = matmul_result.view(*output_size) # ================================================== # Update attention mask for inference. [b, np, s, s] # ================================================== if get_key_value: with torch.no_grad(): if layer_past is not None: attention_mask = attention_mask[ ..., attention_scores.size(3) - 1, :attention_scores.size(3)].unsqueeze(2) else: attention_mask = attention_mask[ ..., :attention_scores.size(3), :attention_scores.size(3)] # =========================== # Attention probs and dropout # =========================== # attention scores and attention mask [b, np, s, s] attention_probs = self.scale_mask_softmax(attention_scores, attention_mask) # This is actually dropping out entire tokens to attend to, which might # seem a bit unusual, but is taken from the original Transformer paper. with mpu.get_cuda_rng_tracker().fork(): attention_probs = self.attention_dropout(attention_probs) # ========================= # Context layer. [s, b, hp] # ========================= # value_layer -> context layer. # [s, b, np, hn] --> [b, np, s, hn] # context layer shape: [b, np, s, hn] output_size = (value_layer.size(1), value_layer.size(2), value_layer.size(0), value_layer.size(3)) # change view [s, b * np, hn] value_layer = value_layer.contiguous().view(output_size[2], output_size[0] * output_size[1], -1) # change view [b * np, s, s] attention_probs = attention_probs.view(output_size[0] * output_size[1], output_size[2], -1) # matmul: [b * np, s, hn] context_layer = torch.bmm(attention_probs, value_layer.transpose(0,1)) # change view [b, np, s, hn] context_layer = context_layer.view(*output_size) # [b, np, s, hn] --> [s, b, np, hn] context_layer = context_layer.permute(2, 0, 1, 3).contiguous() # [s, b, np, hn] --> [s, b, hp] new_context_layer_shape = context_layer.size()[:-2] + \ (self.hidden_size_per_partition,) context_layer = context_layer.view(*new_context_layer_shape) # ================= # Output. [s, b, h] # ================= output, bias = self.dense(context_layer) if get_key_value: output = [output, present] return output, bias def bias_dropout_add(x, bias, residual, prob, training) : # type: (Tensor, Tensor, Tensor, float, bool) -> Tensor out = torch.nn.functional.dropout(x + bias, p=prob, training=training) out = residual + out return out def get_bias_dropout_add(training): def _bias_dropout_add(x, bias, residual, prob): return bias_dropout_add(x, bias, residual, prob, training) return _bias_dropout_add @torch.jit.script def bias_dropout_add_fused_train(x, bias, residual, prob) : # type: (Tensor, Tensor, Tensor, float) -> Tensor return bias_dropout_add(x, bias, residual, prob, True) @torch.jit.script def bias_dropout_add_fused_inference(x, bias, residual, prob) : # type: (Tensor, Tensor, Tensor, float) -> Tensor return bias_dropout_add(x, bias, residual, prob, False) class ParallelTransformerLayer(MegatronModule): """A single transformer layer. Transformore layer takes input with size [b, s, h] and returns an output of the same size. """ def __init__(self, attention_mask_func, init_method, output_layer_init_method, layer_number): args = get_args() super(ParallelTransformerLayer, self).__init__() self.layer_number = layer_number self.apply_residual_connection_post_layernorm \ = args.apply_residual_connection_post_layernorm # Layernorm on the input data. self.input_layernorm = LayerNorm( args.hidden_size, eps=args.layernorm_epsilon) # Self attention. self.attention = ParallelSelfAttention(attention_mask_func, init_method, output_layer_init_method, layer_number) self.hidden_dropout = args.hidden_dropout self.bias_dropout_fusion = args.bias_dropout_fusion # Layernorm on the input data. self.post_attention_layernorm = LayerNorm( args.hidden_size, eps=args.layernorm_epsilon) # MLP self.mlp = ParallelMLP(init_method, output_layer_init_method) def forward(self, hidden_states, attention_mask, layer_past=None, get_key_value=False): # hidden_states: [b, s, h] # Layer norm at the begining of the transformer layer. layernorm_output = self.input_layernorm(hidden_states) # Self attention. attention_output, attention_bias = \ self.attention(layernorm_output, attention_mask, layer_past=layer_past, get_key_value=get_key_value) if get_key_value: attention_output, presents = attention_output # Residual connection. if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = hidden_states # jit scripting for a nn.module (with dropout) is not # trigerring the fusion kernel. For now, we use two # different nn.functional routines to account for varying # dropout semantics during training and inference phases. if self.bias_dropout_fusion: if self.training: bias_dropout_add_func = bias_dropout_add_fused_train else: bias_dropout_add_func = bias_dropout_add_fused_inference else: bias_dropout_add_func = get_bias_dropout_add(self.training) #re-enable torch grad to enable fused optimization. with torch.enable_grad(): layernorm_input = bias_dropout_add_func( attention_output, attention_bias.expand_as(residual), residual, self.hidden_dropout) # Layer norm post the self attention. layernorm_output = self.post_attention_layernorm(layernorm_input) # MLP. mlp_output, mlp_bias = self.mlp(layernorm_output) # Second residual connection. if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = layernorm_input #re-enable torch grad to enable fused optimization. with torch.enable_grad(): output = bias_dropout_add_func( mlp_output, mlp_bias.expand_as(residual), residual, self.hidden_dropout) if get_key_value: output = [output, presents] return output class ParallelTopQueryLayer(MegatronModule): """A single top query layer. Top query layer takes input with size [b, s, h] and returns an output of the same size. """ def __init__(self, attention_mask_func, init_method, output_layer_init_method, layer_number): args = get_args() super(ParallelTopQueryLayer, self).__init__() self.layer_number = layer_number self.apply_residual_connection_post_layernorm \ = args.apply_residual_connection_post_layernorm # Layernorm on the input data. self.input_layernorm = LayerNorm( args.hidden_size, eps=args.layernorm_epsilon) # Self attention. self.attention = ParallelTopQuerySelfAttention(attention_mask_func, init_method, output_layer_init_method, layer_number) self.hidden_dropout = args.hidden_dropout self.bias_dropout_fusion = args.bias_dropout_fusion # Layernorm on the input data. self.post_attention_layernorm = LayerNorm( args.hidden_size, eps=args.layernorm_epsilon) # MLP self.mlp = ParallelMLP(init_method, output_layer_init_method) def forward(self, hidden_states, query_hidden_state, attention_mask, layer_past=None, get_key_value=False): # hidden_states: [b, s, h] assert query_hidden_state != None # Layer norm at the begining of the transformer layer. layernorm_output = self.input_layernorm(hidden_states) # Self attention. attention_output, attention_bias = \ self.attention(layernorm_output, query_hidden_state, attention_mask, layer_past=layer_past, get_key_value=get_key_value) if get_key_value: attention_output, presents = attention_output # Residual connection. if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = hidden_states # jit scripting for a nn.module (with dropout) is not # trigerring the fusion kernel. For now, we use two # different nn.functional routines to account for varying # dropout semantics during training and inference phases. if self.bias_dropout_fusion: if self.training: bias_dropout_add_func = bias_dropout_add_fused_train else: bias_dropout_add_func = bias_dropout_add_fused_inference else: bias_dropout_add_func = get_bias_dropout_add(self.training) #re-enable torch grad to enable fused optimization. with torch.enable_grad(): layernorm_input = bias_dropout_add_func( attention_output, attention_bias.expand_as(residual), residual, self.hidden_dropout) # Layer norm post the self attention. layernorm_output = self.post_attention_layernorm(layernorm_input) # MLP. mlp_output, mlp_bias = self.mlp(layernorm_output) # Second residual connection. if self.apply_residual_connection_post_layernorm: residual = layernorm_output else: residual = layernorm_input #re-enable torch grad to enable fused optimization. with torch.enable_grad(): output = bias_dropout_add_func( mlp_output, mlp_bias.expand_as(residual), residual, self.hidden_dropout) if get_key_value: output = [output, presents] return output class ParallelTransformer(MegatronModule): """Transformer class.""" def __init__(self, attention_mask_func, init_method, output_layer_init_method): super(ParallelTransformer, self).__init__() args = get_args() # Store activation checkpoiting flag. self.checkpoint_activations = args.checkpoint_activations self.checkpoint_num_layers = args.checkpoint_num_layers # Number of layers: self.num_layers = args.num_layers self.num_unique_layers = args.num_unique_layers ################# assert self.num_unique_layers is None ################# if self.num_unique_layers is None: self.num_unique_layers = self.num_layers assert self.num_layers % self.num_unique_layers == 0, \ 'number of layers should be divisible by number of unique layers' self.param_sharing_style = args.param_sharing_style # Transformer layers. def build_layer(layer_number): return ParallelTransformerLayer( attention_mask_func, init_method, output_layer_init_method, layer_number) self.layers = torch.nn.ModuleList( [build_layer(i + 1) for i in range(self.num_unique_layers)]) self.topQueryLayer = ParallelTopQueryLayer( attention_mask_func, init_method, output_layer_init_method, self.num_unique_layers) # Print layer ordering. # if self.num_layers != self.num_unique_layers: # if torch.distributed.get_rank() == 0: # print('> will be using the following layer ordering:') # for i in range(self.num_layers): # print(' layer id: {:3d} --> unique layer id: ' # '{:3d}'.format(i, self._get_layer_index(i)), # flush=True) # Final layer norm before output. self.final_layernorm = LayerNorm( args.hidden_size, eps=args.layernorm_epsilon) def _get_layer_index(self, layer_number): if self.param_sharing_style == 'grouped': return layer_number % self.num_unique_layers if self.param_sharing_style == 'spaced': return layer_number // (self.num_layers // self.num_unique_layers) assert False, 'should not be here' def _get_layer(self, layer_number): return self.layers[self._get_layer_index(layer_number)] def _checkpointed_forward(self, hidden_states, attention_mask): """Forward method with activation checkpointing.""" def custom(start, end): def custom_forward(*inputs): x_ = inputs[0] for index in range(start, end): layer = self._get_layer(index) x_ = layer(x_, inputs[1]) return x_ return custom_forward # Make sure memory is freed. mpu.reset_checkpointed_activations_memory_buffer() l = 0 while l < self.num_layers: hidden_states = mpu.checkpoint( custom(l, l + self.checkpoint_num_layers), hidden_states, attention_mask) l += self.checkpoint_num_layers return hidden_states def forward(self, hidden_states, query_hidden_state, attention_mask, layer_past=None, get_key_value=False): # Checks if layer_past is not None: assert get_key_value, \ 'for not None values in layer_past, ' \ 'expected get_key_value to be set' if get_key_value: assert not self.checkpoint_activations, \ 'get_key_value does not work with ' \ 'activation checkpointing' # data format change to avoid explicit tranposes : [b s h] --> [s b h] hidden_states = hidden_states.transpose(0, 1).contiguous() query_hidden_state = query_hidden_state.transpose(0, 1).contiguous() if self.checkpoint_activations: hidden_states = self._checkpointed_forward(hidden_states, attention_mask) else: if get_key_value: presents = [] for index in range(self.num_layers): layer = self._get_layer(index) past = None if layer_past is not None: past = layer_past[index] hidden_states = layer(hidden_states, attention_mask, layer_past=past, get_key_value=get_key_value) if get_key_value: hidden_states, present = hidden_states presents.append(present) hidden_states_ = self.final_layernorm(hidden_states) ################################# #top query layer ################################# past = None if layer_past is not None: past = layer_past[self.num_layers] hidden_states = self.topQueryLayer(hidden_states_, query_hidden_state, attention_mask, layer_past=past, get_key_value=get_key_value) if get_key_value: hidden_states, present = hidden_states presents.append(present) # reverting data format change [s b h] --> [b s h] output = hidden_states.transpose(0, 1).contiguous() # Final layer norm. if get_key_value: output = [output, presents] return output pangu/megatron/model/utils.py000066400000000000000000000060041430334424000166130ustar00rootroot00000000000000# 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. """Utilities for models.""" import math import torch import sys, os sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) print(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) from megatron.model.transformer import LayerNorm def init_method_normal(sigma): """Init method based on N(0, sigma).""" def init_(tensor): return torch.nn.init.normal_(tensor, mean=0.0, std=sigma) return init_ def scaled_init_method_normal(sigma, num_layers): """Init method based on N(0, sigma/sqrt(2*num_layers).""" std = sigma / math.sqrt(2.0 * num_layers) def init_(tensor): return torch.nn.init.normal_(tensor, mean=0.0, std=std) return init_ def get_linear_layer(rows, columns, init_method): """Simple linear layer with weight initialization.""" layer = torch.nn.Linear(rows, columns) init_method(layer.weight) with torch.no_grad(): layer.bias.zero_() return layer @torch.jit.script def gelu_impl(x): """OpenAI's gelu implementation.""" return 0.5 * x * (1.0 + torch.tanh(0.7978845608028654 * x * (1.0 + 0.044715 * x * x))) def openai_gelu(x): return gelu_impl(x) #This is actually Python equivalent of torch.nn.functional.gelu(), also with type hints for ONNX exporter @torch.jit.script def erf_gelu(x): return x * 0.5 * (torch.erf(x / 1.41421).to(dtype=x.dtype)+torch.ones_like(x).to(dtype=x.dtype)) def get_params_for_weight_decay_optimization(module): """Divide params into with-weight-decay and without-weight-decay groups. Layernorms and baises will have no weight decay but the rest will. """ weight_decay_params = {'params': []} no_weight_decay_params = {'params': [], 'weight_decay': 0.0} for module_ in module.modules(): if isinstance(module_, LayerNorm): no_weight_decay_params['params'].extend( [p for p in list(module_._parameters.values()) if p is not None]) else: weight_decay_params['params'].extend( [p for n, p in list(module_._parameters.items()) if p is not None and n != 'bias']) no_weight_decay_params['params'].extend( [p for n, p in list(module_._parameters.items()) if p is not None and n == 'bias']) return weight_decay_params, no_weight_decay_params pangu/megatron/module.py000066400000000000000000000021351430334424000156410ustar00rootroot00000000000000# 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. """Megatron Module""" import torch class MegatronModule(torch.nn.Module): """Megatron specific extentions of torch Module.""" def __init__(self): super(MegatronModule, self).__init__() def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): """Use this function to override the state dict for saving checkpoints.""" return self.state_dict(destination, prefix, keep_vars) pangu/megatron/mpu/000077500000000000000000000000001430334424000146025ustar00rootroot00000000000000pangu/megatron/mpu/__init__.py000066400000000000000000000040531430334424000167150ustar00rootroot00000000000000# 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. """Model parallel utility interface.""" from .cross_entropy import vocab_parallel_cross_entropy from .data import broadcast_data from .grads import clip_grad_norm from .initialize import is_unitialized from .initialize import destroy_model_parallel from .initialize import get_data_parallel_group from .initialize import get_data_parallel_rank from .initialize import get_data_parallel_world_size from .initialize import get_model_parallel_group from .initialize import get_model_parallel_rank, set_model_parallel_rank from .initialize import get_model_parallel_src_rank from .initialize import get_model_parallel_world_size, set_model_parallel_world_size from .initialize import initialize_model_parallel from .initialize import model_parallel_is_initialized from .layers import LayerNorm from .layers import ColumnParallelLinear from .layers import RowParallelLinear from .layers import VocabParallelEmbedding from .mappings import copy_to_model_parallel_region from .mappings import gather_from_model_parallel_region from .mappings import reduce_from_model_parallel_region from .mappings import scatter_to_model_parallel_region from .random import checkpoint from .random import get_cuda_rng_tracker from .random import init_checkpointed_activations_memory_buffer from .random import model_parallel_cuda_manual_seed from .random import reset_checkpointed_activations_memory_buffer from .utils import divide from .utils import split_tensor_along_last_dim pangu/megatron/mpu/cross_entropy.py000066400000000000000000000113051430334424000200650ustar00rootroot00000000000000# 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. import torch from .initialize import get_model_parallel_group from .initialize import get_model_parallel_rank from .initialize import get_model_parallel_world_size from .utils import VocabUtility class _VocabParallelCrossEntropy(torch.autograd.Function): @staticmethod def forward(ctx, vocab_parallel_logits, target): # Maximum value along vocab dimension across all GPUs. logits_max = torch.max(vocab_parallel_logits, dim=-1)[0] torch.distributed.all_reduce(logits_max, op=torch.distributed.ReduceOp.MAX, group=get_model_parallel_group()) # Subtract the maximum value. vocab_parallel_logits.sub_(logits_max.unsqueeze(dim=-1)) # Get the partition's vocab indecies get_vocab_range = VocabUtility.vocab_range_from_per_partition_vocab_size partition_vocab_size = vocab_parallel_logits.size()[-1] rank = get_model_parallel_rank() world_size = get_model_parallel_world_size() vocab_start_index, vocab_end_index = get_vocab_range( partition_vocab_size, rank, world_size) # Create a mask of valid vocab ids (1 means it needs to be masked). target_mask = (target < vocab_start_index) | (target >= vocab_end_index) masked_target = target.clone() - vocab_start_index masked_target[target_mask] = 0 # Get predicted-logits = logits[target]. # For Simplicity, we convert logits to a 2-D tensor with size # [*, partition-vocab-size] and target to a 1-D tensor of size [*]. logits_2d = vocab_parallel_logits.view(-1, partition_vocab_size) masked_target_1d = masked_target.view(-1) arange_1d = torch.arange(start=0, end=logits_2d.size()[0], device=logits_2d.device) predicted_logits_1d = logits_2d[arange_1d, masked_target_1d] predicted_logits_1d = predicted_logits_1d.clone().contiguous() predicted_logits = predicted_logits_1d.view_as(target) predicted_logits[target_mask] = 0.0 # All reduce is needed to get the chunks from other GPUs. torch.distributed.all_reduce(predicted_logits, op=torch.distributed.ReduceOp.SUM, group=get_model_parallel_group()) # Sum of exponential of logits along vocab dimension across all GPUs. exp_logits = vocab_parallel_logits torch.exp(vocab_parallel_logits, out=exp_logits) sum_exp_logits = exp_logits.sum(dim=-1) torch.distributed.all_reduce(sum_exp_logits, op=torch.distributed.ReduceOp.SUM, group=get_model_parallel_group()) # Loss = log(sum(exp(logits))) - predicted-logit. loss = torch.log(sum_exp_logits) - predicted_logits # Store softmax, target-mask and masked-target for backward pass. exp_logits.div_(sum_exp_logits.unsqueeze(dim=-1)) ctx.save_for_backward(exp_logits, target_mask, masked_target_1d) return loss @staticmethod def backward(ctx, grad_output): # Retreive tensors from the forward path. softmax, target_mask, masked_target_1d = ctx.saved_tensors # All the inputs have softmax as thier gradient. grad_input = softmax # For simplicity, work with the 2D gradient. partition_vocab_size = softmax.size()[-1] grad_2d = grad_input.view(-1, partition_vocab_size) # Add the gradient from matching classes. arange_1d = torch.arange(start=0, end=grad_2d.size()[0], device=grad_2d.device) grad_2d[arange_1d, masked_target_1d] -= ( 1.0 - target_mask.view(-1).float()) # Finally elementwise multiplication with the output gradients. grad_input.mul_(grad_output.unsqueeze(dim=-1)) return grad_input, None def vocab_parallel_cross_entropy(vocab_parallel_logits, target): """Helper function for the cross entropy.""" return _VocabParallelCrossEntropy.apply(vocab_parallel_logits, target) pangu/megatron/mpu/data.py000066400000000000000000000076621430334424000161000ustar00rootroot00000000000000# 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. import torch from .initialize import get_model_parallel_group from .initialize import get_model_parallel_rank from .initialize import get_model_parallel_src_rank _MAX_DATA_DIM = 4 def _check_data_types(keys, data, target_dtype): """Check that all the keys have the same target data type.""" for key in keys: assert data[key].dtype == target_dtype, '{} has data type {} which '\ 'is different than {}'.format(key, data[key].dtype, target_dtype) def _build_key_size_numel_dictionaries(keys, data): """Build the size on rank 0 and broadcast.""" max_dim = _MAX_DATA_DIM sizes = [0 for _ in range(max_dim) for _ in keys] # Pack the sizes on rank zero. if get_model_parallel_rank() == 0: offset = 0 for key in keys: assert data[key].dim() < max_dim, 'you should increase MAX_DATA_DIM' size = data[key].size() for i, s in enumerate(size): sizes[i + offset] = s offset += max_dim # Move to GPU and broadcast. sizes_cuda = torch.cuda.LongTensor(sizes) torch.distributed.broadcast(sizes_cuda, get_model_parallel_src_rank(), group=get_model_parallel_group()) # Move back to cpu and unpack. sizes_cpu = sizes_cuda.cpu() key_size = {} key_numel = {} total_numel = 0 offset = 0 for key in keys: i = 0 size = [] numel = 1 while sizes_cpu[offset + i] > 0: this_size = sizes_cpu[offset + i] size.append(this_size) numel *= this_size i += 1 key_size[key] = size key_numel[key] = numel total_numel += numel offset += max_dim return key_size, key_numel, total_numel def broadcast_data(keys, data, datatype): """Broadcast data from rank zero of each model parallel group to the members of the same model parallel group. Arguments: keys: list of keys in the data disctionary to be broadcasted data: data dictionary of string keys and cpu tensor values. datatype: torch data type of all tensors in data associated with keys. """ # Build (key, size) and (key, number of elements) dictionaries along # with the total number of elements on all ranks. key_size, key_numel, total_numel = _build_key_size_numel_dictionaries(keys, data) # Pack on rank zero. if get_model_parallel_rank() == 0: # Check that all keys have the same data type. _check_data_types(keys, data, datatype) # Flatten the data associated with the keys flatten_data = torch.cat( [data[key].contiguous().view(-1) for key in keys], dim=0).cuda() else: flatten_data = torch.empty(total_numel, device=torch.cuda.current_device(), dtype=datatype) # Boradcast torch.distributed.broadcast(flatten_data, get_model_parallel_src_rank(), group=get_model_parallel_group()) # Unpack output = {} offset = 0 for key in keys: size = key_size[key] numel = key_numel[key] output[key] = flatten_data.narrow(0, offset, numel).view(size) offset += numel return output pangu/megatron/mpu/grads.py000066400000000000000000000114421430334424000162560ustar00rootroot00000000000000# 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. # Parts of the code here are adapted from PyTorch # repo: https://github.com/pytorch/pytorch import torch from torch._six import inf try: from apex.multi_tensor_apply import multi_tensor_applier import amp_C except Exception as e: print('WARNING: APEX is not installed, multi_tensor_applier will not be available.') from .initialize import get_model_parallel_group from .initialize import get_model_parallel_rank def l2_grad_clipper(parameters, max_norm): """Efficient L2 norm gradient clipping.""" overflow_buf = torch.zeros(1, dtype=torch.int, device='cuda') # Make sure we have an iterable. if isinstance(parameters, torch.Tensor): parameters = [parameters] # Filter parameters with gradients. parameters_with_grads = list(filter( lambda p: p.grad is not None, parameters)) # Filter parameters for norm calculations. mp_rank_is_zero = (get_model_parallel_rank() == 0) parameters_for_norm = list(filter( lambda p: p.model_parallel or mp_rank_is_zero, parameters_with_grads)) # Calculate L2 norm. norm, _ = multi_tensor_applier( amp_C.multi_tensor_l2norm, overflow_buf, [parameters_for_norm], False # no per-parameter norm ) # Sum across all model parallel GPUs. norm_2 = norm * norm torch.distributed.all_reduce(norm_2, op=torch.distributed.ReduceOp.SUM, group=get_model_parallel_group()) total_norm = norm_2.item() ** 0.5 # Scale to get max_norm. clip_coef = float(max_norm) / (total_norm + 1.0e-6) grads = [p.grad for p in parameters_with_grads] if clip_coef < 1.0: multi_tensor_applier( amp_C.multi_tensor_scale, overflow_buf, [grads, grads], clip_coef) return total_norm def clip_grad_norm(parameters, max_norm, norm_type=2): """Clips gradient norm of an iterable of parameters. This is adapted from torch.nn.utils.clip_grad.clip_grad_norm_ and added functionality to handle model parallel parameters. Note that the gradients are modified in place. Arguments: parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a single Tensor that will have gradients normalized max_norm (float or int): max norm of the gradients norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for infinity norm. Returns: Total norm of the parameters (viewed as a single vector). """ if isinstance(parameters, torch.Tensor): parameters = [parameters] parameters = list(filter(lambda p: p.grad is not None, parameters)) max_norm = float(max_norm) norm_type = float(norm_type) if norm_type == inf: total_norm = max(p.grad.data.abs().max() for p in parameters) total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)]) # Take max across all GPUs. torch.distributed.all_reduce(total_norm_cuda, op=torch.distributed.ReduceOp.MAX, group=get_model_parallel_group()) total_norm = total_norm_cuda[0].item() clip_coef = max_norm / (total_norm + 1e-6) if clip_coef < 1: for p in parameters: p.grad.data.mul_(clip_coef) #elif norm_type == 2: # total_norm = l2_grad_clipper(parameters, max_norm) else: total_norm = 0 for p in parameters: if p.model_parallel or (get_model_parallel_rank() == 0): param_norm = p.grad.data.norm(norm_type) total_norm += param_norm.item() ** norm_type # Sum across all model parallel GPUs. total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)]) torch.distributed.all_reduce(total_norm_cuda, op=torch.distributed.ReduceOp.SUM, group=get_model_parallel_group()) total_norm = total_norm_cuda[0].item() ** (1. / norm_type) clip_coef = max_norm / (total_norm + 1e-6) if clip_coef < 1: for p in parameters: p.grad.data.mul_(clip_coef) return total_norm pangu/megatron/mpu/initialize.py000066400000000000000000000127031430334424000173200ustar00rootroot00000000000000# 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. """Model and data parallel groups.""" import torch from .utils import ensure_divisibility # Model parallel group that the current rank belongs to. _MODEL_PARALLEL_GROUP = None # Data parallel group that the current rank belongs to. _DATA_PARALLEL_GROUP = None # These values enable us to change the mpu sizes on the fly. _MPU_WORLD_SIZE = None _MPU_RANK = None def is_unitialized(): """Useful for code segments that may be accessed with or without mpu initialization""" return _DATA_PARALLEL_GROUP is None def initialize_model_parallel(model_parallel_size_): """ Initialize model data parallel groups. Arguments: model_parallel_size: number of GPUs used to parallelize model. Let's say we have a total of 8 GPUs denoted by g0 ... g7 and we use 2 GPUs to parallelize the model. The present function will create 4 model parallel groups and 2 data parallel grous as: 4 model parallel groups: [g0, g1], [g2, g3], [g4, g5], [g6, g7] 2 data parallel groups: [g0, g2, g4, g6], [g1, g3, g5, g7] Note that for efficiency, the caller should make sure adjacent ranks are on the same DGX box. For example if we are using 2 DGX-1 boxes with a total of 16 GPUs, rank 0 to 7 belong to the first box and ranks 8 to 15 belong to the second box. """ if torch.distributed.get_rank() == 0: print('> initializing model parallel with size {}'.format( model_parallel_size_)) # Get world size and rank. Ensure some consistencies. assert torch.distributed.is_initialized() world_size = torch.distributed.get_world_size() model_parallel_size = min(model_parallel_size_, world_size) ensure_divisibility(world_size, model_parallel_size) rank = torch.distributed.get_rank() # Build the data parallel groups. global _DATA_PARALLEL_GROUP assert _DATA_PARALLEL_GROUP is None, \ 'data parallel group is already initialized' for i in range(model_parallel_size): ranks = range(i, world_size, model_parallel_size) group = torch.distributed.new_group(ranks) if i == (rank % model_parallel_size): _DATA_PARALLEL_GROUP = group # Build the model parallel groups. global _MODEL_PARALLEL_GROUP assert _MODEL_PARALLEL_GROUP is None, \ 'model parallel group is already initialized' for i in range(world_size // model_parallel_size): ranks = range(i * model_parallel_size, (i + 1) * model_parallel_size) group = torch.distributed.new_group(ranks) if i == (rank // model_parallel_size): _MODEL_PARALLEL_GROUP = group def model_parallel_is_initialized(): """Check if model and data parallel groups are initialized.""" if _MODEL_PARALLEL_GROUP is None or _DATA_PARALLEL_GROUP is None: return False return True def get_model_parallel_group(): """Get the model parallel group the caller rank belongs to.""" assert _MODEL_PARALLEL_GROUP is not None, \ 'model parallel group is not initialized' return _MODEL_PARALLEL_GROUP def get_data_parallel_group(): """Get the data parallel group the caller rank belongs to.""" assert _DATA_PARALLEL_GROUP is not None, \ 'data parallel group is not initialized' return _DATA_PARALLEL_GROUP def set_model_parallel_world_size(world_size): """Set the model parallel size""" global _MPU_WORLD_SIZE _MPU_WORLD_SIZE = world_size def get_model_parallel_world_size(): """Return world size for the model parallel group.""" global _MPU_WORLD_SIZE if _MPU_WORLD_SIZE is not None: return _MPU_WORLD_SIZE return torch.distributed.get_world_size(group=get_model_parallel_group()) def set_model_parallel_rank(rank): """Set model parallel rank.""" global _MPU_RANK _MPU_RANK = rank def get_model_parallel_rank(): """Return my rank for the model parallel group.""" global _MPU_RANK if _MPU_RANK is not None: return _MPU_RANK return torch.distributed.get_rank(group=get_model_parallel_group()) def get_model_parallel_src_rank(): """Calculate the global rank corresponding to a local rank zeor in the model parallel group.""" global_rank = torch.distributed.get_rank() local_world_size = get_model_parallel_world_size() return (global_rank // local_world_size) * local_world_size def get_data_parallel_world_size(): """Return world size for the data parallel group.""" return torch.distributed.get_world_size(group=get_data_parallel_group()) def get_data_parallel_rank(): """Return my rank for the data parallel group.""" return torch.distributed.get_rank(group=get_data_parallel_group()) def destroy_model_parallel(): """Set the groups to none.""" global _MODEL_PARALLEL_GROUP _MODEL_PARALLEL_GROUP = None global _DATA_PARALLEL_GROUP _DATA_PARALLEL_GROUP = None pangu/megatron/mpu/layers.py000066400000000000000000000372361430334424000164660ustar00rootroot00000000000000# 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. # Parts of the code here are adapted from PyTorch # repo: https://github.com/pytorch/pytorch import math import torch import torch.nn.functional as F import torch.nn.init as init from torch.nn.parameter import Parameter try: from apex.normalization.fused_layer_norm import FusedLayerNorm as LayerNorm # Try to use FusedLayerNorm from Apex - this will trigger an error. _ = LayerNorm(8, eps=1e-5) except Exception as e: print('WARNING: APEX is not installed, using torch.nn.LayerNorm ' 'instead of apex.normalization.FusedLayerNorm!') from torch.nn import LayerNorm from .initialize import get_model_parallel_rank from .initialize import get_model_parallel_world_size from .mappings import copy_to_model_parallel_region from .mappings import gather_from_model_parallel_region from .mappings import reduce_from_model_parallel_region from .mappings import scatter_to_model_parallel_region from .random import get_cuda_rng_tracker from .utils import divide from .utils import split_tensor_along_last_dim from .utils import VocabUtility from megatron import get_args # class LayerNorm(torch.nn.Module): # def __init__(self, normalized_shape, eps=1e-5): # super(LayerNorm, self).__init__() # # Initialize master weight # self.weight = Parameter(torch.Tensor(normalized_shape)) # self.bias = Parameter(torch.Tensor(normalized_shape)) # torch.nn.init.ones_(self.weight) # torch.nn.init.ones_(self.bias) # self.eps = eps # # def forward(self, x): # mean = torch.mean(x, -1, keepdim=True) # diff = torch.sub(x, mean) # variance = torch.mean(torch.square(diff), -1,keepdim=True) # variance_eps = torch.sqrt(torch.add(variance, self.eps)) # output_ = torch.div(diff, variance_eps) # output = torch.add(torch.mul(output_, self.weight), self.bias) # return output def _initialize_affine_weight_gpu(weight, init_method, partition_dim, stride=1): """Initialize affine weight for model parallel on GPU.""" weight.model_parallel = True weight.partition_dim = partition_dim weight.partition_stride = stride weight.stride = stride with get_cuda_rng_tracker().fork(): init_method(weight) def _initialize_affine_weight_cpu(weight, output_size, input_size, per_partition_size, partition_dim, init_method, stride=1, return_master_weight=False): """Initialize affine weight for model parallel. Build the master weight on all processes and scatter the relevant chunk.""" weight.model_parallel = True weight.partition_dim = partition_dim weight.partition_stride = stride weight.stride = stride # Initialize master weight master_weight = torch.empty(output_size, input_size, dtype=torch.float, requires_grad=False) init_method(master_weight) args = get_args() master_weight = master_weight.to(dtype=args.params_dtype) # Split and copy per_partition_per_stride_size = divide(per_partition_size, stride) weight_list = torch.split(master_weight, per_partition_per_stride_size, dim=partition_dim) rank = get_model_parallel_rank() world_size = get_model_parallel_world_size() my_weight_list = weight_list[rank::world_size] with torch.no_grad(): torch.cat(my_weight_list, dim=partition_dim, out=weight) if return_master_weight: return master_weight return None class VocabParallelEmbedding(torch.nn.Module): """Embedding parallelized in the vocabulary dimension. This is mainly adapted from torch.nn.Embedding and all the default values are kept. Arguments: num_embeddings: vocabulary size. embedding_dim: size of hidden state. init_method: method to initialize weights. """ def __init__(self, num_embeddings, embedding_dim, init_method=init.xavier_normal_): super(VocabParallelEmbedding, self).__init__() # Keep the input dimensions. self.num_embeddings = num_embeddings self.embedding_dim = embedding_dim # Set the detauls for compatibility. self.padding_idx = None self.max_norm = None self.norm_type = 2. self.scale_grad_by_freq = False self.sparse = False self._weight = None self.model_parallel_size = get_model_parallel_world_size() # Divide the weight matrix along the vocaburaly dimension. self.vocab_start_index, self.vocab_end_index = \ VocabUtility.vocab_range_from_global_vocab_size( self.num_embeddings, get_model_parallel_rank(), self.model_parallel_size) self.num_embeddings_per_partition = self.vocab_end_index - \ self.vocab_start_index # Allocate weights and initialize. args = get_args() if args.use_cpu_initialization: self.weight = Parameter(torch.empty( self.num_embeddings_per_partition, self.embedding_dim, dtype=args.params_dtype)) _initialize_affine_weight_cpu( self.weight, self.num_embeddings, self.embedding_dim, self.num_embeddings_per_partition, 0, init_method) else: self.weight = Parameter(torch.empty( self.num_embeddings_per_partition, self.embedding_dim, device=torch.cuda.current_device(), dtype=args.params_dtype)) _initialize_affine_weight_gpu(self.weight, init_method, partition_dim=0, stride=1) def forward(self, input_): if self.model_parallel_size > 1: # Build the mask. input_mask = (input_ < self.vocab_start_index) | \ (input_ >= self.vocab_end_index) # Mask the input. masked_input = input_.clone() - self.vocab_start_index masked_input[input_mask] = 0 else: masked_input = input_ # Get the embeddings. output_parallel = F.embedding(masked_input, self.weight, self.padding_idx, self.max_norm, self.norm_type, self.scale_grad_by_freq, self.sparse) # Mask the output embedding. if self.model_parallel_size > 1: output_parallel[input_mask, :] = 0.0 # Reduce across all the model parallel GPUs. output = reduce_from_model_parallel_region(output_parallel) return output class ColumnParallelLinear(torch.nn.Module): """Linear layer with column parallelism. The linear layer is defined as Y = XA + b. A is parallelized along its second dimension as A = [A_1, ..., A_p]. Arguments: input_size: first dimension of matrix A. output_size: second dimension of matrix A. bias: If true, add bias gather_output: If true, call all-gether on output and make Y avaiable to all GPUs, otherwise, every GPU will have its output which is Y_i = XA_i init_method: method to initialize weights. Note that bias is always set to zero. stride: For the strided linear layers. keep_master_weight_for_test: This was added for testing and should be set to False. It returns the master weights used for initialization. skip_bias_add: This was added to enable performance optimations where bias can be fused with other elementwise operations. we skip adding bias but instead return it. """ def __init__(self, input_size, output_size, bias=True, gather_output=True, init_method=init.xavier_normal_, stride=1, keep_master_weight_for_test=False, skip_bias_add=False): super(ColumnParallelLinear, self).__init__() # Keep input parameters self.input_size = input_size self.output_size = output_size self.gather_output = gather_output # Divide the weight matrix along the last dimension. world_size = get_model_parallel_world_size() self.output_size_per_partition = divide(output_size, world_size) self.skip_bias_add = skip_bias_add # Parameters. # Note: torch.nn.functional.linear performs XA^T + b and as a result # we allocate the transpose. # Initialize weight. args = get_args() if args.use_cpu_initialization: self.weight = Parameter(torch.empty(self.output_size_per_partition, self.input_size, dtype=args.params_dtype)) self.master_weight = _initialize_affine_weight_cpu( self.weight, self.output_size, self.input_size, self.output_size_per_partition, 0, init_method, stride=stride, return_master_weight=keep_master_weight_for_test) else: self.weight = Parameter(torch.empty( self.output_size_per_partition, self.input_size, device=torch.cuda.current_device(), dtype=args.params_dtype)) _initialize_affine_weight_gpu(self.weight, init_method, partition_dim=0, stride=stride) if bias: if args.use_cpu_initialization: self.bias = Parameter(torch.empty( self.output_size_per_partition, dtype=args.params_dtype)) else: self.bias = Parameter(torch.empty( self.output_size_per_partition, device=torch.cuda.current_device(), dtype=args.params_dtype)) self.bias.model_parallel = True self.bias.partition_dim = 0 self.bias.stride = stride # Always initialize bias to zero. with torch.no_grad(): self.bias.zero_() else: self.register_parameter('bias', None) def forward(self, input_): # Set up backprop all-reduce. input_parallel = copy_to_model_parallel_region(input_) # Matrix multiply. bias = self.bias if not self.skip_bias_add else None output_parallel = F.linear(input_parallel, self.weight, bias) if self.gather_output: # All-gather across the partitions. output = gather_from_model_parallel_region(output_parallel) else: output = output_parallel output_bias = self.bias if self.skip_bias_add else None return output, output_bias class RowParallelLinear(torch.nn.Module): """Linear layer with row parallelism. The linear layer is defined as Y = XA + b. A is parallelized along its first dimension and X along its second dimension as: - - | A_1 | | . | A = | . | X = [X_1, ..., X_p] | . | | A_p | - - Arguments: input_size: first dimension of matrix A. output_size: second dimension of matrix A. bias: If true, add bias. Note that bias is not parallelized. input_is_parallel: If true, we assume that the input is already split across the GPUs and we do not split again. init_method: method to initialize weights. Note that bias is always set to zero. stride: For the strided linear layers. keep_master_weight_for_test: This was added for testing and should be set to False. It returns the master weights used for initialization. skip_bias_add: This was added to enable performance optimations where bias can be fused with other elementwise operations. we skip adding bias but instead return it. """ def __init__(self, input_size, output_size, bias=True, input_is_parallel=False, init_method=init.xavier_normal_, stride=1, keep_master_weight_for_test=False, skip_bias_add=False): super(RowParallelLinear, self).__init__() # Keep input parameters self.input_size = input_size self.output_size = output_size self.input_is_parallel = input_is_parallel # Divide the weight matrix along the last dimension. world_size = get_model_parallel_world_size() self.input_size_per_partition = divide(input_size, world_size) self.skip_bias_add = skip_bias_add # Parameters. # Note: torch.nn.functional.linear performs XA^T + b and as a result # we allocate the transpose. # Initialize weight. args = get_args() if args.use_cpu_initialization: self.weight = Parameter(torch.empty(self.output_size, self.input_size_per_partition, dtype=args.params_dtype)) self.master_weight = _initialize_affine_weight_cpu( self.weight, self.output_size, self.input_size, self.input_size_per_partition, 1, init_method, stride=stride, return_master_weight=keep_master_weight_for_test) else: self.weight = Parameter(torch.empty( self.output_size, self.input_size_per_partition, device=torch.cuda.current_device(), dtype=args.params_dtype)) _initialize_affine_weight_gpu(self.weight, init_method, partition_dim=1, stride=stride) if bias: if args.use_cpu_initialization: self.bias = Parameter(torch.empty(self.output_size, dtype=args.params_dtype)) else: self.bias = Parameter(torch.empty( self.output_size, device=torch.cuda.current_device(), dtype=args.params_dtype)) # Always initialize bias to zero. with torch.no_grad(): self.bias.zero_() else: self.register_parameter('bias', None) def forward(self, input_): # Set up backprop all-reduce. if self.input_is_parallel: input_parallel = input_ else: input_parallel = scatter_to_model_parallel_region(input_) # Matrix multiply. output_parallel = F.linear(input_parallel, self.weight) # All-reduce across all the partitions. output_ = reduce_from_model_parallel_region(output_parallel) if not self.skip_bias_add: output = output_ + self.bias if self.bias is not None else output_ output_bias = None else: output = output_ output_bias = self.bias return output, output_bias pangu/megatron/mpu/mappings.py000066400000000000000000000103241430334424000167720ustar00rootroot00000000000000# 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. import torch from .initialize import get_model_parallel_group, get_model_parallel_world_size, get_model_parallel_rank from .utils import split_tensor_along_last_dim def _reduce(input_): """All-reduce the the input tensor across model parallel group.""" # Bypass the function if we are using only 1 GPU. if get_model_parallel_world_size()==1: return input_ # All-reduce. torch.distributed.all_reduce(input_, group=get_model_parallel_group()) return input_ def _split(input_): """Split the tensor along its last dimension and keep the corresponding slice.""" world_size = get_model_parallel_world_size() # Bypass the function if we are using only 1 GPU. if world_size==1: return input_ # Split along last dimension. input_list = split_tensor_along_last_dim(input_, world_size) # Note: torch.split does not create contiguous tensors by default. rank = get_model_parallel_rank() output = input_list[rank].contiguous() return output def _gather(input_): """Gather tensors and concatinate along the last dimension.""" world_size = get_model_parallel_world_size() # Bypass the function if we are using only 1 GPU. if world_size==1: return input_ # Size and dimension. last_dim = input_.dim() - 1 rank = get_model_parallel_rank() tensor_list = [torch.empty_like(input_) for _ in range(world_size)] tensor_list[rank] = input_ torch.distributed.all_gather(tensor_list, input_, group=get_model_parallel_group()) # Note: torch.cat already creates a contiguous tensor. output = torch.cat(tensor_list, dim=last_dim).contiguous() return output class _CopyToModelParallelRegion(torch.autograd.Function): """Pass the input to the model parallel region.""" @staticmethod def symbolic(graph, input_): return input_ @staticmethod def forward(ctx, input_): return input_ @staticmethod def backward(ctx, grad_output): return _reduce(grad_output) class _ReduceFromModelParallelRegion(torch.autograd.Function): """All-redcue the input from the model parallel region.""" @staticmethod def symbolic(graph, input_): return _reduce(input_) @staticmethod def forward(ctx, input_): return _reduce(input_) @staticmethod def backward(ctx, grad_output): return grad_output class _ScatterToModelParallelRegion(torch.autograd.Function): """Split the input and keep only the corresponding chuck to the rank.""" @staticmethod def symbolic(graph, input_): return _split(input_) @staticmethod def forward(ctx, input_): return _split(input_) @staticmethod def backward(ctx, grad_output): return _gather(grad_output) class _GatherFromModelParallelRegion(torch.autograd.Function): """Gather the input from model parallel region and concatinate.""" @staticmethod def symbolic(graph, input_): return _gather(input_) @staticmethod def forward(ctx, input_): return _gather(input_) @staticmethod def backward(ctx, grad_output): return _split(grad_output) # ----------------- # Helper functions. # ----------------- def copy_to_model_parallel_region(input_): return _CopyToModelParallelRegion.apply(input_) def reduce_from_model_parallel_region(input_): return _ReduceFromModelParallelRegion.apply(input_) def scatter_to_model_parallel_region(input_): return _ScatterToModelParallelRegion.apply(input_) def gather_from_model_parallel_region(input_): return _GatherFromModelParallelRegion.apply(input_) pangu/megatron/mpu/random.py000066400000000000000000000311241430334424000164350ustar00rootroot00000000000000# 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. # Parts of the code here are adapted from PyTorch # repo: https://github.com/pytorch/pytorch import contextlib import torch from torch import _C from torch.cuda import _lazy_call, device as device_ctx_manager from torch.utils.checkpoint import detach_variable from megatron import get_args from megatron.memory import allocate_mem_buff from .initialize import get_data_parallel_rank from .initialize import get_model_parallel_group from .initialize import get_model_parallel_rank from .initialize import get_model_parallel_world_size # Default name for the model parallel rng tracker. _MODEL_PARALLEL_RNG_TRACKER_NAME = 'model-parallel-rng' # Whether apply model parallelsim to checkpointed hidden states. _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER = None def init_checkpointed_activations_memory_buffer(): """Initializ the memory buffer for the checkpointed activations.""" args = get_args() per_layer = args.batch_size * args.max_position_embeddings * \ args.hidden_size // args.model_parallel_size assert args.num_layers % args.checkpoint_num_layers == 0, \ 'number of layers is not divisible by checkpoint-num-layers' num_checkpointer_layers = args.num_layers // args.checkpoint_num_layers numel = per_layer * num_checkpointer_layers dtype = torch.half if not args.fp16: dtype = torch.float global _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER assert _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER is None, \ 'checkpointed activations memory buffer is already allocated.' _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER = allocate_mem_buff( 'checkpointed activations', numel, dtype, track_usage=False) def reset_checkpointed_activations_memory_buffer(): """Reset the memory used for checkpointing.""" if _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER is not None: _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER.reset() def _set_cuda_rng_state(new_state, device=-1): """Sets the random number generator state of the current GPU. Argumentss: new_state (torch.ByteTensor): The desired state This function is adapted from PyTorch repo (torch.cuda.set_rng_state) with a single change: the input state is not cloned. Cloning caused major performance issues for +4 GPU cases. """ if hasattr(_C, '_cuda_setRNGState') and callable(_C._cuda_setRNGState): # older PyTorch def cb(): with device_ctx_manager(device): _C._cuda_setRNGState(new_state) else: # newer PyTorch if device == -1: device = torch.device('cuda') elif isinstance(device, str): device = torch.device(device) elif isinstance(device, int): device = torch.device('cuda', device) def cb(): idx = device.index if idx is None: idx = torch.cuda.current_device() default_generator = torch.cuda.default_generators[idx] default_generator.set_state(new_state) _lazy_call(cb) def split_tensor_into_1d_equal_chunks(tensor): """Break a tensor into equal 1D chunks.""" data = tensor.view(-1) partition_size = torch.numel(data) // get_model_parallel_world_size() start_index = partition_size * get_model_parallel_rank() end_index = start_index + partition_size return data[start_index:end_index] def gather_split_1d_tensor(tensor): """Opposite of above function, gather values from model parallel ranks.""" world_size = get_model_parallel_world_size() numel = torch.numel(tensor) numel_gathered = world_size * numel gathered = torch.empty(numel_gathered, dtype=tensor.dtype, device=torch.cuda.current_device(), requires_grad=False) chunks = [gathered[i*numel:(i+1)*numel] for i in range(world_size)] torch.distributed.all_gather(chunks, tensor, group=get_model_parallel_group()) return gathered class CudaRNGStatesTracker: """Tracker for the cuda RNG states. Using the `add` method, a cuda rng state is initialized based on the input `seed` and is assigned to `name`. Later, by forking the rng state, we can perform operations and return to our starting cuda state. """ def __init__(self): # Map from a string name to the cuda rng state. self.states_ = {} # Seeds are just for book keeping and ensure no seed is set twice. self.seeds_ = set() def reset(self): """Set to the initial state (no tracker).""" self.states_ = {} self.seeds_ = set() def get_states(self): """Get rng states. Copy the dictionary so we have direct pointers to the states, not just a pointer to the dictionary.""" states = {} for name in self.states_: states[name] = self.states_[name] return states def set_states(self, states): """Set the rng states. For efficiency purposes, we do not check the size of seed for compatibility.""" self.states_ = states def add(self, name, seed): """Track the rng state.""" # Check seed is not already used. if seed in self.seeds_: raise Exception('seed {} already exists'.format(seed)) self.seeds_.add(seed) # Check that state is not already defined. if name in self.states_: raise Exception('cuda rng state {} already exists'.format(name)) # Get the current rng state. orig_rng_state = torch.cuda.get_rng_state() # Set the new state and store it. torch.cuda.manual_seed(seed) self.states_[name] = torch.cuda.get_rng_state() # Reset rng state to what it was. _set_cuda_rng_state(orig_rng_state) @contextlib.contextmanager def fork(self, name=_MODEL_PARALLEL_RNG_TRACKER_NAME): """Fork the cuda rng state, perform operations, and exit with the original state.""" try: # Check if we have added the state if name not in self.states_: raise Exception('cuda rng state {} is not added'.format(name)) # Store current rng state. orig_cuda_rng_state = torch.cuda.get_rng_state() # Set rng state to the desired one _set_cuda_rng_state(self.states_[name]) except: print("\n"+"@"*30) print('cuda rng state {} is not added'.format(name)) print("@"*30+"\n") # Do the stuff we wanted to do. try: yield finally: pass try: # Update the current rng state for later use. self.states_[name] = torch.cuda.get_rng_state() # And set the state to the original state we started with. _set_cuda_rng_state(orig_cuda_rng_state) except: print("\n"+"@"*30) print("UnboundLocalError: local variable 'orig_cuda_rng_state' referenced before assignment") print("@"*30+"\n") # RNG tracker object. _CUDA_RNG_STATE_TRACKER = CudaRNGStatesTracker() def get_cuda_rng_tracker(): """Get cuda rng tracker.""" return _CUDA_RNG_STATE_TRACKER def model_parallel_cuda_manual_seed(seed): """Initialize model parallel cuda seed. This function should be called after the model parallel is initialized. Also, no torch.cuda.manual_seed should be called after this function. Basically, this is replacement for that function. Two set of RNG states are tracked: default state: This is for data parallelism and is the same among a set of model parallel GPUs but different across different model paralle groups. This is used for example for dropout in the non-model-parallel regions. model-parallel state: This state is different among a set of model parallel GPUs, but the same across data parallel groups. This is used for example for dropout in model parallel regions. """ # 2718 is just for fun and any POSITIVE value will work. offset = seed + 2718 model_parallel_seed = offset + get_model_parallel_rank() # Data parallel gets the original sedd. data_parallel_seed = seed if torch.distributed.get_rank() == 0: print('> initializing model parallel cuda seeds on global rank {}, ' 'model parallel rank {}, and data parallel rank {} with ' 'model parallel seed: {} and data parallel seed: {}'.format( torch.distributed.get_rank(), get_model_parallel_rank(), get_data_parallel_rank(), model_parallel_seed, data_parallel_seed), flush=True) _CUDA_RNG_STATE_TRACKER.reset() # Set the default state. torch.cuda.manual_seed(data_parallel_seed) # and model parallel state. _CUDA_RNG_STATE_TRACKER.add(_MODEL_PARALLEL_RNG_TRACKER_NAME, model_parallel_seed) class CheckpointFunction(torch.autograd.Function): """This function is adapted from torch.utils.checkpoint with two main changes: 1) torch.cuda.set_rng_state is replaced with `_set_cuda_rng_state` 2) the states in the model parallel tracker are also properly tracked/set/reset. """ @staticmethod def forward(ctx, run_function, *args): ctx.run_function = run_function # Copy the rng states. ctx.fwd_cpu_rng_state = torch.get_rng_state() ctx.fwd_cuda_rng_state = torch.cuda.get_rng_state() ctx.fwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states() with torch.no_grad(): outputs = run_function(*args) # Divide hidden states across model parallel group and only keep # the chunk corresponding to the current rank. if _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER is not None: ctx.input_0_shape = args[0].data.shape args[0].data = split_tensor_into_1d_equal_chunks(args[0].data) args[0].data = _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER.add( args[0].data) # Store everything. ctx.save_for_backward(*args) return outputs @staticmethod def backward(ctx, *args): if not torch.autograd._is_checkpoint_valid(): raise RuntimeError("Checkpointing is not compatible with .grad(), " "please use .backward() if possible") inputs = ctx.saved_tensors if _CHECKPOINTED_ACTIVATIONS_MEMORY_BUFFER is not None: inputs[0].data = gather_split_1d_tensor(inputs[0].data) inputs[0].data = inputs[0].data.view(ctx.input_0_shape) # Store the current states. bwd_cpu_rng_state = torch.get_rng_state() bwd_cuda_rng_state = torch.cuda.get_rng_state() bwd_cuda_rng_state_tracker = get_cuda_rng_tracker().get_states() # Set the states to what it used to be before the forward pass. torch.set_rng_state(ctx.fwd_cpu_rng_state) _set_cuda_rng_state(ctx.fwd_cuda_rng_state) get_cuda_rng_tracker().set_states(ctx.fwd_cuda_rng_state_tracker) # Compute the forward pass. detached_inputs = detach_variable(inputs) with torch.enable_grad(): outputs = ctx.run_function(*detached_inputs) # Set the states back to what it was at the start of this function. torch.set_rng_state(bwd_cpu_rng_state) _set_cuda_rng_state(bwd_cuda_rng_state) get_cuda_rng_tracker().set_states(bwd_cuda_rng_state_tracker) if isinstance(outputs, torch.Tensor): outputs = (outputs,) torch.autograd.backward(outputs, args) grads = tuple(inp.grad if isinstance(inp, torch.Tensor) else inp for inp in detached_inputs) return (None,) + grads def checkpoint(function, *args): """Checkpoint a model or part of the model. This has been directly copied from torch.utils.checkpoint.""" return CheckpointFunction.apply(function, *args) pangu/megatron/mpu/tests/000077500000000000000000000000001430334424000157445ustar00rootroot00000000000000pangu/megatron/mpu/tests/__init__.py000066400000000000000000000000001430334424000200430ustar00rootroot00000000000000pangu/megatron/mpu/tests/commons.py000066400000000000000000000050741430334424000177770ustar00rootroot00000000000000# 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. import argparse import os import random import numpy import torch import mpu class IdentityLayer(torch.nn.Module): def __init__(self, size, scale=1.0): super(IdentityLayer, self).__init__() self.weight = torch.nn.Parameter(scale * torch.randn(size)) def forward(self): return self.weight def set_random_seed(seed): """Set random seed for reproducability.""" random.seed(seed) numpy.random.seed(seed) torch.manual_seed(seed) mpu.model_parallel_cuda_manual_seed(seed) def initialize_distributed(backend='nccl'): """Initialize torch.distributed.""" # Get local rank in case it is provided. parser = argparse.ArgumentParser() parser.add_argument('--local_rank', type=int, default=None, help='local rank passed from distributed launcher') args = parser.parse_args() local_rank = args.local_rank # Get rank and world size. rank = int(os.getenv('RANK', '0')) world_size = int(os.getenv("WORLD_SIZE", '1')) print('> initializing torch.distributed with local rank: {}, ' 'rank: {}, world size: {}'.format(local_rank, rank, world_size)) # Set the device id. device = rank % torch.cuda.device_count() if local_rank is not None: device = local_rank torch.cuda.set_device(device) # Call the init process. init_method = 'tcp://' master_ip = os.getenv('MASTER_ADDR', 'localhost') master_port = os.getenv('MASTER_PORT', '6000') init_method += master_ip + ':' + master_port torch.distributed.init_process_group( backend=backend, world_size=world_size, rank=rank, init_method=init_method) def print_separator(message): torch.distributed.barrier() filler_len = (78 - len(message)) // 2 filler = '-' * filler_len string = '\n' + filler + ' {} '.format(message) + filler if torch.distributed.get_rank() == 0: print(string, flush=True) torch.distributed.barrier() pangu/megatron/mpu/tests/test_cross_entropy.py000066400000000000000000000074341430334424000222760ustar00rootroot00000000000000# 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. from commons import set_random_seed from commons import IdentityLayer from commons import print_separator from commons import initialize_distributed from mpu.cross_entropy import vocab_parallel_cross_entropy import mpu import torch.nn.functional as F import torch import random import sys sys.path.append("../..") def torch_cross_entropy(batch_size, seq_length, vocab_size, logits_scale, seed): set_random_seed(seed) identity = IdentityLayer((batch_size, seq_length, vocab_size), scale=logits_scale).cuda() logits = identity() target = torch.cuda.LongTensor( size=(batch_size, seq_length)).random_(0, vocab_size) loss = F.cross_entropy(logits.view(-1, logits.size()[-1]), target.view(-1), reduction='none').view_as(target).mean() loss.backward() return loss, identity.weight.grad def mpu_cross_entropy(batch_size, seq_length, vocab_size, logits_scale, seed): set_random_seed(seed) identity = IdentityLayer((batch_size, seq_length, vocab_size), scale=logits_scale).cuda() logits = identity() logits_parallel = mpu.scatter_to_model_parallel_region(logits) target = torch.cuda.LongTensor( size=(batch_size, seq_length)).random_(0, vocab_size) loss = vocab_parallel_cross_entropy(logits_parallel, target).mean() loss.backward() return loss, identity.weight.grad def test_cross_entropy(model_parallel_size): if torch.distributed.get_rank() == 0: print('> testing cross entropy with model parallel size {} ...'. format(model_parallel_size)) mpu.initialize_model_parallel(model_parallel_size) model_parallel_size = mpu.get_model_parallel_world_size() batch_size = 13 seq_length = 17 vocab_size_per_partition = 11 logits_scale = 1000.0 vocab_size = vocab_size_per_partition * model_parallel_size seed = 1234 loss_torch, grad_torch = torch_cross_entropy(batch_size, seq_length, vocab_size, logits_scale, seed) loss_mpu, grad_mpu = mpu_cross_entropy(batch_size, seq_length, vocab_size, logits_scale, seed) error = loss_torch.sub_(loss_mpu).abs().max() print(' max error in loss on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 1.0e-6 error = grad_torch.sub_(grad_mpu).abs().max() print(' max error in grad on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 1.0e-6 # Reset groups mpu.destroy_model_parallel() torch.distributed.barrier() if torch.distributed.get_rank() == 0: print('>> passed the test :-)') if __name__ == '__main__': initialize_distributed() world_size = torch.distributed.get_world_size() model_parallel_size = 1 while model_parallel_size <= world_size: print_separator('test cross entropy') test_cross_entropy(model_parallel_size) model_parallel_size *= 2 pangu/megatron/mpu/tests/test_data.py000066400000000000000000000055431430334424000202750ustar00rootroot00000000000000# 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. from commons import print_separator from commons import initialize_distributed from mpu import data as data_utils import mpu import torch import functools import operator import sys sys.path.append("../..") def test_boradcast_data(model_parallel_size): if torch.distributed.get_rank() == 0: print('> testing boradcast_data with model parallel size {} ...'. format(model_parallel_size)) mpu.initialize_model_parallel(model_parallel_size) torch.manual_seed(1234 + mpu.get_data_parallel_rank()) model_parallel_size = mpu.get_model_parallel_world_size() key_size_t = {'key1': [7, 11], 'key2': [8, 2, 1], 'key3': [13], 'key4': [5, 1, 2], 'key5': [5, 12]} keys = list(key_size_t.keys()) data = {} data_t = {} for key in key_size_t: data[key] = torch.LongTensor(size=key_size_t[key]).random_(0, 1000) data_t[key] = data[key].clone() data['keyX'] = torch.FloatTensor(size=(5, )).random_(0, 1000) data_t['keyX'] = data['keyX'].clone() if mpu.get_model_parallel_rank() != 0: data = None data_utils._check_data_types(keys, data_t, torch.int64) key_size, key_numel, \ total_numel = data_utils._build_key_size_numel_dictionaries(keys, data) for key in keys: assert key_size[key] == key_size_t[key] total_numel_t = 0 for key in keys: target_size = functools.reduce(operator.mul, key_size_t[key], 1) assert key_numel[key] == target_size total_numel_t += target_size assert total_numel == total_numel_t data_b = data_utils.broadcast_data(keys, data, torch.int64) for key in keys: tensor = data_t[key].cuda() assert data_b[key].sub(tensor).abs().max() == 0 # Reset groups mpu.destroy_model_parallel() torch.distributed.barrier() if torch.distributed.get_rank() == 0: print('>> passed the test :-)') if __name__ == '__main__': initialize_distributed() world_size = torch.distributed.get_world_size() model_parallel_size = 1 while model_parallel_size <= world_size: print_separator('test test boradcast data') test_boradcast_data(model_parallel_size) model_parallel_size *= 2 pangu/megatron/mpu/tests/test_initialize.py000066400000000000000000000066471430334424000215330ustar00rootroot00000000000000# 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. from commons import print_separator from commons import initialize_distributed import mpu import torch import sys sys.path.append("../..") def test_initialize_model_parallel(model_parallel_size): if torch.distributed.get_rank() == 0: print('> testing initialize_model_parallel with size {} ...'.format( model_parallel_size)) model_parallel_size_ = min(model_parallel_size, torch.distributed.get_world_size()) assert not mpu.model_parallel_is_initialized() mpu.initialize_model_parallel(model_parallel_size_) assert mpu.model_parallel_is_initialized() # Checks. def check(group, world_size, rank): assert world_size == torch.distributed.get_world_size(group=group) assert rank == torch.distributed.get_rank(group=group) # Model parallel. world_size = model_parallel_size_ rank = torch.distributed.get_rank() % model_parallel_size_ assert world_size == mpu.get_model_parallel_world_size() assert rank == mpu.get_model_parallel_rank() check(mpu.get_model_parallel_group(), world_size, rank) # Data parallel. world_size = torch.distributed.get_world_size() // model_parallel_size_ rank = torch.distributed.get_rank() // model_parallel_size assert world_size == mpu.get_data_parallel_world_size() assert rank == mpu.get_data_parallel_rank() check(mpu.get_data_parallel_group(), world_size, rank) # Reset groups mpu.destroy_model_parallel() torch.distributed.barrier() if torch.distributed.get_rank() == 0: print('>> passed the test :-)') def test_get_model_parallel_src_rank(model_parallel_size_): if torch.distributed.get_rank() == 0: print('> testing get_model_parallel_src_rank with size {} ...'.format( model_parallel_size_)) model_parallel_size = min(model_parallel_size_, torch.distributed.get_world_size()) assert not mpu.model_parallel_is_initialized() mpu.initialize_model_parallel(model_parallel_size) assert mpu.model_parallel_is_initialized() # Checks src_rank = torch.distributed.get_rank() - mpu.get_model_parallel_rank() assert mpu.get_model_parallel_src_rank() == src_rank # Reset groups mpu.destroy_model_parallel() torch.distributed.barrier() if torch.distributed.get_rank() == 0: print('>> passed the test :-)') if __name__ == '__main__': initialize_distributed() world_size = torch.distributed.get_world_size() model_parallel_size = 1 while model_parallel_size <= world_size: print_separator('test initialize model parallel') test_initialize_model_parallel(model_parallel_size) print_separator('test model parallel source rank') test_get_model_parallel_src_rank(model_parallel_size) model_parallel_size *= 2 pangu/megatron/mpu/tests/test_layers.py000066400000000000000000000454571430334424000206730ustar00rootroot00000000000000# 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. from mpu import layers from commons import set_random_seed from commons import print_separator from commons import initialize_distributed import mpu from torch.nn.parameter import Parameter import torch.nn.init as init import torch import random import sys sys.path.append("../..") def test_parallel_embedding(model_parallel_size): if torch.distributed.get_rank() == 0: print('> testing parallel embedding with model parallel size {} ...'. format(model_parallel_size)) mpu.initialize_model_parallel(model_parallel_size) model_parallel_size = mpu.get_model_parallel_world_size() batch_size = 17 seq_length = 23 vocab_size = 48 hidden_size = 16 seed = 1236 set_random_seed(123) input_data = torch.LongTensor( size=(batch_size, seq_length)).random_(0, vocab_size).cuda() loss_weight = torch.randn([batch_size, seq_length, hidden_size]).cuda() set_random_seed(seed) embedding_original = torch.nn.Embedding(vocab_size, hidden_size).cuda() output = embedding_original(input_data) loss_original = torch.mul(output, loss_weight).sum() loss_original.backward() set_random_seed(seed) embedding_parallel = layers.ParallelEmbedding( vocab_size, hidden_size, init_method=init.normal_).cuda() output = embedding_parallel(input_data) loss_parallel = torch.mul(output, loss_weight).sum() loss_parallel.backward() set_random_seed(seed) embedding_vocab_parallel = layers.VocabParallelEmbedding( vocab_size, hidden_size, init_method=init.normal_).cuda() output = embedding_vocab_parallel(input_data) loss_vocab_parallel = torch.mul(output, loss_weight).sum() loss_vocab_parallel.backward() torch.distributed.barrier() error = loss_parallel.sub(loss_original).abs() print(' error in loss (parallel) on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 1.0e-12, 'error: {}'.format(error) torch.distributed.barrier() error = loss_vocab_parallel.sub(loss_original).abs() print(' error in loss (vocab parallel) on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 1.0e-12, 'error: {}'.format(error) weight_grad_orig = torch.split(embedding_original.weight.grad, hidden_size // model_parallel_size, 1)[mpu.get_model_parallel_rank()] error = embedding_parallel.weight.grad.sub(weight_grad_orig).abs().max() print(' error in grad (parallel) on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 1.0e-12, 'error: {}'.format(error) weight_grad_orig = torch.split(embedding_original.weight.grad, vocab_size // model_parallel_size, 0)[mpu.get_model_parallel_rank()] error = embedding_vocab_parallel.weight.grad.sub( weight_grad_orig).abs().max() print(' error in grad (vocab parallel) on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 1.0e-12, 'error: {}'.format(error) # Reset groups mpu.destroy_model_parallel() torch.distributed.barrier() if torch.distributed.get_rank() == 0: print('>> passed the test :-)') def test_initialize_affine_weight(model_parallel_size): mpu.initialize_model_parallel(model_parallel_size) if torch.distributed.get_rank() == 0: print('> testing initialize_affine_weight with model parallel ' 'size: {}'.format(model_parallel_size)) model_parallel_size = mpu.get_model_parallel_world_size() seed = 12345 input_size_coeff = 13 input_size = input_size_coeff * model_parallel_size output_size_coeff = 17 output_size = output_size_coeff * model_parallel_size # --------------- # Column parallel # --------------- weight = torch.empty(output_size_coeff, input_size) set_random_seed(seed) layers._initialize_affine_weight(weight, output_size, input_size, output_size_coeff, 0, torch.nn.init.normal_) # Target. set_random_seed(seed) master_weight = torch.empty(output_size, input_size) torch.nn.init.normal_(master_weight) rank = mpu.get_model_parallel_rank() my_weight = torch.split(master_weight, output_size_coeff, dim=0)[rank].contiguous().clone() # Compare. error = weight.sub(my_weight).abs().max() torch.distributed.barrier() print(' column parallel max error (should be zero) on global rank ' '{}: {}'.format(torch.distributed.get_rank(), error)) assert error < 1.0e-6 # ------------ # Row parallel # ------------ weight = torch.empty(output_size, input_size_coeff) set_random_seed(seed) mpu.layers._initialize_affine_weight(weight, output_size, input_size, input_size_coeff, 1, torch.nn.init.normal_) # Target. set_random_seed(seed) master_weight = torch.empty(output_size, input_size) torch.nn.init.normal_(master_weight) rank = mpu.get_model_parallel_rank() my_weight = torch.split(master_weight, input_size_coeff, dim=1)[rank].contiguous().clone() # Compare. error = weight.sub(my_weight).abs().max() torch.distributed.barrier() print(' row parallel max error (should be zero) on global rank ' '{}: {}'.format(torch.distributed.get_rank(), error)) assert error < 1.0e-6 # Reset groups mpu.destroy_model_parallel() torch.distributed.barrier() if torch.distributed.get_rank() == 0: print(' >> passed the test :-)') class IdentityLayer2D(torch.nn.Module): def __init__(self, m, n): super(IdentityLayer2D, self).__init__() self.weight = Parameter(torch.Tensor(m, n)) torch.nn.init.xavier_normal_(self.weight) def forward(self): return self.weight def test_column_parallel_linear(model_parallel_size): mpu.initialize_model_parallel(model_parallel_size) if torch.distributed.get_rank() == 0: print('> testing ColumnParallelLinear with model parallel ' 'size: {}'.format(model_parallel_size)) model_parallel_size = mpu.get_model_parallel_world_size() seed = 12345 set_random_seed(seed) input_size_coeff = 13 input_size = input_size_coeff * model_parallel_size output_size_coeff = 17 output_size = output_size_coeff * model_parallel_size batch_size = 7 # Network identity_layer = IdentityLayer2D(batch_size, input_size).cuda() linear_layer = mpu.ColumnParallelLinear( input_size, output_size, keep_master_weight_for_test=True).cuda() loss_weight = torch.randn([batch_size, output_size]).cuda() # Forward input_ = identity_layer() output = linear_layer(input_) loss = torch.mul(output, loss_weight).sum() # Backward loss.backward() # Values. dLdY = loss_weight X = identity_layer.weight A = linear_layer.master_weight.cuda() dLdA = torch.matmul(dLdY.t(), X) dLdb = torch.matmul(torch.ones(batch_size, 1).cuda().t(), dLdY).view(-1) dLdX = torch.matmul(dLdY, A) rank = mpu.get_model_parallel_rank() my_dLdA = torch.split(dLdA, output_size_coeff, dim=0)[rank].contiguous().clone() error = my_dLdA.sub(linear_layer.weight.grad).abs().max() torch.distributed.barrier() print(' error in dLdA on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 1.0e-6 my_dLdb = torch.split(dLdb, output_size_coeff, dim=0)[rank].contiguous().clone() error = my_dLdb.sub(linear_layer.bias.grad).abs().max() torch.distributed.barrier() print(' error in dLdb on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 1.0e-6 error = dLdX.sub(identity_layer.weight.grad).abs().max() torch.distributed.barrier() print(' error in dLdX on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 1.0e-6 # Reset groups mpu.destroy_model_parallel() torch.distributed.barrier() if torch.distributed.get_rank() == 0: print(' >> passed the test :-)') def test_row_parallel_linear(model_parallel_size): mpu.initialize_model_parallel(model_parallel_size) if torch.distributed.get_rank() == 0: print('> testing RowParallelLinear with model parallel ' 'size: {}'.format(model_parallel_size)) model_parallel_size = mpu.get_model_parallel_world_size() seed = 12345 set_random_seed(seed) input_size_coeff = 13 input_size = input_size_coeff * model_parallel_size output_size_coeff = 17 output_size = output_size_coeff * model_parallel_size batch_size = 7 # Network identity_layer = IdentityLayer2D(batch_size, input_size).cuda() linear_layer = mpu.RowParallelLinear( input_size, output_size, keep_master_weight_for_test=True).cuda() loss_weight = torch.randn([batch_size, output_size]).cuda() # Forward input_ = identity_layer() output = linear_layer(input_) loss = torch.mul(output, loss_weight).sum() # Backward loss.backward() # Values. dLdY = loss_weight X = identity_layer.weight A = linear_layer.master_weight.cuda() dLdA = torch.matmul(dLdY.t(), X) dLdb = torch.matmul(torch.ones(batch_size, 1).cuda().t(), dLdY).view(-1) dLdX = torch.matmul(dLdY, A) rank = mpu.get_model_parallel_rank() my_dLdA = torch.split(dLdA, input_size_coeff, dim=1)[rank].contiguous().clone() error = my_dLdA.sub(linear_layer.weight.grad).abs().max() torch.distributed.barrier() print(' error in dLdA on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 1.0e-6 error = dLdb.sub(linear_layer.bias.grad).abs().max() torch.distributed.barrier() print(' error in dLdb on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 1.0e-6 error = dLdX.sub(identity_layer.weight.grad).abs().max() torch.distributed.barrier() print(' error in dLdX on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 1.0e-6 # Reset groups mpu.destroy_model_parallel() torch.distributed.barrier() if torch.distributed.get_rank() == 0: print(' >> passed the test :-)') class IdentityLayer3D(torch.nn.Module): def __init__(self, m, n, k): super(IdentityLayer3D, self).__init__() self.weight = Parameter(torch.Tensor(m, n, k)) torch.nn.init.xavier_normal_(self.weight) def forward(self): return self.weight def parallel_self_attention(model_parallel_size, num_att_heads_per_partition, hidden_size_per_att_head, dropout_prob, batch_size, sequence_length): mpu.initialize_model_parallel(model_parallel_size) model_parallel_size = mpu.get_model_parallel_world_size() seed = 12345 set_random_seed(seed) num_att_heads = num_att_heads_per_partition * \ torch.distributed.get_world_size() hidden_size = hidden_size_per_att_head * num_att_heads # Network identity_layer = IdentityLayer3D(batch_size, sequence_length, hidden_size).cuda() attention_layer = mpu.BertParallelSelfAttention(hidden_size, num_att_heads, dropout_prob).cuda() loss_weight = torch.randn([batch_size, sequence_length, hidden_size]).cuda() attention_mask = torch.randn([batch_size, 1, 1, sequence_length]).cuda() # Forward input_ = identity_layer() output = attention_layer(input_, attention_mask) loss = torch.mul(output, loss_weight).sum() # Backward loss.backward() rank = mpu.get_model_parallel_rank() mpu.destroy_model_parallel() return rank, hidden_size, model_parallel_size, loss, \ attention_layer, identity_layer def test_parallel_self_attention(model_parallel_size): if torch.distributed.get_rank() == 0: print('> testing ParallelSelfAttention with model parallel ' 'size: {}'.format(model_parallel_size)) num_att_heads_per_partition = 3 hidden_size_per_att_head = 7 dropout_prob = 0.0 # has to be zero batch_size = 5 sequence_length = 13 rank_1, hideen_size_1, model_parallel_size_1, loss_1, \ attention_layer_1, identity_layer_1 = parallel_self_attention( 1, num_att_heads_per_partition, hidden_size_per_att_head, dropout_prob, batch_size, sequence_length) rank, hidden_size, model_parallel_size, loss, \ attention_layer, identity_layer = parallel_self_attention( model_parallel_size, num_att_heads_per_partition, hidden_size_per_att_head, dropout_prob, batch_size, sequence_length) assert hideen_size_1 == hidden_size error = loss_1.sub(loss).abs().max() torch.distributed.barrier() print(' loss error on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 5.0e-6 my_lin_grad_list = torch.split( attention_layer_1.query_key_value.weight.grad, hidden_size // model_parallel_size, 0)[rank::model_parallel_size] my_lin_grad = torch.cat(my_lin_grad_list, dim=0) error = my_lin_grad.sub( attention_layer.query_key_value.weight.grad).abs().max() torch.distributed.barrier() print(' weight gradient error on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 5.0e-6 error = identity_layer_1.weight.grad.sub( identity_layer.weight.grad).abs().max() torch.distributed.barrier() print(' input gradient error on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 5.0e-6 torch.distributed.barrier() if torch.distributed.get_rank() == 0: print(' >> passed the test :-)') def parallel_transformer(model_parallel_size, num_att_heads_per_partition, hidden_size_per_att_head, batch_size, sequence_length): mpu.initialize_model_parallel(model_parallel_size) model_parallel_size = mpu.get_model_parallel_world_size() seed = 12345 set_random_seed(seed) num_att_heads = num_att_heads_per_partition * \ torch.distributed.get_world_size() hidden_size = hidden_size_per_att_head * num_att_heads intermediate_size = 4 * hidden_size # Network identity_layer = IdentityLayer3D(batch_size, sequence_length, hidden_size).cuda() transformer_layer = mpu.BertParallelTransformerLayer( hidden_size, intermediate_size, num_att_heads, 0.0, 0.0, torch.nn.functional.relu, 1.0e-5).cuda() loss_weight = torch.randn([batch_size, sequence_length, hidden_size]).cuda() attention_mask = torch.randn([batch_size, 1, 1, sequence_length]).cuda() # Forward input_ = identity_layer() output = transformer_layer(input_, attention_mask) loss = torch.mul(output, loss_weight).sum() # Backward loss.backward() rank = mpu.get_model_parallel_rank() mpu.destroy_model_parallel() return rank, hidden_size, model_parallel_size, loss, \ transformer_layer, identity_layer def test_parallel_transformer_layer(model_parallel_size): if torch.distributed.get_rank() == 0: print('> testing ParallelTransformerLayer with model parallel ' 'size: {}'.format(model_parallel_size)) num_att_heads_per_partition = 3 hidden_size_per_att_head = 7 batch_size = 5 sequence_length = 13 rank_1, hidden_size_1, model_parallel_size_1, loss_1, \ transformer_layer_1, identity_layer_1 = parallel_transformer( 1, num_att_heads_per_partition, hidden_size_per_att_head, batch_size, sequence_length) rank, hidden_size, model_parallel_size, loss, \ transformer_layer, identity_layer = parallel_transformer( model_parallel_size, num_att_heads_per_partition, hidden_size_per_att_head, batch_size, sequence_length) error = loss_1.sub(loss).abs().max() torch.distributed.barrier() print(' loss error on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 5.0e-5, 'error: {}'.format(error) error = identity_layer_1.weight.grad.sub( identity_layer.weight.grad).abs().max() torch.distributed.barrier() print(' input gradient error on global rank {}: {}'.format( torch.distributed.get_rank(), error)) assert error < 5.0e-5, 'error: {}'.format(error) torch.distributed.barrier() if torch.distributed.get_rank() == 0: print(' >> passed the test :-)') if __name__ == '__main__': torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False initialize_distributed() world_size = torch.distributed.get_world_size() print_separator('test initialize affine weight') model_parallel_size = 1 while model_parallel_size <= world_size: test_initialize_affine_weight(model_parallel_size) model_parallel_size *= 2 model_parallel_size = 1 while model_parallel_size <= world_size: print_separator('test parallel embedding') test_parallel_embedding(model_parallel_size) model_parallel_size *= 2 print_separator('test column-parallel linear') model_parallel_size = 1 while model_parallel_size <= world_size: test_column_parallel_linear(model_parallel_size) model_parallel_size *= 2 print_separator('test row-parallel linear') model_parallel_size = 1 while model_parallel_size <= world_size: test_row_parallel_linear(model_parallel_size) model_parallel_size *= 2 print_separator('test parallel self-attention') model_parallel_size = 1 while model_parallel_size <= world_size: test_parallel_self_attention(model_parallel_size) model_parallel_size *= 2 print_separator('test parallel transformer') model_parallel_size = 1 while model_parallel_size <= world_size: test_parallel_transformer_layer(model_parallel_size) model_parallel_size *= 2 pangu/megatron/mpu/tests/test_random.py000066400000000000000000000150471430334424000206440ustar00rootroot00000000000000# 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. from commons import print_separator from commons import initialize_distributed import mpu import torch import sys sys.path.append("../..") def test_set_cuda_rng_state(model_parallel_size): if torch.distributed.get_rank() == 0: print('> testing set_rng_state with size {} ...'. format(model_parallel_size)) mpu.initialize_model_parallel(model_parallel_size) model_parallel_size = mpu.get_model_parallel_world_size() size = 123 seed = 1234 torch.cuda.manual_seed(1234) tensor = torch.cuda.FloatTensor(size) # Get the state rng_state = torch.cuda.get_rng_state() rng_state_copy = rng_state.clone() # Do some stuff. for _ in range(5): torch.randn(size, out=tensor) result_1 = tensor.clone() assert rng_state.sub(rng_state_copy).max() == 0 assert torch.cuda.get_rng_state().sub(rng_state_copy).max() > 0 # State should be different. new_rng_state = torch.cuda.get_rng_state() max_diff = new_rng_state.sub(rng_state).max() print(' max diff in rng state (should be non-zero) on global rank {}: {}'. format(torch.distributed.get_rank(), max_diff)) assert max_diff > 0 # Reset the rng state and do the same stuff. mpu.random._set_cuda_rng_state(rng_state) for _ in range(5): torch.randn(size, out=tensor) mpu.random._set_cuda_rng_state(rng_state) for _ in range(5): torch.randn(size, out=tensor) result_2 = tensor.clone() # Results should be the same error = result_2.sub(result_1).abs().max() print(' max error in generated tensors (should be zero) on ' 'global rank {}: {}'.format(torch.distributed.get_rank(), error)) assert error < 1.0e-6 # Input state should have remained intact. error = rng_state.sub(rng_state_copy).max() print(' max error in rng state (should be zero) on global rank {}: {}'. format(torch.distributed.get_rank(), error)) assert error == 0 # Reset groups mpu.destroy_model_parallel() torch.distributed.barrier() if torch.distributed.get_rank() == 0: print('>> passed the test :-)') def test_cuda_rng_tracker(model_parallel_size): if torch.distributed.get_rank() == 0: print('> testing cuda rng tracker with size {} ...'. format(model_parallel_size)) mpu.initialize_model_parallel(model_parallel_size) model_parallel_size = mpu.get_model_parallel_world_size() seed_1 = 1234 seed_2 = 4321 size = [12, 21] tensor = torch.cuda.FloatTensor(size) # Set to seed_1 and generate two tensors. torch.cuda.manual_seed(seed_1) torch.randn(size, out=tensor) target_11 = tensor.clone() torch.randn(size, out=tensor) target_12 = tensor.clone() # Set to seed_2 and generate two tensors. torch.cuda.manual_seed(seed_2) torch.randn(size, out=tensor) target_21 = tensor.clone() torch.randn(size, out=tensor) target_22 = tensor.clone() # Now if we interleave seed_1 and seed_2, # we should still get the same tensors torch.cuda.manual_seed(seed_1) mpu.get_cuda_rng_tracker().add('test', seed_2) torch.randn(size, out=tensor) result_11 = tensor.clone() with mpu.get_cuda_rng_tracker().fork('test'): torch.randn(size, out=tensor) result_21 = tensor.clone() torch.randn(size, out=tensor) result_12 = tensor.clone() with mpu.get_cuda_rng_tracker().fork('test'): torch.randn(size, out=tensor) result_22 = tensor.clone() diff = result_11.sub(result_21).abs().max() diff = min(diff, result_12.sub(result_22).abs().max()) print(' max diff in generated tensors (should be non-zero) on ' 'global rank {}: {}'.format(torch.distributed.get_rank(), diff)) assert diff > 1.0e-6 error = max(result_11.sub(target_11).abs().max(), result_12.sub(target_12).abs().max()) error = max(error, result_21.sub(target_21).abs().max()) error = max(error, result_22.sub(target_22).abs().max()) print(' max error in generated tensors (should be zero) on ' 'global rank {}: {}'.format(torch.distributed.get_rank(), error)) assert error < 1.0e-6 # Reset the tracker mpu.get_cuda_rng_tracker().reset() # Reset groups mpu.destroy_model_parallel() torch.distributed.barrier() if torch.distributed.get_rank() == 0: print('>> passed the test :-)') def test_model_parallel_cuda_manual_seed(model_parallel_size): if torch.distributed.get_rank() == 0: print('> testing model parallel cuda manual seed with size {} ...'. format(model_parallel_size)) mpu.initialize_model_parallel(model_parallel_size) model_parallel_size = mpu.get_model_parallel_world_size() mpu.model_parallel_cuda_manual_seed(12345) assert torch.cuda.initial_seed() == 12345 with mpu.get_cuda_rng_tracker().fork(): assert torch.cuda.initial_seed() == (12345 + 2718 + mpu.get_model_parallel_rank()) # Reset the tracker mpu.get_cuda_rng_tracker().reset() # Reset groups mpu.destroy_model_parallel() torch.distributed.barrier() if torch.distributed.get_rank() == 0: print('>> passed the test :-)') if __name__ == '__main__': initialize_distributed() world_size = torch.distributed.get_world_size() model_parallel_size = 1 while model_parallel_size <= world_size: print_separator('test set rng state') test_set_cuda_rng_state(model_parallel_size) model_parallel_size *= 2 model_parallel_size = 1 while model_parallel_size <= world_size: print_separator('test cuda rng tracker') test_cuda_rng_tracker(model_parallel_size) model_parallel_size *= 2 model_parallel_size = 1 while model_parallel_size <= world_size: print_separator('test model parallel cuda manual seed') test_model_parallel_cuda_manual_seed(model_parallel_size) model_parallel_size *= 2 pangu/megatron/mpu/utils.py000066400000000000000000000052271430334424000163220ustar00rootroot00000000000000# 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. import torch def ensure_divisibility(numerator, denominator): """Ensure that numerator is divisible by the denominator.""" assert numerator % denominator == 0, '{} is not divisible by {}'.format( numerator, denominator) def divide(numerator, denominator): """Ensure that numerator is divisible by the denominator and return the division value.""" ensure_divisibility(numerator, denominator) return numerator // denominator def split_tensor_along_last_dim(tensor, num_partitions, contiguous_split_chunks=False): """Split a tensor along its last dimension. Arguments: tensor: input tensor. num_partitions: number of partitions to split the tensor contiguous_split_chunks: If True, make each chunk contiguous in memory. """ # Get the size and dimension. last_dim = tensor.dim() - 1 last_dim_size = divide(tensor.size()[last_dim], num_partitions) # Split. tensor_list = torch.split(tensor, last_dim_size, dim=last_dim) # Note: torch.split does not create contiguous tensors by default. if contiguous_split_chunks: return tuple(chunk.contiguous() for chunk in tensor_list) return tensor_list class VocabUtility: """Split the vocabulary into `world_size` chunks amd return the first and last index of the vocabulary belonging to the `rank` partition: Note that indecies in [fist, last)""" @staticmethod def vocab_range_from_per_partition_vocab_size(per_partition_vocab_size, rank, world_size): index_f = rank * per_partition_vocab_size index_l = index_f + per_partition_vocab_size return index_f, index_l @staticmethod def vocab_range_from_global_vocab_size(global_vocab_size, rank, world_size): per_partition_vocab_size = divide(global_vocab_size, world_size) return VocabUtility.vocab_range_from_per_partition_vocab_size( per_partition_vocab_size, rank, world_size) pangu/megatron/package_info.py000066400000000000000000000023031430334424000167570ustar00rootroot00000000000000# 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. MAJOR = 1 MINOR = 1.2 # Use the following formatting: (major, minor) VERSION = (MAJOR, MINOR) __version__ = '.'.join(map(str, VERSION)) __package_name__ = 'megatron-lm' __contact_names__ = 'NVIDIA INC' __url__ = 'https://github.com/NVIDIA/Megatron-LM' __download_url__ = 'https://github.com/NVIDIA/Megatron-LM/releases' __description__ = 'Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism.' __license__ = 'See https://github.com/NVIDIA/Megatron-LM/blob/master/LICENSE' __keywords__ = 'deep learning, Megatron, gpu, NLP, nvidia, pytorch, torch, language' pangu/megatron/text_generation_utils.py000066400000000000000000000367361430334424000210110ustar00rootroot00000000000000# 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. """Utilities for generating text.""" import copy import json import os import time import torch import torch.nn.functional as F from megatron import get_args from megatron import get_tokenizer from megatron import mpu from megatron.utils import get_ltor_masks_and_position_ids def get_batch(context_tokens): """Generate batch from context tokens.""" args = get_args() tokenizer = get_tokenizer() # Move to GPU. tokens = context_tokens.view(args.batch_size, -1).contiguous().cuda() # Get the attention mask and postition ids. attention_mask, _, position_ids = get_ltor_masks_and_position_ids( tokens, tokenizer.eod, args.reset_position_ids, args.reset_attention_mask, args.eod_mask_loss) return tokens, attention_mask, position_ids def top_k_logits(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')): """ This function has been mostly taken from huggingface conversational ai code at https://medium.com/huggingface/how-to-build-a-state-of-the-art- conversational-ai-with-transfer-learning-2d818ac26313 """ if top_k > 0: # Remove all tokens with a probability less than the # last token of the top-k indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = filter_value if top_p > 0.0: # Cconvert to 1D sorted_logits, sorted_indices = torch.sort( logits, descending=True, dim=-1) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold sorted_indices_to_remove = cumulative_probs > top_p # Shift the indices to the right to keep also the first token # above the threshold sorted_indices_to_remove[..., 1:] \ = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 for i in range(sorted_indices.size(0)): indices_to_remove = sorted_indices[i][sorted_indices_to_remove[i]] logits[i][indices_to_remove] = filter_value return logits def generate_samples_input_from_file(model): args = get_args() tokenizer = get_tokenizer() # Read the sample file and open the output file. assert args.sample_input_file is not None, \ 'sample input file is not provided.' if mpu.get_model_parallel_rank() == 0: fname = open(args.sample_input_file, "r") all_raw_text = fname.readlines() input_count = len(all_raw_text) input_pos = 0 if args.sample_output_file is None: sample_output_file = args.sample_input_file + ".out" print('could not find `sample-output-file`, setting ' 'it to {}'.format(sample_output_file)) fname_out = open(sample_output_file, "w+") context_count = 0 model.eval() with torch.no_grad(): while True: torch.distributed.barrier(group=mpu.get_model_parallel_group()) terminate_runs = 0 if mpu.get_model_parallel_rank() == 0: raw_text = all_raw_text[input_pos] input_pos += 1 if input_pos == input_count: raw_text = "stop" if "stop" in raw_text: terminate_runs = 1 else: context_tokens = tokenizer.tokenize(raw_text) context_length = len(context_tokens) if context_length >= (args.seq_length // 2): print("\nContext length", context_length, "\nPlease give smaller context (half of the " "sequence length)!", flush=True) continue else: context_tokens = tokenizer.tokenize("EMPTY TEXT") context_length = len(context_tokens) terminate_runs_tensor = torch.cuda.LongTensor([terminate_runs]) torch.distributed.broadcast(terminate_runs_tensor, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group()) terminate_runs = terminate_runs_tensor[0].item() if terminate_runs == 1: return token_stream = get_token_stream(model, [context_tokens]) for _, decode_tokens in enumerate(token_stream): decode_tokens, _ = decode_tokens decode_tokens = decode_tokens[0].cpu().numpy().tolist() if mpu.get_model_parallel_rank() == 0: os.system('clear') print("\nContext:", raw_text, flush=True) trim_decode_tokens = tokenizer.detokenize( decode_tokens)[len(raw_text):] print("\nMegatron-LM:", trim_decode_tokens, flush=True) fname_out.write("\nContext:") fname_out.write(raw_text) fname_out.write("\n\nMegatron-LM:") fname_out.write(trim_decode_tokens) fname_out.write("\n") raw_text = None torch.distributed.barrier(group=mpu.get_model_parallel_group()) context_count += 1 def generate_samples_interactive(model, print_frequency=24): args = get_args() tokenizer = get_tokenizer() context_count = 0 model.eval() with torch.no_grad(): while True: torch.distributed.barrier(group=mpu.get_model_parallel_group()) terminate_runs = 0 if mpu.get_model_parallel_rank() == 0: os.system('clear') raw_text = input("\nContext prompt (stop to exit) >>> ") while not raw_text: print('Prompt should not be empty!') raw_text = input("\nContext prompt (stop to exit) >>> ") if "stop" in raw_text: terminate_runs = 1 else: context_tokens = tokenizer.tokenize(raw_text) context_length = len(context_tokens) if context_length >= (args.seq_length // 2): print("\nContext length", context_length, "\nPlease give smaller context (half of the " "sequence length)!", flush=True) continue else: context_tokens = tokenizer.tokenize("EMPTY TEXT") context_length = len(context_tokens) terminate_runs_tensor = torch.cuda.LongTensor([terminate_runs]) torch.distributed.broadcast(terminate_runs_tensor, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group()) terminate_runs = terminate_runs_tensor[0].item() if terminate_runs == 1: return token_stream = get_token_stream(model, [context_tokens]) for counter, decode_tokens in enumerate(token_stream): decode_tokens, _ = decode_tokens decode_tokens = decode_tokens[0].cpu().numpy().tolist() if mpu.get_model_parallel_rank() == 0 and \ counter % print_frequency == 0: os.system('clear') print("\nContext:", raw_text, flush=True) trim_decode_tokens = tokenizer.detokenize( decode_tokens)[len(raw_text):] print("\nMegatron-LM:", trim_decode_tokens, flush=True) if mpu.get_model_parallel_rank() == 0: os.system('clear') print("\nContext:", raw_text, flush=True) trim_decode_tokens = tokenizer.detokenize( decode_tokens)[len(raw_text):] print("\nMegatron-LM:", trim_decode_tokens, flush=True) raw_text = None torch.distributed.barrier(group=mpu.get_model_parallel_group()) context_count += 1 if mpu.get_model_parallel_rank() == 0: input("\nPress any key to continue >>>") def generate_samples_unconditional(model): args = get_args() tokenizer = get_tokenizer() num_samples = args.num_samples context_tokens = [[tokenizer.eod] for _ in range(args.batch_size)] ctr = 0 while True: start_time = time.time() for token_stream in get_token_stream(model, copy.deepcopy(context_tokens)): pass if ctr % args.log_interval == 0: print('Avg s/batch:', (time.time() - start_time) / min(args.log_interval, ctr + 1)) start_time = time.time() length = len(token_stream) token_batch = token_stream[0].cpu().numpy().tolist() length_batch = token_stream[1].cpu().numpy().tolist() for tokens, length in zip(token_batch, length_batch): tokens = tokens[1:length - 1] text = tokenizer.detokenize(tokens) is_finished = length < args.seq_length - 1 datum = {'text': text, 'length': length - 1, 'finished': is_finished} yield datum ctr += 1 if ctr >= num_samples: break if ctr >= num_samples: break def generate_and_write_samples_unconditional(model): args = get_args() assert args.genfile is not None with open(args.genfile, 'w') as f: for datum in generate_samples_unconditional(model): f.write(json.dumps(datum) + '\n') def pad_batch(batch, pad_id, args): context_lengths = [] for tokens in batch: context_length = len(tokens) if context_length < args.seq_length: tokens.extend([pad_id] * (args.seq_length - context_length)) context_lengths.append(context_length) return batch, context_lengths def get_token_stream(model, context_tokens): args = get_args() tokenizer = get_tokenizer() context_tokens, context_lengths = pad_batch(context_tokens, tokenizer.eod, args) context_tokens_tensor = torch.cuda.LongTensor(context_tokens) context_length_tensor = torch.cuda.LongTensor(context_lengths) torch.distributed.broadcast(context_length_tensor, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group()) torch.distributed.broadcast(context_tokens_tensor, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group()) context_length = context_length_tensor.min().item() tokens, attention_mask, position_ids = get_batch(context_tokens_tensor) batch_token_iterator = sample_sequence_batch(model, context_tokens_tensor, context_length_tensor, attention_mask, position_ids) for tokens, lengths in batch_token_iterator: context_length += 1 yield tokens[:, :context_length], lengths def switch(val1, val2, boolean): boolean = boolean.type_as(val1) return (1 - boolean) * val1 + boolean * val2 def sample_sequence_batch(model, context_tokens, context_lengths, attention_mask, position_ids, maxlen=None, type_ids=None): args = get_args() tokenizer = get_tokenizer() model.eval() with torch.no_grad(): context_length = context_lengths.min().item() eos_id = tokenizer.eod counter = 0 org_context_length = context_length layer_past = None batch_size = context_tokens.size(0) is_done = torch.zeros([batch_size]).byte().cuda() tokens = context_tokens if maxlen is None: maxlen = args.seq_length - 1 if maxlen > (org_context_length + args.out_seq_length): maxlen = org_context_length + args.out_seq_length lengths = torch.ones([batch_size]).long().cuda() * maxlen while context_length <= (maxlen): if args.recompute: logits = model(tokens, position_ids, attention_mask, tokentype_ids=type_ids, forward_method_parallel_output=False) logits = logits[:, context_length - 1, :] else: types2use = None if counter == 0: tokens2use = tokens[:, :context_length] positions2use = position_ids[:, :context_length] if type_ids is not None: types2use = type_ids[:, :context_length] else: tokens2use = tokens[:, context_length - 1].view( batch_size, -1) positions2use = position_ids[:, context_length - 1].view( batch_size, -1) if type_ids is not None: types2use = type_ids[:, context_length - 1].view( batch_size, -1) logits, layer_past = model(tokens2use, positions2use, attention_mask, layer_past=layer_past, get_key_value=True, tokentype_ids=types2use, forward_method_parallel_output=False) logits = logits[:, -1].view(batch_size, -1).contiguous() if args.greedy: prev = torch.argmax(logits, dim=-1).view(-1) else: logits = logits.float() logits /= args.temperature logits = top_k_logits(logits, top_k=args.top_k, top_p=args.top_p) log_probs = F.softmax(logits, dim=-1) prev = torch.multinomial(log_probs, num_samples=1).view(-1) print_logits = [] for p in prev: print_logits.append([logits[i, p].item() for i in range(batch_size)]) started = context_lengths <= context_length tokens[:, context_length] = switch( tokens[:, context_length].view(-1), prev, started) context_length += 1 counter += 1 done_token = (prev == eos_id).byte() & started.byte() just_finished = (done_token & ~is_done).bool() lengths[just_finished.view(-1)] = context_length is_done = is_done | done_token done = torch.all(is_done) yield tokens, lengths if done: break pangu/megatron/tokenizer/000077500000000000000000000000001430334424000160135ustar00rootroot00000000000000pangu/megatron/tokenizer/__init__.py000066400000000000000000000012321430334424000201220ustar00rootroot00000000000000# 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. from .tokenizer import build_tokenizer pangu/megatron/tokenizer/bert_tokenization.py000066400000000000000000000323561430334424000221300ustar00rootroot00000000000000# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Tokenization classes.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import collections import re import unicodedata import six def validate_case_matches_checkpoint(do_lower_case, init_checkpoint): """Checks whether the casing config is consistent with the checkpoint name.""" # The casing has to be passed in by the user and there is no explicit check # as to whether it matches the checkpoint. The casing information probably # should have been stored in the bert_config.json file, but it's not, so # we have to heuristically detect it to validate. if not init_checkpoint: return m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint) if m is None: return model_name = m.group(1) lower_models = [ "uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12", "multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12" ] cased_models = [ "cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16", "multi_cased_L-12_H-768_A-12" ] is_bad_config = False if model_name in lower_models and not do_lower_case: is_bad_config = True actual_flag = "False" case_name = "lowercased" opposite_flag = "True" if model_name in cased_models and do_lower_case: is_bad_config = True actual_flag = "True" case_name = "cased" opposite_flag = "False" if is_bad_config: raise ValueError( "You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. " "However, `%s` seems to be a %s model, so you " "should pass in `--do_lower_case=%s` so that the fine-tuning matches " "how the model was pre-training. If this error is wrong, please " "just comment out this check." % (actual_flag, init_checkpoint, model_name, case_name, opposite_flag)) def convert_to_unicode(text): """Converts `text` to Unicode (if it's not already), assuming utf-8 input.""" if six.PY3: if isinstance(text, str): return text elif isinstance(text, bytes): return text.decode("utf-8", "ignore") else: raise ValueError("Unsupported string type: %s" % (type(text))) elif six.PY2: if isinstance(text, str): return text.decode("utf-8", "ignore") elif isinstance(text, unicode): return text else: raise ValueError("Unsupported string type: %s" % (type(text))) else: raise ValueError("Not running on Python2 or Python 3?") def printable_text(text): """Returns text encoded in a way suitable for print or `tf.logging`.""" # These functions want `str` for both Python2 and Python3, but in one case # it's a Unicode string and in the other it's a byte string. if six.PY3: if isinstance(text, str): return text elif isinstance(text, bytes): return text.decode("utf-8", "ignore") else: raise ValueError("Unsupported string type: %s" % (type(text))) elif six.PY2: if isinstance(text, str): return text elif isinstance(text, unicode): return text.encode("utf-8") else: raise ValueError("Unsupported string type: %s" % (type(text))) else: raise ValueError("Not running on Python2 or Python 3?") def load_vocab(vocab_file): """Loads a vocabulary file into a dictionary.""" vocab = collections.OrderedDict() index = 0 with open(vocab_file, "r") as reader: while True: token = convert_to_unicode(reader.readline()) if not token: break token = token.strip() vocab[token] = index index += 1 return vocab def convert_by_vocab(vocab, items): """Converts a sequence of [tokens|ids] using the vocab.""" output = [] for item in items: output.append(vocab[item]) return output def convert_tokens_to_ids(vocab, tokens): return convert_by_vocab(vocab, tokens) def convert_ids_to_tokens(inv_vocab, ids): return convert_by_vocab(inv_vocab, ids) def whitespace_tokenize(text): """Runs basic whitespace cleaning and splitting on a piece of text.""" text = text.strip() if not text: return [] tokens = text.split() return tokens class FullTokenizer(object): """Runs end-to-end tokenziation.""" def __init__(self, vocab_file, do_lower_case=True): self.vocab = load_vocab(vocab_file) self.inv_vocab = {v: k for k, v in self.vocab.items()} self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case) self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab) def tokenize(self, text): split_tokens = [] for token in self.basic_tokenizer.tokenize(text): for sub_token in self.wordpiece_tokenizer.tokenize(token): split_tokens.append(sub_token) return split_tokens def convert_tokens_to_ids(self, tokens): return convert_by_vocab(self.vocab, tokens) def convert_ids_to_tokens(self, ids): return convert_by_vocab(self.inv_vocab, ids) def vocab_size(self): return len(self.vocab) class BasicTokenizer(object): """Runs basic tokenization (punctuation splitting, lower casing, etc.).""" def __init__(self, do_lower_case=True): """Constructs a BasicTokenizer. Args: do_lower_case: Whether to lower case the input. """ self.do_lower_case = do_lower_case def tokenize(self, text): """Tokenizes a piece of text.""" text = convert_to_unicode(text) text = self._clean_text(text) # This was added on November 1st, 2018 for the multilingual and Chinese # models. This is also applied to the English models now, but it doesn't # matter since the English models were not trained on any Chinese data # and generally don't have any Chinese data in them (there are Chinese # characters in the vocabulary because Wikipedia does have some Chinese # words in the English Wikipedia.). text = self._tokenize_chinese_chars(text) orig_tokens = whitespace_tokenize(text) split_tokens = [] for token in orig_tokens: if self.do_lower_case: token = token.lower() token = self._run_strip_accents(token) split_tokens.extend(self._run_split_on_punc(token)) output_tokens = whitespace_tokenize(" ".join(split_tokens)) return output_tokens def _run_strip_accents(self, text): """Strips accents from a piece of text.""" text = unicodedata.normalize("NFD", text) output = [] for char in text: cat = unicodedata.category(char) if cat == "Mn": continue output.append(char) return "".join(output) def _run_split_on_punc(self, text): """Splits punctuation on a piece of text.""" chars = list(text) i = 0 start_new_word = True output = [] while i < len(chars): char = chars[i] if _is_punctuation(char): output.append([char]) start_new_word = True else: if start_new_word: output.append([]) start_new_word = False output[-1].append(char) i += 1 return ["".join(x) for x in output] def _tokenize_chinese_chars(self, text): """Adds whitespace around any CJK character.""" output = [] for char in text: cp = ord(char) if self._is_chinese_char(cp): output.append(" ") output.append(char) output.append(" ") else: output.append(char) return "".join(output) def _is_chinese_char(self, cp): """Checks whether CP is the codepoint of a CJK character.""" # This defines a "chinese character" as anything in the CJK Unicode block: # https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block) # # Note that the CJK Unicode block is NOT all Japanese and Korean characters, # despite its name. The modern Korean Hangul alphabet is a different block, # as is Japanese Hiragana and Katakana. Those alphabets are used to write # space-separated words, so they are not treated specially and handled # like the all of the other languages. if ((cp >= 0x4E00 and cp <= 0x9FFF) or # (cp >= 0x3400 and cp <= 0x4DBF) or # (cp >= 0x20000 and cp <= 0x2A6DF) or # (cp >= 0x2A700 and cp <= 0x2B73F) or # (cp >= 0x2B740 and cp <= 0x2B81F) or # (cp >= 0x2B820 and cp <= 0x2CEAF) or (cp >= 0xF900 and cp <= 0xFAFF) or # (cp >= 0x2F800 and cp <= 0x2FA1F)): # return True return False def _clean_text(self, text): """Performs invalid character removal and whitespace cleanup on text.""" output = [] for char in text: cp = ord(char) if cp == 0 or cp == 0xfffd or _is_control(char): continue if _is_whitespace(char): output.append(" ") else: output.append(char) return "".join(output) class WordpieceTokenizer(object): """Runs WordPiece tokenziation.""" def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200): self.vocab = vocab self.unk_token = unk_token self.max_input_chars_per_word = max_input_chars_per_word def tokenize(self, text): """Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the given vocabulary. For example: input = "unaffable" output = ["un", "##aff", "##able"] Args: text: A single token or whitespace separated tokens. This should have already been passed through `BasicTokenizer. Returns: A list of wordpiece tokens. """ text = convert_to_unicode(text) output_tokens = [] for token in whitespace_tokenize(text): chars = list(token) if len(chars) > self.max_input_chars_per_word: output_tokens.append(self.unk_token) continue is_bad = False start = 0 sub_tokens = [] while start < len(chars): end = len(chars) cur_substr = None while start < end: substr = "".join(chars[start:end]) if start > 0: substr = "##" + substr if substr in self.vocab: cur_substr = substr break end -= 1 if cur_substr is None: is_bad = True break sub_tokens.append(cur_substr) start = end if is_bad: output_tokens.append(self.unk_token) else: output_tokens.extend(sub_tokens) return output_tokens def _is_whitespace(char): """Checks whether `chars` is a whitespace character.""" # \t, \n, and \r are technically contorl characters but we treat them # as whitespace since they are generally considered as such. if char == " " or char == "\t" or char == "\n" or char == "\r": return True cat = unicodedata.category(char) if cat == "Zs": return True return False def _is_control(char): """Checks whether `chars` is a control character.""" # These are technically control characters but we count them as whitespace # characters. if char == "\t" or char == "\n" or char == "\r": return False cat = unicodedata.category(char) if cat in ("Cc", "Cf"): return True return False def _is_punctuation(char): """Checks whether `chars` is a punctuation character.""" cp = ord(char) # We treat all non-letter/number ASCII as punctuation. # Characters such as "^", "$", and "`" are not in the Unicode # Punctuation class but we treat them as punctuation anyways, for # consistency. if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)): return True cat = unicodedata.category(char) if cat.startswith("P"): return True return False pangu/megatron/tokenizer/gpt2_tokenization.py000066400000000000000000000322231430334424000220410ustar00rootroot00000000000000# coding=utf-8 # 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 try: from functools import lru_cache except ImportError: # Just a dummy decorator to get the checks to run on python2 # because honestly I don't want to support a byte-level unicode BPE # tokenizer on python 2 right now. def lru_cache(): return lambda func: func 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 signficant 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 wont 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)))) self.cache = {} # 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)) def bpe(self, token): if token in self.cache: return self.cache[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) self.cache[token] = 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(u'#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) + u'\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 + u'\n') index += 1 return vocab_file, merge_file, special_tokens_file pangu/megatron/tokenizer/tokenization_jieba.py000066400000000000000000000063311430334424000222400ustar00rootroot00000000000000# coding=utf-8 # 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 import sentencepiece as spm import jieba try: from functools import lru_cache except ImportError: # Just a dummy decorator to get the checks to run on python2 # because honestly I don't want to support a byte-level unicode BPE tokenizer on python 2 right now. def lru_cache(): return lambda func: func class JIEBATokenizer(object): def __init__(self, vocab_file, max_len=None): self.max_len = max_len if max_len is not None else int(1e12) # self.encoder = json.load(open(vocab_file)) model_file = vocab_file + ".model" vocab_file = vocab_file + ".vocab" f = open(vocab_file,'r', encoding='utf-8') lines = f.readlines() self.encoder = {} for line in enumerate(lines): key = line[1].split('\t')[0] self.encoder[key] = line[0] self.decoder = {v:k for k,v in self.encoder.items()} self.sp = spm.SentencePieceProcessor(model_file=model_file) self.translator = str.maketrans(" \n", "\u2582\u2583") self.eod_id = self.encoder[''] self.eot_id = self.encoder[''] self.pad_id = self.encoder[''] self.unk = 0 @property def vocab_size(self): return len(self.encoder) def __len__(self): return len(self.encoder) + len(self.special_tokens) @property def eod(self): return self.eot_id def tokenize(self, text): """ Tokenize a string. """ seg_list = [x.translate(self.translator) for x in jieba.cut(text, cut_all=False)] new_seg = " ".join(seg_list) return self.sp.encode(new_seg) def convert_tokens_to_ids(self, tokens): return tokens def convert_ids_to_tokens(self, ids): return self.decode(ids) def detokenize(self, token_ids): return self.decode(token_ids) def encode(self, text): res = self.tokenize(text) return res def decode(self, tokens): text = self.sp.decode(tokens) text = text.replace(' ', '').replace('\u2582', ' ').replace('\u2583', '\n') return text if __name__ == '__main__': ids = [26, 80, 373, 5440, 2527, 1663, 11, 1268, 4896, 7902] vocab_file = '/userhome/pclproject/gpt/Megatron-LM-1.1-Pangu/megatron/tokenizer/bpe_4w_pcl/vocab' tokenizer = JIEBATokenizer(vocab_file) print(tokenizer.convert_ids_to_tokens(ids)) passpangu/megatron/tokenizer/tokenizer.py000066400000000000000000000147631430334424000204120ustar00rootroot00000000000000# 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. """Megatron tokenizers.""" from abc import ABC from abc import abstractmethod from .bert_tokenization import FullTokenizer as FullBertTokenizer from .gpt2_tokenization import GPT2Tokenizer from .tokenization_jieba import JIEBATokenizer def build_tokenizer(args): """Initialize tokenizer.""" if args.rank == 0: print('> building {} tokenizer ...'.format(args.tokenizer_type), flush=True) # Select and instantiate the tokenizer. assert args.vocab_file is not None if args.tokenizer_type == 'BertWordPieceLowerCase': tokenizer = _BertWordPieceTokenizer(vocab_file=args.vocab_file, lower_case=True) elif args.tokenizer_type == 'BertWordPieceCase': tokenizer = _BertWordPieceTokenizer(vocab_file=args.vocab_file, lower_case=False) elif args.tokenizer_type == 'GPT2BPETokenizer': # assert args.merge_file is not None # tokenizer = _GPT2BPETokenizer(args.vocab_file, args.merge_file) tokenizer = JIEBATokenizer(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 if args.rank == 0: print(' > padded vocab (size: {}) with {} dummy tokens ' '(new size: {})'.format( orig_vocab_size, after - orig_vocab_size, after), flush=True) 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 _BertWordPieceTokenizer(AbstractTokenizer): """Original BERT wordpiece tokenizer.""" def __init__(self, vocab_file, lower_case=True): if lower_case: name = 'BERT Lower Case' else: name = 'BERT Upper Case' super().__init__(name) self.tokenizer = FullBertTokenizer(vocab_file, do_lower_case=lower_case) self.cls_id = self.tokenizer.vocab['[CLS]'] self.sep_id = self.tokenizer.vocab['[SEP]'] self.pad_id = self.tokenizer.vocab['[PAD]'] self.mask_id = self.tokenizer.vocab['[MASK]'] @property def vocab_size(self): return self.tokenizer.vocab_size() @property def vocab(self): return self.tokenizer.vocab @property def inv_vocab(self): return self.tokenizer.inv_vocab def tokenize(self, text): text_tokens = self.tokenizer.tokenize(text) return self.tokenizer.convert_tokens_to_ids(text_tokens) def decode_token_ids(self, token_ids): tokens = self.tokenizer.convert_ids_to_tokens(token_ids) exclude_list = ['[PAD]', '[CLS]'] non_pads = [t for t in tokens if t not in exclude_list] result = "" for s in non_pads: if s.startswith("##"): result += s[2:] else: result += " " + s return result @property def cls(self): return self.cls_id @property def sep(self): return self.sep_id @property def pad(self): return self.pad_id @property def mask(self): return self.mask_id 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 pangu/megatron/training.py000066400000000000000000000516001430334424000161700ustar00rootroot00000000000000# 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. """Pretrain utilities.""" from datetime import datetime import math import sys import torch from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP from apex.optimizers import FusedAdam as Adam from megatron import get_args from megatron import get_timers from megatron import get_tensorboard_writer from megatron import mpu from megatron import print_rank_0 from megatron.checkpointing import load_checkpoint from megatron.checkpointing import save_checkpoint from megatron.fp16 import FP16_Module from megatron.fp16 import FP16_Optimizer from megatron.initialize import initialize_megatron from megatron.learning_rates import AnnealingLR from megatron.model import DistributedDataParallel as LocalDDP from megatron.model import get_params_for_weight_decay_optimization from megatron.model.realm_model import ICTBertModel from megatron.utils import check_adlr_autoresume_termination from megatron.utils import make_data_loader from megatron.utils import report_memory def pretrain(train_valid_test_dataset_provider, model_provider, forward_step_func, extra_args_provider=None, args_defaults={}): """Main training program. This function will run the followings in the order provided: 1) initialize Megatron. 2) setup model, optimizer and lr schedule using the model_provider. 3) call train_val_test_data_provider to get train/val/test datasets. 4) train the modle using the forward_step_func. Arguments: train_valid_test_dataset_provider: a function that takes the size of train/valid/test dataset and returns `train, valid, test` datasets. model_provider: a function that returns a vanilla version of the model. By vanilla we mean a simple model on cpu with no fp16 or ddp. forward_step_func: a function that takes a `data iterator` and `model`, and returns a `loss` scalar with a dictionary with key:values being the info we would like to monitor during training, for example `lm-loss: value`. We also require that this function add `batch generator` to the timers class. extra_args_provider: a function that takes a parser and adds arguments to it. It is used for programs to add their own arguments. args_defaults: a dictionary from argument-name to argument-value. It to set already parse arguments. """ # Initalize and get arguments, timers, and Tensorboard writer. initialize_megatron(extra_args_provider=extra_args_provider, args_defaults=args_defaults) args = get_args() timers = get_timers() # Model, optimizer, and learning rate. timers('model and optimizer').start() model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider) timers('model and optimizer').stop() # Data stuff. timers('train/valid/test data iterators').start() train_data_iterator, valid_data_iterator, test_data_iterator \ = build_train_valid_test_data_iterators( train_valid_test_dataset_provider) timers('train/valid/test data iterators').stop() # Print setup timing. print_rank_0('done with setups ...') timers.log(['model and optimizer', 'train/valid/test data iterators']) print_rank_0('training ...') iteration = 0 if args.do_train and args.train_iters > 0: iteration, _ = train(forward_step_func, model, optimizer, lr_scheduler, train_data_iterator, valid_data_iterator) if args.do_valid: prefix = 'the end of training for val data' evaluate_and_print_results(prefix, forward_step_func, valid_data_iterator, model, iteration, False) if args.save and iteration != 0: save_checkpoint(iteration, model, optimizer, lr_scheduler) if args.do_test: # Run on test data. prefix = 'the end of training for test data' evaluate_and_print_results(prefix, forward_step_func, test_data_iterator, model, 0, True) def get_model(model_provider_func): """Build the model.""" args = get_args() # Build model on cpu. model = model_provider_func() # Print number of parameters. if mpu.get_data_parallel_rank() == 0: print(' > number of parameters on model parallel rank {}: {}'.format( mpu.get_model_parallel_rank(), sum([p.nelement() for p in model.parameters()])), flush=True) # GPU allocation. model.cuda(torch.cuda.current_device()) # Fp16 conversion. if args.fp16: model = FP16_Module(model) # Wrap model for distributed training.""" if args.DDP_impl == 'torch': i = torch.cuda.current_device() model = torchDDP(model, device_ids=[i], output_device=i, process_group=mpu.get_data_parallel_group()) return model if args.DDP_impl == 'local': model = LocalDDP(model) return model raise NotImplementedError('Unknown DDP implementation specified: {}. ' 'Exiting.'.format(args.DDP_impl)) def get_optimizer(model): """Set up the optimizer.""" args = get_args() # Build parameter groups (weight decay and non-decay). while isinstance(model, (torchDDP, LocalDDP, FP16_Module)): model = model.module param_groups = get_params_for_weight_decay_optimization(model) # Add model parallel attribute if it is not set. for param_group in param_groups: for param in param_group['params']: if not hasattr(param, 'model_parallel'): param.model_parallel = False # Use Adam. optimizer = Adam(param_groups, lr=args.lr, weight_decay=args.weight_decay) # Wrap into fp16 optimizer. if args.fp16: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale, dynamic_loss_scale=args.dynamic_loss_scale, dynamic_loss_args={ 'scale_window': args.loss_scale_window, 'min_scale': args.min_scale, 'delayed_shift': args.hysteresis}) return optimizer def get_learning_rate_scheduler(optimizer): """Build the learning rate scheduler.""" args = get_args() # Add linear learning rate scheduler. if args.lr_decay_iters is not None: num_iters = args.lr_decay_iters else: num_iters = args.train_iters num_iters = max(1, num_iters) init_step = 0 warmup_iter = args.warmup * num_iters lr_scheduler = AnnealingLR( optimizer, start_lr=args.lr, warmup_iter=warmup_iter, total_iters=num_iters, decay_style=args.lr_decay_style, last_iter=init_step, min_lr=args.min_lr, use_checkpoint_lr_scheduler=args.use_checkpoint_lr_scheduler, override_lr_scheduler=args.override_lr_scheduler) return lr_scheduler def setup_model_and_optimizer(model_provider_func): """Setup model and optimizer.""" args = get_args() model = get_model(model_provider_func) optimizer = get_optimizer(model) lr_scheduler = get_learning_rate_scheduler(optimizer) if args.load is not None: args.iteration = load_checkpoint(model, optimizer, lr_scheduler) else: args.iteration = 0 # get model without FP16 and/or TorchDDP wrappers unwrapped_model = model while hasattr(unwrapped_model, 'module'): unwrapped_model = unwrapped_model.module if args.iteration == 0 and hasattr(unwrapped_model, 'init_state_dict_from_bert'): print("Initializing ICT from pretrained BERT model", flush=True) unwrapped_model.init_state_dict_from_bert() return model, optimizer, lr_scheduler def backward_step(optimizer, model, loss): """Backward step.""" args = get_args() timers = get_timers() # Backward pass. timers('backward-backward').start() optimizer.zero_grad(set_grads_to_None=True) if args.fp16: optimizer.backward(loss, update_master_grads=False) else: loss.backward() timers('backward-backward').stop() # All-reduce if needed. if args.DDP_impl == 'local': timers('backward-allreduce').start() model.allreduce_params(reduce_after=False, fp32_allreduce=args.fp32_allreduce) timers('backward-allreduce').stop() # Update master gradients. timers('backward-master-grad').start() if args.fp16: optimizer.update_master_grads() timers('backward-master-grad').stop() # Clipping gradients helps prevent the exploding gradient. timers('backward-clip-grad').start() if args.clip_grad > 0: if not args.fp16: mpu.clip_grad_norm(model.parameters(), args.clip_grad) else: optimizer.clip_master_grads(args.clip_grad) timers('backward-clip-grad').stop() def train_step(forward_step_func, data_iterator, model, optimizer, lr_scheduler): """Single training step.""" args = get_args() timers = get_timers() # Forward model for one step. timers('forward').start() loss, loss_reduced = forward_step_func(data_iterator, model) timers('forward').stop() # Calculate gradients, reduce across processes, and clip. timers('backward').start() backward_step(optimizer, model, loss) timers('backward').stop() # Update parameters. timers('optimizer').start() optimizer.step() timers('optimizer').stop() # Update learning rate. skipped_iter = 0 if not (args.fp16 and optimizer.overflow): lr_scheduler.step() else: skipped_iter = 1 return loss_reduced, skipped_iter def training_log(loss_dict, total_loss_dict, learning_rate, iteration, loss_scale, report_memory_flag): """Log training information such as losses, timing, ....""" args = get_args() timers = get_timers() writer = get_tensorboard_writer() # Update losses. for key in loss_dict: total_loss_dict[key] = total_loss_dict.get(key, 0.) + loss_dict[key] # Logging. timers_to_log = [] def add_to_logging(name): if name in timers.timers: timers_to_log.append(name) add_to_logging('forward') add_to_logging('backward') add_to_logging('backward-backward') add_to_logging('backward-allreduce') add_to_logging('backward-master-grad') add_to_logging('backward-clip-grad') add_to_logging('optimizer') add_to_logging('batch generator') # Tensorboard values. if writer and torch.distributed.get_rank() == 0: writer.add_scalar('learning_rate', learning_rate, iteration) for key in loss_dict: writer.add_scalar(key, loss_dict[key], iteration) if args.fp16: writer.add_scalar('loss_scale', loss_scale, iteration) normalizer = iteration % args.log_interval if normalizer == 0: normalizer = args.log_interval timers.write(timers_to_log, writer, iteration, normalizer=normalizer) if iteration % args.log_interval == 0: elapsed_time = timers('interval time').elapsed() if writer and torch.distributed.get_rank() == 0: writer.add_scalar('iteration_time', elapsed_time / args.log_interval, iteration) log_string = ' iteration {:8d}/{:8d} |'.format(iteration, args.train_iters) log_string += ' elapsed time per iteration (ms): {:.1f} |'.format( elapsed_time * 1000.0 / args.log_interval) log_string += ' learning rate: {:.3E} |'.format(learning_rate) for key in total_loss_dict: avg = total_loss_dict[key].item() / args.log_interval log_string += ' {}: {:.6E} |'.format(key, avg) total_loss_dict[key] = 0.0 if args.fp16: log_string += ' loss scale: {:.1f} |'.format(loss_scale) print_rank_0(log_string) if report_memory_flag: report_memory('after {} iterations'.format(iteration)) report_memory_flag = False timers.log(timers_to_log, normalizer=args.log_interval) return report_memory_flag def train(forward_step_func, model, optimizer, lr_scheduler, train_data_iterator, valid_data_iterator): """Train the model function.""" args = get_args() timers = get_timers() # Turn on training mode which enables dropout. model.train() # Tracking loss. total_loss_dict = {} # Iterations. iteration = args.iteration skipped_iters = 0 timers('interval time').start() report_memory_flag = True while iteration < args.train_iters: loss_dict, skipped_iter = train_step(forward_step_func, train_data_iterator, model, optimizer, lr_scheduler) skipped_iters += skipped_iter iteration += 1 # Logging. loss_scale = None if args.fp16: loss_scale = optimizer.loss_scale report_memory_flag = training_log(loss_dict, total_loss_dict, optimizer.param_groups[0]['lr'], iteration, loss_scale, report_memory_flag) # Autoresume if args.adlr_autoresume and \ (iteration % args.adlr_autoresume_interval == 0): check_adlr_autoresume_termination(iteration, model, optimizer, lr_scheduler) # Checkpointing if args.save and args.save_interval and \ iteration % args.save_interval == 0: save_checkpoint(iteration, model, optimizer, lr_scheduler) # Evaluation if args.eval_interval and iteration % args.eval_interval == 0 and \ args.do_valid: prefix = 'iteration {}'.format(iteration) evaluate_and_print_results(prefix, forward_step_func, valid_data_iterator, model, iteration, False) if args.exit_interval and iteration % args.exit_interval == 0: torch.distributed.barrier() time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S') rank = torch.distributed.get_rank() print_rank_0('rank: {} | time: {} | exiting the program at ' 'iteration {}'.format(rank, time_str, iteration)) sys.exit() return iteration, skipped_iters def evaluate(forward_step_func, data_iterator, model, verbose=False): """Evaluation.""" args = get_args() # Turn on evaluation mode which disables dropout. model.eval() total_loss_dict = {} with torch.no_grad(): iteration = 0 while iteration < args.eval_iters: iteration += 1 if verbose and iteration % args.log_interval == 0: print_rank_0('Evaluating iter {}/{}'.format(iteration, args.eval_iters)) # Forward evaluation. _, loss_dict = forward_step_func(data_iterator, model) # Reduce across processes. for key in loss_dict: total_loss_dict[key] = total_loss_dict.get(key, 0.) + \ loss_dict[key] # Move model back to the train mode. model.train() for key in total_loss_dict: total_loss_dict[key] /= args.eval_iters return total_loss_dict def evaluate_and_print_results(prefix, forward_step_func, data_iterator, model, iteration, verbose=False): """Helper function to evaluate and dump results on screen.""" writer = get_tensorboard_writer() total_loss_dict = evaluate(forward_step_func, data_iterator, model, verbose) string = ' validation loss at {} | '.format(prefix) for key in total_loss_dict: string += '{} value: {:.6E} | '.format(key, total_loss_dict[key].item()) ppl = math.exp(min(20, total_loss_dict[key].item())) string += '{} PPL: {:.6E} | '.format(key, ppl) if writer and torch.distributed.get_rank() == 0: writer.add_scalar('{} value'.format(key), total_loss_dict[key].item(), iteration) writer.add_scalar('{} ppl'.format(key), ppl, iteration) length = len(string) + 1 print_rank_0('-' * length) print_rank_0(string) print_rank_0('-' * length) def build_train_valid_test_data_iterators( build_train_valid_test_datasets_provider): """XXX""" args = get_args() (train_dataloader, valid_dataloader, test_dataloader) = (None, None, None) print_rank_0('> building train, validation, and test datasets ...') # Data loader only on rank 0 of each model parallel group. if mpu.get_model_parallel_rank() == 0: # Rank, size, and global batch size. data_parallel_size = mpu.get_data_parallel_world_size() global_batch_size = args.batch_size * data_parallel_size # Number of train/valid/test samples. train_iters = args.train_iters eval_iters = (train_iters // args.eval_interval + 1) * args.eval_iters test_iters = args.eval_iters train_val_test_num_samples = [train_iters * global_batch_size, eval_iters * global_batch_size, test_iters * global_batch_size] print_rank_0(' > datasets target sizes (minimum size):') print_rank_0(' train: {}'.format(train_val_test_num_samples[0])) print_rank_0(' validation: {}'.format(train_val_test_num_samples[1])) print_rank_0(' test: {}'.format(train_val_test_num_samples[2])) # Build the datasets. train_ds, valid_ds, test_ds = build_train_valid_test_datasets_provider( train_val_test_num_samples) # Build dataloders. train_dataloader = make_data_loader(train_ds) valid_dataloader = make_data_loader(valid_ds) test_dataloader = make_data_loader(test_ds) # Flags to know if we need to do training/validation/testing. do_train = train_dataloader is not None and args.train_iters > 0 do_valid = valid_dataloader is not None and args.eval_iters > 0 do_test = test_dataloader is not None and args.eval_iters > 0 # Need to broadcast num_tokens and num_type_tokens. flags = torch.cuda.LongTensor( [int(do_train), int(do_valid), int(do_test)]) else: flags = torch.cuda.LongTensor([0, 0, 0]) # Broadcast num tokens. torch.distributed.broadcast(flags, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group()) args.do_train = flags[0].item() args.do_valid = flags[1].item() args.do_test = flags[2].item() # Shift the start iterations. if train_dataloader is not None: train_dataloader.batch_sampler.start_iter = args.iteration % \ len(train_dataloader) print_rank_0('setting training data start iteration to {}'. format(train_dataloader.batch_sampler.start_iter)) if valid_dataloader is not None: start_iter_val = (args.iteration // args.eval_interval) * \ args.eval_iters valid_dataloader.batch_sampler.start_iter = start_iter_val % \ len(valid_dataloader) print_rank_0('setting validation data start iteration to {}'. format(valid_dataloader.batch_sampler.start_iter)) # Build iterators. if train_dataloader is not None: train_data_iterator = iter(train_dataloader) else: train_data_iterator = None if valid_dataloader is not None: valid_data_iterator = iter(valid_dataloader) else: valid_data_iterator = None if test_dataloader is not None: test_data_iterator = iter(test_dataloader) else: test_data_iterator = None return train_data_iterator, valid_data_iterator, test_data_iterator pangu/megatron/utils.py000066400000000000000000000147111430334424000155170ustar00rootroot00000000000000# 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. """General utilities.""" import sys import torch from megatron import get_args from megatron import print_rank_0 from megatron import get_adlr_autoresume from megatron import mpu from megatron.checkpointing import save_checkpoint from megatron.data.samplers import DistributedBatchSampler from megatron.fp16 import FP16_Optimizer def reduce_losses(losses): """Reduce a tensor of losses across all GPUs.""" reduced_losses = torch.cat( [loss.clone().detach().view(1) for loss in losses]) torch.distributed.all_reduce(reduced_losses) reduced_losses = reduced_losses / torch.distributed.get_world_size() return reduced_losses def report_memory(name): """Simple GPU memory report.""" mega_bytes = 1024.0 * 1024.0 string = name + ' memory (MB)' string += ' | allocated: {}'.format( torch.cuda.memory_allocated() / mega_bytes) string += ' | max allocated: {}'.format( torch.cuda.max_memory_allocated() / mega_bytes) string += ' | reserved: {}'.format(torch.cuda.memory_reserved() / mega_bytes) string += ' | max reserved: {}'.format( torch.cuda.max_memory_reserved() / mega_bytes) print_rank_0(string) def print_params_min_max_norm(optimizer, iteration): """Print min, max, and norm of all parameters.""" index = 0 rank = torch.distributed.get_rank() string = 'iteration, rank, index, model-parallel,min, max, norm\n' optimizer_ = optimizer if isinstance(optimizer, FP16_Optimizer): optimizer_ = optimizer.optimizer for param_group in optimizer_.param_groups: for param in param_group['params']: index += 1 min_ = param.data.min() max_ = param.data.max() norm = param.data.norm() string += '{:7d}, {:4d}, {:4d}, {:2d}, '.format( iteration, rank, index, int(param.model_parallel)) string += '{:.6E}, {:.6E}, {:.6E}\n'.format(min_, max_, norm) print(string, flush=True) def check_adlr_autoresume_termination(iteration, model, optimizer, lr_scheduler): """Check for autoresume signal and exit if it is received.""" args = get_args() autoresume = get_adlr_autoresume() # Add barrier to ensure consistnecy. torch.distributed.barrier() if autoresume.termination_requested(): if args.save: save_checkpoint(iteration, model, optimizer, lr_scheduler) print_rank_0(">>> autoresume termination request found!") if torch.distributed.get_rank() == 0: autoresume.request_resume() print_rank_0(">>> training terminated. Returning") sys.exit(0) def make_data_loader(dataset): """Buld dataloader given an input dataset.""" if dataset is None: return None args = get_args() # Data parallel arguments. world_size = mpu.get_data_parallel_world_size() rank = mpu.get_data_parallel_rank() global_batch_size = args.batch_size * world_size num_workers = args.num_workers # Use a simple sampler with distributed batch sampler. sampler = torch.utils.data.SequentialSampler(dataset) batch_sampler = DistributedBatchSampler(sampler=sampler, batch_size=global_batch_size, drop_last=True, rank=rank, world_size=world_size) # Torch dataloader. return torch.utils.data.DataLoader(dataset, batch_sampler=batch_sampler, num_workers=num_workers, pin_memory=True) def get_ltor_masks_and_position_ids(data, eod_token, reset_position_ids, reset_attention_mask, eod_mask_loss): """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). if reset_attention_mask: att_mask_batch = batch_size else: att_mask_batch = 1 attention_mask = torch.tril(torch.ones( (att_mask_batch, seq_length, seq_length), device=data.device)).view( att_mask_batch, 1, seq_length, seq_length) # Loss mask. loss_mask = torch.ones(data.size(), dtype=torch.float, device=data.device) if eod_mask_loss: loss_mask[data == eod_token] = 0.0 # Position ids. position_ids = torch.arange(seq_length, dtype=torch.long, device=data.device) position_ids = position_ids.unsqueeze(0).expand_as(data) # We need to clone as the ids will be modifed based on batch index. if reset_position_ids: position_ids = position_ids.clone() if reset_position_ids or reset_attention_mask: # Loop through the batches: for b in range(batch_size): # Find indecies where EOD token is. eod_index = position_ids[b, data[b] == eod_token] # Detach indecies from positions if going to modify positions. if reset_position_ids: eod_index = eod_index.clone() # Loop through EOD indecies: prev_index = 0 for j in range(eod_index.size()[0]): i = eod_index[j] # Mask attention loss. if reset_attention_mask: attention_mask[b, 0, (i + 1):, :(i + 1)] = 0 # Reset positions. if reset_position_ids: position_ids[b, (i + 1):] -= (i + 1 - prev_index) prev_index = i + 1 # Convert attention mask to binary: attention_mask = (attention_mask < 0.5) return attention_mask, loss_mask, position_ids pangu/method/000077500000000000000000000000001430334424000134455ustar00rootroot00000000000000pangu/method/README.md000066400000000000000000000017751430334424000147360ustar00rootroot00000000000000# 算法层&任务层 中文|[English](README_EN.md) ## 简介 此目录包含算法层&任务层的实现，应用算法常以一个任务做为实现实例，因此算法层和任务层在实现上归为一层。 ## 内容导引 | 算法 | 项目 | 描述 | | --- | :----------------: | :--------------- | | Model compression | [[目录位置]](./modelcompress)] | | | Fine tuning | [[目录位置]](./finetune)] | | | Prompt tuning | [[目录位置]](./prompttune)] | | | Incontext few shot learning | [[目录位置](./incontext)] | | ## 规划与现状 ### 小样本模型迁移 #### 句子关系 | 模型 | 算法 | CMNLI | | ------- | ------- | :--- | | 鹏城·盘古 | Fine tuning | 完成 | | 鹏城·盘古 | Prompt tuning | 完成 | | 鹏城·盘古 | Incontext few shot learning | 完成 | 注： CMNLI: 自然语言推理中文版[CLUE: A Chinese Language Understanding Evaluation Benchmark]。 #### 其他任务 注：规划中 ### 模型压缩 ### 持续学习 ### 其他 pangu/method/README_EN.md000066400000000000000000000020311430334424000153020ustar00rootroot00000000000000# Algorithm&Task [中文](README.md)|English ## Introduction This directory contains the implementation of algorithm layer and task layer. Application algorithms often take a task as an implementation example, so the algorithm layer and task layer are classified into one layer in implementation. ## Navigation | Algorithm | Project | Describe | | --- | :----------------: | :--------------- | | Model compression | [[path]](./modelcompress)] | | | Fine tuning | [[path]](./finetune)] | | | Prompt tuning | [[path]](./prompttune)] | | | Incontext learning | [[path](./incontext)] | | ## Planning and status ### few shot learing #### sentence pair tasks | Model | Algorithm | CMNLI | | ------- | ------- | :--- | | Pengcheng PanGu | Fine tuning | done | | Pengcheng PanGu | Prompt tuning | done | | Pengcheng PanGu | Incontext learning | done | notes： CMNLI: [CLUE: A Chinese Language Understanding Evaluation Benchmark]。 #### other tasks notes：Planning ### model compression ### continuous learing ### otherspangu/method/__init__.py000066400000000000000000000001211430334424000155500ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/5 # @Author: qing pangu/method/continuous/000077500000000000000000000000001430334424000156535ustar00rootroot00000000000000pangu/method/continuous/__init__.py000066400000000000000000000001211430334424000177560ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/5 # @Author: qing pangu/method/finetune/000077500000000000000000000000001430334424000152625ustar00rootroot00000000000000pangu/method/finetune/README.md000066400000000000000000000026531430334424000165470ustar00rootroot00000000000000# Finetune 预训练微调范式，通过引入较多额外的参数并使用特定任务的目标函数对预训练模型进行微调，将预训练语言模型适应于不同的下游任务。 本目录为鹏程预训练大模型的fine-tune代码仓库，可以用于模型fine-tune的多机多卡训练/测试。 ## 实例 ### 实例1: 鹏程·盘古 句子关系-NLI任务 目前已支持鹏程·盘古模型的在NLI任务的fine-tune，具体实现实例为CMNLI(自然语言推理中文版)任务。 - **算法原理** * 因为鹏程·盘古模型不像BERT模型有CLS位置，所以没法获得句子的表示。 * 处理方法是是将两个句子拼接起来，然后增加特殊token放句子末尾，通过finetune请特殊token获得整体表示，再接一个分类层，将NLI任务转换成三分类。 - **环境** * python依赖包见首页requirements.txt。 * 推荐使用英伟达的官方 docker 镜像docker pull nvcr.io/nvidia/pytorch:20.03-py3，并安装NLTK。 - **模型** * 目前实现基于盘古2.6\13B的pytorch-GPU版。 * 模型下载，请参考[PanGu-Alpha-GPU](https:\//git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha-GPU)。 - **运行** * 参考脚本：scripts/finetune_nli.sh - **性能** * 盘古2.6B-fp16，未充分调优情况下，训练集覆盖6遍时，验证集acc: 50%左右。 * 如充分调优，性能应该有较大提升空间。 pangu/method/finetune/__init__.py000066400000000000000000000001211430334424000173650ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/5 # @Author: qing pangu/method/finetune/datadefine/000077500000000000000000000000001430334424000173465ustar00rootroot00000000000000pangu/method/finetune/datadefine/__init__.py000066400000000000000000000001221430334424000214520ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing pangu/method/finetune/datadefine/dataset.py000066400000000000000000000054711430334424000213540ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/5 # @Author: qing import os import json import torch from torch.utils.data import Dataset class BaseDataset(Dataset): def __init__(self, args, tokenizer, split, ratio=1, num=-1): self.args = args self.tokenizer = tokenizer self.ratio = ratio self.split = split self.pad_id = tokenizer.pad_id self.sep_id = self.tokenizer.encoder[''] self.eod_id = self.tokenizer.encoder[''] self.data, self.max_len = self.process_data() if num > 0: self.data = self.data[:num] def collate(self, samples): bs = len(samples) model_data = { "input_ids": torch.ones(bs, self.args.seq_length, dtype=torch.long) * self.pad_id, "label_ids": torch.ones(bs, 1, dtype=torch.long), "context_lengths": torch.ones(bs, 1, dtype=torch.long) } for i, samp in enumerate(samples): seq_len = len(samp["input_ids"]) model_data["input_ids"][i][:seq_len] = torch.tensor(samp["input_ids"], dtype=torch.long) model_data["label_ids"][i][0] = samp["label_ids"] model_data["context_lengths"][i][0] = seq_len return model_data def __len__(self): return len(self.data) def __getitem__(self, idx): return self.data[idx] class NliDataset(BaseDataset): LABELS = {'contradiction': 0, 'entailment': 1, 'neutral': 2} def __init__(self, args, tokenizer, split, ratio=1, num=-1): super().__init__(args, tokenizer, split, ratio, num) def process_data(self): data = [] sizes = [] data_path = os.path.join(self.args.data_path, self.split) with open(data_path, "r", encoding="utf-8") as f: data_lines = f.readlines() print(f"All {self.split} case num: {len(data_lines)}.") for i, instance in enumerate(data_lines): instance = json.loads(instance) sentence1 = instance["sentence1"] sentence2 = instance["sentence2"] label = instance.get("label", "-100") if label not in self.LABELS: # print(f"Worng label: {label}.") continue input_id_1 = self.tokenizer.encode(sentence1) input_id_2 = self.tokenizer.encode(sentence2) # 用两个在预训练中没用到过的token做句子分隔和结束，结束token用于输到分类器 input_id = input_id_1 + [self.sep_id] + input_id_2 + [self.eod_id] label_id = self.LABELS.get(label, -100) sizes.append(len(input_id)) data.append({ "idx": i, "input_ids": input_id, "label_ids": label_id, }) max_len = max(sizes) return data, max_lenpangu/method/finetune/model/000077500000000000000000000000001430334424000163625ustar00rootroot00000000000000pangu/method/finetune/model/__init__.py000066400000000000000000000001221430334424000204660ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing pangu/method/finetune/model/gpt2_model_ft.py000066400000000000000000000102731430334424000214640ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/9 # @Author: qing # 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. """GPT-2 model.""" import torch from megatron import get_args from megatron import mpu from megatron.model import GPT2Model from megatron.module import MegatronModule from megatron.model.language_model import parallel_lm_logits from megatron.model.language_model import get_language_model from megatron.model.utils import init_method_normal from megatron.model.utils import scaled_init_method_normal def gpt2_attention_mask_func(attention_scores, ltor_mask): attention_scores.masked_fill_(ltor_mask, -10000.0) return attention_scores class GPT2ModelFT(GPT2Model): def __init__(self, num_tokentypes=0, parallel_output=True): super().__init__(num_tokentypes=num_tokentypes, parallel_output=parallel_output) def forward(self, input_ids, position_ids, attention_mask, labels=None, tokentype_ids=None, layer_past=None, get_key_value=False, forward_method_parallel_output=None, fine_tune=True): ############################## debug mp ################################## # import numpy as np # from megatron import print_rank_0 # from megatron import get_tokenizer # print_rank_0(input_ids.shape) # input_ids = [39,2401,17,4448,615,5188,2783,2762,10,2371,5720,11050,3373,24420,1613] # input_ids.extend([6] * (1024 - len(input_ids))) # input_ids = torch.Tensor(np.atleast_2d(input_ids)).long().cuda() # print_rank_0('='*100) # print_rank_0('='*100) # print_rank_0('input id is: ') # tokenizer = get_tokenizer() # for ids in input_ids.cpu().numpy().tolist(): # print_rank_0(ids) # # print_rank_0(tokenizer.convert_ids_to_tokens(ids)) # print_rank_0('*'*50) ################################################################ # Language model. lm_output = self.language_model(input_ids, position_ids, attention_mask, tokentype_ids=tokentype_ids, layer_past=layer_past, get_key_value=get_key_value) ######################### debug mp ####################################### # with torch.no_grad(): # logits = lm_output.cpu().numpy() # index = np.argmax(logits, axis=2) # ids_ = index.tolist() # for ids in ids_: # print_rank_0(ids) # print_rank_0(tokenizer.convert_ids_to_tokens(ids)) ################################################################ if fine_tune: return lm_output if get_key_value: lm_output, presents = lm_output lm_output = torch.add(lm_output,0) # Output. parallel_output = self.parallel_output if forward_method_parallel_output is not None: parallel_output = forward_method_parallel_output output = parallel_lm_logits( lm_output, self.language_model.embedding.word_embeddings.weight, parallel_output) if get_key_value: output = [output, presents] if labels is None: return output else: if self.fp16_lm_cross_entropy: assert output.dtype == torch.half loss = mpu.vocab_parallel_cross_entropy(output, labels) else: loss = mpu.vocab_parallel_cross_entropy(output.float(), labels) return losspangu/method/finetune/model/gpt2_model_ft_head.py000066400000000000000000000042571430334424000224520ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/6 # @Author: qing import torch import torch.nn.functional as F from torch import nn from megatron import get_args from megatron.module import MegatronModule from megatron.model.utils import init_method_normal from method.finetune.model.gpt2_model_ft import GPT2ModelFT class GPT2ModelFTHead(MegatronModule): def __init__(self, num_tokentypes=0, parallel_output=True, num_labels=3): super(GPT2ModelFTHead, self).__init__() args = get_args() self.gpt = GPT2ModelFT(num_tokentypes=num_tokentypes, parallel_output=parallel_output) self.liner = nn.Linear(args.hidden_size, num_labels, bias=False) self.init_method= init_method_normal(args.init_method_std) self.init_method(self.liner.weight) # for param in self.gpt.parameters(): # param.requires_grad = False def forward(self, input_ids, position_ids, attention_mask, context_lengths, labels=None, target=None, tokentype_ids=None, layer_past=None, get_key_value=False, forward_method_parallel_output=None): lm_output = self.gpt(input_ids, position_ids, attention_mask) # lm_output_ft = torch.stack([torch.mean(lm_output[i, :context_lengths[i][0], :], dim=0) for i in range(lm_output.shape[0])]) lm_output_ft = torch.stack([lm_output[i, context_lengths[i][0]-1, :] for i in range(lm_output.shape[0])]) # lm_output_ft = torch.mean(lm_output_ft, dim=0) output = self.liner(lm_output_ft) predict = torch.max(output.data, 1)[1] if target is not None: loss = F.cross_entropy(output, target=target.view(-1)) return loss, predict else: return output, predict # def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): # # state_dict_ = {} # state_dict_[self._language_model_key] \ # = self.language_model.state_dict_for_save_checkpoint(destination, prefix, keep_vars) # return state_dict_ # def load_state_dict(self, state_dict, strict=True): """Customized load.""" self.gpt.load_state_dict(state_dict, strict=strict)pangu/method/finetune/paras/000077500000000000000000000000001430334424000163705ustar00rootroot00000000000000pangu/method/finetune/paras/__init__.py000066400000000000000000000001221430334424000204740ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing pangu/method/finetune/scripts/000077500000000000000000000000001430334424000167515ustar00rootroot00000000000000pangu/method/finetune/scripts/__init__.py000066400000000000000000000001221430334424000210550ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing pangu/method/finetune/scripts/finetune_nli.sh000066400000000000000000000034171430334424000217710ustar00rootroot00000000000000#! /bin/bash #export CUDA_VISIBLE_DEVICES="0,1" #export NCCL_DEBUG="INFO" WORKING_DIR=/userhome/gpt3/PanGu-Alpha-Application MODEL_PATH=/root/pangu/model/pangu_fp16_8mp_2b6 MODEL_SAVE_PATH=/root/pangu/model/pangu_fp16_8mp_2b6_ft_cmnli DATA_PATH=/root/pangu/data/cmnli_public GPUS_PER_NODE=16 MASTER_ADDR=localhost MASTER_PORT=6000 NNODES=1 NODE_RANK=0 WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT" python -u -m torch.distributed.launch $DISTRIBUTED_ARGS \ ${WORKING_DIR}/method/finetune/tasks/finetune_nli.py \ --model-parallel-size 8 \ --num-layers 31 \ --hidden-size 2560 \ --num-attention-heads 32 \ --batch-size 64 \ --seq-length 1024 \ --max-position-embeddings 1024 \ --train-iters 50000 \ --lr-decay-iters 1000 \ --save $MODEL_SAVE_PATH \ --load $MODEL_PATH \ --data-path $DATA_PATH \ --vocab-file ${WORKING_DIR}/megatron/bpe_4w_pcl/vocab \ --merge-file gpt2-merges.txt \ --data-impl mmap \ --split 949,50,1 \ --distributed-backend nccl \ --lr 0.00005 \ --lr-decay-style cosine \ --min-lr 1.0e-6 \ --weight-decay 1e-2 \ --clip-grad 1.0 \ --warmup .01 \ --log-interval 100 \ --save-interval 10000 \ --eval-interval 500 \ --eval-iters 100 \ --attention-dropout 0.1 \ --hidden-dropout 0.1 \ --seed 1234 \ --finetune \ --DDP-impl "torch"\ --checkpoint-activations \ --distribute-checkpointed-activations \ --fp16-lm-cross-entropy \ --use-cpu-initialization \ --make-vocab-size-divisible-by 1 \ pangu/method/finetune/tasks/000077500000000000000000000000001430334424000164075ustar00rootroot00000000000000pangu/method/finetune/tasks/__init__.py000066400000000000000000000001221430334424000205130ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing pangu/method/finetune/tasks/finetune.py000066400000000000000000000554051430334424000206070ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/6 # @Author: qing import math import sys import torch from datetime import datetime from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP from apex.optimizers import FusedAdam as Adam from sklearn import metrics from itertools import cycle from megatron import get_args, get_tokenizer from megatron import get_timers from megatron import get_tensorboard_writer from megatron import mpu from megatron import print_rank_0 from megatron.checkpointing import save_checkpoint, load_checkpoint from megatron.fp16 import FP16_Module from megatron.fp16 import FP16_Optimizer from megatron.initialize import initialize_megatron from megatron.learning_rates import AnnealingLR from megatron.model import DistributedDataParallel as LocalDDP from megatron.model import get_params_for_weight_decay_optimization from megatron.utils import check_adlr_autoresume_termination from megatron.utils import make_data_loader from megatron.utils import report_memory from megatron.data.samplers import DistributedBatchSampler def finetune(train_valid_test_dataset_provider, model_provider, forward_step_func, extra_args_provider=None, args_defaults={}): """Main training program. This function will run the followings in the order provided: 1) initialize Megatron. 2) setup model, optimizer and lr schedule using the model_provider. 3) call train_val_test_data_provider to get train/val/test datasets. 4) train the modle using the forward_step_func. Arguments: train_valid_test_dataset_provider: a function that takes the size of train/valid/test dataset and returns `train, valid, test` datasets. model_provider: a function that returns a vanilla version of the model. By vanilla we mean a simple model on cpu with no fp16 or ddp. forward_step_func: a function that takes a `data iterator` and `model`, and returns a `loss` scalar with a dictionary with key:values being the info we would like to monitor during training, for example `lm-loss: value`. We also require that this function add `batch generator` to the timers class. extra_args_provider: a function that takes a parser and adds arguments to it. It is used for programs to add their own arguments. args_defaults: a dictionary from argument-name to argument-value. It to set already parse arguments. """ # Initalize and get arguments, timers, and Tensorboard writer. initialize_megatron(extra_args_provider=extra_args_provider, args_defaults=args_defaults) args = get_args() timers = get_timers() # Model, optimizer, and learning rate. timers('model and optimizer').start() model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider) timers('model and optimizer').stop() # Data stuff. timers('train/valid/test data iterators').start() train_data_iterator, valid_data_iterator, test_data_iterator = build_train_valid_test_data_iterators(train_valid_test_dataset_provider) timers('train/valid/test data iterators').stop() # Print setup timing. print_rank_0('done with setups ...') timers.log(['model and optimizer', 'train/valid/test data iterators']) print_rank_0('training ...') iteration = 0 if args.do_train and args.train_iters > 0: iteration, _ = train(forward_step_func, model, optimizer, lr_scheduler, train_data_iterator, valid_data_iterator) if args.do_valid: prefix = 'the end of training for val data' evaluate_and_print_results(prefix, forward_step_func, valid_data_iterator, model, iteration, False) if args.save and iteration != 0: save_checkpoint(iteration, model, optimizer, lr_scheduler) if args.do_test: # Run on test data. prefix = 'the end of training for test data' evaluate_and_print_results(prefix, forward_step_func, test_data_iterator, model, 0, True) def get_model(model_provider_func): """Build the model.""" args = get_args() # Build model on cpu. model = model_provider_func() # Print number of parameters. if mpu.get_data_parallel_rank() == 0: print(' > number of parameters on model parallel rank {}: {}'.format( mpu.get_model_parallel_rank(), sum([p.nelement() for p in model.parameters()])), flush=True) # GPU allocation. model.cuda(torch.cuda.current_device()) # Fp16 conversion. if args.fp16: model = FP16_Module(model) # Wrap model for distributed training.""" if args.DDP_impl == 'torch': i = torch.cuda.current_device() model = torchDDP(model, device_ids=[i], output_device=i, process_group=mpu.get_data_parallel_group()) return model if args.DDP_impl == 'local': model = LocalDDP(model) return model raise NotImplementedError('Unknown DDP implementation specified: {}. ' 'Exiting.'.format(args.DDP_impl)) def get_optimizer(model): """Set up the optimizer.""" args = get_args() # Build parameter groups (weight decay and non-decay). while isinstance(model, (torchDDP, LocalDDP, FP16_Module)): model = model.module # model = model.liner param_groups = get_params_for_weight_decay_optimization(model) # Add model parallel attribute if it is not set. for param_group in param_groups: for param in param_group['params']: if not hasattr(param, 'model_parallel'): param.model_parallel = False # Use Adam. optimizer = Adam(param_groups, lr=args.lr, weight_decay=args.weight_decay) # Wrap into fp16 optimizer. if args.fp16: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale, dynamic_loss_scale=args.dynamic_loss_scale, dynamic_loss_args={ 'scale_window': args.loss_scale_window, 'min_scale': args.min_scale, 'delayed_shift': args.hysteresis}) return optimizer def get_learning_rate_scheduler(optimizer): """Build the learning rate scheduler.""" args = get_args() # Add linear learning rate scheduler. if args.lr_decay_iters is not None: num_iters = args.lr_decay_iters else: num_iters = args.train_iters num_iters = max(1, num_iters) init_step = 0 warmup_iter = args.warmup * num_iters lr_scheduler = AnnealingLR( optimizer, start_lr=args.lr, warmup_iter=warmup_iter, total_iters=num_iters, decay_style=args.lr_decay_style, last_iter=init_step, min_lr=args.min_lr, use_checkpoint_lr_scheduler=args.use_checkpoint_lr_scheduler, override_lr_scheduler=args.override_lr_scheduler) return lr_scheduler def add_new_token(model): tokenizer = get_tokenizer() tokenizer.add_special_tokens({'seq_token': '[SQ]'}) tokenizer.add_special_tokens({'eot_token': '[ET]'}) while isinstance(model, (torchDDP, LocalDDP, FP16_Module)): model = model.module model.gpt.resize_token_embeddings(len(tokenizer)) def setup_model_and_optimizer(model_provider_func): """Setup model and optimizer.""" args = get_args() model = get_model(model_provider_func) # add_new_token(model) optimizer = get_optimizer(model) lr_scheduler = get_learning_rate_scheduler(optimizer) if args.load is not None: args.iteration = load_checkpoint(model, optimizer, lr_scheduler) else: args.iteration = 0 # get model without FP16 and/or TorchDDP wrappers unwrapped_model = model while hasattr(unwrapped_model, 'module'): unwrapped_model = unwrapped_model.module if args.iteration == 0 and hasattr(unwrapped_model, 'init_state_dict_from_bert'): print("Initializing ICT from pretrained BERT model", flush=True) unwrapped_model.init_state_dict_from_bert() return model, optimizer, lr_scheduler def backward_step(optimizer, model, loss): """Backward step.""" args = get_args() timers = get_timers() # Backward pass. timers('backward-backward').start() optimizer.zero_grad(set_grads_to_None=True) if args.fp16: optimizer.backward(loss, update_master_grads=False) else: loss.backward() timers('backward-backward').stop() # All-reduce if needed. if args.DDP_impl == 'local': timers('backward-allreduce').start() model.allreduce_params(reduce_after=False, fp32_allreduce=args.fp32_allreduce) timers('backward-allreduce').stop() # Update master gradients. timers('backward-master-grad').start() if args.fp16: optimizer.update_master_grads() timers('backward-master-grad').stop() # Clipping gradients helps prevent the exploding gradient. timers('backward-clip-grad').start() if args.clip_grad > 0: if not args.fp16: mpu.clip_grad_norm(model.parameters(), args.clip_grad) else: optimizer.clip_master_grads(args.clip_grad) timers('backward-clip-grad').stop() def train_step(forward_step_func, data_iterator, model, optimizer, lr_scheduler): """Single training step.""" args = get_args() timers = get_timers() # Forward model for one step. timers('forward').start() loss, loss_reduced, target, predict = forward_step_func(data_iterator, model) # print_rank_0(f"target: {target.view(-1).tolist()}.") # print_rank_0(f"predict: {predict.view(-1).tolist()}.") # print_rank_0(f"loss_reduced: {loss_reduced}.") timers('forward').stop() torch.cuda.empty_cache() # Calculate gradients, reduce across processes, and clip. timers('backward').start() backward_step(optimizer, model, loss) timers('backward').stop() torch.cuda.empty_cache() # Update parameters. timers('optimizer').start() optimizer.step() timers('optimizer').stop() torch.cuda.empty_cache() # Update learning rate. skipped_iter = 0 if not (args.fp16 and optimizer.overflow): lr_scheduler.step() else: skipped_iter = 1 return loss_reduced, skipped_iter def training_log(loss_dict, total_loss_dict, learning_rate, iteration, loss_scale, report_memory_flag): """Log training information such as losses, timing, ....""" args = get_args() timers = get_timers() writer = get_tensorboard_writer() # Update losses. for key in loss_dict: total_loss_dict[key] = total_loss_dict.get(key, 0.) + loss_dict[key] # Logging. timers_to_log = [] def add_to_logging(name): if name in timers.timers: timers_to_log.append(name) add_to_logging('forward') add_to_logging('backward') add_to_logging('backward-backward') add_to_logging('backward-allreduce') add_to_logging('backward-master-grad') add_to_logging('backward-clip-grad') add_to_logging('optimizer') add_to_logging('batch generator') # Tensorboard values. if writer and torch.distributed.get_rank() == 0: writer.add_scalar('learning_rate', learning_rate, iteration) for key in loss_dict: writer.add_scalar(key, loss_dict[key], iteration) if args.fp16: writer.add_scalar('loss_scale', loss_scale, iteration) normalizer = iteration % args.log_interval if normalizer == 0: normalizer = args.log_interval timers.write(timers_to_log, writer, iteration, normalizer=normalizer) if iteration % args.log_interval == 0: elapsed_time = timers('interval time').elapsed() if writer and torch.distributed.get_rank() == 0: writer.add_scalar('iteration_time', elapsed_time / args.log_interval, iteration) log_string = ' iteration {:8d}/{:8d} |'.format(iteration, args.train_iters) log_string += ' elapsed time per iteration (ms): {:.1f} |'.format( elapsed_time * 1000.0 / args.log_interval) log_string += ' learning rate: {:.3E} |'.format(learning_rate) for key in total_loss_dict: avg = total_loss_dict[key].item() / args.log_interval log_string += ' {}: {:.6E} |'.format(key, avg) total_loss_dict[key] = 0.0 if args.fp16: log_string += ' loss scale: {:.1f} |'.format(loss_scale) print_rank_0(log_string) if report_memory_flag: report_memory('after {} iterations'.format(iteration)) report_memory_flag = False timers.log(timers_to_log, normalizer=args.log_interval) return report_memory_flag def train(forward_step_func, model, optimizer, lr_scheduler, train_data_iterator, valid_data_iterator): """Train the model function.""" args = get_args() timers = get_timers() # Turn on training mode which enables dropout. model.train() # Tracking loss. total_loss_dict = {} # Iterations. iteration = args.iteration skipped_iters = 0 timers('interval time').start() report_memory_flag = True while iteration < args.train_iters: loss_dict, skipped_iter = train_step(forward_step_func, train_data_iterator, model, optimizer, lr_scheduler) skipped_iters += skipped_iter iteration += 1 # Logging. loss_scale = None if args.fp16: loss_scale = optimizer.loss_scale report_memory_flag = training_log(loss_dict, total_loss_dict, optimizer.param_groups[0]['lr'], iteration, loss_scale, report_memory_flag) # Autoresume if args.adlr_autoresume and \ (iteration % args.adlr_autoresume_interval == 0): check_adlr_autoresume_termination(iteration, model, optimizer, lr_scheduler) # Checkpointing if args.save and args.save_interval and \ iteration % args.save_interval == 0: save_checkpoint(iteration, model, optimizer, lr_scheduler) # Evaluation if args.eval_interval and iteration % args.eval_interval == 0 and \ args.do_valid: prefix = 'iteration {}'.format(iteration) evaluate_and_print_results(prefix, forward_step_func, valid_data_iterator, model, iteration, False) if args.exit_interval and iteration % args.exit_interval == 0: torch.distributed.barrier() time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S') rank = torch.distributed.get_rank() print_rank_0('rank: {} | time: {} | exiting the program at ' 'iteration {}'.format(rank, time_str, iteration)) sys.exit() return iteration, skipped_iters def evaluate(forward_step_func, data_iterator, model, verbose=False): """Evaluation.""" args = get_args() # Turn on evaluation mode which disables dropout. model.eval() total_loss_dict = {} labels_all, predicts_all = [], [] with torch.no_grad(): iteration = 0 while iteration < args.eval_iters: iteration += 1 if verbose and iteration % args.log_interval == 0: print_rank_0('Evaluating iter {}/{}'.format(iteration, args.eval_iters)) # Forward evaluation. loss, loss_dict, target, predict = forward_step_func(data_iterator, model) labels_all.extend(target.view(-1).tolist()) predicts_all.extend(predict.view(-1).tolist()) # Reduce across processes. for key in loss_dict: total_loss_dict[key] = total_loss_dict.get(key, 0.) + loss_dict[key] acc = metrics.accuracy_score(labels_all, predicts_all) report = metrics.classification_report(labels_all, predicts_all) confusion = metrics.confusion_matrix(labels_all, predicts_all) acc = torch.tensor(acc).cuda() acc_list = [torch.zeros_like(acc) for _ in range(mpu.get_data_parallel_world_size())] torch.distributed.all_gather(acc_list, acc, mpu.get_data_parallel_group()) acc = torch.stack(acc_list, 0).view(-1).tolist() print(f"Evaluate acc:\n {acc}") print(f"Evaluate report:\n {report}") print(f"Evaluate confusion:\n {confusion}") # Move model back to the train mode. model.train() for key in total_loss_dict: total_loss_dict[key] /= args.eval_iters return total_loss_dict def evaluate_and_print_results(prefix, forward_step_func, data_iterator, model, iteration, verbose=False): """Helper function to evaluate and dump results on screen.""" writer = get_tensorboard_writer() total_loss_dict = evaluate(forward_step_func, data_iterator, model, verbose) string = ' validation loss at {} | '.format(prefix) for key in total_loss_dict: string += '{} value: {:.6E} | '.format(key, total_loss_dict[key].item()) ppl = math.exp(min(20, total_loss_dict[key].item())) string += '{} PPL: {:.6E} | '.format(key, ppl) if writer and torch.distributed.get_rank() == 0: writer.add_scalar('{} value'.format(key), total_loss_dict[key].item(), iteration) writer.add_scalar('{} ppl'.format(key), ppl, iteration) length = len(string) + 1 print_rank_0('-' * length) print_rank_0(string) print_rank_0('-' * length) def make_data_loader(dataset): """Buld dataloader given an input dataset.""" if dataset is None: return None args = get_args() # Data parallel arguments. world_size = mpu.get_data_parallel_world_size() rank = mpu.get_data_parallel_rank() global_batch_size = args.batch_size * world_size num_workers = args.num_workers # Use a simple sampler with distributed batch sampler. sampler = torch.utils.data.SequentialSampler(dataset) batch_sampler = DistributedBatchSampler(sampler=sampler, batch_size=global_batch_size, drop_last=True, rank=rank, world_size=world_size) return torch.utils.data.DataLoader(dataset, batch_sampler=batch_sampler, num_workers=num_workers, pin_memory=True, collate_fn=dataset.collate) def build_train_valid_test_data_iterators( build_train_valid_test_datasets_provider): """XXX""" args = get_args() (train_dataloader, valid_dataloader, test_dataloader) = (None, None, None) print_rank_0('> building train, validation, and test datasets ...') # Data loader only on rank 0 of each model parallel group. if mpu.get_model_parallel_rank() == 0: # Rank, size, and global batch size. data_parallel_size = mpu.get_data_parallel_world_size() global_batch_size = args.batch_size * data_parallel_size # Number of train/valid/test samples. train_iters = args.train_iters eval_iters = args.eval_iters test_iters = args.eval_iters train_val_test_num_samples = [train_iters * global_batch_size, eval_iters * global_batch_size, test_iters * global_batch_size] print_rank_0(' > datasets target sizes (minimum size):') print_rank_0(' train: {}'.format(train_val_test_num_samples[0])) print_rank_0(' validation: {}'.format(train_val_test_num_samples[1])) print_rank_0(' test: {}'.format(train_val_test_num_samples[2])) # Build the datasets. train_ds, valid_ds, test_ds = build_train_valid_test_datasets_provider(train_val_test_num_samples) # Build dataloders. train_dataloader = make_data_loader(train_ds) valid_dataloader = make_data_loader(valid_ds) test_dataloader = make_data_loader(test_ds) # Flags to know if we need to do training/validation/testing. do_train = train_dataloader is not None and args.train_iters > 0 do_valid = valid_dataloader is not None and args.eval_iters > 0 do_test = test_dataloader is not None and args.eval_iters > 0 # Need to broadcast num_tokens and num_type_tokens. flags = torch.cuda.LongTensor([int(do_train), int(do_valid), int(do_test)]) else: flags = torch.cuda.LongTensor([0, 0, 0]) # Broadcast num tokens. torch.distributed.broadcast(flags, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group()) args.do_train = flags[0].item() args.do_valid = flags[1].item() args.do_test = flags[2].item() # Shift the start iterations. if train_dataloader is not None: train_dataloader.batch_sampler.start_iter = args.iteration % len(train_dataloader) print_rank_0('setting training data start iteration to {}'.format(train_dataloader.batch_sampler.start_iter)) if valid_dataloader is not None: start_iter_val = (args.iteration // args.eval_interval) * args.eval_iters valid_dataloader.batch_sampler.start_iter = start_iter_val % len(valid_dataloader) print_rank_0('setting validation data start iteration to {}'.format(valid_dataloader.batch_sampler.start_iter)) # Build iterators. if train_dataloader is not None: train_data_iterator = iter(train_dataloader) train_data_iterator = cycle(train_data_iterator) else: train_data_iterator = None if valid_dataloader is not None: valid_data_iterator = iter(valid_dataloader) valid_data_iterator = cycle(valid_data_iterator) else: valid_data_iterator = None if test_dataloader is not None: test_data_iterator = iter(test_dataloader) test_data_iterator = cycle(test_data_iterator) else: test_data_iterator = None return train_data_iterator, valid_data_iterator, test_data_iterator pangu/method/finetune/tasks/finetune_nli.py000066400000000000000000000071501430334424000214430ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/5 # @Author: qing import os import sys import torch sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))) from megatron import get_args from megatron import print_rank_0 from megatron import get_timers from megatron import get_tokenizer from megatron import mpu from megatron.utils import get_ltor_masks_and_position_ids from megatron.utils import reduce_losses from method.finetune.datadefine.dataset import NliDataset from method.finetune.model.gpt2_model_ft_head import GPT2ModelFTHead from method.finetune.tasks.finetune import finetune def model_provider(): """Build the model.""" print_rank_0('building GPT2 model ...') # model = GPT2Model(num_tokentypes=0, parallel_output=True) model = GPT2ModelFTHead(num_tokentypes=0, parallel_output=True) return model def get_batch(data_iterator): """Generate a batch""" args = get_args() tokenizer = get_tokenizer() # Items and their type. keys = ['input_ids', 'label_ids', 'context_lengths'] datatype = torch.int64 # Broadcast data. if data_iterator is not None: data = next(data_iterator) else: data = None data_b = mpu.broadcast_data(keys, data, datatype) # Unpack. tokens_ = data_b['input_ids'].long() target = data_b['label_ids'].long() context_lengths = data_b['context_lengths'].long() labels = tokens_[:, 1:].contiguous() tokens = tokens_[:, :-1].contiguous() context_lengths[context_lengths>=args.seq_length] = args.seq_length-1 # Get the masks and postition ids. attention_mask, loss_mask, position_ids = get_ltor_masks_and_position_ids( tokens, tokenizer.eod, args.reset_position_ids, args.reset_attention_mask, args.eod_mask_loss) # # Loss mask for finetune # loss_mask_ft = torch.ones(tokens.size(), dtype=torch.float, device=tokens.device) # loss_mask_ft[tokens == tokenizer.pad_id] = 0.0 # loss_mask_ft = loss_mask_ft.unsqueeze(dim=-1) return tokens, labels, target, loss_mask, attention_mask, position_ids, context_lengths def forward_step(data_iterator, model): """Forward step.""" args = get_args() timers = get_timers() # Get the batch. timers('batch generator').start() tokens, labels, target, loss_mask, attention_mask, position_ids, context_lengths = get_batch(data_iterator) # print(f"tokens shape: {tokens.shape}") timers('batch generator').stop() # Forward model. # losses = model(tokens, position_ids, attention_mask, labels=labels) losses, predicts = model(tokens, position_ids, attention_mask, context_lengths, target=target) loss_mask = loss_mask.view(-1) loss = torch.sum(losses.view(-1) * loss_mask) / loss_mask.sum() # Reduce loss for logging. reduced_loss = reduce_losses([loss]) return loss, {'lm loss': reduced_loss[0]}, target, predicts def train_valid_test_datasets_provider(train_val_test_num_samples): """Build train, valid, and test datasets.""" args = get_args() tokenizer = get_tokenizer() print_rank_0('> building train, validation, and test datasets for pangu ...') train_ds = NliDataset(args, tokenizer, "train.json") valid_ds = NliDataset(args, tokenizer, "dev.json") test_ds = NliDataset(args, tokenizer, "dev.json") print_rank_0("> finished creating pangu finetune datasets ...") return train_ds, valid_ds, test_ds if __name__ == "__main__": finetune(train_valid_test_datasets_provider, model_provider, forward_step, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'})pangu/method/generate/000077500000000000000000000000001430334424000152375ustar00rootroot00000000000000pangu/method/generate/__init__.py000066400000000000000000000001211430334424000173420ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/5 # @Author: qing pangu/method/incontext/000077500000000000000000000000001430334424000154605ustar00rootroot00000000000000pangu/method/incontext/README.md000066400000000000000000000026261430334424000167450ustar00rootroot00000000000000# Incontext learning 上下文小样本学习范式，通过给模型输入任务提示和相关示例使用预训练语言模型，无需重新训练模型和增加额外参数，更接近人类解决新任务的机制。 本目录为鹏程预训练大模型的Incontext learning代码仓库，可以用于模型Incontext learning的多机多卡测试。 ## 实例 ### 实例1: 鹏程·盘古 句子关系-NLI任务 目前已支持鹏程·盘古模型的在NLI任务的Incontext learning，任务实例为CMNLI(自然语言推理中文版)任务。 - **算法原理** * 将输入嵌入到prompt中同时加上示例输入给模型，类似人类解决新任务的机制。 * 分类类型任务：将任务分解为困惑度比较任务。 * 具体请参参考盘古技术报告："PANGU-α: LARGE-SCALE AUTOREGRESSIVE PRETRAINED CHINESE LANGUAGE MODELS WITH AUTO-PARALLEL COMPUTATION"。 - **环境** * python依赖包见首页requirements.txt。 * 推荐使用英伟达的官方 docker 镜像docker pull nvcr.io/nvidia/pytorch:20.03-py3，并安装NLTK。 - **模型** * 目前实现基于盘古2.6\13B的pytorch-GPU版。 * 模型下载，请参考[PanGu-Alpha-GPU](https:\//git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha-GPU)。 - **运行** * 参考脚本：scripts/incontext_ppl_nli.sh - **性能** * 盘古2.6B-fp16，zero shot，验证集acc: 45%左右。pangu/method/incontext/__init__.py000066400000000000000000000001211430334424000175630ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/5 # @Author: qing pangu/method/incontext/datadefine/000077500000000000000000000000001430334424000175445ustar00rootroot00000000000000pangu/method/incontext/datadefine/__init__.py000066400000000000000000000001221430334424000216500ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing pangu/method/incontext/datadefine/dataset.py000066400000000000000000000075271430334424000215560ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing #!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/11 # @Author: qing import os import json import torch from torch.utils.data import Dataset class BaseDataset(Dataset): def __init__(self, args, tokenizer, split, ratio=1, num=-1): self.args = args self.tokenizer = tokenizer self.ratio = ratio self.split = split self.pad_id = tokenizer.pad_id self.sep_id = self.tokenizer.encoder[''] self.eod_id = self.tokenizer.encoder[''] self.data, self.max_len = self.process_data() if num > 0: self.data = self.data[:num] def collate(self, samples): bs = len(samples) model_data = { "input_ids": torch.ones(bs, self.args.seq_length, dtype=torch.long) * self.pad_id, "label_ids": torch.ones(bs, dtype=torch.long), "idxs": torch.ones(bs, dtype=torch.long), "context_lengths": torch.ones(bs, 1, dtype=torch.long), "loss_mask": torch.zeros(bs, self.args.seq_length, dtype=torch.long) } for i, samp in enumerate(samples): seq_len = len(samp["input_ids"]) loss_mask_index_left = samp["loss_mask_index_left"] model_data["input_ids"][i][:seq_len] = torch.tensor(samp["input_ids"], dtype=torch.long) model_data["label_ids"][i] = samp["label_ids"] model_data["idxs"][i] = samp["idx"] model_data["context_lengths"][i][0] = seq_len model_data["loss_mask"][i][loss_mask_index_left:seq_len] = 1 return model_data def __len__(self): return len(self.data) def __getitem__(self, idx): return self.data[idx] class NliDataset(BaseDataset): LABELS = {'contradiction': 0, 'entailment': 1, 'neutral': 2} template = ["{sentence1}？", "{label}，", "{sentence2}"] def __init__(self, args, tokenizer, split, ratio=1, num=-1): self.candicate_lb_ids = None self.max_num = 100000 super().__init__(args, tokenizer, split, ratio, num) def get_candicate_lb_ids(self): if not self.candicate_lb_ids: self.candicate_lb_ids = [self.tokenizer.encode(l) for l in ["错，", "对，", "或，"]] return self.candicate_lb_ids def process_data(self): data = [] sizes = [] data_path = os.path.join(self.args.data_path, self.split) with open(data_path, "r", encoding="utf-8") as f: data_lines = f.readlines() print(f"All {self.split} case num: {len(data_lines)}.") for i, instance in enumerate(data_lines): if i>=self.max_num: break instance = json.loads(instance) sentence1 = instance["sentence1"] sentence2 = instance["sentence2"] label = instance.get("label", "-100") if label not in self.LABELS: continue s1_str = self.template[0].replace("{sentence1}", sentence1) s2_str = self.template[2].replace("{sentence2}", sentence2) label_idx = self.LABELS.get(label, -100) s1_id = self.tokenizer.encode(s1_str) s2_id = self.tokenizer.encode(s2_str) candicate_lb_ids = self.get_candicate_lb_ids() for j, candicate_lb_id in enumerate(candicate_lb_ids): input_id = s1_id + candicate_lb_id + s2_id loss_mask_index_left = len(s1_id + candicate_lb_id) label_id = int(j==label_idx) sizes.append(len(input_id)) data.append({ "idx": i, "input_ids": input_id, "label_ids": label_id, "loss_mask_index_left": loss_mask_index_left }) max_len = max(sizes) return data, max_lenpangu/method/incontext/scripts/000077500000000000000000000000001430334424000171475ustar00rootroot00000000000000pangu/method/incontext/scripts/incontext_ppl_nli.sh000066400000000000000000000020401430334424000232270ustar00rootroot00000000000000#! /bin/bash #export CUDA_VISIBLE_DEVICES="0,1" #export NCCL_DEBUG="INFO" WORKING_DIR=/userhome/gpt3/PanGu-Alpha-Application MODEL_PATH=/root/pangu/model/Pangu-alpha_2.6B_mgt DATA_PATH=/root/pangu/data/cmnli_public GPUS_PER_NODE=1 MASTER_ADDR=localhost MASTER_PORT=6000 NNODES=1 NODE_RANK=0 WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT" python -u -m torch.distributed.launch $DISTRIBUTED_ARGS \ ${WORKING_DIR}/method/incontext/tasks/infer_ppl_nli.py \ --model-parallel-size 1 \ --num-layers 31 \ --hidden-size 2560 \ --num-attention-heads 32 \ --max-position-embeddings 1024 \ --tokenizer-type GPT2BPETokenizer \ --fp16 \ --batch-size 3 \ --seq-length 1024 \ --load $MODEL_PATH \ --data-path $DATA_PATH \ --vocab-file ${WORKING_DIR}/megatron/bpe_4w_pcl/vocab \ --merge-file gpt2-merges.txt \ --no-load-rng \ pangu/method/incontext/tasks/000077500000000000000000000000001430334424000166055ustar00rootroot00000000000000pangu/method/incontext/tasks/__init__.py000066400000000000000000000001221430334424000207110ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing pangu/method/incontext/tasks/infer_ppl.py000066400000000000000000000102631430334424000211370ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing import torch from tqdm import tqdm from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP from megatron import get_args, mpu from megatron import get_tokenizer from megatron.checkpointing import load_checkpoint from megatron.initialize import initialize_megatron from megatron.model import GPT2Model from megatron.fp16 import FP16_Module from megatron.model import DistributedDataParallel as LocalDDP from megatron.utils import get_ltor_masks_and_position_ids def get_model(): """Build the model.""" args = get_args() # Build model on cpu. model = GPT2Model(num_tokentypes=0, parallel_output=False) # Print number of parameters. if mpu.get_data_parallel_rank() == 0: rank_id = mpu.get_model_parallel_rank() parameter_num = sum([p.nelement() for p in model.parameters()]) print(f" > number of parameters on model parallel rank {rank_id}: {parameter_num}.", flush=True) # GPU allocation. model.cuda(torch.cuda.current_device()) # Fp16 conversion. if args.fp16: model = FP16_Module(model) # Wrap model for distributed training.""" if args.DDP_impl == 'torch': i = torch.cuda.current_device() model = torchDDP(model, device_ids=[i], output_device=i, process_group=mpu.get_data_parallel_group()) return model if args.DDP_impl == 'local': model = LocalDDP(model) return model raise NotImplementedError('Unknown DDP implementation specified: {}. Exiting.'.format(args.DDP_impl)) def do_infer_ppl(load_data_fn): initialize_megatron(args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}) args = get_args() tokenizer = get_tokenizer() model = get_model() load_checkpoint(model, None, None) model.eval() dataloader = load_data_fn() torch.distributed.barrier() loss_all, idx_all, label_all = [], [], [] with torch.no_grad(): for batch in tqdm(dataloader, disable=(torch.distributed.get_rank() != 0)): input_ids = batch['input_ids'].long().cuda() loss_mask = batch['loss_mask'].float().cuda() label_ids = batch['label_ids'].cuda() idxs = batch['idxs'].cuda() labels = input_ids[:, 1:].contiguous() tokens = input_ids[:, :-1].contiguous() loss_mask = loss_mask[:, :-1].contiguous() attention_mask, _, position_ids = get_ltor_masks_and_position_ids(tokens, tokenizer.eod, args.reset_position_ids, args.reset_attention_mask, args.eod_mask_loss) loss_ = model(tokens, position_ids, attention_mask, labels=labels) loss = torch.sum(loss_ * loss_mask, dim=-1) / loss_mask.sum(dim=-1) # print(f"loss: {loss}") # print(f"loss.shape: {loss.shape}") loss_list = [torch.zeros_like(loss) for _ in range(mpu.get_data_parallel_world_size())] torch.distributed.all_gather(loss_list, loss, mpu.get_data_parallel_group()) loss_list = torch.stack(loss_list, 0).view(-1).tolist() idxs_list = [torch.zeros_like(idxs) for _ in range(mpu.get_data_parallel_world_size())] torch.distributed.all_gather(idxs_list, idxs, mpu.get_data_parallel_group()) idxs_list = torch.stack(idxs_list, 0).view(-1).tolist() label_list = [torch.zeros_like(label_ids) for _ in range(mpu.get_data_parallel_world_size())] torch.distributed.all_gather(label_list, label_ids, mpu.get_data_parallel_group()) label_list = torch.stack(label_list, 0).view(-1).tolist() # print(f"loss_list: {loss_list}") # print(f"idxs_list: {idxs_list}") # print(f"label_list: {label_list}") loss_all.extend(loss_list) idx_all.extend(idxs_list) label_all.extend(label_list) return loss_all, idx_all, label_allpangu/method/incontext/tasks/infer_ppl_nli.py000066400000000000000000000056021430334424000220020ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/16 # @Author: qing import os import sys import torch import numpy as np sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))) from megatron import print_rank_0 from megatron import get_tokenizer from megatron import get_args, mpu from megatron.data.samplers import DistributedBatchSampler from method.incontext.datadefine.dataset import NliDataset from method.incontext.tasks.infer_ppl import do_infer_ppl def make_data_loader(dataset): """Buld dataloader given an input dataset.""" if dataset is None: return None args = get_args() # Data parallel arguments. world_size = mpu.get_data_parallel_world_size() rank = mpu.get_data_parallel_rank() global_batch_size = args.batch_size * world_size num_workers = args.num_workers # Use a simple sampler with distributed batch sampler. sampler = torch.utils.data.SequentialSampler(dataset) batch_sampler = DistributedBatchSampler(sampler=sampler, batch_size=global_batch_size, drop_last=False, rank=rank, world_size=world_size) return torch.utils.data.DataLoader(dataset, batch_sampler=batch_sampler, num_workers=num_workers, pin_memory=True, collate_fn=dataset.collate) def load_data(): """Build train, valid, and test datasets.""" args = get_args() tokenizer = get_tokenizer() print_rank_0('> building test dataset for nli ...') test_ds = NliDataset(args, tokenizer, "dev.json") print_rank_0("> finished creating nli incontext dataset ...") print_rank_0('> building test dataloader for nli ...') test_dataloader = make_data_loader(test_ds) print_rank_0("> finished creating nli incontext dataloader ...") return test_dataloader def evaluate_results(loss_all, idx_all, label_all): idx_set = list(set(idx_all)) loss_all = np.array(loss_all) idx_all = np.array(idx_all) label_all = np.array(label_all) print_rank_0(f"loss_all[:75]: {loss_all[:75]}.") print_rank_0(f"idx_all[:75]: {idx_all[:75]}.") print_rank_0(f"label_all[:75]: {label_all[:75]}.") cnt = 0 for idx in idx_set: losses = loss_all[idx_all==idx] pred_idx = np.argmin(losses) if label_all[idx_all==idx][pred_idx]: cnt += 1 acc = cnt/len(idx_set) return acc def do_infer_ppl_nli(): loss_all, idx_all, label_all = do_infer_ppl(load_data) acc = evaluate_results(loss_all, idx_all, label_all) print_rank_0(f"Evaluate acc:\n {acc}.") if __name__=="__main__": do_infer_ppl_nli() pangu/method/kgenhance/000077500000000000000000000000001430334424000153705ustar00rootroot00000000000000pangu/method/kgenhance/__init__.py000066400000000000000000000001211430334424000174730ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/5 # @Author: qing pangu/method/modelcompress/000077500000000000000000000000001430334424000163215ustar00rootroot00000000000000pangu/method/modelcompress/README.md000066400000000000000000000002311430334424000175740ustar00rootroot00000000000000# 模型压缩 将鹏程·盘古-13B/2.6B由8卡压缩到1卡上推理[[参见](https://git.openi.org.cn/PCL-Platform.Intelligence/Model-Compression)]。pangu/method/modelcompress/__init__.py000066400000000000000000000001211430334424000204240ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/5 # @Author: qing pangu/method/prompttune/000077500000000000000000000000001430334424000156625ustar00rootroot00000000000000pangu/method/prompttune/README.md000066400000000000000000000027531430334424000171500ustar00rootroot00000000000000# Prompttune 预训练加提示的范式，这种范式不是让预训练的语言模型适应下游任务，而是形式化下游任务使之在提示(prompt)的帮助更适合预训练模型的原始任务来解决问题。 本目录为鹏程预训练大模型的prompt-tune代码仓库，可以用于模型prompt-tune的多机多卡训练/测试。 ## 实例 ### 实例1: 鹏程·盘古 PET 句子关系-NLI任务 目前已支持鹏程·盘古模型的在NLI任务的prompt-tune，算法实例基于PET的思想，任务实例为CMNLI(自然语言推理中文版)任务。 - **算法原理** * 将输入嵌入到prompt中，label映射到特定的token或词上，从而将任务转换成生成任务。 * PET可以看作是一种手工prompt+tuning的方式，具体可参考论文：“It’s Not Just Size That Matters:Small Language Models Are Also Few-Shot Learners”。 - **环境** * python依赖包见首页requirements.txt。 * 推荐使用英伟达的官方 docker 镜像docker pull nvcr.io/nvidia/pytorch:20.03-py3，并安装NLTK。 - **模型** * 目前实现基于盘古2.6\13B的pytorch-GPU版。 * 模型下载，请参考[PanGu-Alpha-GPU](https:\//git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha-GPU)。 - **运行** * 参考脚本：scripts/prompttune_nli.sh - **性能** * 盘古2.6B-fp16，未充分调优情况下，训练集覆盖6遍时，验证集acc: 55%左右。 * 如充分调优，性能应该有较大提升空间。pangu/method/prompttune/__init__.py000066400000000000000000000001211430334424000177650ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/5 # @Author: qing pangu/method/prompttune/datadefine/000077500000000000000000000000001430334424000177465ustar00rootroot00000000000000pangu/method/prompttune/datadefine/__init__.py000066400000000000000000000001221430334424000220520ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/11 # @Author: qing pangu/method/prompttune/datadefine/dataset.py000066400000000000000000000062651430334424000217560ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/11 # @Author: qing import os import json import torch from torch.utils.data import Dataset class BaseDataset(Dataset): def __init__(self, args, tokenizer, split, ratio=1, num=-1): self.args = args self.tokenizer = tokenizer self.ratio = ratio self.split = split self.pad_id = tokenizer.pad_id self.sep_id = self.tokenizer.encoder[''] self.eod_id = self.tokenizer.encoder[''] self.data, self.max_len = self.process_data() if num > 0: self.data = self.data[:num] def collate(self, samples): bs = len(samples) model_data = { "input_ids": torch.ones(bs, self.args.seq_length, dtype=torch.long) * self.pad_id, "label_ids": torch.ones(bs, 1, dtype=torch.long), "context_lengths": torch.ones(bs, 1, dtype=torch.long), "loss_mask": torch.zeros(bs, self.args.seq_length, dtype=torch.long) } for i, samp in enumerate(samples): seq_len = len(samp["input_ids"]) model_data["input_ids"][i][:seq_len] = torch.tensor(samp["input_ids"], dtype=torch.long) model_data["label_ids"][i][0] = samp["label_ids"] model_data["context_lengths"][i][0] = seq_len model_data["loss_mask"][i, seq_len-2] = 1 return model_data def __len__(self): return len(self.data) def __getitem__(self, idx): return self.data[idx] class NliDataset(BaseDataset): LABELS = {'contradiction': 0, 'entailment': 1, 'neutral': 2} template = "句子一：{sentence1}。句子二：{sentence2}。句子一与句子二的关系是：1、矛盾，2、蕴含，3、中立。答案：{label}" def __init__(self, args, tokenizer, split, ratio=1, num=-1): super().__init__(args, tokenizer, split, ratio, num) @staticmethod def get_cand_ids(tokenizer): cand_ids = [tokenizer.encode(str(i)) for i in range(1, 4)] cand_ids = [cand_id[0] for cand_id in cand_ids] # print(f"cand_ids: {cand_ids}.") return cand_ids def process_data(self): data = [] sizes = [] data_path = os.path.join(self.args.data_path, self.split) with open(data_path, "r", encoding="utf-8") as f: data_lines = f.readlines() print(f"All {self.split} case num: {len(data_lines)}.") for i, instance in enumerate(data_lines): instance = json.loads(instance) sentence1 = instance["sentence1"] sentence2 = instance["sentence2"] label = instance.get("label", "-100") if label not in self.LABELS: continue label_id = self.LABELS.get(label, -100) input_id_str = self.template.replace("{sentence1}", sentence1).replace("{sentence2}", sentence2).replace("{label}", str(label_id+1)) input_id = self.tokenizer.encode(input_id_str) sizes.append(len(input_id)) data.append({ "idx": i, "input_ids": input_id, "label_ids": label_id, }) max_len = max(sizes) return data, max_lenpangu/method/prompttune/model/000077500000000000000000000000001430334424000167625ustar00rootroot00000000000000pangu/method/prompttune/model/__init__.py000066400000000000000000000001221430334424000210660ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/12 # @Author: qing pangu/method/prompttune/model/gpt2_model_pt.py000066400000000000000000000041061430334424000220740ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/12 # @Author: qing import torch from com.utils.mpu_util import mp_gather from megatron import mpu from megatron.model import GPT2Model from megatron.model.language_model import parallel_lm_logits def gpt2_attention_mask_func(attention_scores, ltor_mask): attention_scores.masked_fill_(ltor_mask, -10000.0) return attention_scores class GPT2ModelPT(GPT2Model): def __init__(self, num_tokentypes=0, parallel_output=True): super().__init__(num_tokentypes=num_tokentypes, parallel_output=parallel_output) def forward(self, input_ids, position_ids, attention_mask, labels=None, tokentype_ids=None, layer_past=None, get_key_value=False,forward_method_parallel_output=None): lm_output = self.language_model(input_ids, position_ids, attention_mask, tokentype_ids=tokentype_ids, layer_past=layer_past, get_key_value=get_key_value) if get_key_value: lm_output, presents = lm_output lm_output = torch.add(lm_output,0) parallel_output = self.parallel_output if forward_method_parallel_output is not None: parallel_output = forward_method_parallel_output output = parallel_lm_logits(lm_output, self.language_model.embedding.word_embeddings.weight, parallel_output) if get_key_value: output = [output, presents] if labels is None: return output else: if self.fp16_lm_cross_entropy: assert output.dtype == torch.half loss = mpu.vocab_parallel_cross_entropy(output, labels) else: loss = mpu.vocab_parallel_cross_entropy(output.float(), labels) if parallel_output: output_gather = mp_gather(output) else: output_gather = output return loss, output_gather pangu/method/prompttune/paras/000077500000000000000000000000001430334424000167705ustar00rootroot00000000000000pangu/method/prompttune/paras/__init__.py000066400000000000000000000001221430334424000210740ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing pangu/method/prompttune/paras/custom_arguments.py000066400000000000000000000005711430334424000227440ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/13 # @Author: qing def add_custom_args(parser): group = parser.add_argument_group(title='custom_args') group.add_argument('--gradient-accumulation-steps', type=int, default=1, help="Number of updates steps to accumulate before performing a backward/update pass.") return parserpangu/method/prompttune/scripts/000077500000000000000000000000001430334424000173515ustar00rootroot00000000000000pangu/method/prompttune/scripts/prompttune_nli.sh000066400000000000000000000035641430334424000227740ustar00rootroot00000000000000#! /bin/bash #export CUDA_VISIBLE_DEVICES="0,1" #export NCCL_DEBUG="INFO" WORKING_DIR=/userhome/gpt3/PanGu-Alpha-Application MODEL_PATH=/root/pangu/model/pangu_fp16_8mp_2b6 MODEL_SAVE_PATH=/root/pangu/model/pangu_fp16_8mp_2b6_pt_cmnli DATA_PATH=/root/pangu/data/cmnli_public TENSORBOARD_PATH=/root/pangu/log/tensorboard_nli_pt GPUS_PER_NODE=8 MASTER_ADDR=localhost MASTER_PORT=6000 NNODES=1 NODE_RANK=0 WORLD_SIZE=$(($GPUS_PER_NODE*$NNODES)) DISTRIBUTED_ARGS="--nproc_per_node $GPUS_PER_NODE --nnodes $NNODES --node_rank $NODE_RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT" python -u -m torch.distributed.launch $DISTRIBUTED_ARGS \ ${WORKING_DIR}/method/prompttune/tasks/prompt_tune_nli.py \ --model-parallel-size 8 \ --num-layers 31 \ --hidden-size 2560 \ --num-attention-heads 32 \ --batch-size 64 \ --seq-length 1024 \ --max-position-embeddings 1024 \ --train-iters 50000 \ --lr-decay-iters 1000 \ --save $MODEL_SAVE_PATH \ --load $MODEL_PATH \ --data-path $DATA_PATH \ --tensorboard-dir $TENSORBOARD_PATH \ --vocab-file ${WORKING_DIR}/megatron/bpe_4w_pcl/vocab \ --merge-file gpt2-merges.txt \ --data-impl mmap \ --split 949,50,1 \ --distributed-backend nccl \ --lr 0.00005 \ --lr-decay-style cosine \ --min-lr 1.0e-6 \ --weight-decay 1e-2 \ --clip-grad 1.0 \ --warmup .01 \ --log-interval 100 \ --save-interval 10000 \ --eval-interval 500 \ --eval-iters 200 \ --attention-dropout 0.1 \ --hidden-dropout 0.1 \ --seed 1234 \ --finetune \ --DDP-impl "torch"\ --checkpoint-activations \ --distribute-checkpointed-activations \ --fp16-lm-cross-entropy \ --use-cpu-initialization \ --make-vocab-size-divisible-by 1 \ pangu/method/prompttune/tasks/000077500000000000000000000000001430334424000170075ustar00rootroot00000000000000pangu/method/prompttune/tasks/__init__.py000066400000000000000000000001221430334424000211130ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/12 # @Author: qing pangu/method/prompttune/tasks/prompt_tune.py000066400000000000000000000564301430334424000217450ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/12 # @Author: qing import math import sys import torch from sklearn import metrics from itertools import cycle from datetime import datetime from apex.optimizers import FusedAdam as Adam from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP from megatron import get_args, get_tokenizer from megatron import get_timers from megatron import get_tensorboard_writer from megatron import mpu from megatron import print_rank_0 from megatron.checkpointing import load_checkpoint from megatron.checkpointing import save_checkpoint from megatron.fp16 import FP16_Module from megatron.fp16 import FP16_Optimizer from megatron.initialize import initialize_megatron from megatron.learning_rates import AnnealingLR from megatron.model import DistributedDataParallel as LocalDDP from megatron.model import get_params_for_weight_decay_optimization from megatron.utils import check_adlr_autoresume_termination from megatron.utils import make_data_loader from megatron.utils import report_memory from megatron.data.samplers import DistributedBatchSampler def prompt_tune_train(train_valid_test_dataset_provider, model_provider, forward_step_func, extra_args_provider=None, args_defaults={}): """Main training program. This function will run the followings in the order provided: 1) initialize Megatron. 2) setup model, optimizer and lr schedule using the model_provider. 3) call train_val_test_data_provider to get train/val/test datasets. 4) train the modle using the forward_step_func. Arguments: train_valid_test_dataset_provider: a function that takes the size of train/valid/test dataset and returns `train, valid, test` datasets. model_provider: a function that returns a vanilla version of the model. By vanilla we mean a simple model on cpu with no fp16 or ddp. forward_step_func: a function that takes a `data iterator` and `model`, and returns a `loss` scalar with a dictionary with key:values being the info we would like to monitor during training, for example `lm-loss: value`. We also require that this function add `batch generator` to the timers class. extra_args_provider: a function that takes a parser and adds arguments to it. It is used for programs to add their own arguments. args_defaults: a dictionary from argument-name to argument-value. It to set already parse arguments. """ # Initalize and get arguments, timers, and Tensorboard writer. initialize_megatron(extra_args_provider=extra_args_provider, args_defaults=args_defaults) args = get_args() timers = get_timers() # Model, optimizer, and learning rate. timers('model and optimizer').start() model, optimizer, lr_scheduler = setup_model_and_optimizer(model_provider) timers('model and optimizer').stop() # Data stuff. timers('train/valid/test data iterators').start() train_data_iterator, valid_data_iterator, test_data_iterator = build_train_valid_test_data_iterators(train_valid_test_dataset_provider) timers('train/valid/test data iterators').stop() # Print setup timing. print_rank_0('done with setups ...') timers.log(['model and optimizer', 'train/valid/test data iterators']) print_rank_0('training ...') iteration = 0 if args.do_train and args.train_iters > 0: iteration, _ = train(forward_step_func, model, optimizer, lr_scheduler, train_data_iterator, valid_data_iterator) if args.do_valid: prefix = 'the end of training for val data' evaluate_and_print_results(prefix, forward_step_func, valid_data_iterator, model, iteration, False) if args.save and iteration != 0: save_checkpoint(iteration, model, optimizer, lr_scheduler) if args.do_test: # Run on test data. prefix = 'the end of training for test data' evaluate_and_print_results(prefix, forward_step_func, test_data_iterator, model, 0, True) def get_model(model_provider_func): """Build the model.""" args = get_args() # Build model on cpu. model = model_provider_func() # Print number of parameters. if mpu.get_data_parallel_rank() == 0: print(' > number of parameters on model parallel rank {}: {}'.format( mpu.get_model_parallel_rank(), sum([p.nelement() for p in model.parameters()])), flush=True) # GPU allocation. model.cuda(torch.cuda.current_device()) # Fp16 conversion. if args.fp16: model = FP16_Module(model) # Wrap model for distributed training.""" if args.DDP_impl == 'torch': i = torch.cuda.current_device() model = torchDDP(model, device_ids=[i], output_device=i, process_group=mpu.get_data_parallel_group()) return model if args.DDP_impl == 'local': model = LocalDDP(model) return model raise NotImplementedError('Unknown DDP implementation specified: {}. ' 'Exiting.'.format(args.DDP_impl)) def get_optimizer(model): """Set up the optimizer.""" args = get_args() # Build parameter groups (weight decay and non-decay). while isinstance(model, (torchDDP, LocalDDP, FP16_Module)): model = model.module # model = model.liner param_groups = get_params_for_weight_decay_optimization(model) # Add model parallel attribute if it is not set. for param_group in param_groups: for param in param_group['params']: if not hasattr(param, 'model_parallel'): param.model_parallel = False # Use Adam. optimizer = Adam(param_groups, lr=args.lr, weight_decay=args.weight_decay) # Wrap into fp16 optimizer. if args.fp16: optimizer = FP16_Optimizer(optimizer, static_loss_scale=args.loss_scale, dynamic_loss_scale=args.dynamic_loss_scale, dynamic_loss_args={ 'scale_window': args.loss_scale_window, 'min_scale': args.min_scale, 'delayed_shift': args.hysteresis}) return optimizer def get_learning_rate_scheduler(optimizer): """Build the learning rate scheduler.""" args = get_args() # Add linear learning rate scheduler. if args.lr_decay_iters is not None: num_iters = args.lr_decay_iters else: num_iters = args.train_iters num_iters = max(1, num_iters) init_step = 0 warmup_iter = args.warmup * num_iters lr_scheduler = AnnealingLR( optimizer, start_lr=args.lr, warmup_iter=warmup_iter, total_iters=num_iters, decay_style=args.lr_decay_style, last_iter=init_step, min_lr=args.min_lr, use_checkpoint_lr_scheduler=args.use_checkpoint_lr_scheduler, override_lr_scheduler=args.override_lr_scheduler) return lr_scheduler def add_new_token(model): tokenizer = get_tokenizer() tokenizer.add_special_tokens({'seq_token': '[SQ]'}) tokenizer.add_special_tokens({'eot_token': '[ET]'}) while isinstance(model, (torchDDP, LocalDDP, FP16_Module)): model = model.module model.gpt.resize_token_embeddings(len(tokenizer)) def setup_model_and_optimizer(model_provider_func): """Setup model and optimizer.""" args = get_args() model = get_model(model_provider_func) # add_new_token(model) optimizer = get_optimizer(model) lr_scheduler = get_learning_rate_scheduler(optimizer) if args.load is not None: args.iteration = load_checkpoint(model, optimizer, lr_scheduler) else: args.iteration = 0 # get model without FP16 and/or TorchDDP wrappers unwrapped_model = model while hasattr(unwrapped_model, 'module'): unwrapped_model = unwrapped_model.module if args.iteration == 0 and hasattr(unwrapped_model, 'init_state_dict_from_bert'): print("Initializing ICT from pretrained BERT model", flush=True) unwrapped_model.init_state_dict_from_bert() return model, optimizer, lr_scheduler def backward_step(optimizer, model, loss, iteration): """Backward step.""" args = get_args() timers = get_timers() # 与optimizer.step要后一个batch做optimizer.zero_grad step_flag = (not args.gradient_accumulation_steps) or (iteration % args.gradient_accumulation_steps == 0) # Backward pass. timers('backward-backward').start() if step_flag: optimizer.zero_grad(set_grads_to_None=True) if args.fp16: optimizer.backward(loss, update_master_grads=False) else: loss.backward() timers('backward-backward').stop() # All-reduce if needed. if args.DDP_impl == 'local': timers('backward-allreduce').start() model.allreduce_params(reduce_after=False, fp32_allreduce=args.fp32_allreduce) timers('backward-allreduce').stop() # Update master gradients. timers('backward-master-grad').start() if args.fp16: optimizer.update_master_grads() timers('backward-master-grad').stop() # Clipping gradients helps prevent the exploding gradient. timers('backward-clip-grad').start() if args.clip_grad > 0: if not args.fp16: mpu.clip_grad_norm(model.parameters(), args.clip_grad) else: optimizer.clip_master_grads(args.clip_grad) timers('backward-clip-grad').stop() def train_step(forward_step_func, data_iterator, model, optimizer, lr_scheduler, iteration): """Single training step.""" args = get_args() timers = get_timers() # Forward model for one step. timers('forward').start() loss, loss_reduced, target, predict = forward_step_func(data_iterator, model) if args.gradient_accumulation_steps: loss = loss / args.gradient_accumulation_steps # print_rank_0(f"target: {target.view(-1).tolist()}.") # print_rank_0(f"predict: {predict.view(-1).tolist()}.") # print_rank_0(f"loss_reduced: {loss_reduced}.") timers('forward').stop() torch.cuda.empty_cache() # Calculate gradients, reduce across processes, and clip. timers('backward').start() backward_step(optimizer, model, loss, iteration) timers('backward').stop() torch.cuda.empty_cache() step_flag = (not args.gradient_accumulation_steps) or ((iteration + 1) % args.gradient_accumulation_steps == 0) # Update parameters. timers('optimizer').start() if step_flag: optimizer.step() timers('optimizer').stop() torch.cuda.empty_cache() # Update learning rate. skipped_iter = 0 if not (args.fp16 and optimizer.overflow): if step_flag: lr_scheduler.step() else: skipped_iter = 1 return loss_reduced, skipped_iter def training_log(loss_dict, total_loss_dict, learning_rate, iteration, loss_scale, report_memory_flag): """Log training information such as losses, timing, ....""" args = get_args() timers = get_timers() writer = get_tensorboard_writer() # Update losses. for key in loss_dict: total_loss_dict[key] = total_loss_dict.get(key, 0.) + loss_dict[key] # Logging. timers_to_log = [] def add_to_logging(name): if name in timers.timers: timers_to_log.append(name) add_to_logging('forward') add_to_logging('backward') add_to_logging('backward-backward') add_to_logging('backward-allreduce') add_to_logging('backward-master-grad') add_to_logging('backward-clip-grad') add_to_logging('optimizer') add_to_logging('batch generator') # Tensorboard values. if writer and torch.distributed.get_rank() == 0: writer.add_scalar('learning_rate', learning_rate, iteration) for key in loss_dict: writer.add_scalar(key, loss_dict[key], iteration) if args.fp16: writer.add_scalar('loss_scale', loss_scale, iteration) normalizer = iteration % args.log_interval if normalizer == 0: normalizer = args.log_interval timers.write(timers_to_log, writer, iteration, normalizer=normalizer) if iteration % args.log_interval == 0: elapsed_time = timers('interval time').elapsed() if writer and torch.distributed.get_rank() == 0: writer.add_scalar('iteration_time', elapsed_time / args.log_interval, iteration) log_string = ' iteration {:8d}/{:8d} |'.format(iteration, args.train_iters) log_string += ' elapsed time per iteration (ms): {:.1f} |'.format( elapsed_time * 1000.0 / args.log_interval) log_string += ' learning rate: {:.3E} |'.format(learning_rate) for key in total_loss_dict: avg = total_loss_dict[key].item() / args.log_interval log_string += ' {}: {:.6E} |'.format(key, avg) total_loss_dict[key] = 0.0 if args.fp16: log_string += ' loss scale: {:.1f} |'.format(loss_scale) print_rank_0(log_string) if report_memory_flag: report_memory('after {} iterations'.format(iteration)) report_memory_flag = False timers.log(timers_to_log, normalizer=args.log_interval) return report_memory_flag def train(forward_step_func, model, optimizer, lr_scheduler, train_data_iterator, valid_data_iterator): """Train the model function.""" args = get_args() timers = get_timers() # Turn on training mode which enables dropout. model.train() # Tracking loss. total_loss_dict = {} # Iterations. iteration = args.iteration skipped_iters = 0 timers('interval time').start() report_memory_flag = True while iteration < args.train_iters: loss_dict, skipped_iter = train_step(forward_step_func, train_data_iterator, model, optimizer, lr_scheduler, iteration) skipped_iters += skipped_iter iteration += 1 # Logging. loss_scale = None if args.fp16: loss_scale = optimizer.loss_scale report_memory_flag = training_log(loss_dict, total_loss_dict, optimizer.param_groups[0]['lr'], iteration, loss_scale, report_memory_flag) # Autoresume if args.adlr_autoresume and \ (iteration % args.adlr_autoresume_interval == 0): check_adlr_autoresume_termination(iteration, model, optimizer, lr_scheduler) # Checkpointing if args.save and args.save_interval and \ iteration % args.save_interval == 0: save_checkpoint(iteration, model, optimizer, lr_scheduler) # Evaluation if args.eval_interval and iteration % args.eval_interval == 0 and \ args.do_valid: prefix = 'iteration {}'.format(iteration) evaluate_and_print_results(prefix, forward_step_func, valid_data_iterator, model, iteration, False) if args.exit_interval and iteration % args.exit_interval == 0: torch.distributed.barrier() time_str = datetime.now().strftime('%Y-%m-%d %H:%M:%S') rank = torch.distributed.get_rank() print_rank_0('rank: {} | time: {} | exiting the program at ' 'iteration {}'.format(rank, time_str, iteration)) sys.exit() return iteration, skipped_iters def evaluate(forward_step_func, data_iterator, model, verbose=False): """Evaluation.""" args = get_args() # Turn on evaluation mode which disables dropout. model.eval() total_loss_dict = {} labels_all, predicts_all = [], [] with torch.no_grad(): iteration = 0 while iteration < args.eval_iters: iteration += 1 if verbose and iteration % args.log_interval == 0: print_rank_0('Evaluating iter {}/{}'.format(iteration, args.eval_iters)) # Forward evaluation. loss, loss_dict, target, predict = forward_step_func(data_iterator, model) labels_all.extend(target.view(-1).tolist()) predicts_all.extend(predict.view(-1).tolist()) # Reduce across processes. for key in loss_dict: total_loss_dict[key] = total_loss_dict.get(key, 0.) + loss_dict[key] acc = metrics.accuracy_score(labels_all, predicts_all) report = metrics.classification_report(labels_all, predicts_all) confusion = metrics.confusion_matrix(labels_all, predicts_all) acc = torch.tensor(acc).cuda() acc_list = [torch.zeros_like(acc) for _ in range(mpu.get_data_parallel_world_size())] torch.distributed.all_gather(acc_list, acc, mpu.get_data_parallel_group()) acc = torch.stack(acc_list, 0).view(-1).tolist() print_rank_0(f"Evaluate acc:\n {acc}") print_rank_0(f"Evaluate report:\n {report}") print_rank_0(f"Evaluate confusion:\n {confusion}") # Move model back to the train mode. model.train() for key in total_loss_dict: total_loss_dict[key] /= args.eval_iters return total_loss_dict def evaluate_and_print_results(prefix, forward_step_func, data_iterator, model, iteration, verbose=False): """Helper function to evaluate and dump results on screen.""" writer = get_tensorboard_writer() total_loss_dict = evaluate(forward_step_func, data_iterator, model, verbose) string = ' validation loss at {} | '.format(prefix) for key in total_loss_dict: string += '{} value: {:.6E} | '.format(key, total_loss_dict[key].item()) ppl = math.exp(min(20, total_loss_dict[key].item())) string += '{} PPL: {:.6E} | '.format(key, ppl) if writer and torch.distributed.get_rank() == 0: writer.add_scalar('{} value'.format(key), total_loss_dict[key].item(), iteration) writer.add_scalar('{} ppl'.format(key), ppl, iteration) length = len(string) + 1 print_rank_0('-' * length) print_rank_0(string) print_rank_0('-' * length) def make_data_loader(dataset): """Buld dataloader given an input dataset.""" if dataset is None: return None args = get_args() # Data parallel arguments. world_size = mpu.get_data_parallel_world_size() rank = mpu.get_data_parallel_rank() global_batch_size = args.batch_size * world_size num_workers = args.num_workers # Use a simple sampler with distributed batch sampler. sampler = torch.utils.data.SequentialSampler(dataset) batch_sampler = DistributedBatchSampler(sampler=sampler, batch_size=global_batch_size, drop_last=True, rank=rank, world_size=world_size) return torch.utils.data.DataLoader(dataset, batch_sampler=batch_sampler, num_workers=num_workers, pin_memory=True, collate_fn=dataset.collate) def build_train_valid_test_data_iterators( build_train_valid_test_datasets_provider): """XXX""" args = get_args() (train_dataloader, valid_dataloader, test_dataloader) = (None, None, None) print_rank_0('> building train, validation, and test datasets ...') # Data loader only on rank 0 of each model parallel group. if mpu.get_model_parallel_rank() == 0: # Rank, size, and global batch size. data_parallel_size = mpu.get_data_parallel_world_size() global_batch_size = args.batch_size * data_parallel_size # Number of train/valid/test samples. train_iters = args.train_iters eval_iters = args.eval_iters test_iters = args.eval_iters train_val_test_num_samples = [train_iters * global_batch_size, eval_iters * global_batch_size, test_iters * global_batch_size] print_rank_0(' > datasets target sizes (minimum size):') print_rank_0(' train: {}'.format(train_val_test_num_samples[0])) print_rank_0(' validation: {}'.format(train_val_test_num_samples[1])) print_rank_0(' test: {}'.format(train_val_test_num_samples[2])) # Build the datasets. train_ds, valid_ds, test_ds = build_train_valid_test_datasets_provider(train_val_test_num_samples) # Build dataloders. train_dataloader = make_data_loader(train_ds) valid_dataloader = make_data_loader(valid_ds) test_dataloader = make_data_loader(test_ds) # Flags to know if we need to do training/validation/testing. do_train = train_dataloader is not None and args.train_iters > 0 do_valid = valid_dataloader is not None and args.eval_iters > 0 do_test = test_dataloader is not None and args.eval_iters > 0 # Need to broadcast num_tokens and num_type_tokens. flags = torch.cuda.LongTensor([int(do_train), int(do_valid), int(do_test)]) else: flags = torch.cuda.LongTensor([0, 0, 0]) # Broadcast num tokens. torch.distributed.broadcast(flags, mpu.get_model_parallel_src_rank(), group=mpu.get_model_parallel_group()) args.do_train = flags[0].item() args.do_valid = flags[1].item() args.do_test = flags[2].item() # Shift the start iterations. if train_dataloader is not None: train_dataloader.batch_sampler.start_iter = args.iteration % len(train_dataloader) print_rank_0('setting training data start iteration to {}'.format(train_dataloader.batch_sampler.start_iter)) if valid_dataloader is not None: start_iter_val = (args.iteration // args.eval_interval) * args.eval_iters valid_dataloader.batch_sampler.start_iter = start_iter_val % len(valid_dataloader) print_rank_0('setting validation data start iteration to {}'.format(valid_dataloader.batch_sampler.start_iter)) # Build iterators. if train_dataloader is not None: train_data_iterator = iter(train_dataloader) train_data_iterator = cycle(train_data_iterator) else: train_data_iterator = None if valid_dataloader is not None: valid_data_iterator = iter(valid_dataloader) valid_data_iterator = cycle(valid_data_iterator) else: valid_data_iterator = None if test_dataloader is not None: test_data_iterator = iter(test_dataloader) test_data_iterator = cycle(test_data_iterator) else: test_data_iterator = None return train_data_iterator, valid_data_iterator, test_data_iterator pangu/method/prompttune/tasks/prompt_tune_nli.py000066400000000000000000000106401430334424000226000ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/8/12 # @Author: qing import os import sys import torch sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))) from megatron import get_args from megatron import print_rank_0 from megatron import get_timers from megatron import get_tokenizer from megatron import mpu from megatron.utils import get_ltor_masks_and_position_ids from megatron.utils import reduce_losses from method.prompttune.datadefine.dataset import NliDataset from method.prompttune.model.gpt2_model_pt import GPT2ModelPT from method.prompttune.tasks.prompt_tune import prompt_tune_train from method.prompttune.paras.custom_arguments import add_custom_args def model_provider(): """Build the model.""" print_rank_0('building PT model ...') model = GPT2ModelPT(num_tokentypes=0, parallel_output=True) return model def get_batch(data_iterator): """Generate a batch""" args = get_args() tokenizer = get_tokenizer() # Items and their type. keys = ['input_ids', 'label_ids', 'context_lengths', 'loss_mask'] datatype = torch.int64 # Broadcast data. if data_iterator is not None: data = next(data_iterator) else: data = None data_b = mpu.broadcast_data(keys, data, datatype) # Unpack. tokens_ = data_b['input_ids'].long() target = data_b['label_ids'].long() context_lengths = data_b['context_lengths'].long() loss_mask = data_b['loss_mask'].float() labels = tokens_[:, 1:].contiguous() tokens = tokens_[:, :-1].contiguous() loss_mask = loss_mask[:, :-1].contiguous() context_lengths[context_lengths>=args.seq_length] = args.seq_length-1 # Get the masks and postition ids. attention_mask, _, position_ids = get_ltor_masks_and_position_ids( tokens, tokenizer.eod, args.reset_position_ids, args.reset_attention_mask, args.eod_mask_loss) # loss_mask = torch.ones(tokens.shape, dtype=torch.float, device=tokens.device) # loss_mask[tokens == tokenizer.pad_id] = 0.0 # loss_mask = loss_mask.unsqueeze(dim=-1) return tokens, labels, target, loss_mask, attention_mask, position_ids, context_lengths def get_preds(logits, loss_mask): tokenizer = get_tokenizer() cand_ids = NliDataset.get_cand_ids(tokenizer) output = torch.sum(logits * loss_mask.unsqueeze(-1), 1) / torch.sum(loss_mask, -1).unsqueeze(-1) scores = output[:, cand_ids] preds = torch.argmax(scores, dim=-1) # tensor_list = [torch.zeros_like(preds) for _ in range(mpu.get_data_parallel_world_size())] # torch.distributed.all_gather(tensor_list, preds, mpu.get_data_parallel_group()) # preds = torch.stack(tensor_list, 0).view(-1).cpu().detach().numpy() return preds def forward_step(data_iterator, model, parallel_output=None): """Forward step.""" args = get_args() timers = get_timers() timers('batch generator').start() tokens, labels, target, loss_mask, attention_mask, position_ids, context_lengths = get_batch(data_iterator) timers('batch generator').stop() loss_, logits = model(tokens, position_ids, attention_mask, labels=labels, forward_method_parallel_output=parallel_output) loss = torch.sum(loss_*loss_mask)/loss_mask.sum() reduced_loss = reduce_losses([loss]) # print_rank_0(f"loss_mask shape: {loss_mask.shape}.") # print_rank_0(f"tokens shape: {tokens.shape}.") # print_rank_0(f"loss_ shape: {loss_.shape}.") # print_rank_0(f"loss shape: {loss.shape}.") preds = get_preds(logits, loss_mask) return loss, {'lm loss': reduced_loss[0]}, target, preds def train_valid_test_datasets_provider(train_val_test_num_samples): """Build train, valid, and test datasets.""" args = get_args() tokenizer = get_tokenizer() print_rank_0('> building train, validation, and test datasets for pangu ...') train_ds = NliDataset(args, tokenizer, "train.json") valid_ds = NliDataset(args, tokenizer, "dev.json") test_ds = NliDataset(args, tokenizer, "dev.json") print_rank_0("> finished creating pangu finetune datasets ...") return train_ds, valid_ds, test_ds if __name__ == "__main__": prompt_tune_train(train_valid_test_datasets_provider, model_provider, forward_step, extra_args_provider=add_custom_args, args_defaults={'tokenizer_type': 'GPT2BPETokenizer'})pangu/model/000077500000000000000000000000001430334424000132655ustar00rootroot00000000000000pangu/model/README.md000066400000000000000000000017731430334424000145540ustar00rootroot00000000000000# 鹏程系列大模型 ## 简介 基于分布训练模架、大数据、高效算法训练大模型，构建文本、多语言、多模型、知识图谱等领域的AI底层基础设施。本目录包含了现有的鹏程系列大模型的基本信息。 ## 鹏程系列大模型 | 序号 | 类型 | 语言 | 模型 | 参数 | 结构 | 导航 | | --- | :-----: | :-----: | :-----: | :-----: | :-----: | :-----: | | 1 | NLP | 中文 | 鹏程.盘古 | 200B
13B
2.6B
350M | Transformer decoder | [[链接](pangu)] | | 2 | NLP | 中文 | 鹏程.盘古增强版 | 2.6B
350M | Transformer decoder | [[链接](pangu_evolution)] | ## 进展 - **2021.04.xx**
* 鹏程.盘古发布，包含200B(暂不开源)、13B、2.6B三个参数规模的版本，支持GPU/NPU/Pytorch/Mindspore。 - **2021.11.25**
* 鹏程.盘古增强版发布，包含2.6B一个参数规模的版本。 - **2022.01.25**
* 鹏程.盘古small GPU版发布，参数规模350M。 ## License ```bash 正在添加中 ``` pangu/model/__init__.py000066400000000000000000000001231430334424000153720ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/11/11 # @Author: qing pangu/model/pangu/000077500000000000000000000000001430334424000143775ustar00rootroot00000000000000pangu/model/pangu/README.md000066400000000000000000000046411430334424000156630ustar00rootroot00000000000000# 鹏程.盘古 ## 简介 鹏程.盘古是以鹏城实验室为首的技术团队联合攻关训练出的业界首个2000亿参数以中文为核心的预训练生成语言模型。 支持丰富的场景应用，在知识问答、知识检索、知识推理、阅读理解等文本生成领域表现突出，具备很强的小样本学习能力。 ## 详述 ### 项目主页 NPU版：[[NPU版](https:\//git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha)] GPU版：[[GPU版](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha-GPU)] ### 模型配置 | 模型 | 参数规模 | 结构 | num-layers | hidden-size | ffn-size | heads | | ------ | ------- | ------ | ------ | ------ | ------ | ------ | | 鹏程.盘古 200B | 200B | Transformer decoder | 64 | 16384 | 65536 | 128 | | 鹏程.盘古 13B | 13B | Transformer decoder | 40 | 5120 | 20480 | 40 | | 鹏程.盘古 2.6B | 2.6B | Transformer decoder | 32 | 2560 | 10240 | 32 | | 鹏程.盘古 small| 350M | Transformer decoder | 24 | 1024 | 4096 | 16 | ## 示例-pytorch版 ### 模型下载 | 参数 | 平台 | 模型文件 | 大小 | | ------ | ------- | ------ | ---- | | 350M | pytorch | [pangu-alpha_350m_fp16.zip](https://git.openi.org.cn/attachments/6e516012-18be-4576-a54e-570218bb3dbd) | 600M | **注：pytorch版的`num-layers` 要设为实 `num-layers - 1`** ### 推理 ```bash bash scripts/generate_pangu.sh ``` ### 模型迁移：finetune\promptune\fewshot learning 请参考模型迁移目录[[模型迁移](../../method)] ### 性能 对于每个任务，如果能获取到测试集则在测试集上进行评估，否则在验证集上进行评估。 同时为了降低计算资源消耗，部分任务上从数据中随机采样部分子集进行评估。 盘古2.6B和盘古small的pytorch的zero shot测试结果如下表。
注：测试结果与mindspore版存在少许差异，差异原因是测试设置并未完全一致。 ## 示例-mindspore版 请参考鹏程.盘古项目主页[[Pangu](https:\//git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha)]。 ## 环境 ### pytorch版 支持 python >= 3.6, pytorch >= 1.5, cuda >= 10, nccl >= 2.6, and nltk。 推荐使用英伟达的官方 docker 镜像`docker pull nvcr.io/nvidia/pytorch:20.03-py3`。 ### mindspore版 请参考鹏程.盘古项目主页[[Pangu](https:\//git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha)]。 pangu/model/pangu/__init__.py000066400000000000000000000001221430334424000165030ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2022/1/19 # @Author: qing pangu/model/pangu/inference/000077500000000000000000000000001430334424000163355ustar00rootroot00000000000000pangu/model/pangu/inference/__init__.py000066400000000000000000000001231430334424000204420ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/11/22 # @Author: qing pangu/model/pangu/inference/infer_generate.py000066400000000000000000000170271430334424000216730ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/10/8 # @Author: qing import os import sys import torch import numpy as np import torch.nn.functional as F from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))) from megatron import get_args, mpu from megatron import get_tokenizer from megatron.checkpointing import load_checkpoint from megatron.initialize import initialize_megatron from megatron.fp16 import FP16_Module from megatron.model import GPT2Model from megatron.model import DistributedDataParallel as LocalDDP from megatron.utils import get_ltor_masks_and_position_ids class InferGenerate(object): def __init__(self, model_name=""): initialize_megatron(args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}) self.args = get_args() self.tokenizer = get_tokenizer() self.model = self.get_model(model_name) load_checkpoint(self.model, None, None) self.model.eval() def top_k_logits(self, logits, top_k=0, top_p=0.0, filter_value=-float('Inf')): # This function has been mostly taken from huggingface conversational ai code at # https://medium.com/huggingface/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2d818ac26313 if top_k > 0: # Remove all tokens with a probability less than the last token of the top-k indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = filter_value batch_size = logits.size()[0] if top_p > 0.0: logits = logits.view(batch_size, -1).contiguous() for logit in logits: sorted_logits, sorted_indices = torch.sort(logit, descending=True) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold sorted_indices_to_remove = cumulative_probs > top_p # Shift the indices to the right to keep also the first token above the threshold sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 indices_to_remove = sorted_indices[sorted_indices_to_remove] logit[indices_to_remove] = filter_value logits = logits.view(batch_size, -1).contiguous() return logits def get_model(self, model_name=""): """Build the model.""" args = get_args() # Build model on cpu. if "pangu" == model_name: model = GPT2Model(num_tokentypes=0, parallel_output=False) elif "pangu_prompt" == model_name: from method.prompttune.model.gpt2_model_pt import GPT2ModelPT model = GPT2ModelPT(num_tokentypes=0, parallel_output=False) else: model = GPT2Model(num_tokentypes=0, parallel_output=False) # Print number of parameters. if mpu.get_data_parallel_rank() == 0: rank_id = mpu.get_model_parallel_rank() parameter_num = sum([p.nelement() for p in model.parameters()]) print(f" > number of parameters on model parallel rank {rank_id}: {parameter_num}.", flush=True) # GPU allocation. model.cuda(torch.cuda.current_device()) # Fp16 conversion. if args.fp16: model = FP16_Module(model) # Wrap model for distributed training.""" if args.DDP_impl == 'torch': i = torch.cuda.current_device() model = torchDDP(model, device_ids=[i], output_device=i, process_group=mpu.get_data_parallel_group()) return model if args.DDP_impl == 'local': model = LocalDDP(model) return model raise NotImplementedError('Unknown DDP implementation specified: {}. Exiting.'.format(args.DDP_impl)) def adjust_next_token_scores(self, input_ids, scores, cur_len, eot=None, batch_size=1, num_beams=1): return scores def get_single_generate_data(self, input_str, max_len): origin_input_ids = self.tokenizer.tokenize(input_str) origin_input_ids = np.array(origin_input_ids).reshape(1, -1) if origin_input_ids.shape[-1] >= max_len: origin_input_ids = origin_input_ids[:, -max_len:] bs, valid_length = origin_input_ids.shape context_len = valid_length pad_length = self.args.seq_length - valid_length input_ids = np.pad(origin_input_ids, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, self.tokenizer.pad_id)) input_ids = torch.tensor(input_ids, dtype=torch.long).contiguous().cuda() attention_mask, _, position_ids = get_ltor_masks_and_position_ids(input_ids, self.tokenizer.eod, self.args.reset_position_ids, self.args.reset_attention_mask, self.args.eod_mask_loss) input_data = (input_ids, position_ids, attention_mask, valid_length) return input_data, context_len def do_single_generate(self, input_data, temperature=1, top_k=None, top_p=None, max_num=50): input_ids, position_ids, attention_mask, valid_length = input_data inputs = input_ids cnt = 0 with torch.no_grad(): while valid_length < self.args.seq_length: logits = self.model(inputs, position_ids, attention_mask) next_token_logits = logits[:, valid_length - 1, :] / temperature if top_k is None and top_p is None: next_token = torch.argmax(next_token_logits, dim=-1) else: next_token_logscores = self.top_k_logits(next_token_logits, top_k=top_k, top_p=top_p) probs = F.softmax(next_token_logscores, dim=-1) # probs = next_token_logscores / torch.sum(next_token_logscores, dim=-1) next_token = torch.multinomial(probs.float(), num_samples=1).squeeze(1) if next_token[0] == self.tokenizer.eod or valid_length == self.args.seq_length - 1 or cnt >= max_num: outputs = input_ids break input_ids[:, valid_length] = next_token valid_length += 1 cnt += 1 return outputs def generate(self, input_str, temperature=1, top_k=None, top_p=None, max_len=1000, max_num=50): input_data, context_len = self.get_single_generate_data(input_str, max_len) outputs = self.do_single_generate(input_data, temperature=temperature, top_k=top_k, top_p=top_p, max_num=max_num) outputs = outputs.cpu().numpy() length = np.sum(outputs != self.tokenizer.pad_id) outputs = outputs[0, context_len:length] generate_text = "".join(self.tokenizer.convert_ids_to_tokens(outputs.tolist())) return generate_text if __name__=="__main__": examples = {"自由生成": ["讲一个关于小镇失业青年的故事，关键词：失业、互联网、相亲、中年危机。故事的主角叫大黄，有一天"]} generater = InferGenerate() input_text = examples["自由生成"][0] output_text = generater.generate(input_text) print(f"input_text: \n{input_text} \noutput_text: \n{output_text}") pangu/model/pangu/resource/000077500000000000000000000000001430334424000162265ustar00rootroot00000000000000pangu/model/pangu/resource/fig/000077500000000000000000000000001430334424000167735ustar00rootroot00000000000000pangu/model/pangu/resource/fig/performance.png000066400000000000000000000635321430334424000220130ustar00rootroot00000000000000PNG  IHDRFk4FsRGBgAMA a pHYsttfxfIDATx^;vϔ:!&ɩAȑ+|9˸F :iu TƋ dr}S n ,2~ J  cK  cK1Faaaaaaaay\I]>(,*7 MecI1bayܼhWRhXXn^Dgaaaa+)4F,,7/Z#-YXXXXJ  ͋,,,,,}%ƈEKt?ß?o?S배WRhXXn^DgakBI1bay?͚#O_{,*)4F,,7/Zܺ12whXܼhrr1ۿƈ%ƈEKt'|AB+kTowwc1Rgi/9``1"wOΆ 99h]a?>?^ӎk}|~||~O}%=Ǜjh G==S>&4F,v3FWe>̄}}|n'ͫuj/ASβ1Pb2F>"S'ޫ n-K#mFy+ZHz1rP1n^Ij]@~%֟d iM]'٦#i&-6] "cc:ѧw%։Bݧhh spi y#ݚ3s7XLz/܈6ƈz-D7x1z|}EH$l0_]sF|pwޢ0ap'#pBFGoOzCjgFj֫3X|XxHv /ΐkvZL .$80Fc'o^O1󾞔l0ur~2GsHٴ 2>J5k^\bDEaNՖw!qZ) !Uؽ㕂dew,|ZوV=Ř w5ͮˆkXE񧛦O:~{gD`p1r^^98mXt:hj=̱Z9cT$Jei抙#' j]MB`_Y\Gkw8"Aℨ#,[E /Awُ5Ƙ# ܵ #o itڭ,zƃq׾`sOo_ϦԡƈzedOJˍ:bU;s\K ѧUO.v3Fo &'[K^yy)LZgj?+~b͢-v..KRw ,DssadQ嘵7xzF|FQ63@Oƈz뱺).]w:-^ТcmIn{_9,殘NcG`N/Iv 3Fބp$>~,ZG.g1epqcG‚xѶo|lY7"딦100~\_W;W;vOو(lP~>yJʝ(O[b(g/Z0vuۧ>7lf^ Z}.7eQX] ,qdRm颖b`dXk:!bFɽ}aH[pf$W%{X^]TD[B "YXm[֍a,H47gc\}ۏY|u ?llkNqE4? 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R2hʸDV4{slL/9Gؘ߁E I~zU?WzΌ&CmאoBFK?-ľό >o_$~sY;bu13c$OOlߡ?drt?MIܤ?$Y×͢q,L0n0 l,KlYۯF).xHv/(yŅc|qqIi̩TqiWHE>u4I[ .W;X{̪Ǥ Ob1}!`>~}YڨWW+ ~;CC+늂tN넦v:R^NwqM^5b-15"O&o&cTxub$#+s /j-v,PP -)Ik>Fru H(@X.X̯MJƟ "j =B9Q-"\}D6>E"cy(o=G6\a3zw?F'hɷW/^8m132^n r6?INzFCɸu slPʋ.r.ki-acdEg/JRX+W(@cD|-qNiaBB=sX_H#HVHxf* }q Hk)(nޛ (}(Wy—bVBOi>F;F^DI\ u{e7k sb  M߽Zݺ@0~3? 8V׮A滂5JCf] οZ\/ĎK}Ƹe۵hJ"W'=B^ǞS;F#0];˰|H2>4m=qc ,+dhKbVz"D}bRh6w/1f+]1 =ޡ1ؔ"n`鿰R[lꋺw,s=W0XPŘ ~^ڛL@.x^i[M42ୢ xM5ybv'd3YfÚv0qa _ 1aV~B7F$n7 p󽬏O0c#}ݶ;2>l^c/u3 1*"ՕdMZ"Ւ0g*Rј>omPr8hZ`qN wE8`?ajmRb\H__C&.صO:}OR=''6gijoI4kp8[n[sn؋71FB!B!8TcYXXXXXXXX;F{B풳4Hc+.9\?? ѫ/(IENDB`pangu/model/pangu/scripts/000077500000000000000000000000001430334424000160665ustar00rootroot00000000000000pangu/model/pangu/scripts/generate_pangu.sh000066400000000000000000000014501430334424000214060ustar00rootroot00000000000000#! /bin/bash export CUDA_VISIBLE_DEVICES="0,1" export MASTER_PORT=6600 WORKING_DIR=/userhome/gpt3/PanGu-Alpha-Applications MODEL_PATH=/root/pangu/model/pretrain/pangu_fp16_8mp_350m_pretrain056_051_1209/merged # 2.6B #Layers=31 #Hsize=2560 #Heads=32 # 350M Layers=23 Hsize=1024 Heads=16 python -u ${WORKING_DIR}/model/pangu/inference/infer_generate.py \ --model-parallel-size 1 \ --num-layers $Layers \ --hidden-size $Hsize \ --num-attention-heads $Heads \ --max-position-embeddings 1024 \ --tokenizer-type GPT2BPETokenizer \ --seq-length 1024 \ --load $MODEL_PATH \ --vocab-file ${WORKING_DIR}/megatron/bpe_4w_pcl/vocab \ --merge-file gpt2-merges.txt \ --no-load-rng \ --make-vocab-size-divisible-by 1 \ --fp16 \ pangu/model/pangu_evolution/000077500000000000000000000000001430334424000165035ustar00rootroot00000000000000pangu/model/pangu_evolution/README.md000066400000000000000000000101071430334424000177610ustar00rootroot00000000000000# 鹏程.盘古增强版 中文|[English](README_EN.md) ## 简介 盘古增强版，更强的任务理解与处理能力，更加擅长遵循您的任务说明。更多技术细节会持续更新，敬请关注。 目前盘古增强版2.6B GPU版本的模型推理阶段显存占用控制在7G以内，8G显存的机器即可推理。 **要点：** - 创新应用了多任务学习、任务统一格式、提示微调和持续学习技术，对基本版模型进行了能力扩展和增强，性能得到大幅提升。 - 提供了基于盘古大模型进行提示微调和持续学习的性能优化新范式，更好的识别用户的任务说明，同时做到尽量保持模型的原始生成能力。 - 盘古增强版，参数量2.6B，在中英翻译、开放域知识问答、文本分类、摘要生成等能力提升显著，在一张V100 GPU卡上可以完成多路并行推理。 ## 方法 ### 基本方案 图1显示了用于构建增强模型的主要方法。我们采用了四种技术方法来提高模型的任务理解和处理能力，以及对任务描述的泛化能力。即多任务、统一任务格式、快速调整和持续学习。
### 统一任务格式 我们设计了统一的任务格式，如图2所示。该统一格式旨在减少下游任务之间的差异，提高知识转移和任务描述的泛化能力。使用这种格式，我们在18个任务上构建了50多个提示，以训练盘古增强版模型。
## Performances 我们进行了大量实验来比较盘古增强版和盘古在自然语言理解任务和自然语言生成任务中的表现。这些任务的结果见表1。对于每个任务，如果能获取到测试集则在测试集上进行评估，否则在验证集上进行评估。同时为了降低计算资源消耗，部分任务上从数据中随机采样部分子集进行评估。结果表明，盘古增强版的各项任务的性能远远优于盘古，平均提高1000%以上。虽然盘古增强版模型只训练了5/6个epoch，但它仍在训练中。同时，通过人工评估，我们得出结论，它保持了与盘古模型相同的原始生成能力。
## 模型文件 | 模型文件 | Md5 | 大小 | 参数配置 | | ------ | ---- | ---- | -------- | | [pangu-alpha-evolution_2.6B_fp16 .zip](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha-Applications/datasets?type=0) | e5c8cbb713fd916b12cbed7fb94a1242 | 4.6G | num-layers : 31
hidden-size : 2560
num-attention-heads : 32 | ```txt pangu-alpha-evolution_2.6B_fp16 # 模型目录 -- iter_0001000 # 迭代次数目录 --mp_rank_00 # 模型并行时各个 GPU 的目录 --model_optim_rng.pt # 模型文件 --latest_checkpointed_iteration.txt # 记录 ckpt 的迭代次数文件 ``` **注：`num-layers` 等于 [Pangu](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha) 项目中的 `num-layers - 1`** ## 示例 ### 推理代码 ```bash bash scripts/generate_pangu_evolution.sh ``` ### 示例输入 了解如何为常用用例创建提示。 NLI ```bash Input: 推理关系判断： 前提：新的权利已经足够好了 假设：每个人都很喜欢最新的福利 选项：矛盾，蕴含，中立 答案： Model output: 中立 ``` Classification ```bash Input: 文本分类： 基本上可以说是诈骗 选项：积极，消极 答案： Model output: 消极 ``` QA ```bash Input: 问题：郑州是那个省的 答案： Model output: 河南 ``` Translate ```bash Input: 翻译成英文： 不过他承认，美国与欧洲关系密切。 Model output: He admitted that the United States was relatively friendly with Europe. ``` ## 环境 ### pytorch版 支持 python >= 3.6, pytorch >= 1.5, cuda >= 10, nccl >= 2.6, and nltk。 推荐使用英伟达的官方 docker 镜像`docker pull nvcr.io/nvidia/pytorch:20.03-py3`。 pangu/model/pangu_evolution/README_EN.md000066400000000000000000000067561430334424000203620ustar00rootroot00000000000000# PanGu-Alpha-Evolution [中文](README.md)|English ## Introduction PanGu-Alpha-Evolution is an enhanced version of Pangu-Alpha, which can better understand and process tasks, and better follow your task description. More technical details will be updated continuously, please pay attention. ## Methods ### Scheme The illustration of the main methods used to build the enhanced model is shown in Figure 1. We adopted four technical methods to improve the model's task understanding and processing capabilities, as well as the generalization capabilities of task descriptions. That is multitasking, unified task format, prompt tune and continuous learning.
### Unified task format We designed the unified task format shown in Figure 2. The unified format is designed to reduce the differences between downstream tasks and promote the ability of knowledge transfer and task description generalization. Using this format, we built more than 50 prompts on 18 tasks to train the PanGu-Alpha-Evolution model.
## Performances We conducted extensive experiments to compare the performance of PanGu-Alpha-Evolution and PanGu-Alpha on both natural language understanding tasks and natural language generation tasks. The results of those tasks are reported in Table 1. The results show that the performance of each task of PanGu-Alpha-Evolution is far superior to PanGu-Alpha, and the average improvement is more than 1000%. Although the PanGu-Alpha-Evolution Model has only been trained for 5/6 epochs, it is still training. Meanwhile, through manual evaluation, we conclude that it maintains the same original generation ability as the PanGu-Alpha model.
## Model files | Model file | Md5 | Size | Config | | ------ | ---- | ---- | -------- | | [pangu-alpha-evolution_2.6B_fp16 .zip](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha-Applications/datasets?type=0) | e5c8cbb713fd916b12cbed7fb94a1242 | 4.6G | num-layers : 31
hidden-size : 2560
num-attention-heads : 32 | ```txt pangu-alpha-evolution_2.6B_fp16 -- iter_0001000 --mp_rank_00 --model_optim_rng.pt --latest_checkpointed_iteration.txt ``` **notes：`num-layers` equals `num-layers - 1` in project [Pangu](https://git.openi.org.cn/PCL-Platform.Intelligence/PanGu-Alpha).** ## Examples ### Inference Inference code. ```bash bash scripts/generate_pangu_evolution.sh ``` ### Use cases Learn how to create prompts for popular use cases. NLI ```bash Input: 推理关系判断： 前提：新的权利已经足够好了 假设：每个人都很喜欢最新的福利 选项：矛盾，蕴含，中立 答案： Model output: 中立 ``` Classification ```bash Input: 文本分类： 基本上可以说是诈骗 选项：积极，消极 答案： Model output: 消极 ``` QA ```bash Input: 问题：郑州是那个省的 答案： Model output: 河南 ``` Translate ```bash Input: 翻译成英文： 不过他承认，美国与欧洲关系密切。 Model output: He admitted that the United States was relatively friendly with Europe. ``` ## Environment ### Pytorch version python >= 3.6, pytorch >= 1.5, cuda >= 10, nccl >= 2.6, and nltk. We recommend using NVIDIA's official docker image `docker pull nvcr.io/nvidia/pytorch:20.03-py3`. pangu/model/pangu_evolution/__init__.py000066400000000000000000000001231430334424000206100ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/11/11 # @Author: qing pangu/model/pangu_evolution/inference/000077500000000000000000000000001430334424000204415ustar00rootroot00000000000000pangu/model/pangu_evolution/inference/__init__.py000066400000000000000000000001231430334424000225460ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/11/22 # @Author: qing pangu/model/pangu_evolution/inference/infer_generate.py000066400000000000000000000160541430334424000237760ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/10/8 # @Author: qing import os import sys import torch import numpy as np import torch.nn.functional as F from torch.nn.parallel.distributed import DistributedDataParallel as torchDDP sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(__file__)))))) from megatron import get_args, mpu from megatron import get_tokenizer from megatron.checkpointing import load_checkpoint from megatron.initialize import initialize_megatron from megatron.fp16 import FP16_Module from megatron.model import DistributedDataParallel as LocalDDP from megatron.utils import get_ltor_masks_and_position_ids from model.pangu_evolution.model.model_define import PanguModelEnhance class InferGenerate(object): def __init__(self): initialize_megatron(args_defaults={'tokenizer_type': 'GPT2BPETokenizer'}) self.args = get_args() self.tokenizer = get_tokenizer() self.model = self.get_model() load_checkpoint(self.model, None, None) self.model.eval() def top_k_logits(self, logits, top_k=0, top_p=0.0, filter_value=-float('Inf')): # This function has been mostly taken from huggingface conversational ai code at # https://medium.com/huggingface/how-to-build-a-state-of-the-art-conversational-ai-with-transfer-learning-2d818ac26313 if top_k > 0: # Remove all tokens with a probability less than the last token of the top-k indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None] logits[indices_to_remove] = filter_value batch_size = logits.size()[0] if top_p > 0.0: logits = logits.view(batch_size, -1).contiguous() for logit in logits: sorted_logits, sorted_indices = torch.sort(logit, descending=True) cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1) # Remove tokens with cumulative probability above the threshold sorted_indices_to_remove = cumulative_probs > top_p # Shift the indices to the right to keep also the first token above the threshold sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone() sorted_indices_to_remove[..., 0] = 0 indices_to_remove = sorted_indices[sorted_indices_to_remove] logit[indices_to_remove] = filter_value logits = logits.view(batch_size, -1).contiguous() return logits def get_model(self): """Build the model.""" args = get_args() # Build model on cpu. model = PanguModelEnhance(num_tokentypes=0, parallel_output=False) # Print number of parameters. if mpu.get_data_parallel_rank() == 0: rank_id = mpu.get_model_parallel_rank() parameter_num = sum([p.nelement() for p in model.parameters()]) print(f" > number of parameters on model parallel rank {rank_id}: {parameter_num}.", flush=True) # GPU allocation. model.cuda(torch.cuda.current_device()) # Fp16 conversion. if args.fp16: model = FP16_Module(model) # Wrap model for distributed training.""" if args.DDP_impl == 'torch': i = torch.cuda.current_device() model = torchDDP(model, device_ids=[i], output_device=i, process_group=mpu.get_data_parallel_group()) return model if args.DDP_impl == 'local': model = LocalDDP(model) return model raise NotImplementedError('Unknown DDP implementation specified: {}. Exiting.'.format(args.DDP_impl)) def get_single_generate_data(self, input_str, max_len): origin_input_ids = self.tokenizer.tokenize(input_str) origin_input_ids = np.array(origin_input_ids).reshape(1, -1) if origin_input_ids.shape[-1] >= max_len: origin_input_ids = origin_input_ids[:, -max_len:] bs, valid_length = origin_input_ids.shape context_len = valid_length pad_length = self.args.seq_length - valid_length input_ids = np.pad(origin_input_ids, ((0, 0), (0, pad_length)), 'constant', constant_values=(0, self.tokenizer.pad_id)) input_ids = torch.tensor(input_ids, dtype=torch.long).contiguous().cuda() attention_mask, _, position_ids = get_ltor_masks_and_position_ids(input_ids, self.tokenizer.eod, self.args.reset_position_ids, self.args.reset_attention_mask, self.args.eod_mask_loss) input_data = (input_ids, position_ids, attention_mask, valid_length) return input_data, context_len def do_single_generate(self, input_data, temperature=1, top_k=None, top_p=None, max_num=50): input_ids, position_ids, attention_mask, valid_length = input_data inputs = input_ids, None cnt = 0 with torch.no_grad(): while valid_length < self.args.seq_length: logits = self.model(inputs, position_ids, attention_mask) next_token_logits = logits[:, valid_length - 1, :] / temperature if top_k is None and top_p is None: next_token = torch.argmax(next_token_logits, dim=-1) else: next_token_logscores = self.top_k_logits(next_token_logits, top_k=top_k, top_p=top_p) probs = F.softmax(next_token_logscores, dim=-1) # probs = next_token_logscores / torch.sum(next_token_logscores, dim=-1) next_token = torch.multinomial(probs.float(), num_samples=1).squeeze(1) if next_token[0] == self.tokenizer.eod or valid_length == self.args.seq_length - 1 or cnt >= max_num: outputs = input_ids break input_ids[:, valid_length] = next_token valid_length += 1 cnt += 1 return outputs def generate(self, input_str, temperature=1, top_k=None, top_p=None, max_len=1000, max_num=50): input_data, context_len = self.get_single_generate_data(input_str, max_len) outputs = self.do_single_generate(input_data, temperature=temperature, top_k=top_k, top_p=top_p, max_num=max_num) outputs = outputs.cpu().numpy() length = np.sum(outputs != self.tokenizer.pad_id) outputs = outputs[0, context_len:length] generate_text = "".join(self.tokenizer.convert_ids_to_tokens(outputs.tolist())) return generate_text if __name__=="__main__": examples = {"文本分类": ["文本分类：\n基本上可以说是诈骗\n选项：积极，消极\n答案："]} generater = InferGenerate() input_text = examples["文本分类"][0] output_text = generater.generate(input_text) print(f"input_text: \n{input_text} \noutput_text: \n{output_text}") pangu/model/pangu_evolution/model/000077500000000000000000000000001430334424000176035ustar00rootroot00000000000000pangu/model/pangu_evolution/model/__init__.py000066400000000000000000000001211430334424000217060ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/9/7 # @Author: qing pangu/model/pangu_evolution/model/model_define.py000066400000000000000000000247441430334424000226020ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/9/24 # @Author: qing import torch from megatron import mpu from megatron import get_args from megatron.model import GPT2Model from megatron.module import MegatronModule from megatron.model.transformer import ParallelTransformer from megatron.model.gpt2_model import gpt2_attention_mask_func from megatron.model.language_model import parallel_lm_logits, Embedding, Pooler from megatron.model.utils import init_method_normal, scaled_init_method_normal from com.utils.mpu_util import mp_gather from model.pangu_evolution.model.top_layer import TopEmbedding class TransformerLanguageModelBase(MegatronModule): """Transformer language model. Arguments: transformer_hparams: transformer hyperparameters attention_mask_func: a function that takes `unmaksed-attention-scores` with size [b, np, s, s] and an `attention-mask` and will apply the masking. The function should return a masked score of the same size [b, np, s, s]. masked-attention-scores = attention_mask_func( unmaksed-attention-scores, attention-mask) vocab_size: vocabulary size max_sequence_length: maximum size of sequence. This is used for positional embedding embedding_dropout_prob: dropout probability for embeddings num_tokentypes: size of the token-type embeddings. 0 value will ignore this embedding """ def __init__(self, attention_mask_func, init_method, output_layer_init_method, num_tokentypes=0, add_pooler=False): super(TransformerLanguageModelBase, self).__init__() args = get_args() self.hidden_size = args.hidden_size self.num_tokentypes = num_tokentypes self.init_method = init_method self.add_pooler = add_pooler # Embeddings self.embedding = Embedding(self.hidden_size, args.padded_vocab_size, args.max_position_embeddings, args.hidden_dropout, self.init_method, self.num_tokentypes) self._embedding_key = 'embedding' self._topQueryEmbedding_key = 'topQueryEmbedding' # Transformer self.transformer = ParallelTransformer(attention_mask_func, self.init_method, output_layer_init_method) self._transformer_key = 'transformer' # Pooler if self.add_pooler: self.pooler = Pooler(self.hidden_size, self.init_method) self._pooler_key = 'pooler' def forward(self, input_ids, position_ids, attention_mask, tokentype_ids=None, layer_past=None, get_key_value=False, pooling_sequence_index=0): raise NotImplementedError("TransformerLanguageModelBase has to implement forward method.") def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): """For easy load.""" state_dict_ = {} state_dict_[self._embedding_key] = self.embedding.state_dict_for_save_checkpoint(destination, prefix, keep_vars) state_dict_[self._transformer_key] = self.transformer.state_dict_for_save_checkpoint(destination, prefix, keep_vars) if self.add_pooler: state_dict_[self._pooler_key] = self.pooler.state_dict_for_save_checkpoint(destination, prefix, keep_vars) return state_dict_ def load_state_dict(self, state_dict, strict=True): """Customized load.""" # Embedding. if self._embedding_key in state_dict: state_dict_ = state_dict[self._embedding_key] else: # for backward compatibility. state_dict_ = {} for key in state_dict.keys(): if '_embeddings' in key: state_dict_[key] = state_dict[key] self.embedding.load_state_dict(state_dict_, strict=strict) # Transformer. if self._transformer_key in state_dict: state_dict_ = state_dict[self._transformer_key] else: # for backward compatibility. state_dict_ = {} for key in state_dict.keys(): if 'transformer.' in key: state_dict_[key.split('transformer.')[1]] = state_dict[key] self.transformer.load_state_dict(state_dict_, strict=strict) # Pooler. if self.add_pooler: assert 'pooler' in state_dict, 'could not find data for pooler in the checkpoint' self.pooler.load_state_dict(state_dict[self._pooler_key], strict=strict) class TransformerLanguageModelEnhance(TransformerLanguageModelBase): def __init__(self, attention_mask_func, init_method, output_layer_init_method, num_tokentypes=0, add_pooler=False, num_tasktypes=0): super().__init__(attention_mask_func, init_method, output_layer_init_method, num_tokentypes, add_pooler) args = get_args() self.num_tasktypes = num_tasktypes # top embeddings self.top_embedding = TopEmbedding(self.hidden_size, args.padded_vocab_size, args.max_position_embeddings, args.hidden_dropout, self.init_method, self.num_tasktypes) self._top_embedding_key = 'task_embedding' def forward(self, inputs, position_ids, attention_mask, tokentype_ids=None, layer_past=None, get_key_value=False, pooling_sequence_index=0): input_ids, task_ids = inputs embedding_output = self.embedding(input_ids, position_ids, tokentype_ids=tokentype_ids) top_embedding_out = self.top_embedding(position_ids, tasktype_ids=task_ids) transformer_output = self.transformer(embedding_output, top_embedding_out, attention_mask, layer_past=layer_past, get_key_value=get_key_value) if self.add_pooler: pooled_output = self.pooler(transformer_output, pooling_sequence_index) return transformer_output, pooled_output return transformer_output def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): """For easy load.""" state_dict_ = super().state_dict_for_save_checkpoint(destination, prefix, keep_vars) state_dict_[self._top_embedding_key] = self.top_embedding.state_dict_for_save_checkpoint(destination, prefix, keep_vars) return state_dict_ def load_state_dict(self, state_dict, strict=True): """Customized load.""" super().load_state_dict(state_dict, strict) if self._top_embedding_key in state_dict: state_dict_ = state_dict[self._top_embedding_key] elif self._topQueryEmbedding_key in state_dict: state_dict_ = state_dict[self._topQueryEmbedding_key] else: state_dict_ = {} for key in state_dict.keys(): if '_embeddings' in key: state_dict_[key] = state_dict[key] self.top_embedding.load_state_dict(state_dict_, strict=strict) def get_enhance_lm(attention_mask_func, num_tokentypes, add_pooler, num_tasktypes, init_method=None, scaled_init_method=None): """Build language model and return along with the key to save.""" args = get_args() if init_method is None: init_method = init_method_normal(args.init_method_std) if scaled_init_method is None: scaled_init_method = scaled_init_method_normal(args.init_method_std, args.num_layers) enhance_lm = TransformerLanguageModelEnhance( attention_mask_func=attention_mask_func, init_method=init_method, output_layer_init_method=scaled_init_method, num_tokentypes=num_tokentypes, add_pooler=add_pooler, num_tasktypes=num_tasktypes) # key used for checkpoints. language_model_key = 'language_model' return enhance_lm, language_model_key class PanguModelEnhance(GPT2Model): def __init__(self, num_tasktypes=0, num_tokentypes=0, parallel_output=True): super().__init__(num_tokentypes=num_tokentypes, parallel_output=parallel_output, should_get_lm=False) args = get_args() self.language_model, self._language_model_key = get_enhance_lm( attention_mask_func=gpt2_attention_mask_func, num_tokentypes=num_tokentypes, add_pooler=False, num_tasktypes=num_tasktypes, init_method=init_method_normal(args.init_method_std), scaled_init_method=scaled_init_method_normal(args.init_method_std, args.num_layers)) def forward(self, inputs, position_ids, attention_mask, labels=None, tokentype_ids=None, layer_past=None, get_key_value=False, forward_method_parallel_output=None, parallel_logits=True): lm_output = self.language_model(inputs, position_ids, attention_mask, tokentype_ids=tokentype_ids, layer_past=layer_past, get_key_value=get_key_value) if get_key_value: lm_output, presents = lm_output lm_output = torch.add(lm_output, 0) parallel_output = self.parallel_output if forward_method_parallel_output is not None: parallel_output = forward_method_parallel_output output = parallel_lm_logits(lm_output, self.language_model.embedding.word_embeddings.weight, parallel_output) # print(f"output.shape: {output.shape}") if get_key_value: output = [output, presents] if labels is None: return output else: if self.fp16_lm_cross_entropy: assert output.dtype == torch.half loss = mpu.vocab_parallel_cross_entropy(output, labels) else: loss = mpu.vocab_parallel_cross_entropy(output.float(), labels) if not parallel_logits: output_gather = mp_gather(output) else: output_gather = output return loss, output_gatherpangu/model/pangu_evolution/model/top_layer.py000066400000000000000000000127561430334424000221660ustar00rootroot00000000000000#!/usr/bin/env python # -*- coding: utf-8 -*- # @Date: 2021/9/24 # @Author: qing import torch from megatron.module import MegatronModule class TopEmbedding(MegatronModule): """Language model embeddings. Arguments: hidden_size: hidden size vocab_size: vocabulary size max_sequence_length: maximum size of sequence. This is used for positional embedding embedding_dropout_prob: dropout probability for embeddings init_method: weight initialization method num_tokentypes: size of the token-type embeddings. 0 value will ignore this embedding """ def __init__(self, hidden_size, vocab_size, max_sequence_length, embedding_dropout_prob, init_method, num_tokentypes=0, top_query_switch=True, task_switch=False): super().__init__() self.hidden_size = hidden_size self.init_method = init_method self.num_tokentypes = num_tokentypes self.top_query_switch=top_query_switch self.task_switch=task_switch # Top query position embedding (serial). if self.top_query_switch: self.top_query_embeddings = torch.nn.Embedding(max_sequence_length, self.hidden_size) self._top_query_embeddings_key = 'top_query_embeddings' # Initialize the top query position embeddings. self.init_method(self.top_query_embeddings.weight) else: self.top_query_embeddings = None # Token type embedding. if self.task_switch and self.num_tokentypes > 0: self.tokentype_embeddings = torch.nn.Embedding(self.num_tokentypes, self.hidden_size) self._tokentype_embeddings_key = 'tasktype_embeddings' # Initialize the token-type embeddings. self.init_method(self.tokentype_embeddings.weight) else: self.tokentype_embeddings = None # Embeddings dropout self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob) def add_tokentype_embeddings(self, num_tokentypes): """Add token-type embedding. This function is provided so we can add token-type embeddings in case the pretrained model does not have it. This allows us to load the model normally and then add this embedding. """ if self.tokentype_embeddings is not None: raise Exception('tokentype embeddings is already initialized') if torch.distributed.get_rank() == 0: print('adding embedding for {} tokentypes'.format(num_tokentypes), flush=True) self.num_tokentypes = num_tokentypes self.tokentype_embeddings = torch.nn.Embedding(num_tokentypes, self.hidden_size) # Initialize the token-type embeddings. self.init_method(self.tokentype_embeddings.weight) def forward(self, position_ids, tasktype_ids): # Embeddings. embeddings = None if self.task_switch and self.top_query_switch: embeddings = self.tokentype_embeddings(tasktype_ids) embeddings = embeddings + self.top_query_embeddings(position_ids) elif self.task_switch: embeddings = self.tokentype_embeddings(tasktype_ids) elif self.top_query_switch: embeddings = self.top_query_embeddings(position_ids) else: embeddings = embeddings if embeddings != None: embeddings = self.embedding_dropout(embeddings) return embeddings def state_dict_for_save_checkpoint(self, destination=None, prefix='', keep_vars=False): """For easy load.""" state_dict_ = {} if self.top_query_switch: state_dict_[self._top_query_embeddings_key] = self.top_query_embeddings.state_dict(destination, prefix, keep_vars) if self.task_switch and self.num_tokentypes > 0: state_dict_[self._tokentype_embeddings_key] = self.tokentype_embeddings.state_dict(destination, prefix, keep_vars) return state_dict_ def load_state_dict(self, state_dict, strict=True): """Customized load.""" # Position embedding. if self._top_query_embeddings_key in state_dict: state_dict_ = state_dict[self._top_query_embeddings_key] else: # for backward compatibility. state_dict_ = {} for key in state_dict.keys(): if 'top_query_embeddings' in key: state_dict_[key.split('top_query_embeddings.')[1]] = state_dict[key] if self.top_query_switch: self.top_query_embeddings.load_state_dict(state_dict_, strict=strict) # Tokentype embedding. if self.task_switch and self.num_tokentypes > 0: state_dict_ = {} if self._tokentype_embeddings_key in state_dict: state_dict_ = state_dict[self._tokentype_embeddings_key] else: # for backward compatibility. for key in state_dict.keys(): if 'tasktype_embeddings' in key: state_dict_[key.split('tasktype_embeddings.')[1]] = state_dict[key] if len(state_dict_.keys()) > 0: self.tokentype_embeddings.load_state_dict(state_dict_, strict=strict) else: print('***WARNING*** expected tasktype embeddings in the checkpoint but could not find it', flush=True)pangu/model/pangu_evolution/resource/000077500000000000000000000000001430334424000203325ustar00rootroot00000000000000pangu/model/pangu_evolution/resource/fig/000077500000000000000000000000001430334424000210775ustar00rootroot00000000000000pangu/model/pangu_evolution/resource/fig/frame.png000066400000000000000000001107711430334424000227060ustar00rootroot00000000000000PNG  IHDR+rsRGBgAMA a pHYsttfxIDATx^{.W]˟sFGjNQ5VY8?NQ<08s)` F9Bc ABwN%\vs#!ٹ>~ګk?Ow?T}}իW^O?k۫QM2=d""""""""2z4DDDDDDDDdh$ѣI&""""""""GLDDDDDDDDF&M2=d""""""""2z4DDDDDDDDdh$ѣI&""""""""GLDDDDDDDDF&٫^xRJR/}Kz!DDDDdmNJRO*e$""""ݰI&"76$QI&"""-d"1h),M2n$AL)5dit&l dJ!k&ً^[)?[M2?i)5n$~[)*d"1 $SjI&"7DƉ&l C4ɔ4DD8$a&R&H'0y;nk*ԆiաCo:~֯]h)5ni'qҩI;xRL,{ꩧzvz0IԸI&"DIg&OoѣOM"QEi4ɖ{vuɴRj{;V/mO"2 :1ɮC{GRDd0]OдЃMSF2+/V:lcm&d,O&$S4ɖcq|==7@]+6L}7~=ۿna7h6'dё =$;Ua]2SŬ.֋4z&&JI8 c,uU9V:VJm;f׉]9ID&tJЃMS&?G~1|x %&YjԠ ,k%,_'y0Փz7cyi4"fҕ6YssҲP,OJeAu#O_l􌵨gO:Qd3v]%dJS_m=ID&tJЃMS& / Ka„CnxP8dNL(7XT^/0^gl'ɷHX'p]q{kNߔ:\e (k6Fi-fɳRj:u]wݵ;,G M263d 3%$̖&#a`&8rcϤ *J밽ub;y:6Oˉ#J0P0ШcjVƭy,dy~gc&bijȾ{ug8m$ DdxhH =$;U5 fY/5bxM& VZ' :<EyiT0rlȇb9MTQvҷM'mc$[}N?tć+5f=r/ػwVmO"2<4DSh*HJaVR:)53A 0Jvtդ:bg~P#S麩ȃLI|YS:?ZlÙd;$[GJ'JqɸRDELD:`fMSI1,5WHk=9aΔ &h&f>R~ADXdOvCQ?/26Bu[iVJ4#Ƒ Rj\2e$N1ATaNj`` nẺKFQNY^:WƬ2t6i˚M[Ï1 .Mňqt¬+EdY4DS fv$;U1%c(Lbf#9 (7_gIQ^LCM1 aܚ.N6BM0X4N&b8R:aVJKƕ",d")3;h*1 &L'fN@r ud<i? 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