From 977a942f774dd7770a20750155ac2ad3e8fac8be Mon Sep 17 00:00:00 2001 From: Sean Date: Thu, 11 Nov 2021 10:24:48 +0800 Subject: [PATCH 01/20] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 26c70c3..dcef6ab 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,6 @@ # TDEER -Official Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021) +Official Code For [TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations](https://aclanthology.org/2021.emnlp-main.635/) (EMNLP2021) ## Overview From d65703758677cb5c46953fe2482759a00acb80cb Mon Sep 17 00:00:00 2001 From: Sean Date: Thu, 11 Nov 2021 10:30:44 +0800 Subject: [PATCH 02/20] Update README.md update citation --- README.md | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index dcef6ab..8b6f0aa 100644 --- a/README.md +++ b/README.md @@ -194,12 +194,23 @@ If you use our code in your research, please cite our work: ```bibtex -@inproceedings{li2021tdeer, - title={TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations}, - author={Li, Xianming and Luo, Xiaotian and Dong, Chenghao and Yang, Daichuan and Luan, Beidi and He, Zhen}, - booktitle={Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)}, - year={2021} +@inproceedings{li-etal-2021-tdeer, + title = "{TDEER}: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations", + author = "Li, Xianming and + Luo, Xiaotian and + Dong, Chenghao and + Yang, Daichuan and + Luan, Beidi and + He, Zhen", + booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing", + month = nov, + year = "2021", + address = "Online and Punta Cana, Dominican Republic", + publisher = "Association for Computational Linguistics", + url = "https://aclanthology.org/2021.emnlp-main.635", + pages = "8055--8064", } + ``` ## Contact From b80daf1c81deceeb524ce8b1c97137ddb835a695 Mon Sep 17 00:00:00 2001 From: Sean Date: Thu, 11 Nov 2021 10:46:55 +0800 Subject: [PATCH 03/20] Update README.md typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8b6f0aa..a512290 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ Official Code For [TDEER: An Efficient Translating Decoding Schema for Joint Ext ## Overview -TDEER is an efficient model for joint extraction of entities and relations. Unlike the common decoding approach that predicting the relation between subject and object, we adopt the proposed translating decoding schema: subject + relation -> objects, to decode triples. By the proposed translating decoding schema, TDEER can handle the overlapping triple problem effectively and efficiently. The following figure is an illustration of our models. +TDEER is an efficient model for joint extraction of entities and relations. Unlike the common decoding approach that predicts the relation between subject and object, we adopt the proposed translating decoding schema: subject + relation -> objects, to decode triples. By the proposed translating decoding schema, TDEER can handle the overlapping triple problem effectively and efficiently. The following figure is an illustration of our models. ![overview](docs/TDEER-Overview.png) From 171ccf0717b308df9621ca4ccb9c32ab142d03e3 Mon Sep 17 00:00:00 2001 From: Sean Date: Thu, 11 Nov 2021 11:02:05 +0800 Subject: [PATCH 04/20] Update README.md add paperwithcode badges --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index a512290..7615c69 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,9 @@ +[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/tdeer-an-efficient-translating-decoding/joint-entity-and-relation-extraction-on-nyt)](https://paperswithcode.com/sota/joint-entity-and-relation-extraction-on-nyt?p=tdeer-an-efficient-translating-decoding) +[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/tdeer-an-efficient-translating-decoding/joint-entity-and-relation-extraction-on-1)](https://paperswithcode.com/sota/joint-entity-and-relation-extraction-on-1?p=tdeer-an-efficient-translating-decoding) +[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/tdeer-an-efficient-translating-decoding/relation-extraction-on-nyt)](https://paperswithcode.com/sota/relation-extraction-on-nyt?p=tdeer-an-efficient-translating-decoding) +[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/tdeer-an-efficient-translating-decoding/relation-extraction-on-webnlg)](https://paperswithcode.com/sota/relation-extraction-on-webnlg?p=tdeer-an-efficient-translating-decoding) + + # TDEER Official Code For [TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations](https://aclanthology.org/2021.emnlp-main.635/) (EMNLP2021) From 3f304b6b6dcda2a397ed9787f764e8dfbd132db2 Mon Sep 17 00:00:00 2001 From: Sean Date: Thu, 11 Nov 2021 17:08:44 +0800 Subject: [PATCH 05/20] Update README.md add emoji --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7615c69..7f1a2e0 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/tdeer-an-efficient-translating-decoding/relation-extraction-on-webnlg)](https://paperswithcode.com/sota/relation-extraction-on-webnlg?p=tdeer-an-efficient-translating-decoding) -# TDEER +# TDEER 🦌🦒 Official Code For [TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations](https://aclanthology.org/2021.emnlp-main.635/) (EMNLP2021) From 35a17191050250b31c0e84a39d8ba8c47ace2017 Mon Sep 17 00:00:00 2001 From: Sean Date: Wed, 22 Dec 2021 10:50:53 +0800 Subject: [PATCH 06/20] Update README.md add tensorflow version --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 7f1a2e0..9b9297f 100644 --- a/README.md +++ b/README.md @@ -27,6 +27,7 @@ You can install the required dependencies by the following script. pip install -r requirements.txt ``` +For tensorflow version, we recommend `tensorflow-gpu==1.15.0`. ### 2. Prepare Data From c4f848fdb4b4238e310a92ce10c958d8c7cd2654 Mon Sep 17 00:00:00 2001 From: Sean Date: Wed, 22 Dec 2021 10:51:26 +0800 Subject: [PATCH 07/20] Update requirements.txt remove TensorFlow dependency --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 99d13c4..bedd737 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,7 +1,6 @@ tokenizers boltons Keras>=2.3.1 -tensorflow tqdm click langml From 23cb59ad4d5a86d7b6d0c90eaa893b6a23039c25 Mon Sep 17 00:00:00 2001 From: Sean Date: Wed, 22 Dec 2021 10:53:30 +0800 Subject: [PATCH 08/20] Update README.md update enviroment --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9b9297f..635b4a6 100644 --- a/README.md +++ b/README.md @@ -21,13 +21,14 @@ TDEER is an efficient model for joint extraction of entities and relations. Unli We conducted experiments under python3.7 and used GPUs device to accelerate computing. -You can install the required dependencies by the following script. +You should first prepare the tensorflow version in terms of your GPU environment. For tensorflow version, we recommend `tensorflow-gpu==1.15.0`. + +Then, you can install the other required dependencies by the following script. ```bash pip install -r requirements.txt ``` -For tensorflow version, we recommend `tensorflow-gpu==1.15.0`. ### 2. Prepare Data From faaf2376c86ba280c317bc28ec34bc4ba32c8850 Mon Sep 17 00:00:00 2001 From: Sean Date: Wed, 22 Dec 2021 17:56:08 +0800 Subject: [PATCH 09/20] Update README.md acknowledgment --- README.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/README.md b/README.md index 635b4a6..b890f68 100644 --- a/README.md +++ b/README.md @@ -221,6 +221,11 @@ If you use our code in your research, please cite our work: ``` +## Acknowledgment + +Some of our codes are inspired by [weizhepei/CasRel](https://github.com/weizhepei/CasRel). Thanks for their excellent work. + + ## Contact If you have any questions about the paper or code, you can From 1f36f68036ba03a86391bd79e244bb66d54a82bb Mon Sep 17 00:00:00 2001 From: Sean Date: Fri, 21 Jan 2022 10:35:43 +0800 Subject: [PATCH 10/20] langml version --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index bedd737..f74f7f0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,4 +3,4 @@ boltons Keras>=2.3.1 tqdm click -langml +langml<0.2.0 From 983e504d663db8689ac83dd25f1e55b6035d5f67 Mon Sep 17 00:00:00 2001 From: Sean Date: Thu, 10 Feb 2022 11:53:01 +0800 Subject: [PATCH 11/20] Update README.md --- ckpts/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ckpts/README.md b/ckpts/README.md index bd30641..65f26ca 100644 --- a/ckpts/README.md +++ b/ckpts/README.md @@ -1,3 +1,3 @@ The `ckpts` folder saves pre-trained TDEER models. -Click [Google Drive]() \| [Baidu NetDisk]() to download pre-trained TDEER models and then uncompress to this folder. +use `git-lfs` to download the pretrained models. From f97727df506790edfcb28d65c4970a6f67fec9ab Mon Sep 17 00:00:00 2001 From: Sean Date: Thu, 10 Feb 2022 11:53:32 +0800 Subject: [PATCH 12/20] Update README.md --- data/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/data/README.md b/data/README.md index 59537cf..6917119 100644 --- a/data/README.md +++ b/data/README.md @@ -1,3 +1,3 @@ We follow [weizhepei/CasRel](https://github.com/weizhepei/CasRel) to prepare datas. -You could download our preprocessed datasets ([Google Drive]() | [Baidu NetDisk]()). +Please use `git-lfs` to download the preprocessed data. From bd05d990c6f1d07b01c986502945a00f0a014a55 Mon Sep 17 00:00:00 2001 From: Sean Date: Thu, 10 Feb 2022 12:01:52 +0800 Subject: [PATCH 13/20] Update README.md --- ckpts/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ckpts/README.md b/ckpts/README.md index 65f26ca..d715026 100644 --- a/ckpts/README.md +++ b/ckpts/README.md @@ -1,3 +1,3 @@ The `ckpts` folder saves pre-trained TDEER models. -use `git-lfs` to download the pretrained models. +You can use `git-lfs` to download the pretrained models. From d9c95d92ef55a4f4bd87239d1810fc8eab4270f2 Mon Sep 17 00:00:00 2001 From: Sean Date: Wed, 16 Feb 2022 09:53:42 +0800 Subject: [PATCH 14/20] Update requirements.txt --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index f74f7f0..f7a5fc4 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,6 @@ tokenizers boltons -Keras>=2.3.1 +Keras==2.3.1 tqdm click langml<0.2.0 From bd51835664f78d234739ac81ee66eaf4cb480578 Mon Sep 17 00:00:00 2001 From: Sean Date: Mon, 7 Mar 2022 14:14:22 +0800 Subject: [PATCH 15/20] remove duplicated code --- utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/utils.py b/utils.py index d5f6105..bfbdf9d 100644 --- a/utils.py +++ b/utils.py @@ -66,7 +66,6 @@ def __call__(self, text: str, threshold: float = 0.5) -> Set: batch_sub_entities = [] batch_rel_types = [] for (sub, sub_head, sub_tail) in subjects: - sub = self.decode_entity(text, mapping, sub_head, sub_tail) for rel in relations: batch_sub_heads.append([sub_head]) batch_sub_tails.append([sub_tail]) From c782dad5d84b50d531eecb2eb2be3ed22f5180fe Mon Sep 17 00:00:00 2001 From: Sean Date: Sat, 26 Mar 2022 10:25:36 +0800 Subject: [PATCH 16/20] fix a typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b890f68..e1edf53 100644 --- a/README.md +++ b/README.md @@ -221,7 +221,7 @@ If you use our code in your research, please cite our work: ``` -## Acknowledgment +## Acknowledgement Some of our codes are inspired by [weizhepei/CasRel](https://github.com/weizhepei/CasRel). Thanks for their excellent work. From 81d3b3457e5308547d7d6695b31193bb34d98223 Mon Sep 17 00:00:00 2001 From: Sean Date: Wed, 18 May 2022 09:57:26 +0800 Subject: [PATCH 17/20] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e1edf53..bfd2d96 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ [![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/tdeer-an-efficient-translating-decoding/relation-extraction-on-webnlg)](https://paperswithcode.com/sota/relation-extraction-on-webnlg?p=tdeer-an-efficient-translating-decoding) -# TDEER 🦌🦒 +# TDEER 🦌 Official Code For [TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations](https://aclanthology.org/2021.emnlp-main.635/) (EMNLP2021) From a3780423a2894d00e57de2c677ca3403a105a53c Mon Sep 17 00:00:00 2001 From: innovation64 Date: Wed, 5 Jun 2024 18:23:41 +0800 Subject: [PATCH 18/20] update pytorch version --- ckpts/README.md | 3 - ckpts/ckpts.zip | 3 - 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-You can use `git-lfs` to download the pretrained models. diff --git a/ckpts/ckpts.zip b/ckpts/ckpts.zip deleted file mode 100644 index d7230bf..0000000 --- a/ckpts/ckpts.zip +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:aaefe93dd5d6427897975a33fc9bc0ee99c4282c093ac1a8b75d58b65eb9ee25 -size 1226187952 diff --git a/dataloader.py b/dataloader.py index bda6e34..b49fd3a 100644 --- a/dataloader.py +++ b/dataloader.py @@ -1,50 +1,52 @@ -#! -*- coding:utf-8 -*- +# -*- coding:utf-8 -*- import json from typing import Dict, List, Optional, Tuple from collections import defaultdict import numpy as np -from keras.preprocessing.sequence import pad_sequences +import torch +from torch.utils.data import Dataset, DataLoader +from transformers import BertTokenizer import log - -def find_entity(source: List[int], target: List[int]) -> int: +def find_entity(source, target): + """ Find the start index of the target sequence in the source sequence """ target_len = len(target) - for i in range(len(source)): - if source[i: i + target_len] == target: + for i in range(len(source) - target_len + 1): + if source[i:i+target_len] == target: return i return -1 +def to_tuple(sent): + """ Convert lists to tuples in place """ + sent['triple_list'] = [tuple(triple) for triple in sent['triple_list']] -def to_tuple(sent: str): - """ list to tuple (inplace operation) - """ - triple_list = [] - for triple in sent['triple_list']: - triple_list.append(tuple(triple)) - sent['triple_list'] = triple_list - - -def filter_data(fpath: str, rel2id: Dict): +def filter_data(fpath: str, rel2id: dict): filtered_data = [] - for obj in json.load(open(fpath)): - filtered_triples = [] - if 'NYT11-HRL' in fpath and len(obj['triple_list']) != 1: + try: + with open(fpath, 'r') as file: + data = json.load(file) + except FileNotFoundError: + print("File not found:", fpath) + return filtered_data + except json.JSONDecodeError: + print("Error decoding JSON from:", fpath) + return filtered_data + + for obj in data: + if 'NYT11-HRL' in fpath and len(obj.get('triple_list', [])) != 1: continue - for triple in obj['triple_list']: - if triple[1] not in rel2id: - continue - filtered_triples.append(triple) + filtered_triples = [triple for triple in obj.get('triple_list', []) if triple[1] in rel2id] if not filtered_triples: continue obj['triple_list'] = filtered_triples filtered_data.append(obj) + return filtered_data - -def load_rel(rel_path: str) -> Tuple[Dict, Dict, List, int]: +def load_rel(rel_path: str) -> Tuple[Dict, Dict, List]: id2rel, rel2id = json.load(open(rel_path)) all_rels = list(id2rel.keys()) id2rel = {int(i): j for i, j in id2rel.items()} @@ -64,9 +66,8 @@ def load_data(fpath: str, rel2id: Dict, is_train: bool = False) -> List: log.info(f"data len: {len(data)}") return data - -class DataGenerator: - def __init__(self, datas: List, tokenizer: object, rel2id: Dict, all_rels: List, max_len: int, +class DataGenerator(Dataset): + def __init__(self, datas: List, tokenizer: BertTokenizer, rel2id: Dict, all_rels: List, max_len: int, batch_size: int = 32, max_sample_triples: Optional[int] = None, neg_samples: Optional[int] = None): self.max_sample_triples = max_sample_triples self.neg_samples = neg_samples @@ -85,7 +86,7 @@ def __init__(self, datas: List, tokenizer: object, rel2id: Dict, all_rels: List, pos_datas = [] neg_datas = [] - text_tokened = tokenizer.encode(data['text']) + text_tokened = tokenizer.encode_plus(data['text'], truncation=True, padding='max_length', max_length=max_len) entity_set = set() # (head idx, tail idx) triples_set = set() # (sub head, sub tail, obj head, obj tail, rel) subj_rel_set = set() # (sub head, sub tail, rel) @@ -95,12 +96,12 @@ def __init__(self, datas: List, tokenizer: object, rel2id: Dict, all_rels: List, for triple in data['triple_list']: subj, rel, obj = triple rel_idx = self.rel2id[rel] - subj_tokened = tokenizer.encode(subj) - obj_tokened = tokenizer.encode(obj) - subj_head_idx = find_entity(text_tokened.ids, subj_tokened.ids[1:-1]) - subj_tail_idx = subj_head_idx + len(subj_tokened.ids[1:-1]) - 1 - obj_head_idx = find_entity(text_tokened.ids, obj_tokened.ids[1:-1]) - obj_tail_idx = obj_head_idx + len(obj_tokened.ids[1:-1]) - 1 + subj_tokened = tokenizer.encode_plus(subj, add_special_tokens=False) + obj_tokened = tokenizer.encode_plus(obj, add_special_tokens=False) + subj_head_idx = find_entity(text_tokened['input_ids'], subj_tokened['input_ids']) + subj_tail_idx = subj_head_idx + len(subj_tokened['input_ids']) - 1 + obj_head_idx = find_entity(text_tokened['input_ids'], obj_tokened['input_ids']) + obj_tail_idx = obj_head_idx + len(obj_tokened['input_ids']) - 1 if subj_head_idx == -1 or obj_head_idx == -1: continue entity_set.add((subj_head_idx, subj_tail_idx, 0)) @@ -141,8 +142,8 @@ def __init__(self, datas: List, tokenizer: object, rel2id: Dict, all_rels: List, sample_obj_heads[idx] = 1.0 # postive samples pos_datas.append({ - 'token_ids': text_tokened.ids, - 'segment_ids': text_tokened.type_ids, + 'token_ids': text_tokened['input_ids'], + 'segment_ids': text_tokened['token_type_ids'], 'entity_heads': entity_heads, 'entity_tails': entity_tails, 'rels': rels, @@ -158,8 +159,8 @@ def __init__(self, datas: List, tokenizer: object, rel2id: Dict, all_rels: List, neg_pair = (neg_subj_head_idx, neg_sub_tail_idx, rel_idx) if neg_pair not in subj_rel_set and neg_pair not in neg_history: current_neg_datas.append({ - 'token_ids': text_tokened.ids, - 'segment_ids': text_tokened.type_ids, + 'token_ids': text_tokened['input_ids'], + 'segment_ids': text_tokened['token_type_ids'], 'entity_heads': entity_heads, 'entity_tails': entity_tails, 'rels': rels, @@ -175,8 +176,8 @@ def __init__(self, datas: List, tokenizer: object, rel2id: Dict, all_rels: List, neg_pair = (subj_head_idx, subj_tail_idx, neg_rel_idx) if neg_pair not in subj_rel_set and neg_pair not in neg_history: current_neg_datas.append({ - 'token_ids': text_tokened.ids, - 'segment_ids': text_tokened.type_ids, + 'token_ids': text_tokened['input_ids'], + 'segment_ids': text_tokened['token_type_ids'], 'entity_heads': entity_heads, 'entity_tails': entity_tails, 'rels': rels, @@ -192,8 +193,8 @@ def __init__(self, datas: List, tokenizer: object, rel2id: Dict, all_rels: List, neg_pair = (neg_subj_head_idx, neg_sub_tail_idx, rel_idx) if neg_pair not in subj_rel_set and neg_pair not in neg_history: current_neg_datas.append({ - 'token_ids': text_tokened.ids, - 'segment_ids': text_tokened.type_ids, + 'token_ids': text_tokened['input_ids'], + 'segment_ids': text_tokened['token_type_ids'], 'entity_heads': entity_heads, 'entity_tails': entity_tails, 'rels': rels, @@ -211,54 +212,71 @@ def __init__(self, datas: List, tokenizer: object, rel2id: Dict, all_rels: List, current_datas = pos_datas + neg_datas self.datas.extend(current_datas) - self.steps = len(self.datas) // self.batch_size - if len(self.datas) % self.batch_size != 0: - self.steps += 1 - def __len__(self): - return self.steps - - def __iter__(self, random: bool = False): - idxs = list(range(len(self.datas))) - if random: - np.random.shuffle(idxs) - batch_tokens, batch_segments = [], [] - batch_entity_heads, batch_entity_tails = [], [] - batch_rels = [] - batch_sample_subj_head, batch_sample_subj_tail = [], [] - batch_sample_rel = [] - batch_sample_obj_heads = [] - - for idx in idxs: - obj = self.datas[idx] - batch_tokens.append(obj['token_ids']) - batch_segments.append(obj['segment_ids']) - batch_entity_heads.append(obj['entity_heads']) - batch_entity_tails.append(obj['entity_tails']) - batch_rels.append(obj['rels']) - batch_sample_subj_head.append(obj['sample_subj_head']) - batch_sample_subj_tail.append(obj['sample_subj_tail']) - batch_sample_rel.append(obj['sample_rel']) - batch_sample_obj_heads.append(obj['sample_obj_heads']) - if len(batch_tokens) == self.batch_size or idx == idxs[-1]: - batch_tokens = pad_sequences(batch_tokens, maxlen=self.max_len, padding='post', truncating='post') - batch_segments = pad_sequences(batch_segments, maxlen=self.max_len, padding='post', truncating='post') - batch_entity_heads = pad_sequences(batch_entity_heads, maxlen=self.max_len, value=np.zeros(2)) - batch_entity_tails = pad_sequences(batch_entity_tails, maxlen=self.max_len, value=np.zeros(2)) - batch_rels = np.array(batch_rels) - batch_sample_subj_head = np.array(batch_sample_subj_head) - batch_sample_subj_tail = np.array(batch_sample_subj_tail) - batch_sample_rel = np.array(batch_sample_rel) - batch_sample_obj_heads = np.array(batch_sample_obj_heads) - yield [batch_tokens, batch_segments, batch_entity_heads, batch_entity_tails, batch_rels, batch_sample_subj_head, batch_sample_subj_tail, batch_sample_rel, batch_sample_obj_heads], None - batch_tokens, batch_segments = [], [] - batch_entity_heads, batch_entity_tails = [], [] - batch_rels = [] - batch_sample_subj_head, batch_sample_subj_tail = [], [] - batch_sample_rel = [] - batch_sample_obj_heads = [] - - def forfit(self, random: bool = False): - while True: - for inputs, labels in self.__iter__(random=random): - yield inputs, labels + return len(self.datas) + + def __getitem__(self, idx): + sample = self.datas[idx] + + return { + 'token_ids': sample['token_ids'], + 'segment_ids': sample['segment_ids'], + 'entity_heads': sample['entity_heads'], + 'entity_tails': sample['entity_tails'], + 'rels': sample['rels'], + 'sample_subj_head': sample['sample_subj_head'], + 'sample_subj_tail': sample['sample_subj_tail'], + 'sample_rel': sample['sample_rel'], + 'sample_obj_heads': sample['sample_obj_heads'] + } + + +def collate_fn(batch): + print("Batch input:", batch) + batch_tokens = [item['token_ids'] for item in batch] + batch_attention_masks = [[1] * len(tokens) for tokens in batch_tokens] + batch_segments = [item['segment_ids'] for item in batch] + batch_entity_heads = [item['entity_heads'] for item in batch] + batch_entity_tails = [item['entity_tails'] for item in batch] + batch_rels = [item['rels'] for item in batch] + batch_sample_subj_head = [item['sample_subj_head'] for item in batch] + batch_sample_subj_tail = [item['sample_subj_tail'] for item in batch] + batch_sample_rel = [item['sample_rel'] for item in batch] + batch_sample_obj_heads = [item['sample_obj_heads'] for item in batch] + + # Convert lists to NumPy arrays + batch_tokens_np = np.array(batch_tokens) + batch_attention_masks_np = np.array(batch_attention_masks) + batch_segments_np = np.array(batch_segments) + batch_entity_heads_np = np.array(batch_entity_heads) + batch_entity_tails_np = np.array(batch_entity_tails) + batch_rels_np = np.array(batch_rels) + batch_sample_subj_head_np = np.array(batch_sample_subj_head) + batch_sample_subj_tail_np = np.array(batch_sample_subj_tail) + batch_sample_rel_np = np.array(batch_sample_rel) + batch_sample_obj_heads_np = np.array(batch_sample_obj_heads) + + # Convert NumPy arrays to PyTorch tensors + batch_tokens = torch.tensor(batch_tokens_np, dtype=torch.long) + batch_attention_masks = torch.tensor(batch_attention_masks_np, dtype=torch.long) + batch_segments = torch.tensor(batch_segments_np, dtype=torch.long) + batch_entity_heads = torch.tensor(batch_entity_heads_np, dtype=torch.float) + batch_entity_tails = torch.tensor(batch_entity_tails_np, dtype=torch.float) + batch_rels = torch.tensor(batch_rels_np, dtype=torch.float) + batch_sample_subj_head = torch.tensor(batch_sample_subj_head_np, dtype=torch.long) + batch_sample_subj_tail = torch.tensor(batch_sample_subj_tail_np, dtype=torch.long) + batch_sample_rel = torch.tensor(batch_sample_rel_np, dtype=torch.long) + batch_sample_obj_heads = torch.tensor(batch_sample_obj_heads_np, dtype=torch.float) + + return { + 'token_ids': batch_tokens, + 'attention_mask': batch_attention_masks, + 'segment_ids': batch_segments, + 'entity_heads': batch_entity_heads, + 'entity_tails': batch_entity_tails, + 'rels': batch_rels, + 'sample_subj_head': batch_sample_subj_head, + 'sample_subj_tail': batch_sample_subj_tail, + 'sample_rel': batch_sample_rel, + 'sample_obj_heads': batch_sample_obj_heads + } \ No newline at end of file diff --git a/langml/.bumpversion.cfg b/langml/.bumpversion.cfg deleted file mode 100644 index 6c922aa..0000000 --- a/langml/.bumpversion.cfg +++ /dev/null @@ -1,5 +0,0 @@ -[bumpversion] -files = setup.py langml/__init__.py -commit = True -tag = True -current_version = 0.6.3 diff --git a/langml/.gitignore b/langml/.gitignore deleted file mode 100644 index 6e1079e..0000000 --- a/langml/.gitignore +++ /dev/null @@ -1,103 +0,0 @@ -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python -env/ -build/ -develop-eggs/ -dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -*.egg-info/ -.installed.cfg -*.egg - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -.hypothesis/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx documentation -docs/_build/ - -# PyBuilder -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# pyenv -.python-version - -# celery beat schedule file -celerybeat-schedule - -# SageMath parsed files -*.sage.py - -# dotenv -.env - -# virtualenv -.venv -venv/ -ENV/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs documentation -/site - -# mypy -.mypy_cache/ - -.vscode diff --git a/langml/Dockerfile b/langml/Dockerfile deleted file mode 100644 index dd8e919..0000000 --- a/langml/Dockerfile +++ /dev/null @@ -1,31 +0,0 @@ -FROM ubuntu:16.04 - -ENV LANG en_US.UTF-8 -ENV LANGUAGE en_US:en -ENV LC_ALL en_US.UTF-8 -ENV LD_LIBRARY_PATH /usr/local/lib:$LD_LIBRARY_PATH - -RUN apt-get update && \ - apt-get install -y locales && \ - locale-gen en_US.UTF-8 && \ - DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends apt-utils \ - # dependency for scipy - gfortran libatlas-dev libblas-dev libopenblas-dev liblapack-dev libopenblas-base \ - redis-server wget build-essential curl ca-certificates libssl-dev && \ - DEBIAN_FRONTEND=noninteractive apt-get autoremove -y && \ - rm -rf /var/lib/apt/list/* - -# Install Python 3.7 -RUN echo 'deb http://ppa.launchpad.net/deadsnakes/ppa/ubuntu xenial main' > /etc/apt/sources.list.d/deadsnakes-ubuntu-ppa-xenial.list && \ - apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 6A755776 && \ - apt-get update && \ - DEBIAN_FRONTEND=noninteractive apt-get install -y python3.7 python3.7-dev python3.7-venv && \ - curl -sSL https://bootstrap.pypa.io/get-pip.py | python3.7 \ - && python3.7 -m ensurepip - -ADD dev-requirements.txt /tmp/dev-requirements.txt -ADD requirements.txt /tmp/requirements.txt - -ARG PIP_INDEX_URL -RUN python3.7 -m pip install --no-cache-dir -U pip twine flake8 "tensorflow<2.5.0" && \ - python3.7 -m pip install --no-cache-dir -r /tmp/dev-requirements.txt diff --git a/langml/README.md b/langml/README.md deleted file mode 100644 index b7f98b9..0000000 --- a/langml/README.md +++ /dev/null @@ -1,17 +0,0 @@ -# Installation - - -You can install [langml](https://github.com/4AI/langml) via pip, -``` -pip install langml -``` - -or manually, - -``` -pip install -e . -``` - -# Reference - -- [langml](https://github.com/4AI/langml) \ No newline at end of file diff --git a/langml/dev-requirements.txt b/langml/dev-requirements.txt deleted file mode 100644 index 921f055..0000000 --- a/langml/dev-requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ --r requirements.txt -setuptools -pytest -pytest-cov -pytest-sugar diff --git a/langml/langml/__init__.py b/langml/langml/__init__.py deleted file mode 100644 index 94edd59..0000000 --- a/langml/langml/__init__.py +++ /dev/null @@ -1,11 +0,0 @@ -# -*- coding: utf-8 -*- - -import os - -import tensorflow as tf - - -__version__ = '0.6.3' - -TF_VERSION = int(tf.__version__.split('.')[0]) -TF_KERAS = int(os.getenv('TF_KERAS', 0)) == 1 diff --git a/langml/langml/baselines/__init__.py b/langml/langml/baselines/__init__.py deleted file mode 100644 index 488ff4a..0000000 --- a/langml/langml/baselines/__init__.py +++ /dev/null @@ -1,22 +0,0 @@ -# -*- coding: utf-8 -*- - -from typing import Dict, Any - - -class BaselineModel: - def build_model(self, *args, **kwargs): - raise NotImplementedError - - -class Parameters: - """ Hyper-Parameters - """ - def __init__(self, data: Dict): - for name, value in data.items(): - setattr(self, name, self._wrap(value)) - - def _wrap(self, value: Any): - if isinstance(value, (tuple, list, set, frozenset)): - return type(value)([self._wrap(v) for v in value]) - else: - return Parameters(value) if isinstance(value, dict) else value diff --git a/langml/langml/baselines/clf/__init__.py b/langml/langml/baselines/clf/__init__.py deleted file mode 100644 index 0025d89..0000000 --- a/langml/langml/baselines/clf/__init__.py +++ /dev/null @@ -1,50 +0,0 @@ -# -*- coding: utf-8 -*- - -import os -from typing import Dict, List, Tuple, Union - -import numpy as np -from sklearn.metrics import f1_score, accuracy_score, classification_report - -from langml import TF_VERSION -from langml.tensor_typing import Models - - -class Infer: - def __init__(self, - model: Models, - tokenizer: object, - id2label: Dict, - is_bert: bool = True): - self.model = model - self.tokenizer = tokenizer - self.id2label = id2label - self.is_bert = is_bert - - def __call__(self, text: str): - tokenized = self.tokenizer.encode(text) - token_ids = np.array([tokenized.ids]) - segment_ids = np.array([tokenized.segment_ids]) - if TF_VERSION > 1: - if self.is_bert: - logits = self.model([token_ids, segment_ids]) - else: - logits = self.model([token_ids]) - else: - if self.is_bert: - logits = self.model.predict([token_ids, segment_ids]) - else: - logits = self.model.predict([token_ids]) - pred = np.argmax(logits, 1)[0] - return self.id2label[pred] - - -def compute_detail_metrics(infer: object, datas: List, use_micro=False) -> Tuple[float, float, Union[str, Dict]]: - y_true, y_pred = [], [] - for text, label in datas: - y_pred.append(infer(text)) - y_true.append(label) - acc = accuracy_score(y_true, y_pred) - f1 = f1_score(y_true, y_pred, average='micro' if use_micro else 'macro') - cr = classification_report(y_true=y_true, y_pred=y_pred) - return f1, acc, cr diff --git a/langml/langml/baselines/clf/bert.py b/langml/langml/baselines/clf/bert.py deleted file mode 100644 index d4adac7..0000000 --- a/langml/langml/baselines/clf/bert.py +++ /dev/null @@ -1,51 +0,0 @@ -# -*- coding: utf-8 -*- - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.layers as L -else: - import keras - import keras.layers as L - -from langml.plm.albert import load_albert -from langml.plm.bert import load_bert -from langml.baselines import BaselineModel, Parameters -from langml.tensor_typing import Models - - -class Bert(BaselineModel): - def __init__(self, - config_path: str, - ckpt_path: str, - params: Parameters, - backbone: str = 'roberta'): - self.config_path = config_path - self.ckpt_path = ckpt_path - self.params = params - assert backbone in ['bert', 'roberta', 'albert'] - if backbone == 'albert': - self.load_plm = load_albert - else: - self.load_plm = load_bert - - def build_model(self, lazy_restore=False) -> Models: - if lazy_restore: - model, _, restore_bert_weights = self.load_plm(self.config_path, self.ckpt_path, lazy_restore=True) - else: - model, _ = self.load_plm(self.config_path, self.ckpt_path) - # CLS - output = L.Lambda(lambda x: x[:, 0], name='CLS')(model.output) - output = L.Dense(self.params.tag_size, - name='tag', - activation='softmax')(output) - train_model = keras.Model(model.input, output) - train_model.summary() - train_model.compile(keras.optimizers.Adam(self.params.learning_rate), - loss='sparse_categorical_crossentropy', - metrics=['accuracy']) - # For distributed training, restoring bert weight after model compiling. - if lazy_restore: - restore_bert_weights(model) - - return train_model diff --git a/langml/langml/baselines/clf/bilstm.py b/langml/langml/baselines/clf/bilstm.py deleted file mode 100644 index 15ea476..0000000 --- a/langml/langml/baselines/clf/bilstm.py +++ /dev/null @@ -1,44 +0,0 @@ -# -*- coding: utf-8 -*- - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.backend as K - import tensorflow.keras.layers as L -else: - import keras - import keras.backend as K - import keras.layers as L - -from langml.baselines import BaselineModel, Parameters -from langml.layers import SelfAttention -from langml.tensor_typing import Models - - -class BiLSTM(BaselineModel): - def __init__(self, params: Parameters): - self.params = params - - def build_model(self, with_attention: bool = False) -> Models: - x_in = L.Input(shape=(None, ), name='Input-Token') - x = x_in - x = L.Embedding(self.params.vocab_size, - self.params.embedding_size, - mask_zero=True, - name='embedding')(x) - if with_attention: - x = L.Bidirectional(L.LSTM(self.params.hidden_size, return_sequences=True))(x) - # attn - x = SelfAttention()(x) - x = L.Lambda(lambda x: K.max(x, 1))(x) - else: - x = L.Bidirectional(L.LSTM(self.params.hidden_size))(x) - x = L.Dropout(0.2)(x) - o = L.Dense(self.params.tag_size, name='tag', activation='softmax')(x) - model = keras.Model(x_in, o) - model.summary() - model.compile(keras.optimizers.Adam(self.params.learning_rate), - loss='sparse_categorical_crossentropy', - metrics=['accuracy']) - - return model diff --git a/langml/langml/baselines/clf/cli.py b/langml/langml/baselines/clf/cli.py deleted file mode 100644 index 2400d04..0000000 --- a/langml/langml/baselines/clf/cli.py +++ /dev/null @@ -1,480 +0,0 @@ -# -*- coding: utf-8 -*- - -import os -import json -from typing import Optional -from shutil import copyfile - -import click -from langml import TF_VERSION, TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.backend as K -else: - import keras - import keras.backend as K - -from langml.log import info -from langml.baselines import Parameters -from langml.baselines.clf import Infer, compute_detail_metrics -from langml.baselines.clf.dataloader import load_data, DataGenerator, TFDataGenerator -from langml.model import save_frozen -from langml.tokenizer import WPTokenizer, SPTokenizer - - -MONITOR = 'val_accuracy' if not TF_KERAS or TF_VERSION > 1 else 'val_acc' - - -@click.group() -def clf(): - """classification command line tools""" - pass - - -@clf.command() -@click.option('--backbone', type=str, default='bert', - help='specify backbone: bert | roberta | albert') -@click.option('--epoch', type=int, default=20, help='epochs') -@click.option('--batch_size', type=int, default=32, help='batch size') -@click.option('--learning_rate', type=float, default=2e-5, help='learning rate') -@click.option('--max_len', type=int, default=512, help='max len') -@click.option('--lowercase', is_flag=True, default=False, help='do lowercase') -@click.option('--tokenizer_type', type=str, default=None, - help='specify tokenizer type from [`wordpiece`, `sentencepiece`]') -@click.option('--early_stop', type=int, default=10, help='patience to early stop') -@click.option('--use_micro', is_flag=True, default=False, help='whether to use micro metrics') -@click.option('--config_path', type=str, required=True, help='bert config path') -@click.option('--ckpt_path', type=str, required=True, help='bert checkpoint path') -@click.option('--vocab_path', type=str, required=True, help='bert vocabulary path') -@click.option('--train_path', type=str, required=True, help='train path') -@click.option('--dev_path', type=str, required=True, help='dev path') -@click.option('--test_path', type=str, default=None, help='test path') -@click.option('--save_dir', type=str, required=True, help='dir to save model') -@click.option('--verbose', type=int, default=2, help='0 = silent, 1 = progress bar, 2 = one line per epoch') -@click.option('--distribute', is_flag=True, default=False, help='distributed training') -def bert(backbone: str, epoch: int, batch_size: int, learning_rate: float, max_len: Optional[int], - lowercase: bool, tokenizer_type: Optional[str], early_stop: int, use_micro: bool, - config_path: str, ckpt_path: str, vocab_path: str, train_path: str, dev_path: str, - test_path: str, save_dir: str, verbose: int, distribute: bool): - - # check distribute - if distribute: - assert TF_KERAS, 'Please `export TF_KERAS=1` to support distributed training!' - - from langml.baselines.clf.bert import Bert - - if not os.path.exists(save_dir): - os.makedirs(save_dir) - - train_datas, label2id = load_data(train_path, build_vocab=True) - id2label = {v: k for k, v in label2id.items()} - dev_datas = load_data(dev_path) - test_datas = None - if test_path is not None: - test_datas = load_data(test_path) - info(f'labels: {label2id}') - info(f'train size: {len(train_datas)}') - info(f'valid size: {len(dev_datas)}') - if test_path is not None: - info(f'test size: {len(test_datas)}') - - if tokenizer_type == 'wordpiece': - tokenizer = WPTokenizer(vocab_path, lowercase=lowercase) - elif tokenizer_type == 'sentencepiece': - tokenizer = SPTokenizer(vocab_path, lowercase=lowercase) - else: - # auto deduce - if vocab_path.endswith('.txt'): - info('automatically apply `WPTokenizer`') - tokenizer = WPTokenizer(vocab_path, lowercase=lowercase) - elif vocab_path.endswith('.model'): - info('automatically apply `SPTokenizer`') - tokenizer = SPTokenizer(vocab_path, lowercase=lowercase) - else: - raise ValueError("Langml cannot deduce which tokenizer to apply, please specify `tokenizer_type` manually.") # NOQA - - tokenizer.enable_truncation(max_length=max_len) - params = Parameters({ - 'learning_rate': learning_rate, - 'tag_size': len(label2id), - }) - if distribute: - import tensorflow as tf - # distributed training - strategy = tf.distribute.MirroredStrategy() - with strategy.scope(): - model = Bert(config_path, ckpt_path, params, backbone=backbone).build_model(lazy_restore=True) - else: - model = Bert(config_path, ckpt_path, params, backbone=backbone).build_model() - - early_stop_callback = keras.callbacks.EarlyStopping( - monitor=MONITOR, - min_delta=1e-4, - patience=early_stop, - verbose=0, - mode='auto', - baseline=None, - restore_best_weights=True - ) - save_checkpoint_callback = keras.callbacks.ModelCheckpoint( - os.path.join(save_dir, 'best_model.weights'), - save_best_only=True, - save_weights_only=True, - monitor=MONITOR, - mode='auto') - - if distribute: - info('distributed training! using `TFDataGenerator`') - assert max_len is not None, 'Please specify `max_len`!' - train_generator = TFDataGenerator(max_len, train_datas, tokenizer, label2id, - batch_size=batch_size, is_bert=True) - dev_generator = TFDataGenerator(max_len, dev_datas, tokenizer, label2id, - batch_size=batch_size, is_bert=True) - train_dataset = train_generator() - dev_dataset = dev_generator() - else: - train_generator = DataGenerator(train_datas, tokenizer, label2id, - batch_size=batch_size, is_bert=True) - dev_generator = DataGenerator(dev_datas, tokenizer, label2id, - batch_size=batch_size, is_bert=True) - train_dataset = train_generator.forfit(random=True) - dev_dataset = dev_generator.forfit(random=False) - - model.fit(train_dataset, - steps_per_epoch=len(train_generator), - verbose=verbose, - epochs=epoch, - validation_data=dev_dataset, - validation_steps=len(dev_generator), - callbacks=[early_stop_callback, save_checkpoint_callback]) - - # clear model - del model - if distribute: - del strategy - K.clear_session() - # restore model - model = Bert(config_path, ckpt_path, params, backbone=backbone).build_model() - if TF_KERAS or TF_VERSION > 1: - model.load_weights(os.path.join(save_dir, 'best_model.weights')).expect_partial() - else: - model.load_weights(os.path.join(save_dir, 'best_model.weights')) - # compute detail metrics - info('done to training! start to compute detail metrics...') - infer = Infer(model, tokenizer, id2label, is_bert=True) - _, _, dev_cr = compute_detail_metrics(infer, dev_datas, use_micro=use_micro) - print('develop metrics:') - print(dev_cr) - if test_datas: - _, _, test_cr = compute_detail_metrics(infer, test_datas, use_micro=use_micro) - print('test metrics:') - print(test_cr) - # save model - info('start to save frozen') - save_frozen(model, os.path.join(save_dir, 'frozen_model')) - info('start to save label') - with open(os.path.join(save_dir, 'label2id.json'), 'w', encoding='utf-8') as writer: - json.dump(label2id, writer) - info('copy vocab') - copyfile(vocab_path, os.path.join(save_dir, 'vocab.txt')) - - -@clf.command() -@click.option('--epoch', type=int, default=20, help='epochs') -@click.option('--batch_size', type=int, default=32, help='batch size') -@click.option('--learning_rate', type=float, default=1e-3, help='learning rate') -@click.option('--embedding_size', type=int, default=200, help='embedding size') -@click.option('--filter_size', type=int, default=100, help='filter size of convolution') -@click.option('--max_len', type=int, default=None, help='max len') -@click.option('--lowercase', is_flag=True, default=False, help='do lowercase') -@click.option('--tokenizer_type', type=str, default=None, - help='specify tokenizer type from [`wordpiece`, `sentencepiece`]') -@click.option('--early_stop', type=int, default=10, help='patience to early stop') -@click.option('--use_micro', is_flag=True, default=False, help='whether to use micro metrics') -@click.option('--vocab_path', type=str, required=True, help='vocabulary path') -@click.option('--train_path', type=str, required=True, help='train path') -@click.option('--dev_path', type=str, required=True, help='dev path') -@click.option('--test_path', type=str, default=None, help='test path') -@click.option('--save_dir', type=str, required=True, help='dir to save model') -@click.option('--verbose', type=int, default=2, help='0 = silent, 1 = progress bar, 2 = one line per epoch') -@click.option('--distribute', is_flag=True, default=False, help='distributed training') -def textcnn(epoch: int, batch_size: int, learning_rate: float, embedding_size: int, - filter_size: int, max_len: Optional[int], lowercase: bool, tokenizer_type: Optional[str], - early_stop: int, use_micro: bool, vocab_path: str, train_path: str, dev_path: str, - test_path: str, save_dir: str, verbose: int, distribute: bool): - - # check distribute - if distribute: - assert TF_KERAS, 'please `export TF_KERAS=1` to support distributed training!' - - from langml.baselines.clf.textcnn import TextCNN - - if not os.path.exists(save_dir): - os.makedirs(save_dir) - - train_datas, label2id = load_data(train_path, build_vocab=True) - id2label = {v: k for k, v in label2id.items()} - dev_datas = load_data(dev_path) - test_datas = None - if test_path is not None: - test_datas = load_data(test_path) - info(f'labels: {label2id}') - info(f'train size: {len(train_datas)}') - info(f'valid size: {len(dev_datas)}') - if test_path is not None: - info(f'test size: {len(test_datas)}') - - if tokenizer_type == 'wordpiece': - tokenizer = WPTokenizer(vocab_path, lowercase=lowercase) - elif tokenizer_type == 'sentencepiece': - tokenizer = SPTokenizer(vocab_path, lowercase=lowercase) - else: - # auto deduce - if vocab_path.endswith('.txt'): - info('automatically apply `WPTokenizer`') - tokenizer = WPTokenizer(vocab_path, lowercase=lowercase) - elif vocab_path.endswith('.model'): - info('automatically apply `SPTokenizer`') - tokenizer = SPTokenizer(vocab_path, lowercase=lowercase) - else: - raise ValueError("Langml cannot deduce which tokenizer to apply, please specify `tokenizer_type` manually.") # NOQA - - if max_len is not None: - tokenizer.enable_truncation(max_length=max_len) - params = Parameters({ - 'learning_rate': learning_rate, - 'tag_size': len(label2id), - 'vocab_size': tokenizer.get_vocab_size(), - 'embedding_size': embedding_size, - 'filter_size': filter_size - }) - if distribute: - import tensorflow as tf - # distributed training - strategy = tf.distribute.MirroredStrategy() - with strategy.scope(): - model = TextCNN(params).build_model() - else: - model = TextCNN(params).build_model() - - early_stop_callback = keras.callbacks.EarlyStopping( - monitor=MONITOR, - min_delta=1e-4, - patience=early_stop, - verbose=0, - mode='auto', - baseline=None, - restore_best_weights=True - ) - save_checkpoint_callback = keras.callbacks.ModelCheckpoint( - os.path.join(save_dir, 'best_model.weights'), - save_best_only=True, - save_weights_only=True, - monitor=MONITOR, - mode='auto') - - if distribute: - info('distributed training! using `TFDataGenerator`') - assert max_len is not None, 'Please specify `max_len`!' - train_generator = TFDataGenerator(max_len, train_datas, tokenizer, label2id, - batch_size=batch_size, is_bert=False) - dev_generator = TFDataGenerator(max_len, dev_datas, tokenizer, label2id, - batch_size=batch_size, is_bert=False) - train_dataset = train_generator() - dev_dataset = dev_generator() - else: - train_generator = DataGenerator(train_datas, tokenizer, label2id, - batch_size=batch_size, is_bert=False) - dev_generator = DataGenerator(dev_datas, tokenizer, label2id, - batch_size=batch_size, is_bert=False) - train_dataset = train_generator.forfit(random=True) - dev_dataset = dev_generator.forfit(random=False) - - model.fit(train_dataset, - steps_per_epoch=len(train_generator), - verbose=verbose, - epochs=epoch, - validation_data=dev_dataset, - validation_steps=len(dev_generator), - callbacks=[early_stop_callback, save_checkpoint_callback]) - - # clear model - del model - if distribute: - del strategy - K.clear_session() - # restore model - model = TextCNN(params).build_model() - if TF_KERAS or TF_VERSION > 1: - model.load_weights(os.path.join(save_dir, 'best_model.weights')).expect_partial() - else: - model.load_weights(os.path.join(save_dir, 'best_model.weights')) - # compute detail metrics - info('done to training! start to compute detail metrics...') - infer = Infer(model, tokenizer, id2label, is_bert=False) - _, _, dev_cr = compute_detail_metrics(infer, dev_datas, use_micro=use_micro) - print('develop metrics:') - print(dev_cr) - if test_datas: - _, _, test_cr = compute_detail_metrics(infer, test_datas, use_micro=use_micro) - print('test metrics:') - print(test_cr) - # save model - info('start to save frozen') - save_frozen(model, os.path.join(save_dir, 'frozen_model')) - info('start to save label') - with open(os.path.join(save_dir, 'label2id.json'), 'w', encoding='utf-8') as writer: - json.dump(label2id, writer) - info('copy vocab') - copyfile(vocab_path, os.path.join(save_dir, 'vocab.txt')) - - -@clf.command() -@click.option('--epoch', type=int, default=20, help='epochs') -@click.option('--batch_size', type=int, default=32, help='batch size') -@click.option('--learning_rate', type=float, default=1e-3, help='learning rate') -@click.option('--embedding_size', type=int, default=200, help='embedding size') -@click.option('--hidden_size', type=int, default=128, help='hidden size of lstm') -@click.option('--max_len', type=int, default=None, help='max len') -@click.option('--lowercase', is_flag=True, default=False, help='do lowercase') -@click.option('--tokenizer_type', type=str, default=None, - help='specify tokenizer type from [`wordpiece`, `sentencepiece`]') -@click.option('--early_stop', type=int, default=10, help='patience to early stop') -@click.option('--use_micro', is_flag=True, default=False, help='whether to use micro metrics') -@click.option('--vocab_path', type=str, required=True, help='vocabulary path') -@click.option('--train_path', type=str, required=True, help='train path') -@click.option('--dev_path', type=str, required=True, help='dev path') -@click.option('--test_path', type=str, default=None, help='test path') -@click.option('--save_dir', type=str, required=True, help='dir to save model') -@click.option('--verbose', type=int, default=2, help='0 = silent, 1 = progress bar, 2 = one line per epoch') -@click.option('--with_attention', is_flag=True, default=False, help='apply bilstm attention') -@click.option('--distribute', is_flag=True, default=False, help='distributed training') -def bilstm(epoch: int, batch_size: int, learning_rate: float, embedding_size: int, - hidden_size: int, max_len: Optional[int], lowercase: bool, tokenizer_type: Optional[str], - early_stop: int, use_micro: bool, vocab_path: str, train_path: str, dev_path: str, - test_path: str, save_dir: str, verbose: int, with_attention: bool, distribute: bool): - - # check distribute - if distribute: - assert TF_KERAS, 'please `export TF_KERAS=1` to support distributed training!' - - from langml.baselines.clf.bilstm import BiLSTM as BiLSTM - - if not os.path.exists(save_dir): - os.makedirs(save_dir) - - train_datas, label2id = load_data(train_path, build_vocab=True) - id2label = {v: k for k, v in label2id.items()} - dev_datas = load_data(dev_path) - test_datas = None - if test_path is not None: - test_datas = load_data(test_path) - info(f'labels: {label2id}') - info(f'train size: {len(train_datas)}') - info(f'valid size: {len(dev_datas)}') - if test_path is not None: - info(f'test size: {len(test_datas)}') - - if tokenizer_type == 'wordpiece': - tokenizer = WPTokenizer(vocab_path, lowercase=lowercase) - elif tokenizer_type == 'sentencepiece': - tokenizer = SPTokenizer(vocab_path, lowercase=lowercase) - else: - # auto deduce - if vocab_path.endswith('.txt'): - info('automatically apply `WPTokenizer`') - tokenizer = WPTokenizer(vocab_path, lowercase=lowercase) - elif vocab_path.endswith('.model'): - info('automatically apply `SPTokenizer`') - tokenizer = SPTokenizer(vocab_path, lowercase=lowercase) - else: - raise ValueError("Langml cannot deduce which tokenizer to apply, please specify `tokenizer_type` manually.") # NOQA - - if max_len is not None: - tokenizer.enable_truncation(max_length=max_len) - params = Parameters({ - 'learning_rate': learning_rate, - 'tag_size': len(label2id), - 'vocab_size': tokenizer.get_vocab_size(), - 'embedding_size': embedding_size, - 'hidden_size': hidden_size - }) - if distribute: - import tensorflow as tf - # distributed training - strategy = tf.distribute.MirroredStrategy() - with strategy.scope(): - model = BiLSTM(params).build_model(with_attention=with_attention) - else: - model = BiLSTM(params).build_model(with_attention=with_attention) - - early_stop_callback = keras.callbacks.EarlyStopping( - monitor=MONITOR, - min_delta=1e-4, - patience=early_stop, - verbose=0, - mode='auto', - baseline=None, - restore_best_weights=True - ) - save_checkpoint_callback = keras.callbacks.ModelCheckpoint( - os.path.join(save_dir, 'best_model.weights'), - save_best_only=True, - save_weights_only=True, - monitor=MONITOR, - mode='auto') - - if distribute: - info('distributed training! using `TFDataGenerator`') - assert max_len is not None, 'Please specify `max_len`!' - train_generator = TFDataGenerator(max_len, train_datas, tokenizer, label2id, - batch_size=batch_size, is_bert=False) - dev_generator = TFDataGenerator(max_len, dev_datas, tokenizer, label2id, - batch_size=batch_size, is_bert=False) - train_dataset = train_generator() - dev_dataset = dev_generator() - else: - train_generator = DataGenerator(train_datas, tokenizer, label2id, - batch_size=batch_size, is_bert=False) - dev_generator = DataGenerator(dev_datas, tokenizer, label2id, - batch_size=batch_size, is_bert=False) - train_dataset = train_generator.forfit(random=True) - dev_dataset = dev_generator.forfit(random=False) - - model.fit(train_dataset, - steps_per_epoch=len(train_generator), - verbose=verbose, - epochs=epoch, - validation_data=dev_dataset, - validation_steps=len(dev_generator), - callbacks=[early_stop_callback, save_checkpoint_callback]) - - # clear model - del model - if distribute: - del strategy - K.clear_session() - # restore model - model = BiLSTM(params).build_model(with_attention=with_attention) - if TF_KERAS or TF_VERSION > 1: - model.load_weights(os.path.join(save_dir, 'best_model.weights')).expect_partial() - else: - model.load_weights(os.path.join(save_dir, 'best_model.weights')) - # compute detail metrics - info('done to training! start to compute detail metrics...') - infer = Infer(model, tokenizer, id2label, is_bert=False) - _, _, dev_cr = compute_detail_metrics(infer, dev_datas, use_micro=use_micro) - print('develop metrics:') - print(dev_cr) - if test_datas: - _, _, test_cr = compute_detail_metrics(infer, test_datas, use_micro=use_micro) - print('test metrics:') - print(test_cr) - # save model - info('start to save frozen') - save_frozen(model, os.path.join(save_dir, 'frozen_model')) - info('start to save label') - with open(os.path.join(save_dir, 'label2id.json'), 'w', encoding='utf-8') as writer: - json.dump(label2id, writer) - info('copy vocab') - copyfile(vocab_path, os.path.join(save_dir, 'vocab.txt')) diff --git a/langml/langml/baselines/clf/dataloader.py b/langml/langml/baselines/clf/dataloader.py deleted file mode 100644 index e7eef8a..0000000 --- a/langml/langml/baselines/clf/dataloader.py +++ /dev/null @@ -1,115 +0,0 @@ -# -*- coding: utf-8 -*- - -import json -import math -from random import shuffle -from typing import Dict, List - -import numpy as np -from boltons.iterutils import chunked_iter -import tensorflow as tf -from langml import TF_KERAS -if TF_KERAS: - from tensorflow.keras.preprocessing.sequence import pad_sequences -else: - from keras.preprocessing.sequence import pad_sequences - - -def load_data(fpath: str, build_vocab: bool = False) -> List: - if build_vocab: - label2id = {} - datas = [] - with open(fpath, 'r', encoding='utf-8') as reader: - for line in reader: - line = line.strip() - if not line: - continue - obj = json.loads(line) - if build_vocab and obj['label'] not in label2id: - label2id[obj['label']] = len(label2id) - datas.append((obj['text'], obj['label'])) - if build_vocab: - return datas, label2id - return datas - - -class DataGenerator: - def __init__(self, - datas: List, - tokenizer: object, - label2id: Dict, - batch_size: int = 32, - is_bert: bool = True): - self.batch_size = batch_size - self.is_bert = is_bert - - self.datas = [] - for text, label in datas: - tokened = tokenizer.encode(text) - self.datas.append((tokened.ids, tokened.segment_ids, label2id[label])) - - def __len__(self) -> int: - return math.ceil(len(self.datas) / self.batch_size) - - def __iter__(self, random: bool = False): - if random: - shuffle(self.datas) - - for chunks in chunked_iter(self.datas, self.batch_size): - batch_tokens, batch_segments, batch_labels = [], [], [] - - for token_ids, segment_ids, label in chunks: - batch_tokens.append(token_ids) - batch_segments.append(segment_ids) - batch_labels.append([label]) - - batch_tokens = pad_sequences(batch_tokens, padding='post', truncating='post') - batch_segments = pad_sequences(batch_segments, padding='post', truncating='post') - batch_labels = np.array(batch_labels) - if self.is_bert: - yield [batch_tokens, batch_segments], batch_labels - else: - yield batch_tokens, batch_labels - - def forfit(self, random: bool = False): - while True: - for inputs, labels in self.__iter__(random=random): - yield inputs, labels - - -class TFDataGenerator(DataGenerator): - def __init__(self, - max_len: int, - datas: List, - tokenizer: object, - label2id: Dict, - batch_size: int, - is_bert: bool): - super().__init__(datas, tokenizer, label2id, batch_size=batch_size, is_bert=is_bert) - self.max_len = max_len - - def __call__(self): - def gen_features(): - for token_ids, _, label in self.datas: - token_ids = token_ids[: self.max_len] + [0]*(self.max_len - len(token_ids)) - if self.is_bert: - segment_ids = [0] * len(token_ids) - yield {'Input-Token': token_ids, 'Input-Segment': segment_ids}, [label] - else: - yield token_ids, [label] - - if self.is_bert: - output_types = ({'Input-Token': tf.int64, 'Input-Segment': tf.int64}, tf.int64) - output_shapes = ({'Input-Token': tf.TensorShape((None, )), - 'Input-Segment': tf.TensorShape((None, ))}, - tf.TensorShape((1, ))) - else: - output_types = (tf.int64, tf.int64) - output_shapes = (tf.TensorShape((None, )), tf.TensorShape((1, ))) - dataset = tf.data.Dataset.from_generator(gen_features, - output_types=output_types, - output_shapes=output_shapes) - dataset = dataset.repeat() - dataset = dataset.shuffle(self.batch_size * 1000) - dataset = dataset.batch(self.batch_size) - return dataset diff --git a/langml/langml/baselines/clf/textcnn.py b/langml/langml/baselines/clf/textcnn.py deleted file mode 100644 index 2b8c98d..0000000 --- a/langml/langml/baselines/clf/textcnn.py +++ /dev/null @@ -1,45 +0,0 @@ -# -*- coding: utf-8 -*- - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.layers as L -else: - import keras - import keras.layers as L - -from langml.baselines import BaselineModel, Parameters -from langml.tensor_typing import Models - - -class TextCNN(BaselineModel): - def __init__(self, params: Parameters): - self.params = params - - def build_model(self) -> Models: - x_in = L.Input(shape=(None, ), name='Input-Token') - x = x_in - x = L.Embedding(self.params.vocab_size, - self.params.embedding_size, - name='embedding')(x) - convs = [] - for kernel_size in [3, 4, 5]: - # for convenience, using 1d-conv here. - conv = L.Conv1D(filters=self.params.filter_size, - kernel_size=kernel_size, - strides=1, - padding='valid', - activation='relu')(x) - conv = L.MaxPooling1D()(conv) - conv = L.GlobalMaxPool1D()(conv) - convs.append(conv) - x = L.Concatenate()(convs) - x = L.Dropout(0.2)(x) - o = L.Dense(self.params.tag_size, name='tag', activation='softmax')(x) - model = keras.Model(x_in, o) - model.summary() - model.compile(keras.optimizers.Adam(self.params.learning_rate), - loss='sparse_categorical_crossentropy', - metrics=['accuracy']) - - return model diff --git a/langml/langml/baselines/cli.py b/langml/langml/baselines/cli.py deleted file mode 100644 index d05c27e..0000000 --- a/langml/langml/baselines/cli.py +++ /dev/null @@ -1,16 +0,0 @@ -# -*- coding: utf-8 -*- - -import click - -from langml.baselines.ner.cli import ner -from langml.baselines.clf.cli import clf - - -@click.group() -def baseline(): - """LangML Baseline client""" - pass - - -baseline.add_command(ner) -baseline.add_command(clf) diff --git a/langml/langml/baselines/ner/__init__.py b/langml/langml/baselines/ner/__init__.py deleted file mode 100644 index 73ab8cc..0000000 --- a/langml/langml/baselines/ner/__init__.py +++ /dev/null @@ -1,104 +0,0 @@ -# -*- coding: utf-8 -*- - -import re -from typing import Dict, List, Optional - -import numpy as np -from seqeval.metrics import classification_report - -from langml import TF_VERSION -from langml.utils import bio_decode -from langml.tensor_typing import Models - - -re_split = re.compile(r'.*?[\n。]+') - - -class Infer: - def __init__(self, - model: Models, - tokenizer: object, - id2label: Dict, - max_chunk_len: Optional[int] = None, - is_bert: bool = True): - self.model = model - self.tokenizer = tokenizer - self.id2label = id2label - self.max_chunk_len = max_chunk_len - self.is_bert = is_bert - - def decode_one(self, text: str, base_position: int = 0): - """ - Args: - - text: str - - base_position: int - - Return: - list of tuple: [(entity, start, end, entity_type)] - """ - tokened = self.tokenizer.encode(text) - mapping = self.tokenizer.tokens_mapping(text, tokened.tokens) - token_ids = np.array([tokened.ids]) - segment_ids = np.array([tokened.segment_ids]) - if TF_VERSION > 1: - if self.is_bert: - logits = self.model([token_ids, segment_ids]) - else: - logits = self.model([token_ids]) - else: - if self.is_bert: - logits = self.model.predict([token_ids, segment_ids]) - else: - logits = self.model.predict([token_ids]) - tags = [self.id2label[i] for i in np.argmax(logits[0], axis=1)] - entities = bio_decode(tags) - - res = [] - for s, e, t in entities: - s = mapping[s] - e = mapping[e] - s = 0 if not s else s[0] - e = len(text) - 1 if not e else e[-1] - res.append((base_position + s, base_position + e + 1, text[s: e + 1], t)) - return res - - def __call__(self, text: str): - if self.max_chunk_len is None or len(text) < self.max_chunk_len: - return self.decode_one(text) - sentences = re_split.findall(text) - if not sentences: - return self.decode_one(text) - results = [] - prev, base_position = 0, 0 - for i in range(1, len(sentences)): - current_text = ''.join(sentences[prev: i]) - if len(current_text) <= self.max_chunk_len and len(''.join(sentences[prev: i+1])) > self.max_chunk_len: - results.extend(self.decode_one(current_text, base_position=base_position)) - prev = i - base_position += len(current_text) - results.extend(self.decode_one(''.join(sentences[prev:]), base_position=base_position)) - return results - - -def report_detail_metrics(model: Models, datas: List, id2label: Dict, is_bert: bool = True): - all_preds, all_golds = [], [] - for token_ids, segment_ids, tags in datas: - token_ids = np.array([token_ids]) - segment_ids = np.array([segment_ids]) - if TF_VERSION > 1: - if is_bert: - logits = model([token_ids, segment_ids]) - else: - logits = model([token_ids]) - else: - if is_bert: - logits = model.predict([token_ids, segment_ids]) - else: - logits = model.predict([token_ids]) - gold_tags = [id2label[i] for i in tags] - pred_tags = [id2label[i] for i in np.argmax(logits[0], axis=1)] - assert len(gold_tags) == len(pred_tags), f'g: {len(gold_tags)}, t: {len(pred_tags)}' - all_preds.append(pred_tags) - all_golds.append(gold_tags) - - print(classification_report(all_golds, all_preds, digits=4)) diff --git a/langml/langml/baselines/ner/bert_crf.py b/langml/langml/baselines/ner/bert_crf.py deleted file mode 100644 index e22b388..0000000 --- a/langml/langml/baselines/ner/bert_crf.py +++ /dev/null @@ -1,49 +0,0 @@ -# -*- coding: utf-8 -*- - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.layers as L -else: - import keras - import keras.layers as L - -from langml.plm.albert import load_albert -from langml.plm.bert import load_bert -from langml.layers import CRF -from langml.baselines import BaselineModel, Parameters -from langml.tensor_typing import Models - - -class BertCRF(BaselineModel): - def __init__(self, - config_path: str, - ckpt_path: str, - params: Parameters, - backbone: str = 'roberta'): - self.config_path = config_path - self.ckpt_path = ckpt_path - self.params = params - assert backbone in ['bert', 'roberta', 'albert'] - if backbone == 'albert': - self.load_plm = load_albert - else: - self.load_plm = load_bert - - def build_model(self, lazy_restore=False) -> Models: - if lazy_restore: - model, _, restore_bert_weights = self.load_plm(self.config_path, self.ckpt_path, lazy_restore=True) - else: - model, _ = self.load_plm(self.config_path, self.ckpt_path) - crf = CRF(self.params.tag_size, sparse_target=False, name='crf') - output = L.Dropout(self.params.dropout_rate)(model.output) - output = L.Dense(self.params.tag_size, name='tag')(output) - output = crf(output) - train_model = keras.Model(model.input, output) - train_model.summary() - train_model.compile(keras.optimizers.Adam(self.params.learning_rate), loss=crf.loss, metrics=[crf.accuracy]) - - if lazy_restore: - restore_bert_weights(model) - - return train_model diff --git a/langml/langml/baselines/ner/cli.py b/langml/langml/baselines/ner/cli.py deleted file mode 100644 index b061875..0000000 --- a/langml/langml/baselines/ner/cli.py +++ /dev/null @@ -1,329 +0,0 @@ -# -*- coding: utf-8 -*- - -import os -import json -from typing import Optional -from shutil import copyfile - -from langml import TF_KERAS, TF_VERSION -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.backend as K -else: - import keras - import keras.backend as K -import click - -from langml.log import info -from langml.tokenizer import WPTokenizer, SPTokenizer -from langml.baselines import Parameters -from langml.baselines.ner import report_detail_metrics -from langml.baselines.ner.dataloader import load_data, DataGenerator, TFDataGenerator -from langml.model import save_frozen - - -@click.group() -def ner(): - """ner command line tools""" - pass - - -@ner.command() -@click.option('--backbone', type=str, default='bert', - help='specify backbone: bert | roberta | albert') -@click.option('--epoch', type=int, default=20, help='epochs') -@click.option('--batch_size', type=int, default=32, help='batch size') -@click.option('--learning_rate', type=float, default=2e-5, help='learning rate') -@click.option('--dropout_rate', type=float, default=0.2, help='dropout rate') -@click.option('--max_len', type=int, default=512, help='max len') -@click.option('--lowercase', is_flag=True, default=False, help='do lowercase') -@click.option('--tokenizer_type', type=str, default=None, - help='specify tokenizer type from [`wordpiece`, `sentencepiece`]') -@click.option('--config_path', type=str, required=True, help='bert config path') -@click.option('--ckpt_path', type=str, required=True, help='bert checkpoint path') -@click.option('--vocab_path', type=str, required=True, help='bert vocabulary path') -@click.option('--train_path', type=str, required=True, help='train path') -@click.option('--dev_path', type=str, required=True, help='dev path') -@click.option('--test_path', type=str, default=None, help='test path') -@click.option('--save_dir', type=str, required=True, help='dir to save model') -@click.option('--early_stop', type=int, default=10, help='patience to early stop') -@click.option('--distribute', is_flag=True, default=False, help='distributed training') -@click.option('--verbose', type=int, default=2, help='0 = silent, 1 = progress bar, 2 = one line per epoch') -def bert_crf(backbone: str, epoch: int, batch_size: int, learning_rate: float, - dropout_rate: float, max_len: Optional[int], lowercase: bool, - tokenizer_type: Optional[str], config_path: str, ckpt_path: str, - vocab_path: str, train_path: str, dev_path: str, test_path: str, - save_dir: str, early_stop: int, distribute: bool, verbose: int): - - from langml.baselines.ner.bert_crf import BertCRF - - # check distribute - if distribute: - assert TF_KERAS, 'Please `export TF_KERAS=1` to support distributed training!' - - if not os.path.exists(save_dir): - os.makedirs(save_dir) - - train_datas, label2id = load_data(train_path, build_vocab=True) - id2label = {v: k for k, v in label2id.items()} - dev_datas = load_data(dev_path) - test_datas = None - if test_path is not None: - test_datas = load_data(test_path) - info(f'labels: {label2id}') - info(f'train size: {len(train_datas)}') - info(f'valid size: {len(dev_datas)}') - if test_path is not None: - info(f'test size: {len(test_datas)}') - - if tokenizer_type == 'wordpiece': - tokenizer = WPTokenizer(vocab_path, lowercase=lowercase) - elif tokenizer_type == 'sentencepiece': - tokenizer = SPTokenizer(vocab_path, lowercase=lowercase) - else: - # auto deduce - if vocab_path.endswith('.txt'): - info('automatically apply `WPTokenizer`') - tokenizer = WPTokenizer(vocab_path, lowercase=lowercase) - elif vocab_path.endswith('.model'): - info('automatically apply `SPTokenizer`') - tokenizer = SPTokenizer(vocab_path, lowercase=lowercase) - else: - raise ValueError("Langml cannot deduce which tokenizer to apply, please specify `tokenizer_type` manually.") # NOQA - - tokenizer.enable_truncation(max_length=max_len) - params = Parameters({ - 'learning_rate': learning_rate, - 'dropout_rate': dropout_rate, - 'tag_size': len(label2id), - 'vocab_size': tokenizer.get_vocab_size(), - }) - if distribute: - import tensorflow as tf - # distributed training - strategy = tf.distribute.MirroredStrategy() - with strategy.scope(): - model = BertCRF(config_path, ckpt_path, params, backbone=backbone).build_model(lazy_restore=True) - else: - model = BertCRF(config_path, ckpt_path, params, backbone=backbone).build_model() - - if distribute: - info('distributed training! using `TFDataGenerator`') - assert max_len is not None, 'Please specify `max_len`!' - train_generator = TFDataGenerator(train_datas, tokenizer, label2id, - batch_size=batch_size, max_len=max_len, is_bert=True) - dev_generator = TFDataGenerator(dev_datas, tokenizer, label2id, - batch_size=batch_size, max_len=max_len, is_bert=True) - train_dataset = train_generator() - dev_dataset = dev_generator() - else: - train_generator = DataGenerator(train_datas, tokenizer, label2id, - batch_size=batch_size, max_len=max_len, is_bert=True) - dev_generator = DataGenerator(dev_datas, tokenizer, label2id, - batch_size=batch_size, max_len=max_len, is_bert=True) - train_dataset = train_generator.forfit(random=True) - dev_dataset = dev_generator.forfit(random=False) - - early_stop_callback = keras.callbacks.EarlyStopping( - monitor='val_viterbi_accuracy', - min_delta=1e-4, - patience=early_stop, - verbose=0, - mode='auto', - baseline=None, - restore_best_weights=True - ) - save_checkpoint_callback = keras.callbacks.ModelCheckpoint( - os.path.join(save_dir, 'best_model.weights'), - save_best_only=True, - save_weights_only=True, - monitor='val_viterbi_accuracy', - mode='auto') - model.fit(train_dataset, - steps_per_epoch=len(train_generator), - validation_data=dev_dataset, - validation_steps=len(dev_generator), - verbose=verbose, - epochs=epoch, - callbacks=[early_stop_callback, save_checkpoint_callback]) - # clear model - del model - if distribute: - del strategy - K.clear_session() - # restore model - model = BertCRF(config_path, ckpt_path, params, backbone=backbone).build_model() - if TF_KERAS or TF_VERSION > 1: - model.load_weights(os.path.join(save_dir, 'best_model.weights')).expect_partial() - else: - model.load_weights(os.path.join(save_dir, 'best_model.weights')) - # compute detail metrics - info('done to training! start to compute detail metrics...') - print('develop metrics:') - dev_generator = DataGenerator(dev_datas, tokenizer, label2id, - batch_size=batch_size, max_len=max_len, is_bert=True) - report_detail_metrics(model, dev_generator.datas, id2label, is_bert=True) - if test_datas: - print('test metrics:') - test_generator = DataGenerator(test_datas, tokenizer, label2id, - batch_size=batch_size, max_len=max_len, is_bert=True) - report_detail_metrics(model, test_generator.datas, id2label, is_bert=True) - # save model - info('start to save frozen') - save_frozen(model, os.path.join(save_dir, 'frozen_model')) - info('start to save label') - with open(os.path.join(save_dir, 'label2id.json'), 'w', encoding='utf-8') as writer: - json.dump(label2id, writer) - info('copy vocab') - copyfile(vocab_path, os.path.join(save_dir, 'vocab.txt')) - - -@ner.command() -@click.option('--epoch', type=int, default=20, help='epochs') -@click.option('--batch_size', type=int, default=32, help='batch size') -@click.option('--learning_rate', type=float, default=1e-3, help='learning rate') -@click.option('--dropout_rate', type=float, default=0.2, help='dropout rate') -@click.option('--embedding_size', type=int, default=200, help='embedding size') -@click.option('--hidden_size', type=int, default=128, help='hidden size') -@click.option('--max_len', type=int, default=None, help='max len') -@click.option('--lowercase', is_flag=True, default=False, help='do lowercase') -@click.option('--tokenizer_type', type=str, default=None, - help='specify tokenizer type from [`wordpiece`, `sentencepiece`]') -@click.option('--vocab_path', type=str, required=True, help='vocabulary path') -@click.option('--train_path', type=str, required=True, help='train path') -@click.option('--dev_path', type=str, required=True, help='dev path') -@click.option('--test_path', type=str, default=None, help='test path') -@click.option('--save_dir', type=str, required=True, help='dir to save model') -@click.option('--early_stop', type=int, default=10, help='patience to early stop') -@click.option('--distribute', is_flag=True, default=False, help='distributed training') -@click.option('--verbose', type=int, default=2, help='0 = silent, 1 = progress bar, 2 = one line per epoch') -def lstm_crf(epoch: int, batch_size: int, learning_rate: float, dropout_rate: float, - embedding_size: int, hidden_size: int, max_len: Optional[int], - lowercase: bool, tokenizer_type: Optional[str], vocab_path: str, - train_path: str, dev_path: str, test_path: str, save_dir: str, - early_stop: int, distribute: bool, verbose: int): - - from langml.baselines.ner.lstm_crf import LstmCRF - - # check distribute - if distribute: - assert TF_KERAS, 'Please `export TF_KERAS=1` to support distributed training!' - - if not os.path.exists(save_dir): - os.makedirs(save_dir) - - train_datas, label2id = load_data(train_path, build_vocab=True) - id2label = {v: k for k, v in label2id.items()} - dev_datas = load_data(dev_path) - test_datas = None - if test_path is not None: - test_datas = load_data(test_path) - info(f'labels: {label2id}') - info(f'train size: {len(train_datas)}') - info(f'valid size: {len(dev_datas)}') - if test_path is not None: - info(f'test size: {len(test_datas)}') - - if tokenizer_type == 'wordpiece': - tokenizer = WPTokenizer(vocab_path, lowercase=lowercase) - elif tokenizer_type == 'sentencepiece': - tokenizer = SPTokenizer(vocab_path, lowercase=lowercase) - else: - # auto deduce - if vocab_path.endswith('.txt'): - info('automatically apply `WPTokenizer`') - tokenizer = WPTokenizer(vocab_path, lowercase=lowercase) - elif vocab_path.endswith('.model'): - info('automatically apply `SPTokenizer`') - tokenizer = SPTokenizer(vocab_path, lowercase=lowercase) - else: - raise ValueError("Langml cannot deduce which tokenizer to apply, please specify `tokenizer_type` manually.") # NOQA - - if max_len is not None: - tokenizer.enable_truncation(max_length=max_len) - params = Parameters({ - 'learning_rate': learning_rate, - 'dropout_rate': dropout_rate, - 'tag_size': len(label2id), - 'vocab_size': tokenizer.get_vocab_size(), - 'embedding_size': embedding_size, - 'hidden_size': hidden_size - }) - if distribute: - import tensorflow as tf - # distributed training - strategy = tf.distribute.MirroredStrategy() - with strategy.scope(): - model = LstmCRF(params).build_model() - else: - model = LstmCRF(params).build_model() - - if distribute: - info('distributed training! using `TFDataGenerator`') - assert max_len is not None, 'Please specify `max_len`!' - train_generator = TFDataGenerator(train_datas, tokenizer, label2id, - batch_size=batch_size, max_len=max_len, is_bert=False) - dev_generator = TFDataGenerator(dev_datas, tokenizer, label2id, - batch_size=batch_size, max_len=max_len, is_bert=False) - train_dataset = train_generator() - dev_dataset = dev_generator() - else: - train_generator = DataGenerator(train_datas, tokenizer, label2id, - batch_size=batch_size, max_len=max_len, is_bert=False) - dev_generator = DataGenerator(dev_datas, tokenizer, label2id, - batch_size=batch_size, max_len=max_len, is_bert=False) - train_dataset = train_generator.forfit(random=True) - dev_dataset = dev_generator.forfit(random=False) - - early_stop_callback = keras.callbacks.EarlyStopping( - monitor='val_viterbi_accuracy', - min_delta=1e-4, - patience=early_stop, - verbose=0, - mode='auto', - baseline=None, - restore_best_weights=True - ) - save_checkpoint_callback = keras.callbacks.ModelCheckpoint( - os.path.join(save_dir, 'best_model.weights'), - save_best_only=True, - save_weights_only=True, - monitor='val_viterbi_accuracy', - mode='auto') - model.fit(train_dataset, - steps_per_epoch=len(train_generator), - validation_data=dev_dataset, - validation_steps=len(dev_generator), - verbose=verbose, - epochs=epoch, - callbacks=[early_stop_callback, save_checkpoint_callback]) - # clear model - del model - if distribute: - del strategy - K.clear_session() - # restore model - model = LstmCRF(params).build_model() - if TF_KERAS or TF_VERSION > 1: - model.load_weights(os.path.join(save_dir, 'best_model.weights')).expect_partial() - else: - model.load_weights(os.path.join(save_dir, 'best_model.weights')) - # compute detail metrics - info('done to training! start to compute detail metrics...') - print('develop metrics:') - dev_generator = DataGenerator(dev_datas, tokenizer, label2id, - batch_size=batch_size, max_len=max_len, is_bert=False) - report_detail_metrics(model, dev_generator.datas, id2label, is_bert=False) - if test_datas: - print('test metrics:') - test_generator = DataGenerator(test_datas, tokenizer, label2id, - batch_size=batch_size, max_len=max_len, is_bert=False) - report_detail_metrics(model, test_generator.datas, id2label, is_bert=False) - # save model - info('start to save frozen') - save_frozen(model, os.path.join(save_dir, 'frozen_model')) - info('start to save label') - with open(os.path.join(save_dir, 'label2id.json'), 'w', encoding='utf-8') as writer: - json.dump(label2id, writer) - info('copy vocab') - copyfile(vocab_path, os.path.join(save_dir, 'vocab.txt')) diff --git a/langml/langml/baselines/ner/dataloader.py b/langml/langml/baselines/ner/dataloader.py deleted file mode 100644 index 90aed9e..0000000 --- a/langml/langml/baselines/ner/dataloader.py +++ /dev/null @@ -1,141 +0,0 @@ -# -*- coding: utf-8 -*- - -import math -from random import shuffle -from typing import Dict, List, Optional - -from boltons.iterutils import chunked_iter -import tensorflow as tf -from langml import TF_KERAS -if TF_KERAS: - from tensorflow.keras.preprocessing.sequence import pad_sequences -else: - from keras.preprocessing.sequence import pad_sequences - - -def load_data(fpath: str, build_vocab: bool = False) -> List: - if build_vocab: - label2id = {'O': 0} - datas = [] - with open(fpath, 'r', encoding='utf-8') as reader: - for sentence in reader.read().split('\n\n'): - if not sentence: - continue - data = [] - for chunk in sentence.split('\n'): - try: - segment, label = chunk.split('\t') - if build_vocab: - if label != 'O' and f'B-{label}' not in label2id: - label2id[f'B-{label}'] = len(label2id) - if label != 'O' and f'I-{label}' not in label2id: - label2id[f'I-{label}'] = len(label2id) - data.append((segment, label)) - except ValueError: - print('broken data:', chunk) - datas.append(data) - if build_vocab: - return datas, label2id - return datas - - -class DataGenerator: - def __init__(self, - datas: List, - tokenizer: object, - label2id: Dict, - batch_size: int = 32, - max_len: Optional[int] = None, - is_bert: bool = True): - self.label2id = label2id - self.batch_size = batch_size - self.max_len = max_len - self.is_bert = is_bert - start_token_id = tokenizer.token_to_id(tokenizer.special_tokens.CLS) - end_token_id = tokenizer.token_to_id(tokenizer.special_tokens.SEP) - - self.datas = [] - for data in datas: - token_ids, labels = [start_token_id], [label2id['O']] - for segment, label in data: - tokened = tokenizer.encode(segment) - token_id = tokened.ids[1:-1] - token_ids += token_id - if label == 'O': - labels += [label2id['O']] * len(token_id) - else: - labels += ([label2id[f'B-{label}']] + [label2id[f'I-{label}']] * (len(token_id) - 1)) - assert len(token_ids) == len(labels) - if max_len is not None: - token_ids = token_ids[:max_len - 1] - labels = labels[:max_len - 1] - token_ids += [end_token_id] - labels += [label2id['O']] - segment_ids = [0] * len(token_ids) - self.datas.append((token_ids, segment_ids, labels)) - - def __len__(self) -> int: - return math.ceil(len(self.datas) / self.batch_size) - - def __iter__(self, random: bool = False): - if random: - shuffle(self.datas) - - for chunks in chunked_iter(self.datas, self.batch_size): - batch_tokens, batch_segments, batch_labels = [], [], [] - - for token_ids, segment_ids, labels in chunks: - batch_tokens.append(token_ids) - batch_segments.append(segment_ids) - batch_labels.append(labels) - - batch_tokens = pad_sequences(batch_tokens, padding='post', truncating='post') - batch_segments = pad_sequences(batch_segments, padding='post', truncating='post') - batch_labels = pad_sequences(batch_labels, padding='post', truncating='post') - if self.is_bert: - yield [batch_tokens, batch_segments], batch_labels - else: - yield batch_tokens, batch_labels - - def forfit(self, random: bool = False): - while True: - for inputs, labels in self.__iter__(random=random): - yield inputs, labels - - -class TFDataGenerator(DataGenerator): - def __init__(self, - datas: List, - tokenizer: object, - label2id: Dict, - batch_size: int = 32, - max_len: Optional[int] = None, - is_bert: bool = True): - super().__init__(datas, tokenizer, label2id, batch_size=batch_size, max_len=max_len, is_bert=is_bert) - - def __call__(self): - def gen_features(): - for token_ids, segment_ids, label_ids in self.datas: - token_ids = token_ids[:self.max_len] + [0] * (self.max_len - len(token_ids)) - label_ids = label_ids[:self.max_len] + [self.label2id['O']] * (self.max_len - len(label_ids)) - if self.is_bert: - segment_ids = [0] * len(token_ids) - yield {'Input-Token': token_ids, 'Input-Segment': segment_ids}, label_ids - else: - yield token_ids, label_ids - - if self.is_bert: - output_types = ({'Input-Token': tf.int64, 'Input-Segment': tf.int64}, tf.int64) - output_shapes = ({'Input-Token': tf.TensorShape((None, )), - 'Input-Segment': tf.TensorShape((None, ))}, - tf.TensorShape((None, ))) - else: - output_types = (tf.int64, tf.int64) - output_shapes = (tf.TensorShape((None, )), tf.TensorShape((None, ))) - dataset = tf.data.Dataset.from_generator(gen_features, - output_types=output_types, - output_shapes=output_shapes) - dataset = dataset.repeat() - dataset = dataset.shuffle(self.batch_size * 1000) - dataset = dataset.batch(self.batch_size) - return dataset diff --git a/langml/langml/baselines/ner/lstm_crf.py b/langml/langml/baselines/ner/lstm_crf.py deleted file mode 100644 index e2440df..0000000 --- a/langml/langml/baselines/ner/lstm_crf.py +++ /dev/null @@ -1,31 +0,0 @@ -# -*- coding: utf-8 -*- - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.layers as L -else: - import keras - import keras.layers as L - -from langml.layers import CRF -from langml.baselines import BaselineModel, Parameters -from langml.tensor_typing import Models - - -class LstmCRF(BaselineModel): - def __init__(self, params: Parameters): - self.params = params - - def build_model(self) -> Models: - crf = CRF(self.params.tag_size, sparse_target=False) - model = keras.Sequential() - model.add(L.Embedding(self.params.vocab_size, self.params.embedding_size, mask_zero=False)) - model.add(L.Bidirectional(L.LSTM(self.params.hidden_size, return_sequences=True))) - model.add(L.Dropout(self.params.dropout_rate)) - model.add(L.Dense(self.params.tag_size, name='tag')) - model.add(crf) - model.summary() - model.compile(keras.optimizers.Adam(self.params.learning_rate), loss=crf.loss, metrics=[crf.accuracy]) - - return model diff --git a/langml/langml/cli.py b/langml/langml/cli.py deleted file mode 100644 index e5ac9c8..0000000 --- a/langml/langml/cli.py +++ /dev/null @@ -1,19 +0,0 @@ -# -*- coding: utf-8 -*- - -import click - -import langml - - -@click.group() -@click.version_option(version=langml.__version__) -def cli(): - """LangML client""" - pass - - -def main(): - from langml.baselines.cli import baseline - - cli.add_command(baseline) - cli(prog_name='langml', obj={}) diff --git a/langml/langml/layers/__init__.py b/langml/langml/layers/__init__.py deleted file mode 100644 index 0b1e61c..0000000 --- a/langml/langml/layers/__init__.py +++ /dev/null @@ -1,24 +0,0 @@ -# -*- coding: utf-8 -*- - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras -else: - import keras - -from langml.layers.crf import CRF -from langml.layers.attention import ( - SelfAttention, SelfAdditiveAttention, - ScaledDotProductAttention, MultiHeadAttention -) -from langml.layers.layer_norm import LayerNorm - -custom_objects = {} -custom_objects.update(CRF.get_custom_objects()) -custom_objects.update(SelfAttention.get_custom_objects()) -custom_objects.update(SelfAdditiveAttention.get_custom_objects()) -custom_objects.update(LayerNorm.get_custom_objects()) -custom_objects.update(ScaledDotProductAttention.get_custom_objects()) -custom_objects.update(MultiHeadAttention.get_custom_objects()) - -keras.utils.get_custom_objects().update(custom_objects) diff --git a/langml/langml/layers/attention.py b/langml/langml/layers/attention.py deleted file mode 100644 index 30f752b..0000000 --- a/langml/langml/layers/attention.py +++ /dev/null @@ -1,577 +0,0 @@ -# -*- coding: utf-8 -*- - -from typing import Optional, Union, List - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.backend as K - import tensorflow.keras.layers as L -else: - import keras - import keras.backend as K - import keras.layers as L - -from langml.tensor_typing import Tensors, Activation, Initializer, Constraint, Regularizer - - -class SelfAttention(L.Layer): - def __init__(self, - attention_units: Optional[int] = None, - return_attention: Optional[bool] = False, - is_residual: Optional[bool] = False, - attention_activation: Optional[Activation] = 'relu', - attention_epsilon: Optional[float] = 1e10, - kernel_initializer: Optional[Initializer] = 'glorot_normal', - kernel_regularizer: Optional[Regularizer] = None, - kernel_constraint: Optional[Constraint] = None, - bias_initializer: Optional[Initializer] = 'zeros', - bias_regularizer: Optional[Regularizer] = None, - bias_constraint: Optional[Constraint] = None, - use_attention_bias: Optional[bool] = True, - attention_penalty_weight: Optional[float] = 0.0, - **kwargs): - super(SelfAttention, self).__init__(**kwargs) - - self.supports_masking = True - - self.attention_units = attention_units - self.return_attention = return_attention - self.is_residual = is_residual - self.attention_epsilon = attention_epsilon - self.attention_activation = keras.activations.get(attention_activation) - self.kernel_initializer = keras.initializers.get(kernel_initializer) - self.kernel_regularizer = keras.regularizers.get(kernel_regularizer) - self.kernel_constraint = keras.constraints.get(kernel_constraint) - self.bias_initializer = keras.initializers.get(bias_initializer) - self.bias_regularizer = keras.regularizers.get(bias_regularizer) - self.bias_constraint = keras.constraints.get(bias_constraint) - self.use_attention_bias = use_attention_bias - self.attention_penalty_weight = attention_penalty_weight - - def get_config(self) -> dict: - config = { - "attention_units": self.attention_units, - "return_attention": self.return_attention, - "is_residual": self.is_residual, - "attention_epsilon": self.attention_epsilon, - "attention_activation": keras.activations.serialize(self.attention_activation), - "kernel_initializer": keras.initializers.serialize(self.kernel_initializer), - "kernel_regularizer": keras.regularizers.serialize(self.kernel_regularizer), - "kernel_constraint": keras.constraints.serialize(self.kernel_constraint), - "bias_initializer": keras.initializers.serialize(self.bias_initializer), - "bias_regularizer": keras.regularizers.serialize(self.bias_regularizer), - "bias_constraint": keras.constraints.serialize(self.bias_constraint), - "use_attention_bias": self.use_attention_bias, - "attention_penalty_weight": self.attention_penalty_weight - } - base_config = super(SelfAttention, self).get_config() - - return dict(base_config, **config) - - def build(self, input_shape: Tensors): - feature_dim = int(input_shape[2]) - units = feature_dim if self.attention_units is None else self.attention_units - - self.Wq = self.add_weight(shape=(feature_dim, units), - name=f'{self.name}_Attn_Wq', - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint) - self.Wk = self.add_weight(shape=(feature_dim, units), - name=f'{self.name}_Attn_Wt', - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint) - self.Wv = self.add_weight(shape=(feature_dim, units), - name=f'{self.name}_Attn_Wv', - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint) - if self.use_attention_bias: - self.attn_bias = self.add_weight(shape=(1,), - name=f'{self.name}_Attn_bias', - initializer=self.bias_initializer, - regularizer=self.bias_regularizer, - constraint=self.bias_constraint) - - def call(self, inputs: Tensors, mask: Optional[Tensors] = None, **kwargs) -> Union[List[Tensors], Tensors]: - - q = K.dot(inputs, self.Wq) - k = K.dot(inputs, self.Wk) - v = K.dot(inputs, self.Wv) - if self.attention_activation is not None: - q = self.attention_activation(q) - k = self.attention_activation(k) - v = self.attention_activation(v) - - if self.use_attention_bias: - q += self.attn_bias - k += self.attn_bias - v += self.attn_bias - - e = K.batch_dot(q, k, axes=2) - - if mask is not None: - if len(K.int_shape(mask)) == len(K.int_shape(inputs)) - 1: - mask = K.expand_dims(K.cast(mask, K.floatx()), axis=-1) - e -= self.attention_epsilon * (1.0 - mask) - - a = K.softmax(e) - v_o = K.batch_dot(a, v) - - if self.is_residual: - v_o += v - - if self.attention_penalty_weight > 0.0: - self.add_loss(self._attention_penalty(a)) - - if self.return_attention: - return [v_o, a] - - return v_o - - def compute_mask(self, - inputs: Tensors, - mask: Optional[Tensors] = None) -> Union[List[Union[Tensors, None]], Tensors]: - if self.return_attention: - return [mask, None] - return mask - - def _attention_penalty(self, attention: Tensors) -> Tensors: - batch_size = K.cast(K.shape(attention)[0], K.floatx()) - input_len = K.shape(attention)[-1] - indices = K.expand_dims(K.arange(0, input_len), axis=0) - diagonal = K.expand_dims(K.arange(0, input_len), axis=-1) - eye = K.cast(K.equal(indices, diagonal), K.floatx()) - return self.attention_penalty_weight * K.sum(K.square(K.batch_dot( - attention, K.permute_dimensions(attention, (0, 2, 1))) - eye)) / batch_size - - @staticmethod - def get_custom_objects() -> dict: - return {'SelfAttention': SelfAttention} - - def compute_output_shape(self, input_shape: Tensors) -> Union[List[Tensors], Tensors]: - output_shape = input_shape - if self.return_attention: - attention_shape = (input_shape[0], output_shape[1], input_shape[1]) - return [output_shape, attention_shape] - return output_shape - - -class SelfAdditiveAttention(L.Layer): - def __init__(self, - attention_units: Optional[int] = None, - return_attention: Optional[bool] = False, - is_residual: Optional[bool] = False, - attention_activation: Optional[Activation] = 'relu', - attention_epsilon: Optional[float] = 1e10, - kernel_initializer: Optional[Initializer] = 'glorot_normal', - kernel_regularizer: Optional[Regularizer] = None, - kernel_constraint: Optional[Constraint] = None, - bias_initializer: Optional[Initializer] = 'zeros', - bias_regularizer: Optional[Regularizer] = None, - bias_constraint: Optional[Constraint] = None, - use_attention_bias: Optional[bool] = True, - attention_penalty_weight: Optional[float] = 0.0, - **kwargs): - super(SelfAdditiveAttention, self).__init__(**kwargs) - - self.supports_masking = True - - self.attention_units = attention_units - self.return_attention = return_attention - self.is_residual = is_residual - self.attention_epsilon = attention_epsilon - self.attention_activation = keras.activations.get(attention_activation) - self.kernel_initializer = keras.initializers.get(kernel_initializer) - self.kernel_regularizer = keras.regularizers.get(kernel_regularizer) - self.kernel_constraint = keras.constraints.get(kernel_constraint) - self.bias_initializer = keras.initializers.get(bias_initializer) - self.bias_regularizer = keras.regularizers.get(bias_regularizer) - self.bias_constraint = keras.constraints.get(bias_constraint) - self.use_attention_bias = use_attention_bias - self.attention_penalty_weight = attention_penalty_weight - - def get_config(self) -> dict: - config = { - "attention_units": self.attention_units, - "return_attention": self.return_attention, - "is_residual": self.is_residual, - "attention_epsilon": self.attention_epsilon, - "attention_activation": keras.activations.serialize(self.attention_activation), - "kernel_initializer": keras.initializers.serialize(self.kernel_initializer), - "kernel_regularizer": keras.regularizers.serialize(self.kernel_regularizer), - "kernel_constraint": keras.constraints.serialize(self.kernel_constraint), - "bias_initializer": keras.initializers.serialize(self.bias_initializer), - "bias_regularizer": keras.regularizers.serialize(self.bias_regularizer), - "bias_constraint": keras.constraints.serialize(self.bias_constraint), - "use_attention_bias": self.use_attention_bias, - "attention_penalty_weight": self.attention_penalty_weight - } - base_config = super(SelfAdditiveAttention, self).get_config() - - return dict(base_config, **config) - - def build(self, input_shape: Tensors): - feature_dim = int(input_shape[2]) - units = feature_dim if self.attention_units is None else self.attention_units - - self.Wh = self.add_weight(shape=(feature_dim, units), - name=f'{self.name}_Attn_Wh', - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint) - self.We = self.add_weight(shape=(units, 1), - name=f'{self.name}_Attn_We', - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint) - if self.use_attention_bias: - self.attn_bias = self.add_weight(shape=(1,), - name=f'{self.name}_Attn_bias', - initializer=self.bias_initializer, - regularizer=self.bias_regularizer, - constraint=self.bias_constraint) - - def call(self, inputs: Tensors, mask: Optional[Tensors] = None, **kwargs) -> Union[List[Tensors], Tensors]: - h = K.dot(inputs, self.Wh) - if self.attention_activation is not None: - h = self.attention_activation(h) - if self.use_attention_bias: - h += self.attn_bias - e = K.dot(h, self.We) - if self.use_attention_bias: - e += self.attn_bias - - if mask is not None: - if len(K.int_shape(mask)) == len(K.int_shape(inputs)) - 1: - mask = K.expand_dims(K.cast(mask, K.floatx()), axis=-1) - e -= self.attention_epsilon * (1.0 - mask) - - a = K.softmax(e, axis=1) - v_o = a * inputs - - if self.is_residual: - v_o += inputs - - if self.attention_penalty_weight > 0.0: - self.add_loss(self._attention_penalty(a)) - - if self.return_attention: - return [v_o, a] - - return v_o - - def compute_mask(self, - inputs: Tensors, - mask: Optional[Tensors] = None) -> Union[List[Union[Tensors, None]], Tensors]: - if self.return_attention: - return [mask, None] - return mask - - def _attention_penalty(self, attention: Tensors) -> Tensors: - batch_size = K.cast(K.shape(attention)[0], K.floatx()) - input_len = K.shape(attention)[-1] - indices = K.expand_dims(K.arange(0, input_len), axis=0) - diagonal = K.expand_dims(K.arange(0, input_len), axis=-1) - eye = K.cast(K.equal(indices, diagonal), K.floatx()) - return self.attention_penalty_weight * K.sum(K.square(K.batch_dot( - attention, K.permute_dimensions(attention, (0, 2, 1))) - eye)) / batch_size - - @staticmethod - def get_custom_objects() -> dict: - return {'SelfAdditiveAttention': SelfAdditiveAttention} - - def compute_output_shape(self, input_shape: Tensors) -> Union[List[Tensors], Tensors]: - output_shape = input_shape - if self.return_attention: - attention_shape = (input_shape[0], output_shape[1], input_shape[1]) - return [output_shape, attention_shape] - return output_shape - - -class ScaledDotProductAttention(L.Layer): - r""" ScaledDotProductAttention - - $Attention(Q, K, V) = softmax(\frac{Q K^T}{\sqrt{d_k}}) V$ - - https://arxiv.org/pdf/1706.03762.pdf - """ - def __init__(self, - return_attention: Optional[bool] = False, - history_only: Optional[bool] = False, - **kwargs): - super(ScaledDotProductAttention, self).__init__(**kwargs) - - self.supports_masking = True - - self.return_attention = return_attention - self.history_only = history_only - - def get_config(self) -> dict: - config = { - "return_attention": self.return_attention, - "history_only": self.history_only, - } - base_config = super(ScaledDotProductAttention, self).get_config() - - return dict(base_config, **config) - - def call(self, - inputs: Tensors, - mask: Optional[Union[Tensors, List[Tensors]]] = None, **kwargs) -> Union[List[Tensors], Tensors]: - if isinstance(inputs, list): - q, k, v = inputs - else: - q = k = v = inputs - if isinstance(mask, list): - mask = mask[1] - # e = \frac{QK^T}{\sqrt{d_k}} - # shape: [(B, Lq, D), (B, Lk, D)] -> (B, Lq, Lk) - e = K.batch_dot(q, k, axes=2) / K.sqrt(K.cast(K.shape(q)[-1], dtype=K.floatx())) - if self.history_only: - q_len, k_len = K.shape(q)[1], K.shape(k)[1] - indices = K.expand_dims(K.arange(0, k_len), axis=0) - upper = K.expand_dims(K.arange(0, q_len), axis=-1) - e -= 10000.0 * K.expand_dims(K.cast(indices > upper, K.floatx()), axis=0) - if mask is not None: - e -= 10000.0 * (1.0 - K.cast(K.expand_dims(mask, axis=-2), K.floatx())) - # softmax(e) - e = K.exp(e - K.max(e, axis=-1, keepdims=True)) - attention = e / K.sum(e, axis=-1, keepdims=True) - v = K.batch_dot(attention, v) - if self.return_attention: - return [v, attention] - return v - - def compute_mask(self, - inputs: Tensors, - mask: Optional[Union[Tensors, List[Tensors]]] = None) -> Union[ - List[Union[Tensors, None]], Tensors]: - if isinstance(mask, list): - mask = mask[0] - if self.return_attention: - return [mask, None] - return mask - - @staticmethod - def get_custom_objects() -> dict: - return {'ScaledDotProductAttention': ScaledDotProductAttention} - - def compute_output_shape(self, input_shape: Union[Tensors, List[Tensors]]) -> Union[List[Tensors], Tensors]: - if isinstance(input_shape, list): - q_shape, k_shape, v_shape = input_shape - else: - q_shape = k_shape = v_shape = input_shape - output_shape = q_shape[:-1] + v_shape[-1:] - if self.return_attention: - attention_shape = q_shape[:2] + (k_shape[1],) - return [output_shape, attention_shape] - return output_shape - - -class MultiHeadAttention(L.Layer): - """ MultiHeadAttention - https://arxiv.org/pdf/1706.03762.pdf - """ - def __init__(self, - head_num: int, - return_attention: Optional[bool] = False, - attention_activation: Optional[Activation] = 'relu', - kernel_initializer: Optional[Initializer] = 'glorot_normal', - kernel_regularizer: Optional[Regularizer] = None, - kernel_constraint: Optional[Constraint] = None, - bias_initializer: Optional[Initializer] = 'zeros', - bias_regularizer: Optional[Regularizer] = None, - bias_constraint: Optional[Constraint] = None, - use_attention_bias: Optional[bool] = True, - history_only: Optional[bool] = False, - **kwargs): - super(MultiHeadAttention, self).__init__(**kwargs) - - self.supports_masking = True - - self.head_num = head_num - self.return_attention = return_attention - self.attention_activation = keras.activations.get(attention_activation) - self.kernel_initializer = keras.initializers.get(kernel_initializer) - self.kernel_regularizer = keras.regularizers.get(kernel_regularizer) - self.kernel_constraint = keras.constraints.get(kernel_constraint) - self.bias_initializer = keras.initializers.get(bias_initializer) - self.bias_regularizer = keras.regularizers.get(bias_regularizer) - self.bias_constraint = keras.constraints.get(bias_constraint) - self.use_attention_bias = use_attention_bias - self.history_only = history_only - - def get_config(self) -> dict: - config = { - "head_num": self.head_num, - "return_attention": self.return_attention, - "attention_activation": keras.activations.serialize(self.attention_activation), - "kernel_initializer": keras.initializers.serialize(self.kernel_initializer), - "kernel_regularizer": keras.regularizers.serialize(self.kernel_regularizer), - "kernel_constraint": keras.constraints.serialize(self.kernel_constraint), - "bias_initializer": keras.initializers.serialize(self.bias_initializer), - "bias_regularizer": keras.regularizers.serialize(self.bias_regularizer), - "bias_constraint": keras.constraints.serialize(self.bias_constraint), - "use_attention_bias": self.use_attention_bias, - "history_only": self.history_only - } - base_config = super(MultiHeadAttention, self).get_config() - - return dict(base_config, **config) - - def build(self, input_shape: Tensors): - if isinstance(input_shape, list): - q, k, v = input_shape - else: - q = k = v = input_shape - feature_dim = int(v[-1]) - assert feature_dim % self.head_num == 0, 'feature_dim should be divided by head_num with no remainder' - self.Wq = self.add_weight(shape=(int(q[-1]), feature_dim), - name=f'{self.name}_Wq', - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint) - self.Wk = self.add_weight(shape=(int(k[-1]), feature_dim), - name=f'{self.name}_Wk', - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint) - self.Wv = self.add_weight(shape=(feature_dim, feature_dim), - name=f'{self.name}_Wv', - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint) - self.Wo = self.add_weight(shape=(feature_dim, feature_dim), - name=f'{self.name}_Wo', - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint) - if self.use_attention_bias: - self.bq = self.add_weight(shape=(feature_dim,), - name=f'{self.name}_bq', - initializer=self.bias_initializer, - regularizer=self.bias_regularizer, - constraint=self.bias_constraint) - self.bk = self.add_weight(shape=(feature_dim,), - name=f'{self.name}_bk', - initializer=self.bias_initializer, - regularizer=self.bias_regularizer, - constraint=self.bias_constraint) - self.bv = self.add_weight(shape=(feature_dim,), - name=f'{self.name}_bv', - initializer=self.bias_initializer, - regularizer=self.bias_regularizer, - constraint=self.bias_constraint) - self.bo = self.add_weight(shape=(feature_dim,), - name=f'{self.name}_bo', - initializer=self.bias_initializer, - regularizer=self.bias_regularizer, - constraint=self.bias_constraint) - - @staticmethod - def _reshape_to_batches(x, head_num): - input_shape = K.shape(x) - batch_size, seq_len, feature_dim = input_shape[0], input_shape[1], input_shape[2] - head_dim = feature_dim // head_num - x = K.reshape(x, (batch_size, seq_len, head_num, head_dim)) - x = K.permute_dimensions(x, [0, 2, 1, 3]) - return K.reshape(x, (batch_size * head_num, seq_len, head_dim)) - - @staticmethod - def _reshape_attention_from_batches(x, head_num): - input_shape = K.shape(x) - batch_size, seq_len, feature_dim = input_shape[0], input_shape[1], input_shape[2] - x = K.reshape(x, (batch_size // head_num, head_num, seq_len, feature_dim)) - return K.permute_dimensions(x, [0, 2, 1, 3]) - - @staticmethod - def _reshape_from_batches(x, head_num): - input_shape = K.shape(x) - batch_size, seq_len, feature_dim = input_shape[0], input_shape[1], input_shape[2] - x = K.reshape(x, (batch_size // head_num, head_num, seq_len, feature_dim)) - x = K.permute_dimensions(x, [0, 2, 1, 3]) - return K.reshape(x, (batch_size // head_num, seq_len, feature_dim * head_num)) - - @staticmethod - def _reshape_mask(mask, head_num): - if mask is None: - return mask - seq_len = K.shape(mask)[1] - mask = K.expand_dims(mask, axis=1) - mask = K.tile(mask, [1, head_num, 1]) - return K.reshape(mask, (-1, seq_len)) - - def call(self, inputs: Tensors, mask: Optional[Tensors] = None, **kwargs) -> Tensors: - if isinstance(inputs, list): - q, k, v = inputs - else: - q = k = v = inputs - if isinstance(mask, list): - q_mask, k_mask, v_mask = mask - else: - q_mask = k_mask = v_mask = mask - q = K.dot(q, self.Wq) - k = K.dot(k, self.Wk) - v = K.dot(v, self.Wv) - if self.use_attention_bias: - q += self.bq - k += self.bk - v += self.bv - if self.attention_activation is not None: - q = self.attention_activation(q) - k = self.attention_activation(k) - v = self.attention_activation(v) - scaled_dot_product_attention = ScaledDotProductAttention( - return_attention=True, - history_only=self.history_only, - name=f'{self.name}-Attention', - ) - output, attention = scaled_dot_product_attention( - inputs=[ - self._reshape_to_batches(q, self.head_num), - self._reshape_to_batches(k, self.head_num), - self._reshape_to_batches(v, self.head_num), - ], - mask=[ - self._reshape_mask(q_mask, self.head_num), - self._reshape_mask(k_mask, self.head_num), - self._reshape_mask(v_mask, self.head_num), - ], - ) - attention = self._reshape_attention_from_batches(attention, self.head_num) - output = self._reshape_from_batches(output, self.head_num) - output = K.dot(output, self.Wo) - if self.use_attention_bias: - output += self.bo - if self.attention_activation is not None: - output = self.attention_activation(output) - if self.return_attention: - return [output, attention] - return output - - @staticmethod - def get_custom_objects() -> dict: - return {'MultiHeadAttention': MultiHeadAttention} - - def compute_mask(self, - inputs: Tensors, - mask: Optional[Tensors] = None) -> Union[List[Union[Tensors, None]], Tensors]: - if isinstance(mask, list): - mask = mask[0] - if self.return_attention: - return [mask, None] - return mask - - def compute_output_shape(self, input_shape: Union[Tensors, List[Tensors]]) -> Union[List[Tensors], Tensors]: - if isinstance(input_shape, list): - q_shape, _, v_shape = input_shape - else: - q_shape = _ = v_shape = input_shape - output_shape = q_shape[:-1] + (v_shape[-1],) - if self.return_attention: - attention_shape = (*q_shape[:-1], self.head_num, v_shape[-1]) - return [output_shape, attention_shape] - return output_shape diff --git a/langml/langml/layers/crf.py b/langml/langml/layers/crf.py deleted file mode 100644 index 7ed266b..0000000 --- a/langml/langml/layers/crf.py +++ /dev/null @@ -1,159 +0,0 @@ -# -*- coding: utf-8 -*- - -from typing import Optional, Callable, Union - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras.backend as K - import tensorflow.keras.layers as L -else: - import keras.backend as K - import keras.layers as L - -import tensorflow as tf - -from langml.tensor_typing import Tensors -from langml.third_party.crf import crf_log_likelihood, crf_decode - - -class CRF(L.Layer): - def __init__(self, - output_dim: int, - sparse_target: Optional[bool] = True, - **kwargs): - """ - Args: - output_dim (int): the number of labels to tag each temporal input. - sparse_target (bool): whether the the ground-truth label represented in one-hot. - Input shape: - (batch_size, sentence length, output_dim) - Output shape: - (batch_size, sentence length, output_dim) - - Usage: - >>> from tensorflow.keras.models import Sequential - >>> from tensorflow.keras.layers import Input, Embedding, Bidirectional, LSTM, Dense - - - >>> num_labels = 10 - >>> embedding_size = 100 - >>> hidden_size = 128 - - >>> model = Sequential() - >>> model.add(Embedding(num_labels, embedding_size)) - >>> model.add(Bidirectional(LSTM(hidden_size, return_sequences=True))) - >>> model.add(Dense(num_labels)) - - >>> crf = CRF(num_labels, sparse_target=True) - >>> model.add(crf) - >>> model.compile('adam', loss=crf.loss, metrics=[crf.accuracy]) - """ - super(CRF, self).__init__(**kwargs) - self.support_mask = True - self.output_dim = output_dim - self.sparse_target = sparse_target - self.input_spec = L.InputSpec(min_ndim=3) - self.supports_masking = False - self.sequence_lengths = None - - def build(self, input_shape: Tensors): - assert len(input_shape) == 3 - f_shape = input_shape - input_spec = L.InputSpec(min_ndim=3, axes={-1: f_shape[-1]}) - - if f_shape[-1] is None: - raise ValueError('The last dimension of the inputs to `CRF` ' - 'should be defined. Found `None`.') - if f_shape[-1] != self.output_dim: - raise ValueError('The last dimension of the input shape must be equal to output' - ' shape. Use a linear layer if needed.') - self.input_spec = input_spec - self.transitions = self.add_weight(name='transitions', - shape=[self.output_dim, self.output_dim], - initializer='glorot_uniform', - trainable=True) - self.built = True - - def compute_mask(self, - inputs: Tensors, - mask: Optional[Tensors] = None): - return None - - def call(self, - inputs: Tensors, - sequence_lengths: Optional[Tensors] = None, - training: Optional[Union[bool, int]] = None, - mask: Optional[Tensors] = None, - **kwargs) -> Tensors: - sequences = tf.convert_to_tensor(inputs, dtype=self.dtype) - if sequence_lengths is not None: - assert len(sequence_lengths.shape) == 2 - assert tf.convert_to_tensor(sequence_lengths).dtype == 'int32' - seq_len_shape = tf.convert_to_tensor(sequence_lengths).get_shape().as_list() - assert seq_len_shape[1] == 1 - self.sequence_lengths = K.flatten(sequence_lengths) - else: - self.sequence_lengths = tf.ones(tf.shape(inputs)[0], dtype=tf.int32) * ( - tf.shape(inputs)[1] - ) - - viterbi_sequence, _ = crf_decode(sequences, - self.transitions, - self.sequence_lengths) - output = K.one_hot(viterbi_sequence, self.output_dim) - return K.in_train_phase(sequences, output, training=training) - - @property - def loss(self) -> Callable: - def crf_loss(y_true: Tensors, y_pred: Tensors) -> Tensors: - y_true = K.argmax(y_true, 2) if self.sparse_target else y_true - y_true = K.reshape(y_true, K.shape(y_pred)[:-1]) - log_likelihood, _ = crf_log_likelihood( - y_pred, - K.cast(y_true, dtype='int32'), - self.sequence_lengths, - transition_params=self.transitions, - ) - return K.mean(-log_likelihood) - return crf_loss - - @property - def accuracy(self) -> Callable: - def viterbi_accuracy(y_true: Tensors, y_pred: Tensors) -> Tensors: - y_true = K.argmax(y_true, 2) if self.sparse_target else y_true - y_true = K.reshape(y_true, K.shape(y_pred)[:-1]) - viterbi_sequence, _ = crf_decode( - y_pred, - self.transitions, - self.sequence_lengths - ) - mask = K.all(K.greater(y_pred, -1e6), axis=2) - mask = K.cast(mask, K.floatx()) - y_true = K.cast(y_true, 'int32') - corrects = K.cast(K.equal(y_true, viterbi_sequence), K.floatx()) - return K.sum(corrects * mask) / K.sum(mask) - return viterbi_accuracy - - def compute_output_shape(self, input_shape: Tensors) -> Tensors: - tf.TensorShape(input_shape).assert_has_rank(3) - return input_shape[:2] + (self.output_dim,) - - @property - def trans(self) -> Tensors: - """ transition parameters - """ - return K.eval(self.transitions) - - def get_config(self) -> dict: - config = { - 'output_dim': self.output_dim, - 'sparse_target': self.sparse_target, - 'supports_masking': self.supports_masking, - 'transitions': K.eval(self.transitions) - } - base_config = super(CRF, self).get_config() - return dict(base_config, **config) - - @staticmethod - def get_custom_objects() -> dict: - return {'CRF': CRF} diff --git a/langml/langml/layers/layer_norm.py b/langml/langml/layers/layer_norm.py deleted file mode 100644 index af991d4..0000000 --- a/langml/langml/layers/layer_norm.py +++ /dev/null @@ -1,103 +0,0 @@ -# -*- coding: utf-8 -*- - -from typing import Optional, Union - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.backend as K - import tensorflow.keras.layers as L -else: - import keras - import keras.backend as K - import keras.layers as L - -from langml.tensor_typing import Tensors, Initializer, Constraint, Regularizer - - -class LayerNorm(L.Layer): - def __init__(self, - center: Optional[bool] = True, - scale: Optional[bool] = True, - epsilon: Optional[float] = 1e-7, - gamma_initializer: Optional[Initializer] = 'ones', - gamma_regularizer: Optional[Regularizer] = None, - gamma_constraint: Optional[Constraint] = None, - beta_initializer: Optional[Initializer] = 'zeros', - beta_regularizer: Optional[Regularizer] = None, - beta_constraint: Optional[Constraint] = None, - **kwargs): - super(LayerNorm, self).__init__(**kwargs) - - self.supports_masking = True - - self.center = center - self.scale = scale - self.epsilon = epsilon - self.gamma_initializer = keras.initializers.get(gamma_initializer) - self.gamma_regularizer = keras.regularizers.get(gamma_regularizer) - self.gamma_constraint = keras.constraints.get(gamma_constraint) - self.beta_initializer = keras.initializers.get(beta_initializer) - self.beta_regularizer = keras.regularizers.get(beta_regularizer) - self.beta_constraint = keras.constraints.get(beta_constraint) - - def get_config(self) -> dict: - config = { - "center": self.center, - "scale": self.scale, - "epsilon": self.epsilon, - "gamma_initializer": keras.initializers.serialize(self.gamma_initializer), - "gamma_regularizer": keras.regularizers.serialize(self.gamma_regularizer), - "gamma_constraint": keras.constraints.serialize(self.gamma_constraint), - "beta_initializer": keras.initializers.serialize(self.beta_initializer), - "beta_regularizer": keras.regularizers.serialize(self.beta_regularizer), - "beta_constraint": keras.constraints.serialize(self.beta_constraint) - } - base_config = super(LayerNorm, self).get_config() - - return dict(base_config, **config) - - def build(self, input_shape: Tensors): - shape = input_shape[-1:] - if self.scale: - self.gamma = self.add_weight( - shape=shape, - initializer=self.gamma_initializer, - regularizer=self.gamma_regularizer, - constraint=self.gamma_constraint, - name='gamma', - ) - if self.center: - self.beta = self.add_weight( - shape=shape, - initializer=self.beta_initializer, - regularizer=self.beta_regularizer, - constraint=self.beta_constraint, - name='beta', - ) - super(LayerNorm, self).build(input_shape) - - def call(self, inputs: Tensors, **kwargs) -> Tensors: - # layer norm: specify axis=-1 - mean = K.mean(inputs, axis=-1, keepdims=True) - variance = K.mean(K.square(inputs), axis=-1, keepdims=True) - std = K.sqrt(variance + self.epsilon) - # standard normalization: x = (x - \mu) / \std - outputs = (inputs - mean) / std - if self.scale: - outputs *= self.gamma - if self.center: - outputs += self.beta - return outputs - - def compute_mask(self, - inputs: Tensors, - mask: Optional[Tensors] = None) -> Union[Tensors, None]: - return mask - - @staticmethod - def get_custom_objects() -> dict: - return {'LayerNorm': LayerNorm} - - def compute_output_shape(self, input_shape: Tensors) -> Tensors: - return input_shape diff --git a/langml/langml/log.py b/langml/langml/log.py deleted file mode 100644 index aa50e37..0000000 --- a/langml/langml/log.py +++ /dev/null @@ -1,22 +0,0 @@ -# -*- coding: utf-8 -*- - -import logging -from datetime import datetime -from functools import partial - -logging.addLevelName(logging.WARN, 'WARN') - - -def print_log(level: int, msg: str, *args): - log = '%s %s %s' % ( - datetime.now().strftime('%Y-%m-%d %H:%M:%S'), - logging.getLevelName(level), - msg % args - ) - print(log) - - -debug = partial(print_log, logging.DEBUG) -info = partial(print_log, logging.INFO) -warn = partial(print_log, logging.WARN) -error = partial(print_log, logging.ERROR) diff --git a/langml/langml/model.py b/langml/langml/model.py deleted file mode 100644 index e243aa4..0000000 --- a/langml/langml/model.py +++ /dev/null @@ -1,96 +0,0 @@ -# -*- coding: utf-8 -*- - -import os -import random -import string -from typing import Any - -import tensorflow as tf -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras.backend as K -else: - import keras.backend as K - -from langml.tensor_typing import Models -from langml.log import info, warn -from langml import TF_VERSION - - -SAVED_MODEL_TAG = 'serve' - - -def get_random_string(length): - return ''.join(random.choice(string.ascii_lowercase) for _ in range(length)) - - -def export_model_v1(model, export_model_dir): - """ - :param export_model_dir: type string, save dir for exported model url - :param model_version: type int best - :return:no return - """ - - if os.path.exists(export_model_dir): - warn(f'path `{export_model_dir}` exists!') - export_model_dir = f"{export_model_dir}.{get_random_string(6)}" - warn(f'auto relocation to `{export_model_dir}`') - - os.makedirs(export_model_dir) - - with tf.get_default_graph().as_default(): - info(f"input: {model.input}") - info(f"output: {model.output}") - input_map = {} - if isinstance(model.input, (tuple, list)): - for x in model.input: - input_map[x.name.split(':')[0]] = tf.saved_model.build_tensor_info(x) - else: - input_map[model.input.name.split(':')[0]] = tf.saved_model.build_tensor_info(model.input) - info(f'input map: {input_map}') - output_map = {} - if isinstance(model.output, (tuple, list)): - for x in model.output: - output_map[x.name.split(':')[0]] = tf.saved_model.build_tensor_info(x) - else: - output_map[model.output.name.split(':')[0]] = tf.saved_model.build_tensor_info(model.output) - info(f'output map: {output_map}') - prediction_signature = ( - tf.saved_model.build_signature_def( - inputs=input_map, - outputs=output_map) - ) - info('step1 => prediction_signature created successfully') - builder = tf.saved_model.builder.SavedModelBuilder(export_model_dir) - builder.add_meta_graph_and_variables( - sess=K.get_session(), - tags=[SAVED_MODEL_TAG], - signature_def_map={ - 'predict': prediction_signature, - 'serving_default': prediction_signature, - }, - ) - info(f'step2 => Export path({export_model_dir}) ready to export trained model') - builder.save() - info(f'done! model has saved to {export_model_dir}.') - - -def save_frozen(model: Models, fpath: str): - if int(tf.__version__.split('.')[0]) > 1: - tf.saved_model.save(model, fpath) - else: - info('apply tensorflow 1.x frozen') - export_model_v1(model, fpath) - - -def load_frozen(model_dir: str, - session: Any = None) -> Any: - - if TF_VERSION > 1: - return tf.saved_model.load(model_dir) - - if session is None: - raise ValueError('session is required in tensorflow 1.x') - tf.saved_model.loader.load(session, [SAVED_MODEL_TAG], export_dir=model_dir) - info('done! session has restored.') - return session diff --git a/langml/langml/plm/__init__.py b/langml/langml/plm/__init__.py deleted file mode 100644 index 272b584..0000000 --- a/langml/langml/plm/__init__.py +++ /dev/null @@ -1,29 +0,0 @@ -# -*- coding: utf-8 -*- - -from typing import Callable - -import tensorflow as tf -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras -else: - import keras - -from langml.plm.layers import ( - TokenEmbedding, AbsolutePositionEmbedding, - EmbeddingMatching, Masked, -) - -custom_objects = {} -custom_objects.update(TokenEmbedding.get_custom_objects()) -custom_objects.update(AbsolutePositionEmbedding.get_custom_objects()) -custom_objects.update(EmbeddingMatching.get_custom_objects()) -custom_objects.update(Masked.get_custom_objects()) - -keras.utils.get_custom_objects().update(custom_objects) - - -def load_variables(checkpoint_path: str) -> Callable: - def wrap(varname: str): - return tf.train.load_variable(checkpoint_path, varname) - return wrap diff --git a/langml/langml/plm/albert.py b/langml/langml/plm/albert.py deleted file mode 100644 index a2b7155..0000000 --- a/langml/langml/plm/albert.py +++ /dev/null @@ -1,138 +0,0 @@ -# -*- coding: utf-8 -*- - -import json -from typing import Callable, Optional, Tuple, Union - -import numpy as np - -from langml.tensor_typing import Models -from langml.plm import load_variables -from langml.plm.bert import BERT - - -def load_albert(config_path: str, - checkpoint_path: str, - seq_len: Optional[int] = None, - pretraining: bool = False, - with_mlm: bool = True, - with_nsp: bool = True, - lazy_restore: bool = False, - weight_prefix: Optional[str] = None, - dropout_rate: float = 0.0, - **kwargs) -> Union[Tuple[Models, Callable], Tuple[Models, Callable, Callable]]: - """ Load pretrained ALBERT - Args: - - config_path: str, path of albert config - - checkpoint_path: str, path of albert checkpoint - - seq_len: Optional[int], specify fixed input sequence length, default None - - pretraining: bool, pretraining mode, default False - - with_mlm: bool, whether to use mlm task in pretraining, default True - - with_nsp: bool, whether to use nsp/sop task in pretraining, default True - - lazy_restore: bool, whether to restore pretrained weights lazily, default False. - Set it as True for distributed training. - - weight_prefix: Optional[str], prefix name of weights, default None. - You can set a prefix name in unshared siamese networks. - - dropout_rate: float, dropout rate, default 0. - Return: - - model: keras model - - bert: bert instance - - restore: conditionally, it will return when lazy_restore=True - """ - # initialize model from config - with open(config_path, 'r') as reader: - config = json.load(reader) - if seq_len is not None: - config['max_position_embeddings'] = min(seq_len, config['max_position_embeddings']) - - bert = BERT( - config['vocab_size'], - position_size=config['max_position_embeddings'], - seq_len=seq_len, - embedding_dim=config.get('embedding_size') or config.get('hidden_size'), - hidden_dim=config.get('hidden_size'), - transformer_blocks=config['num_hidden_layers'], - attention_heads=config['num_attention_heads'], - intermediate_size=config['intermediate_size'], - feed_forward_activation=config['hidden_act'], - initializer_range=config['initializer_range'], - dropout_rate=dropout_rate or config.get('hidden_dropout_prob', 0.0), - pretraining=pretraining, - share_weights=True, - weight_prefix=weight_prefix, - **kwargs) - bert.build() - model = bert(with_mlm=with_mlm, with_nsp=with_nsp) - - def restore(model): - variables = load_variables(checkpoint_path) - model.get_layer(name=bert.get_weight_name('Embedding-Token')).set_weights([ - variables('bert/embeddings/word_embeddings'), - ]) - model.get_layer(name=bert.get_weight_name('Embedding-Position')).set_weights([ - variables('bert/embeddings/position_embeddings')[:config['max_position_embeddings'], :], - ]) - model.get_layer(name=bert.get_weight_name('Embedding-Segment')).set_weights([ - variables('bert/embeddings/token_type_embeddings'), - ]) - model.get_layer(name=bert.get_weight_name('Embedding-Norm')).set_weights([ - variables('bert/embeddings/LayerNorm/gamma'), - variables('bert/embeddings/LayerNorm/beta'), - ]) - model.get_layer(name=bert.get_weight_name('Embedding-Mapping')).set_weights([ - variables('bert/encoder/embedding_hidden_mapping_in/kernel'), - variables('bert/encoder/embedding_hidden_mapping_in/bias'), - ]) - # 以下权重共享 - model.get_layer(name=bert.get_weight_name('Transformer-MultiHeadSelfAttention')).set_weights([ - variables('bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/kernel'), - variables('bert/encoder/transformer/group_0/inner_group_0/attention_1/self/key/kernel'), - variables('bert/encoder/transformer/group_0/inner_group_0/attention_1/self/value/kernel'), - variables('bert/encoder/transformer/group_0/inner_group_0/attention_1/output/dense/kernel'), - variables('bert/encoder/transformer/group_0/inner_group_0/attention_1/self/query/bias'), - variables('bert/encoder/transformer/group_0/inner_group_0/attention_1/self/key/bias'), - variables('bert/encoder/transformer/group_0/inner_group_0/attention_1/self/value/bias'), - variables('bert/encoder/transformer/group_0/inner_group_0/attention_1/output/dense/bias'), - ]) - model.get_layer(name=bert.get_weight_name('Transformer-MultiHeadSelfAttention-Norm')).set_weights([ - variables('bert/encoder/transformer/group_0/inner_group_0/LayerNorm/gamma'), - variables('bert/encoder/transformer/group_0/inner_group_0/LayerNorm/beta'), - ]) - model.get_layer(name=bert.get_weight_name('Transformer-FeedForward')).set_weights([ - variables('bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/dense/kernel'), - variables('bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/output/dense/kernel'), - variables('bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/dense/bias'), - variables('bert/encoder/transformer/group_0/inner_group_0/ffn_1/intermediate/output/dense/bias'), - ]) - model.get_layer(name=bert.get_weight_name('Transformer-FeedForward-Norm')).set_weights([ - variables('bert/encoder/transformer/group_0/inner_group_0/LayerNorm_1/gamma'), - variables('bert/encoder/transformer/group_0/inner_group_0/LayerNorm_1/beta'), - ]) - if pretraining: - if with_mlm: - model.get_layer(name=bert.get_weight_name('MLM-Dense')).set_weights([ - variables('cls/predictions/transform/dense/kernel'), - variables('cls/predictions/transform/dense/bias'), - ]) - model.get_layer(name=bert.get_weight_name('MLM-Norm')).set_weights([ - variables('cls/predictions/transform/LayerNorm/gamma'), - variables('cls/predictions/transform/LayerNorm/beta'), - ]) - model.get_layer(name=bert.get_weight_name('MLM-Match')).set_weights([ - variables('cls/predictions/output_bias'), - ]) - if with_nsp: - model.get_layer(name=bert.get_weight_name('NSP-Dense')).set_weights([ - variables('bert/pooler/dense/kernel'), - variables('bert/pooler/dense/bias'), - ]) - model.get_layer(name=bert.get_weight_name('NSP')).set_weights([ - np.transpose(variables('cls/seq_relationship/output_weights')), - variables('cls/seq_relationship/output_bias'), - ]) - return model - - if lazy_restore: - return model, bert, restore - model = restore(model) - return model, bert - diff --git a/langml/langml/plm/bert.py b/langml/langml/plm/bert.py deleted file mode 100644 index 0ca87be..0000000 --- a/langml/langml/plm/bert.py +++ /dev/null @@ -1,343 +0,0 @@ -# -*- coding: utf-8 -*- - -import json -from typing import Callable, List, Optional, Tuple, Union - -import numpy as np -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.layers as L -else: - import keras - import keras.layers as L - -from langml.layers import LayerNorm -from langml.transformer import gelu -from langml.transformer.encoder import TransformerEncoderBlock -from langml.tensor_typing import Activation, Tensors, Models -from langml.plm import TokenEmbedding, AbsolutePositionEmbedding, EmbeddingMatching, Masked, load_variables - - -class BERT: - def __init__(self, - vocab_size: int, - position_size: Optional[int] = 512, - seq_len: Optional[int] = 512, - embedding_dim: Optional[int] = 768, - hidden_dim: Optional[int] = None, - transformer_blocks: Optional[int] = 12, - attention_heads: Optional[int] = 12, - intermediate_size: Optional[int] = 3072, - dropout_rate: Optional[float] = 0.1, - attention_activation: Optional[Activation] = None, - feed_forward_activation: Optional[Activation] = 'gelu', - initializer_range: Optional[float] = 0.02, - pretraining: bool = False, - trainable_prefixs: Optional[List] = None, - share_weights: bool = False, - weight_prefix: Optional[str] = None): - self.vocab_size = vocab_size - self.seq_len = seq_len - self.position_size = position_size - self.embedding_dim = embedding_dim - self.transformer_blocks = transformer_blocks - self.attention_heads = attention_heads - self.intermediate_size = intermediate_size - self.hidden_dim = hidden_dim - self.dropout_rate = dropout_rate - self.attention_activation = attention_activation - self.feed_forward_activation = feed_forward_activation - if self.attention_activation == 'gelu': - self.attention_activation = gelu - if self.feed_forward_activation == 'gelu': - self.feed_forward_activation = gelu - self.pretraining = pretraining - self.trainable_prefixs = trainable_prefixs - if self.trainable_prefixs is None: - self.trainable = True - else: - self.trainable = False - self.share_weights = share_weights - self.weight_prefix = weight_prefix - self.initializer = keras.initializers.TruncatedNormal(stddev=initializer_range) - self.is_embedding_mapping = self.hidden_dim is not None and self.embedding_dim != self.hidden_dim - - def get_weight_name(self, name: str) -> str: - if self.weight_prefix is not None: - return f'{self.weight_prefix}-{name}' - return name - - def build(self): - # emedding layers - self.token_embedding_layer = TokenEmbedding( - input_dim=self.vocab_size, - output_dim=self.embedding_dim, - mask_zero=True, - trainable=self.trainable, - embeddings_initializer=self.initializer, - name=self.get_weight_name('Embedding-Token'), - ) - self.segment_embedding_layer = L.Embedding( - input_dim=2, - output_dim=self.embedding_dim, - trainable=self.trainable, - embeddings_initializer=self.initializer, - name=self.get_weight_name('Embedding-Segment') - ) - self.add_embedding_layer = L.Add(name=self.get_weight_name('Embedding-Token-Segment')) - self.position_embedding_layer = AbsolutePositionEmbedding( - input_dim=self.position_size, - output_dim=self.embedding_dim, - mode='add', - trainable=self.trainable, - embeddings_initializer=self.initializer, - name=self.get_weight_name('Embedding-Position'), - ) - # layernorm - self.embedding_norm_layer = LayerNorm( - trainable=self.trainable, - name=self.get_weight_name('Embedding-Norm'), - ) - # dropout - self.embedding_dropout_layer = L.Dropout( - self.dropout_rate, - name=self.get_weight_name('Embedding-Dropout'), - ) - # embedding mapping - if self.is_embedding_mapping: - self.embedding_mapping_layer = L.Dense( - self.hidden_dim, - kernel_initializer=self.initializer, - name=self.get_weight_name('Embedding-Mapping') - ) - # transformer - self.transformer_layer = TransformerEncoderBlock( - blocks=self.transformer_blocks, - attention_heads=self.attention_heads, - hidden_dim=self.intermediate_size, - attention_activation=self.attention_activation, - feed_forward_activation=self.feed_forward_activation, - dropout_rate=self.dropout_rate, - name=self.get_weight_name('Transformer'), - share_weights=self.share_weights - ) - - def get_inputs(self) -> List[Tensors]: - # Input Placeholder - t_in = L.Input(shape=(self.seq_len, ), name=self.get_weight_name('Input-Token')) - s_in = L.Input(shape=(self.seq_len, ), name=self.get_weight_name('Input-Segment')) - m_in = L.Input(shape=(self.seq_len, ), name=self.get_weight_name('Input-Masked')) - return [t_in, s_in, m_in] - - def get_embedding(self, inputs: List[Tensors]) -> List[Tensors]: - token_embedding, embedding_weights = self.token_embedding_layer(inputs[0]) - segment_embedding = self.segment_embedding_layer(inputs[1]) - token_segment_embedding = self.add_embedding_layer([token_embedding, segment_embedding]) - embedding = self.position_embedding_layer(token_segment_embedding) - return [embedding, embedding_weights] - - def is_trainable(self, layer: L.Layer) -> bool: - if isinstance(self.trainable_prefixs, (list, tuple, set)): - if any(layer.name.startswith(prefix) for prefix in self.trainable_prefixs): - return True - return False - return self.trainable - - def __call__(self, - inputs: Optional[Union[Tuple, List]] = None, - return_model: bool = True, - with_mlm: bool = True, - with_nsp: bool = True) -> Models: - if inputs is None: - inputs = self.get_inputs() - assert isinstance(inputs, (tuple, list)) and len(inputs) > 1, '`inputs` should be a tuple/list consisting of placeholders and stores token, segment, and masked placeholders respectively. Note that the masked placeholder is optional for finetuning.' # NOQA - # embedding - embedding, embedding_weights = self.get_embedding(inputs) - x = self.embedding_norm_layer(embedding) - x = self.embedding_dropout_layer(x) - if self.is_embedding_mapping: - x = self.embedding_mapping_layer(x) - # transformer - x = self.transformer_layer(x) - if self.pretraining: - # pretrain - # don't support parameter sharing for the pretraining phase. - assert with_mlm or with_nsp, '`with_mlm` and `with_nsp` cannot be `False` at the same time' - if with_mlm: - xi = L.Dense( - units=self.embedding_dim, - activation=self.feed_forward_activation, - name=self.get_weight_name('MLM-Dense') - )(x) - xi = LayerNorm(name=self.get_weight_name('MLM-Norm'))(xi) - xi = EmbeddingMatching(name=self.get_weight_name('MLM-Match'))([xi, embedding_weights]) - mask_output = Masked(name=self.get_weight_name('MLM'))([xi, inputs[-1]]) - if with_nsp: - xi = L.Lambda(lambda t: t[:, 0], name=self.get_weight_name('cls'))(x) - xi = L.Dense( - units=self.hidden_dim or self.embedding_dim, - activation='tanh', - name=self.get_weight_name('NSP-Dense'), - )(xi) - nsp_output = L.Dense( - units=2, - activation='softmax', - name=self.get_weight_name('NSP'), - )(xi) - outputs = [] - if with_mlm: - outputs.append(mask_output) - if with_nsp: - outputs.append(nsp_output) - if return_model: - model = keras.models.Model(inputs=inputs, outputs=outputs) - for layer in model.layers: - layer.trainable = self.is_trainable(layer) - return model - return outputs - else: - # finetune - inputs = inputs[:2] - if return_model: - model = keras.models.Model(inputs=inputs, outputs=x) - for layer in model.layers: - layer.trainable = self.is_trainable(layer) - - return model - return x - - -def load_bert(config_path: str, - checkpoint_path: str, - seq_len: Optional[int] = None, - pretraining: bool = False, - with_mlm: bool = True, - with_nsp: bool = True, - lazy_restore: bool = False, - weight_prefix: Optional[str] = None, - dropout_rate: float = 0.0, - **kwargs) -> Union[Tuple[Models, Callable], Tuple[Models, Callable, Callable]]: - """ Load pretrained BERT/RoBERTa - Args: - - config_path: str, path of albert config - - checkpoint_path: str, path of albert checkpoint - - seq_len: Optional[int], specify fixed input sequence length, default None - - pretraining: bool, pretraining mode, default False - - with_mlm: bool, whether to use mlm task in pretraining, default True - - with_nsp: bool, whether to use nsp task in pretraining, default True - - lazy_restore: bool, whether to restore pretrained weights lazily, default False. - Set it as True for distributed training. - - weight_prefix: Optional[str], prefix name of weights, default None. - You can set a prefix name in unshared siamese networks. - - dropout_rate: float, dropout rate, default 0. - Return: - - model: keras model - - bert: bert instance - - restore: conditionally, it will return when lazy_restore=True - """ - # initialize model from config - with open(config_path, 'r') as reader: - config = json.load(reader) - if seq_len is not None: - config['max_position_embeddings'] = min(seq_len, config['max_position_embeddings']) - - bert = BERT( - config['vocab_size'], - position_size=config['max_position_embeddings'], - seq_len=seq_len, - embedding_dim=config.get('embedding_size') or config.get('hidden_size'), - hidden_dim=config.get('hidden_size'), - transformer_blocks=config['num_hidden_layers'], - attention_heads=config['num_attention_heads'], - intermediate_size=config['intermediate_size'], - feed_forward_activation=config['hidden_act'], - initializer_range=config['initializer_range'], - dropout_rate=dropout_rate or config.get('hidden_dropout_prob', 0.0), - pretraining=pretraining, - weight_prefix=weight_prefix, - **kwargs) - bert.build() - model = bert(with_mlm=with_mlm, with_nsp=with_nsp) - - def restore(model): - # restore weights - variables = load_variables(checkpoint_path) - model.get_layer(name=bert.get_weight_name('Embedding-Token')).set_weights([ - variables('bert/embeddings/word_embeddings'), - ]) - model.get_layer(name=bert.get_weight_name('Embedding-Position')).set_weights([ - variables('bert/embeddings/position_embeddings')[:config['max_position_embeddings'], :], - ]) - model.get_layer(name=bert.get_weight_name('Embedding-Segment')).set_weights([ - variables('bert/embeddings/token_type_embeddings'), - ]) - model.get_layer(name=bert.get_weight_name('Embedding-Norm')).set_weights([ - variables('bert/embeddings/LayerNorm/gamma'), - variables('bert/embeddings/LayerNorm/beta'), - ]) - try: - # BERT 并没有这一层 - model.get_layer(name=bert.get_weight_name('Embedding-Mapping')).set_weights([ - variables('bert/encoder/embedding_hidden_mapping_in/kernel'), - variables('bert/encoder/embedding_hidden_mapping_in/bias'), - ]) - except ValueError: - print('Skip Embedding-Mapping') - pass - for i in range(config['num_hidden_layers']): - model.get_layer(name=bert.get_weight_name('Transformer-%d-MultiHeadSelfAttention' % (i + 1))).set_weights([ - variables('bert/encoder/layer_%d/attention/self/query/kernel' % i), - variables('bert/encoder/layer_%d/attention/self/key/kernel' % i), - variables('bert/encoder/layer_%d/attention/self/value/kernel' % i), - variables('bert/encoder/layer_%d/attention/output/dense/kernel' % i), - variables('bert/encoder/layer_%d/attention/self/query/bias' % i), - variables('bert/encoder/layer_%d/attention/self/key/bias' % i), - variables('bert/encoder/layer_%d/attention/self/value/bias' % i), - variables('bert/encoder/layer_%d/attention/output/dense/bias' % i), - ]) - model.get_layer(name=bert.get_weight_name( - 'Transformer-%d-MultiHeadSelfAttention-Norm' % (i + 1)) - ).set_weights([ - variables('bert/encoder/layer_%d/attention/output/LayerNorm/gamma' % i), - variables('bert/encoder/layer_%d/attention/output/LayerNorm/beta' % i), - ]) - model.get_layer(name=bert.get_weight_name('Transformer-%d-FeedForward' % (i + 1))).set_weights([ - variables('bert/encoder/layer_%d/intermediate/dense/kernel' % i), - variables('bert/encoder/layer_%d/output/dense/kernel' % i), - variables('bert/encoder/layer_%d/intermediate/dense/bias' % i), - variables('bert/encoder/layer_%d/output/dense/bias' % i), - ]) - model.get_layer(name=bert.get_weight_name('Transformer-%d-FeedForward-Norm' % (i + 1))).set_weights([ - variables('bert/encoder/layer_%d/output/LayerNorm/gamma' % i), - variables('bert/encoder/layer_%d/output/LayerNorm/beta' % i), - ]) - if pretraining: - if with_mlm: - model.get_layer(name=bert.get_weight_name('MLM-Dense')).set_weights([ - variables('cls/predictions/transform/dense/kernel'), - variables('cls/predictions/transform/dense/bias'), - ]) - model.get_layer(name=bert.get_weight_name('MLM-Norm')).set_weights([ - variables('cls/predictions/transform/LayerNorm/gamma'), - variables('cls/predictions/transform/LayerNorm/beta'), - ]) - model.get_layer(name=bert.get_weight_name('MLM-Match')).set_weights([ - variables('cls/predictions/output_bias'), - ]) - if with_nsp: - model.get_layer(name=bert.get_weight_name('NSP-Dense')).set_weights([ - variables('bert/pooler/dense/kernel'), - variables('bert/pooler/dense/bias'), - ]) - model.get_layer(name=bert.get_weight_name('NSP')).set_weights([ - np.transpose(variables('cls/seq_relationship/output_weights')), - variables('cls/seq_relationship/output_bias'), - ]) - return model - - if lazy_restore: - return model, bert, restore - model = restore(model) - return model, bert - diff --git a/langml/langml/plm/layers.py b/langml/langml/plm/layers.py deleted file mode 100644 index 7dd0036..0000000 --- a/langml/langml/plm/layers.py +++ /dev/null @@ -1,244 +0,0 @@ -# -*- coding: utf-8 -*- - -from typing import Optional, List, Union - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.backend as K - import tensorflow.keras.layers as L -else: - import keras - import keras.backend as K - import keras.layers as L - -from langml.tensor_typing import Tensors, Initializer, Constraint, Regularizer - - -class TokenEmbedding(L.Embedding): - @staticmethod - def get_custom_objects() -> dict: - return {'TokenEmbedding': TokenEmbedding} - - def compute_mask(self, - inputs: Tensors, - mask: Optional[Tensors] = None) -> List[Union[Tensors, None]]: - return [super(TokenEmbedding, self).compute_mask(inputs, mask), None] - - def call(self, inputs: Tensors) -> List[Tensors]: - return [super(TokenEmbedding, self).call(inputs), self.embeddings + 0] - - def compute_output_shape(self, input_shape: Tensors) -> List[Tensors]: - return [super(TokenEmbedding, self).compute_output_shape(input_shape), K.int_shape(self.embeddings)] - - -class AbsolutePositionEmbedding(L.Layer): - def __init__(self, - input_dim: int, - output_dim: int, - mode: Optional[str] = 'add', - embeddings_initializer: Optional[Initializer] = 'uniform', - embeddings_regularizer: Optional[Regularizer] = None, - embeddings_constraint: Optional[Constraint] = None, - mask_zero: Optional[bool] = False, - **kwargs): - """Absolute Position Embedding - # mode: - expand - # Input shape - 2D tensor with shape: `(batch_size, sequence_length)`. - # Output shape - 3D tensor with shape: `(batch_size, sequence_length, output_dim)`. - add - # Input shape - 3D tensor with shape: `(batch_size, sequence_length, feature_dim)`. - # Output shape - 3D tensor with shape: `(batch_size, sequence_length, feature_dim)`. - concat - # Input shape - 3D tensor with shape: `(batch_size, sequence_length, feature_dim)`. - # Output shape - 3D tensor with shape: `(batch_size, sequence_length, feature_dim + output_dim)`. - """ - assert mode in ['expand', 'add', 'concat'], f'not support mode `{mode}`, options: expand | add | concat' - self.input_dim = input_dim - self.output_dim = output_dim - self.mode = mode - self.embeddings_initializer = keras.initializers.get(embeddings_initializer) - self.embeddings_regularizer = keras.regularizers.get(embeddings_regularizer) - self.embeddings_constraint = keras.constraints.get(embeddings_constraint) - self.mask_zero = mask_zero - self.supports_masking = True if mask_zero else False - self.embeddings = None - super(AbsolutePositionEmbedding, self).__init__(**kwargs) - - def get_config(self) -> dict: - config = { - 'input_dim': self.input_dim, - 'output_dim': self.output_dim, - 'mode': self.mode, - 'embeddings_initializer': keras.initializers.serialize(self.embeddings_initializer), - 'embeddings_regularizer': keras.regularizers.serialize(self.embeddings_regularizer), - 'embeddings_constraint': keras.constraints.serialize(self.embeddings_constraint), - 'mask_zero': self.mask_zero - } - base_config = super(AbsolutePositionEmbedding, self).get_config() - return dict(base_config, **config) - - @staticmethod - def get_custom_objects() -> dict: - return {'AbsolutePositionEmbedding': AbsolutePositionEmbedding} - - def build(self, input_shape: Tensors): - if self.mode == 'expand': - self.embeddings = self.add_weight( - shape=(self.input_dim * 2 + 1, self.output_dim), - initializer=self.embeddings_initializer, - name='embeddings', - regularizer=self.embeddings_regularizer, - constraint=self.embeddings_constraint, - ) - else: - self.embeddings = self.add_weight( - shape=(self.input_dim, self.output_dim), - initializer=self.embeddings_initializer, - name='embeddings', - regularizer=self.embeddings_regularizer, - constraint=self.embeddings_constraint, - ) - super(AbsolutePositionEmbedding, self).build(input_shape) - - def compute_mask(self, inputs: Tensors, mask: Optional[Tensors] = None) -> Tensors: - if self.mode == 'expand': - if self.mask_zero: - output_mask = K.not_equal(inputs, self.mask_zero) - else: - output_mask = None - else: - output_mask = mask - return output_mask - - def compute_output_shape(self, input_shape: Tensors) -> Tensors: - if self.mode == 'expand': - return input_shape + (self.output_dim,) - if self.mode == 'concat': - return input_shape[:-1] + (input_shape[-1] + self.output_dim,) - return input_shape - - def call(self, inputs: Tensors, **kwargs) -> Tensors: - if self.mode == 'expand': - inputs = K.cast(inputs, 'int32') - return K.gather( - self.embeddings, - K.minimum(K.maximum(inputs, -self.input_dim), self.input_dim) + self.input_dim, - ) - input_shape = K.shape(inputs) - if self.mode == 'add': - batch_size, seq_len, output_dim = input_shape[0], input_shape[1], input_shape[2] - else: - batch_size, seq_len, output_dim = input_shape[0], input_shape[1], self.output_dim - pos_embeddings = K.tile( - K.expand_dims(self.embeddings[:seq_len, :output_dim], axis=0), - [batch_size, 1, 1], - ) - if self.mode == 'add': - return inputs + pos_embeddings - return K.concatenate([inputs, pos_embeddings], axis=-1) - - -class EmbeddingMatching(L.Layer): - def __init__(self, - initializer: Optional[Initializer] = 'zeros', - regularizer: Optional[Regularizer] = None, - constraint: Optional[Constraint] = None, - use_bias: Optional[bool] = True, - **kwargs): - super(EmbeddingMatching, self).__init__(**kwargs) - self.supports_masking = True - self.initializer = keras.initializers.get(initializer) - self.regularizer = keras.regularizers.get(regularizer) - self.constraint = keras.constraints.get(constraint) - self.use_bias = use_bias - - def get_config(self) -> dict: - config = { - 'initializer': keras.initializers.serialize(self.initializer), - 'regularizer': keras.regularizers.serialize(self.regularizer), - 'constraint': keras.constraints.serialize(self.constraint), - } - base_config = super(EmbeddingMatching, self).get_config() - return dict(base_config, **config) - - def build(self, input_shape: Tensors): - if self.use_bias: - self.bias = self.add_weight( - shape=(int(input_shape[1][0]), ), - initializer=self.initializer, - regularizer=self.regularizer, - constraint=self.constraint, - name='bias', - ) - super(EmbeddingMatching, self).build(input_shape) - - def compute_mask(self, inputs: Tensors, mask: Optional[Tensors] = None) -> Tensors: - if isinstance(mask, list): - return mask[0] - return mask - - def call(self, inputs: Tensors, mask: Optional[Tensors] = None, **kwargs) -> Tensors: - inputs, embeddings = inputs - output = K.dot(inputs, K.transpose(embeddings)) - if self.use_bias: - output = K.bias_add(output, self.bias) - return K.softmax(output) - - @staticmethod - def get_custom_objects() -> dict: - return {'EmbeddingMatching': EmbeddingMatching} - - def compute_output_shape(self, input_shape: Tensors) -> Tensors: - return input_shape[0][:2] + (input_shape[1][0], ) - - -class Masked(L.Layer): - """Generate output mask based on the given mask. - https://arxiv.org/pdf/1810.04805.pdf - """ - - def __init__(self, - return_masked: Optional[bool] = False, - **kwargs): - super(Masked, self).__init__(**kwargs) - self.supports_masking = True - self.return_masked = return_masked - - @staticmethod - def get_custom_objects() -> dict: - return {'Masked': Masked} - - def get_config(self) -> dict: - config = { - 'return_masked': self.return_masked, - } - base_config = super(Masked, self).get_config() - return dict(base_config, **config) - - def compute_mask(self, - inputs: Tensors, - mask: Optional[Tensors] = None) -> Union[List[Union[Tensors, None]], Tensors]: - token_mask = K.not_equal(inputs[1], 0) - masked = K.all(K.stack([token_mask, mask[0]], axis=0), axis=0) - if self.return_masked: - return [masked, None] - return masked - - def call(self, inputs: Tensors, mask: Optional[Tensors] = None, **kwargs) -> Tensors: - output = inputs[0] + 0 - if self.return_masked: - return [output, K.cast(self.compute_mask(inputs, mask)[0], K.floatx())] - return output - - def compute_output_shape(self, input_shape: Tensors) -> Union[List[Tensors], Tensors]: - if self.return_masked: - return [input_shape[0], (2, ) + input_shape[1]] - return input_shape[0] diff --git a/langml/langml/prompt/__init__.py b/langml/langml/prompt/__init__.py deleted file mode 100644 index 298b04d..0000000 --- a/langml/langml/prompt/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -# -*- coding: utf-8 -*- - -from langml.prompt.prompt import Prompt # NOQA diff --git a/langml/langml/prompt/prompt.py b/langml/langml/prompt/prompt.py deleted file mode 100644 index 9c4e4b4..0000000 --- a/langml/langml/prompt/prompt.py +++ /dev/null @@ -1,276 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -Prompt-Base finetune. -""" - -import re -from typing import Dict, List, Optional, Tuple, Union - -import numpy as np - -from langml.log import info -from langml.plm.bert import load_bert -from langml.plm.albert import load_albert -from langml import TF_KERAS, TF_VERSION -if TF_KERAS: - import tensorflow.keras as keras - from tensorflow.keras.preprocessing.sequence import pad_sequences -else: - import keras - from keras.preprocessing.sequence import pad_sequences - -from langml.tokenizer import Tokenizer, WPTokenizer, SPTokenizer - - -re_unused = re.compile(r'\[unused[0-9]+\]') - - -class DataGenerator: - def __init__(self, - template_ids: List[int], - datas: List[str], - labels: List[Union[str, List[str]]], - tokenizer: Tokenizer, - label2id: Dict, - mask_id: int, - batch_size: int = 16): - self.batch_size = batch_size - self.mask_id = mask_id - self.datas = [] - for data, label in zip(datas, labels): - tokened = tokenizer.encode(data) - token_ids = tokened.ids - token_ids = [token_ids[0]] + template_ids + token_ids[1:-1] + [token_ids[-1]] - segment_ids = [0] * len(token_ids) - mask_ids = (np.array(token_ids) == self.mask_id).astype('int') - if isinstance(label, str): - label = [label] - else: - label = list(label) - assert len(label) == mask_ids.sum(), 'number of [MASK] should be equal with number of label' - mask_ids = mask_ids.tolist() - output_ids = [] - label = label[::-1] - for token_id, mask_id in zip(token_ids, mask_ids): - if mask_id == 1: - output_ids.append(label2id[label.pop()]) - else: - output_ids.append(token_id) - self.datas.append({ - 'token_ids': token_ids, - 'segment_ids': segment_ids, - 'mask_ids': mask_ids, - 'output_ids': output_ids - }) - - self.steps = len(self.datas) // self.batch_size - if len(self.datas) % self.batch_size != 0: - self.steps += 1 - - def __len__(self): - return self.steps - - def __iter__(self, random: bool = True): - idxs = list(range(len(self.datas))) - if random: - np.random.shuffle(idxs) - batch_tokens, batch_segments, batch_mask_ids, batch_outputs = [], [], [], [] - for idx in idxs: - obj = self.datas[idx] - batch_tokens.append(obj['token_ids']) - batch_segments.append(obj['segment_ids']) - batch_mask_ids.append(obj['mask_ids']) - batch_outputs.append(obj['output_ids']) - - if len(batch_tokens) == self.batch_size or idx == idxs[-1]: - batch_outputs = pad_sequences(batch_outputs, truncating='post', padding='post') - batch_outputs = np.expand_dims(batch_outputs, axis=-1) - batch_tokens = pad_sequences(batch_tokens, truncating='post', padding='post') - batch_segments = pad_sequences(batch_segments, truncating='post', padding='post') - batch_mask_ids = pad_sequences(batch_mask_ids, truncating='post', padding='post') - yield [batch_tokens, batch_segments, batch_mask_ids], [batch_outputs] - batch_tokens, batch_segments, batch_mask_ids, batch_outputs = [], [], [], [] - - def forfit(self, random: bool = True): - while True: - for inputs, labels in self.__iter__(random=random): - yield inputs, labels - - -class Prompt: - def __init__(self, - config_path: str, - checkpoint_path: str, - vocab_path: str, - verbalizer: Dict, - template: str, - backbone: str = 'bert', - tokenizer: Optional[Tokenizer] = None, - lowercase: bool = False, - max_length: int = 512, - special_tokens: Optional[List[str]] = None, - lazy_restore: bool = False): - """ - Args: - - config_path: str, path of PLM config - - checkpoint_path: str, path of PLM checkpoint - - vocab_path: str, path of vocabulary - - verbalizer, Dict, the mapping of tokens to factual labels. - Please assure vocabulary contain input tokens. - Strongly recommend using unused tokens as verbalizer tokens. - - template: str, prompt template - - backbone: str, optional, specify PLM backbones, options: bert, roberta, albert - - tokenizer: Optional[Tokenizer], optional, options: SPTokenizer, WPTokenizer - - lowercase: bool, optional, whether to do lowercase - - max_length: int, optional, max length of tokenizer, default 512 - - special_tokens: Optional[List[str]], specify special tokens, default None - - lazy_restore: bool, whether to lazy restore PLMs, set `True` if train distributely. - """ - self.vocab_path = vocab_path - self.lowercase = lowercase - self.lazy_restore_callback = None - if tokenizer is None: - if vocab_path.endswith('.txt'): - info('automatically apply `WPTokenizer`') - tokenizer = WPTokenizer - elif vocab_path.endswith('.model'): - info('automatically apply `SPTokenizer`') - tokenizer = SPTokenizer - else: - raise ValueError("Langml cannot deduce which tokenizer to apply, please assign `tokenizer` manually.") # NOQA - - self.tokenizer = tokenizer(vocab_path, lowercase=lowercase) - if special_tokens is not None: - self.tokenizer.add_special_tokens(special_tokens) - self.label2id = self._label_map(verbalizer) - self.id2label = {idx: label for label, idx in self.label2id.items()} - self.mask_id = self.tokenizer.token_to_id(self.tokenizer.special_tokens.MASK) - - self.template_ids = self.get_template_ids(template) - self.tokenizer.enable_truncation(max_length=max_length - len(self.template_ids)) - - if backbone == 'albert': - load_model = load_albert - elif backbone == 'bert': - load_model = load_bert - if lazy_restore: - self.model, _, self.lazy_restore_callback = load_model( - config_path, checkpoint_path, - pretraining=True, with_nsp=False, lazy_restore=True - ) - else: - self.model, _ = load_model( - config_path, checkpoint_path, - pretraining=True, with_nsp=False, - ) - self.model.summary() - - def get_template_ids(self, template: str) -> List[int]: - """ Get template token ids - Args: - - template: str - Return: - List[int] - """ - template_ids = [] - start = 0 - for match in re.finditer(r'\[MASK\]', template): - span = match.span() - template_ids += self.tokenizer.encode(template[start: span[0]]).ids[1:-1] - template_ids += [self.mask_id] - start = span[1] - if template[start:]: - template_ids += self.tokenizer.encode(template[start:]).ids[1:-1] - return template_ids - - def get_available_unused_tokens(self) -> List[Tuple[str, int]]: - """ Return available unused tokens. - Strongly recommend using unused tokens as verbalizer tokens. - """ - unuseds = [] - for token, idx in self.tokenizer.get_vocab().items(): - if re_unused.match(token) and idx not in self.label2id.values(): - unuseds.append((token, idx)) - unuseds.sort(key=lambda x: x[1]) - return unuseds - - def _label_map(self, verbalizer: Dict) -> Dict: - """ map token to factual labels - """ - label2id = {} - unk_id = self.tokenizer.token_to_id(self.tokenizer.special_tokens.UNK) - for token, label in verbalizer.items(): - token_id = self.tokenizer.token_to_id(token) - assert token_id is not None and token_id != unk_id, f'the token `{token}` is not found in vocabulary' - label2id[label] = token_id - return label2id - - def fit(self, - datas: List[str], - labels: List[Union[str, List[str]]], - epoch: int = 20, - batch_size: int = 16, - learning_rate: float = 2e-5, - do_shuffle: bool = True, - verbose: int = 1, - callbacks: Optional[List] = None): - """ fit model - Args: - - datas: List[str], texts - - labels: List[Union[str, List[str]]], labels - - epoch: int - - batch_size: int - - learning_rate: float - - do_shuffle: whether to shuffle data in training phase - - verbose: int, 0 = silent, 1 = progress bar, 2 = one line per epoch - - callbacks: keras callbacks - """ - assert len(datas) == len(labels), "datas should have an equal size with labels" - self.model.compile( - optimizer=keras.optimizers.Adam(learning_rate), - loss='sparse_categorical_crossentropy', - ) - if self.lazy_restore_callback is not None: - self.lazy_restore_callback(self.model) - generator = DataGenerator(self.template_ids, datas, labels, self.tokenizer, - self.label2id, mask_id=self.mask_id, batch_size=batch_size) - info('start to train...') - self.model.fit( - generator.forfit(random=do_shuffle), - steps_per_epoch=len(generator), - epochs=epoch, - callbacks=callbacks, - verbose=verbose - ) - - def predict(self, text: str) -> List: - """ inference - Args: - - text: str - """ - tokened = self.tokenizer.encode(text) - token_ids = tokened.ids - token_ids = [token_ids[0]] + self.template_ids + token_ids[1:-1] + [token_ids[-1]] - segment_ids = [0] * len(token_ids) - token_ids = np.array([token_ids]) - segment_ids = np.array([segment_ids]) - mask_ids = (token_ids == self.mask_id).astype('int') - if TF_VERSION > 1: - mask_output = self.model([token_ids, segment_ids, mask_ids]) - mask_output = mask_output[0].numpy() - else: - mask_output = self.model.predict([token_ids, segment_ids, mask_ids]) - mask_output = mask_output[0] - pred = np.argmax(mask_output, axis=1) - output = pred * mask_ids - output = output[output > 0].tolist() - return [self.id2label.get(idx, '') for idx in output] - - def save(self, save_path: str): - self.model.save_weights(save_path) - info(f'model successfully saved to {save_path}!') - - def load(self, model_path: str): - self.model.load_weights(model_path) - info('model successfully loaded!') diff --git a/langml/langml/tensor_typing.py b/langml/langml/tensor_typing.py deleted file mode 100644 index 2f61c3a..0000000 --- a/langml/langml/tensor_typing.py +++ /dev/null @@ -1,61 +0,0 @@ -# -*- coding: utf-8 -*- - -from typing import Union, Callable, List - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras -else: - import keras - -import numpy as np -import tensorflow as tf - - -Number = Union[ - float, - int, - np.float16, - np.float32, - np.float64, - np.int8, - np.int16, - np.int32, - np.int64, - np.uint8, - np.uint16, - np.uint32, - np.uint64, -] - -Initializer = Union[None, dict, str, Callable, keras.initializers.Initializer] -Regularizer = Union[None, dict, str, Callable, keras.regularizers.Regularizer] -Constraint = Union[None, dict, str, Callable, keras.constraints.Constraint] -Activation = Union[None, str, Callable] -Optimizer = Union[keras.optimizers.Optimizer, str] - -try: - from tensorflow.python.keras.engine.keras_tensor import KerasTensor - - Tensors = Union[ - List[Union[Number, list]], - tuple, - Number, - np.ndarray, - tf.Tensor, - tf.SparseTensor, - tf.Variable, - KerasTensor - ] -except ImportError: - Tensors = Union[ - List[Union[Number, list]], - tuple, - Number, - np.ndarray, - tf.Tensor, - tf.SparseTensor, - tf.Variable, - ] - -Models = keras.models.Model diff --git a/langml/langml/third_party/__init__.py b/langml/langml/third_party/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/langml/langml/third_party/conlleval.py b/langml/langml/third_party/conlleval.py deleted file mode 100644 index ac817c7..0000000 --- a/langml/langml/third_party/conlleval.py +++ /dev/null @@ -1,305 +0,0 @@ -# Python version of the evaluation script from CoNLL'00- -# Originates from: https://github.com/spyysalo/conlleval.py - - -# Intentional differences: -# - accept any space as delimiter by default -# - optional file argument (default STDIN) -# - option to set boundary (-b argument) -# - LaTeX output (-l argument) not supported -# - raw tags (-r argument) not supported - -# add function :evaluate(predicted_label, ori_label): which will not read from file - -import sys -import re -import codecs -from collections import defaultdict, namedtuple - -ANY_SPACE = '' - - -class FormatError(Exception): - pass - -Metrics = namedtuple('Metrics', 'tp fp fn prec rec fscore') - - -class EvalCounts(object): - def __init__(self): - self.correct_chunk = 0 # number of correctly identified chunks - self.correct_tags = 0 # number of correct chunk tags - self.found_correct = 0 # number of chunks in corpus - self.found_guessed = 0 # number of identified chunks - self.token_counter = 0 # token counter (ignores sentence breaks) - - # counts by type - self.t_correct_chunk = defaultdict(int) - self.t_found_correct = defaultdict(int) - self.t_found_guessed = defaultdict(int) - - -def parse_args(argv): - import argparse - parser = argparse.ArgumentParser( - description='evaluate tagging results using CoNLL criteria', - formatter_class=argparse.ArgumentDefaultsHelpFormatter - ) - arg = parser.add_argument - arg('-b', '--boundary', metavar='STR', default='-X-', - help='sentence boundary') - arg('-d', '--delimiter', metavar='CHAR', default=ANY_SPACE, - help='character delimiting items in input') - arg('-o', '--otag', metavar='CHAR', default='O', - help='alternative outside tag') - arg('file', nargs='?', default=None) - return parser.parse_args(argv) - - -def parse_tag(t): - m = re.match(r'^([^-]*)-(.*)$', t) - return m.groups() if m else (t, '') - - -def evaluate(iterable, options=None, delimiter=None): - if options is None: - options = parse_args([]) # use defaults - if delimiter is not None: - options.delimiter = delimiter - - counts = EvalCounts() - num_features = None # number of features per line - in_correct = False # currently processed chunks is correct until now - last_correct = 'O' # previous chunk tag in corpus - last_correct_type = '' # type of previously identified chunk tag - last_guessed = 'O' # previously identified chunk tag - last_guessed_type = '' # type of previous chunk tag in corpus - - for line in iterable: - line = line.rstrip('\r\n') - if not line: - continue - - if options.delimiter == ANY_SPACE: - features = line.split() - else: - features = line.split(options.delimiter) - - if num_features is None: - num_features = len(features) - elif num_features != len(features) and len(features) != 0: - print('broken line:', line) - raise FormatError('unexpected number of features: %d (%d)' % - (len(features), num_features)) - - if len(features) == 0 or features[0] == options.boundary: - features = [options.boundary, 'O', 'O'] - if len(features) < 3: - raise FormatError('unexpected number of features in line %s' % line) - - guessed, guessed_type = parse_tag(features.pop()) - correct, correct_type = parse_tag(features.pop()) - first_item = features.pop(0) - - if first_item == options.boundary: - guessed = 'O' - - end_correct = end_of_chunk(last_correct, correct, - last_correct_type, correct_type) - end_guessed = end_of_chunk(last_guessed, guessed, - last_guessed_type, guessed_type) - start_correct = start_of_chunk(last_correct, correct, - last_correct_type, correct_type) - start_guessed = start_of_chunk(last_guessed, guessed, - last_guessed_type, guessed_type) - - if in_correct: - if (end_correct and end_guessed and - last_guessed_type == last_correct_type): - in_correct = False - counts.correct_chunk += 1 - counts.t_correct_chunk[last_correct_type] += 1 - elif (end_correct != end_guessed or guessed_type != correct_type): - in_correct = False - - if start_correct and start_guessed and guessed_type == correct_type: - in_correct = True - - if start_correct: - counts.found_correct += 1 - counts.t_found_correct[correct_type] += 1 - if start_guessed: - counts.found_guessed += 1 - counts.t_found_guessed[guessed_type] += 1 - if first_item != options.boundary: - if correct == guessed and guessed_type == correct_type: - counts.correct_tags += 1 - counts.token_counter += 1 - - last_guessed = guessed - last_correct = correct - last_guessed_type = guessed_type - last_correct_type = correct_type - - if in_correct: - counts.correct_chunk += 1 - counts.t_correct_chunk[last_correct_type] += 1 - - return counts - - - -def uniq(iterable): - seen = set() - return [i for i in iterable if not (i in seen or seen.add(i))] - - -def calculate_metrics(correct, guessed, total): - tp, fp, fn = correct, guessed-correct, total-correct - p = 0 if tp + fp == 0 else 1.*tp / (tp + fp) - r = 0 if tp + fn == 0 else 1.*tp / (tp + fn) - f = 0 if p + r == 0 else 2 * p * r / (p + r) - return Metrics(tp, fp, fn, p, r, f) - - -def metrics(counts): - c = counts - overall = calculate_metrics( - c.correct_chunk, c.found_guessed, c.found_correct - ) - by_type = {} - for t in uniq(list(c.t_found_correct) + list(c.t_found_guessed)): - by_type[t] = calculate_metrics( - c.t_correct_chunk[t], c.t_found_guessed[t], c.t_found_correct[t] - ) - return overall, by_type - - -def report(counts, out=None): - if out is None: - out = sys.stdout - - overall, by_type = metrics(counts) - - c = counts - out.write('processed %d tokens with %d phrases; ' % - (c.token_counter, c.found_correct)) - out.write('found: %d phrases; correct: %d.\n' % - (c.found_guessed, c.correct_chunk)) - - if c.token_counter > 0: - out.write('accuracy: %6.2f%%; ' % - (100.*c.correct_tags/c.token_counter)) - out.write('precision: %6.2f%%; ' % (100.*overall.prec)) - out.write('recall: %6.2f%%; ' % (100.*overall.rec)) - out.write('FB1: %6.2f\n' % (100.*overall.fscore)) - - for i, m in sorted(by_type.items()): - out.write('%17s: ' % i) - out.write('precision: %6.2f%%; ' % (100.*m.prec)) - out.write('recall: %6.2f%%; ' % (100.*m.rec)) - out.write('FB1: %6.2f %d\n' % (100.*m.fscore, c.t_found_guessed[i])) - - -def report_notprint(counts, out=None): - if out is None: - out = sys.stdout - - overall, by_type = metrics(counts) - - c = counts - final_report = [] - line = [] - line.append('processed %d tokens with %d phrases; ' % - (c.token_counter, c.found_correct)) - line.append('found: %d phrases; correct: %d.\n' % - (c.found_guessed, c.correct_chunk)) - final_report.append("".join(line)) - - if c.token_counter > 0: - line = [] - line.append('accuracy: %6.2f%%; ' % - (100.*c.correct_tags/c.token_counter)) - line.append('precision: %6.2f%%; ' % (100.*overall.prec)) - line.append('recall: %6.2f%%; ' % (100.*overall.rec)) - line.append('FB1: %6.2f\n' % (100.*overall.fscore)) - final_report.append("".join(line)) - - for i, m in sorted(by_type.items()): - line = [] - line.append('%17s: ' % i) - line.append('precision: %6.2f%%; ' % (100.*m.prec)) - line.append('recall: %6.2f%%; ' % (100.*m.rec)) - line.append('FB1: %6.2f %d\n' % (100.*m.fscore, c.t_found_guessed[i])) - final_report.append("".join(line)) - return final_report - - -def end_of_chunk(prev_tag, tag, prev_type, type_): - # check if a chunk ended between the previous and current word - # arguments: previous and current chunk tags, previous and current types - chunk_end = False - - if prev_tag == 'E': chunk_end = True - if prev_tag == 'S': chunk_end = True - - if prev_tag == 'B' and tag == 'B': chunk_end = True - if prev_tag == 'B' and tag == 'S': chunk_end = True - if prev_tag == 'B' and tag == 'O': chunk_end = True - if prev_tag == 'I' and tag == 'B': chunk_end = True - if prev_tag == 'I' and tag == 'S': chunk_end = True - if prev_tag == 'I' and tag == 'O': chunk_end = True - - if prev_tag != 'O' and prev_tag != '.' and prev_type != type_: - chunk_end = True - - # these chunks are assumed to have length 1 - if prev_tag == ']': chunk_end = True - if prev_tag == '[': chunk_end = True - - return chunk_end - - -def start_of_chunk(prev_tag, tag, prev_type, type_): - # check if a chunk started between the previous and current word - # arguments: previous and current chunk tags, previous and current types - chunk_start = False - - if tag == 'B': chunk_start = True - if tag == 'S': chunk_start = True - - if prev_tag == 'E' and tag == 'E': chunk_start = True - if prev_tag == 'E' and tag == 'I': chunk_start = True - if prev_tag == 'S' and tag == 'E': chunk_start = True - if prev_tag == 'S' and tag == 'I': chunk_start = True - if prev_tag == 'O' and tag == 'E': chunk_start = True - if prev_tag == 'O' and tag == 'I': chunk_start = True - - if tag != 'O' and tag != '.' and prev_type != type_: - chunk_start = True - - # these chunks are assumed to have length 1 - if tag == '[': chunk_start = True - if tag == ']': chunk_start = True - - return chunk_start - - -def return_report(input_file): - with codecs.open(input_file, "r", "utf8") as f: - counts = evaluate(f) - return report_notprint(counts) - - -def main(argv): - args = parse_args(argv[1:]) - - if args.file is None: - counts = evaluate(sys.stdin, args) - else: - with open(args.file) as f: - counts = evaluate(f, args) - report(counts) - -if __name__ == '__main__': - sys.exit(main(sys.argv)) diff --git a/langml/langml/third_party/crf.py b/langml/langml/third_party/crf.py deleted file mode 100644 index c2d13ed..0000000 --- a/langml/langml/third_party/crf.py +++ /dev/null @@ -1,693 +0,0 @@ -# -*- coding: utf-8 -*- - -# This code implements basic operations of CRF -# Modified from https://github.com/tensorflow/addons (compatible with keras, tf.keras) - -# Copyright 2019 The TensorFlow Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# ============================================================================== - -import os -from typing import Optional, Union, List, Tuple - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.backend as K -else: - import keras - import keras.backend as K - -import numpy as np -import tensorflow as tf -from typeguard import typechecked - -from langml.tensor_typing import Tensors - - -def viterbi_decode(score: Tensors, trans: Tensors) -> Tuple[Tensors, Tensors]: - """ - Args: - score: A [seq_len, num_tags] matrix of unary potentials. - trans: A [num_tags, num_tags] matrix of binary potentials. - Returns: - viterbi: A [seq_len] list of integers containing the highest scoring tag - indices. - viterbi_score: A float containing the score for the Viterbi sequence. - """ - trellis = np.zeros_like(score) - backpointers = np.zeros_like(score, dtype=np.int32) - trellis[0] = score[0] - - for t in range(1, score.shape[0]): - v = np.expand_dims(trellis[t - 1], 1) + trans - trellis[t] = score[t] + np.max(v, 0) - backpointers[t] = np.argmax(v, 0) - - viterbi = [np.argmax(trellis[-1])] - for bp in reversed(backpointers[1:]): - viterbi.append(bp[viterbi[-1]]) - viterbi.reverse() - - viterbi_score = np.max(trellis[-1]) - return viterbi, viterbi_score - - -def _generate_zero_filled_state_for_cell(cell, inputs, batch_size, dtype): - """Generate a zero filled tensor with shape [batch_size, state_size].""" - if inputs is not None: - batch_size = K.shape(inputs)[0] - dtype = K.shape(inputs) - - return K.zeros(shape=(batch_size, cell.state_size), dtype=dtype) - - -def crf_filtered_inputs(inputs: Tensors, tag_bitmap: Tensors) -> tf.Tensor: - """Constrains the inputs to filter out certain tags at each time step. - tag_bitmap limits the allowed tags at each input time step. - This is useful when an observed output at a given time step needs to be - constrained to a selected set of tags. - Args: - inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials - to use as input to the CRF layer. - tag_bitmap: A [batch_size, max_seq_len, num_tags] boolean tensor - representing all active tags at each index for which to calculate the - unnormalized score. - Returns: - filtered_inputs: A [batch_size] vector of unnormalized sequence scores. - """ - - # set scores of filtered out inputs to be -inf. - filtered_inputs = tf.where( - tag_bitmap, - inputs, - tf.fill(K.shape(inputs), K.cast(float("-inf"), K.dtype(inputs))), - ) - return filtered_inputs - - -def crf_sequence_score( - inputs: Tensors, - tag_indices: Tensors, - sequence_lengths: Tensors, - transition_params: Tensors, -) -> tf.Tensor: - """Computes the unnormalized score for a tag sequence. - Args: - inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials - to use as input to the CRF layer. - tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which - we compute the unnormalized score. - sequence_lengths: A [batch_size] vector of true sequence lengths. - transition_params: A [num_tags, num_tags] transition matrix. - Returns: - sequence_scores: A [batch_size] vector of unnormalized sequence scores. - """ - tag_indices = K.cast(tag_indices, dtype='int32') - sequence_lengths = K.cast(sequence_lengths, dtype='int32') - - # If max_seq_len is 1, we skip the score calculation and simply gather the - # unary potentials of the single tag. - def _single_seq_fn(): - batch_inds = K.reshape(K.arange(0, K.shape(inputs)[0]), [-1, 1]) - indices = K.concatenate([batch_inds, tf.zeros_like(batch_inds)], axis=1) - - tag_inds = tf.gather_nd(tag_indices, indices) - tag_inds = K.reshape(tag_inds, [-1, 1]) - indices = K.concatenate([indices, tag_inds], axis=1) - - sequence_scores = tf.gather_nd(inputs, indices) - - sequence_scores = tf.where( - tf.less_equal(sequence_lengths, 0), - tf.zeros_like(sequence_scores), - sequence_scores, - ) - return sequence_scores - - def _multi_seq_fn(): - # Compute the scores of the given tag sequence. - unary_scores = crf_unary_score(tag_indices, sequence_lengths, inputs) - binary_scores = crf_binary_score( - tag_indices, sequence_lengths, transition_params - ) - sequence_scores = unary_scores + binary_scores - return sequence_scores - - return K.switch(K.equal(K.shape(inputs)[1], 1), _single_seq_fn, _multi_seq_fn) - - -def crf_multitag_sequence_score( - inputs: Tensors, - tag_bitmap: Tensors, - sequence_lengths: Tensors, - transition_params: Tensors, -) -> tf.Tensor: - """Computes the unnormalized score of all tag sequences matching - tag_bitmap. - tag_bitmap enables more than one tag to be considered correct at each time - step. This is useful when an observed output at a given time step is - consistent with more than one tag, and thus the log likelihood of that - observation must take into account all possible consistent tags. - Using one-hot vectors in tag_bitmap gives results identical to - crf_sequence_score. - Args: - inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials - to use as input to the CRF layer. - tag_bitmap: A [batch_size, max_seq_len, num_tags] boolean tensor - representing all active tags at each index for which to calculate the - unnormalized score. - sequence_lengths: A [batch_size] vector of true sequence lengths. - transition_params: A [num_tags, num_tags] transition matrix. - Returns: - sequence_scores: A [batch_size] vector of unnormalized sequence scores. - """ - tag_bitmap = K.cast(tag_bitmap, dtype='bool') - sequence_lengths = K.cast(sequence_lengths, dtype='int32') - filtered_inputs = crf_filtered_inputs(inputs, tag_bitmap) - - # If max_seq_len is 1, we skip the score calculation and simply gather the - # unary potentials of all active tags. - def _single_seq_fn(): - return tf.reduce_logsumexp(filtered_inputs, axis=[1, 2], keepdims=False) - - def _multi_seq_fn(): - # Compute the logsumexp of all scores of sequences - # matching the given tags. - return crf_log_norm( - inputs=filtered_inputs, - sequence_lengths=sequence_lengths, - transition_params=transition_params, - ) - - return K.switch(K.equal(K.shape(inputs)[1], 1), _single_seq_fn, _multi_seq_fn) - - -def crf_log_norm( - inputs: Tensors, sequence_lengths: Tensors, transition_params: Tensors -) -> tf.Tensor: - """Computes the normalization for a CRF. - Args: - inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials - to use as input to the CRF layer. - sequence_lengths: A [batch_size] vector of true sequence lengths. - transition_params: A [num_tags, num_tags] transition matrix. - Returns: - log_norm: A [batch_size] vector of normalizers for a CRF. - """ - sequence_lengths = K.cast(sequence_lengths, dtype='int32') - # Split up the first and rest of the inputs in preparation for the forward - # algorithm. - first_input = inputs[:, :1, :] - # first_input = tf.slice(inputs, [0, 0, 0], [-1, 1, -1]) - first_input = K.squeeze(first_input, axis=1) - - # If max_seq_len is 1, we skip the algorithm and simply reduce_logsumexp - # over the "initial state" (the unary potentials). - def _single_seq_fn(): - log_norm = tf.reduce_logsumexp(first_input, [1]) - # Mask `log_norm` of the sequences with length <= zero. - log_norm = tf.where( - tf.less_equal(sequence_lengths, 0), tf.zeros_like(log_norm), log_norm - ) - return log_norm - - def _multi_seq_fn(): - """Forward computation of alpha values.""" - rest_of_input = inputs[:, 1:, :] - # rest_of_input = tf.slice(inputs, [0, 1, 0], [-1, -1, -1]) - # Compute the alpha values in the forward algorithm in order to get the - # partition function. - - alphas = crf_forward( - rest_of_input, first_input, transition_params, sequence_lengths - ) - log_norm = tf.reduce_logsumexp(alphas, [1]) - # Mask `log_norm` of the sequences with length <= zero. - log_norm = tf.where( - tf.less_equal(sequence_lengths, 0), tf.zeros_like(log_norm), log_norm - ) - return log_norm - - return K.switch(K.equal(K.shape(inputs)[1], 1), _single_seq_fn, _multi_seq_fn) - - -def crf_log_likelihood( - inputs: Tensors, - tag_indices: Tensors, - sequence_lengths: Tensors, - transition_params: Optional[Tensors] = None, -) -> tf.Tensor: - """Computes the log-likelihood of tag sequences in a CRF. - Args: - inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials - to use as input to the CRF layer. - tag_indices: A [batch_size, max_seq_len] matrix of tag indices for which - we compute the log-likelihood. - sequence_lengths: A [batch_size] vector of true sequence lengths. - transition_params: A [num_tags, num_tags] transition matrix, - if available. - Returns: - log_likelihood: A [batch_size] `Tensor` containing the log-likelihood of - each example, given the sequence of tag indices. - transition_params: A [num_tags, num_tags] transition matrix. This is - either provided by the caller or created in this function. - """ - # inputs = tf.convert_to_tensor(inputs) - # cast type to handle different types - tag_indices = K.cast(tag_indices, dtype='int32') - sequence_lengths = K.cast(sequence_lengths, dtype='int32') - - transition_params = K.cast(transition_params, K.dtype(inputs)) - sequence_scores = crf_sequence_score( - inputs, tag_indices, sequence_lengths, transition_params - ) - log_norm = crf_log_norm(inputs, sequence_lengths, transition_params) - - # Normalize the scores to get the log-likelihood per example. - log_likelihood = sequence_scores - log_norm - return log_likelihood, transition_params - - -def crf_unary_score( - tag_indices: Tensors, sequence_lengths: Tensors, inputs: Tensors -) -> tf.Tensor: - """Computes the unary scores of tag sequences. - Args: - tag_indices: A [batch_size, max_seq_len] matrix of tag indices. - sequence_lengths: A [batch_size] vector of true sequence lengths. - inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials. - Returns: - unary_scores: A [batch_size] vector of unary scores. - """ - tag_indices = K.cast(tag_indices, dtype='int32') - sequence_lengths = K.cast(sequence_lengths, dtype='int32') - - batch_size = K.shape(inputs)[0] - max_seq_len = K.shape(inputs)[1] - num_tags = K.shape(inputs)[2] - - flattened_inputs = K.reshape(inputs, [-1]) - - offsets = K.expand_dims(K.arange(0, batch_size) * max_seq_len * num_tags, 1) - offsets += K.expand_dims(K.arange(0, max_seq_len) * num_tags, 0) - - flattened_tag_indices = K.reshape(offsets + tag_indices, [-1]) - - unary_scores = K.reshape( - tf.gather(flattened_inputs, flattened_tag_indices), [batch_size, max_seq_len] - ) - - masks = tf.sequence_mask( - sequence_lengths, maxlen=K.shape(tag_indices)[1], dtype=unary_scores.dtype - ) - - unary_scores = tf.reduce_sum(unary_scores * masks, 1) - return unary_scores - - -def crf_binary_score( - tag_indices: Tensors, sequence_lengths: Tensors, transition_params: Tensors -) -> tf.Tensor: - """Computes the binary scores of tag sequences. - Args: - tag_indices: A [batch_size, max_seq_len] matrix of tag indices. - sequence_lengths: A [batch_size] vector of true sequence lengths. - transition_params: A [num_tags, num_tags] matrix of binary potentials. - Returns: - binary_scores: A [batch_size] vector of binary scores. - """ - tag_indices = K.cast(tag_indices, dtype='int32') - sequence_lengths = K.cast(sequence_lengths, dtype='int32') - - num_tags = K.shape(transition_params)[0] - num_transitions = K.shape(tag_indices)[1] - 1 - - # Truncate by one on each side of the sequence to get the start and end - # indices of each transition. - start_tag_indices = tag_indices[:, :num_transitions] - # start_tag_indices = tf.slice(tag_indices, [0, 0], [-1, num_transitions]) - end_tag_indices = tag_indices[:, 1:num_transitions + 1] - # end_tag_indices = tf.slice(tag_indices, [0, 1], [-1, num_transitions]) - - # Encode the indices in a flattened representation. - flattened_transition_indices = start_tag_indices * num_tags + end_tag_indices - flattened_transition_params = K.reshape(transition_params, [-1]) - - # Get the binary scores based on the flattened representation. - binary_scores = tf.gather(flattened_transition_params, flattened_transition_indices) - - masks = tf.sequence_mask( - sequence_lengths, maxlen=K.shape(tag_indices)[1], dtype=binary_scores.dtype - ) - truncated_masks = masks[:, 1:] - # truncated_masks = tf.slice(masks, [0, 1], [-1, -1]) - binary_scores = tf.reduce_sum(binary_scores * truncated_masks, 1) - - return binary_scores - - -def crf_forward( - inputs: Tensors, - state: Tensors, - transition_params: Tensors, - sequence_lengths: Tensors, -) -> tf.Tensor: - """Computes the alpha values in a linear-chain CRF. - See http://www.cs.columbia.edu/~mcollins/fb.pdf for reference. - Args: - inputs: A [batch_size, num_tags] matrix of unary potentials. - state: A [batch_size, num_tags] matrix containing the previous alpha - values. - transition_params: A [num_tags, num_tags] matrix of binary potentials. - This matrix is expanded into a [1, num_tags, num_tags] in preparation - for the broadcast summation occurring within the cell. - sequence_lengths: A [batch_size] vector of true sequence lengths. - Returns: - new_alphas: A [batch_size, num_tags] matrix containing the - new alpha values. - """ - sequence_lengths = K.cast(sequence_lengths, dtype='int32') - - last_index = tf.maximum( - tf.constant(0, dtype=sequence_lengths.dtype), sequence_lengths - 1 - ) - inputs = tf.transpose(inputs, [1, 0, 2]) - transition_params = K.expand_dims(transition_params, 0) - - def _scan_fn(_state, _inputs): - _state = K.expand_dims(_state, 2) - transition_scores = _state + transition_params - new_alphas = _inputs + tf.reduce_logsumexp(transition_scores, [1]) - return new_alphas - - all_alphas = tf.transpose(tf.scan(_scan_fn, inputs, state), [1, 0, 2]) - # add first state for sequences of length 1 - all_alphas = K.concatenate([K.expand_dims(state, 1), all_alphas], 1) - - idxs = tf.stack([K.arange(0, K.shape(last_index)[0]), last_index], axis=1) - return tf.gather_nd(all_alphas, idxs) - - -class AbstractRNNCell(keras.layers.Layer): - """Abstract object representing an RNN cell. - This is the base class for implementing RNN cells with custom behavior. - Every `RNNCell` must have the properties below and implement `call` with - the signature `(output, next_state) = call(input, state)`. - Examples: - ```python - class MinimalRNNCell(AbstractRNNCell): - def __init__(self, units, **kwargs): - self.units = units - super(MinimalRNNCell, self).__init__(**kwargs) - @property - def state_size(self): - return self.units - def build(self, input_shape): - self.kernel = self.add_weight(shape=(input_shape[-1], self.units), - initializer='uniform', - name='kernel') - self.recurrent_kernel = self.add_weight( - shape=(self.units, self.units), - initializer='uniform', - name='recurrent_kernel') - self.built = True - def call(self, inputs, states): - prev_output = states[0] - h = K.dot(inputs, self.kernel) - output = h + K.dot(prev_output, self.recurrent_kernel) - return output, output - ``` - This definition of cell differs from the definition used in the literature. - In the literature, 'cell' refers to an object with a single scalar output. - This definition refers to a horizontal array of such units. - An RNN cell, in the most abstract setting, is anything that has - a state and performs some operation that takes a matrix of inputs. - This operation results in an output matrix with `self.output_size` columns. - If `self.state_size` is an integer, this operation also results in a new - state matrix with `self.state_size` columns. If `self.state_size` is a - (possibly nested tuple of) TensorShape object(s), then it should return a - matching structure of Tensors having shape `[batch_size].concatenate(s)` - for each `s` in `self.batch_size`. - """ - - def call(self, inputs, states): - """The function that contains the logic for one RNN step calculation. - Args: - inputs: the input tensor, which is a slide from the overall RNN input by - the time dimension (usually the second dimension). - states: the state tensor from previous step, which has the same shape - as `(batch, state_size)`. In the case of timestep 0, it will be the - initial state user specified, or zero filled tensor otherwise. - Returns: - A tuple of two tensors: - 1. output tensor for the current timestep, with size `output_size`. - 2. state tensor for next step, which has the shape of `state_size`. - """ - raise NotImplementedError('Abstract method') - - @property - def state_size(self): - """size(s) of state(s) used by this cell. - It can be represented by an Integer, a TensorShape or a tuple of Integers - or TensorShapes. - """ - raise NotImplementedError('Abstract method') - - @property - def output_size(self): - """Integer or TensorShape: size of outputs produced by this cell.""" - raise NotImplementedError('Abstract method') - - def get_initial_state(self, inputs=None, batch_size=None, dtype=None): - return _generate_zero_filled_state_for_cell(self, inputs, batch_size, dtype) - - -class CrfDecodeForwardRnnCell(AbstractRNNCell): - """Computes the forward decoding in a linear-chain CRF.""" - - @typechecked - def __init__(self, transition_params: Tensors, **kwargs): - """Initialize the CrfDecodeForwardRnnCell. - Args: - transition_params: A [num_tags, num_tags] matrix of binary - potentials. This matrix is expanded into a - [1, num_tags, num_tags] in preparation for the broadcast - summation occurring within the cell. - """ - super().__init__(**kwargs) - self.supports_masking = True - self._transition_params = K.expand_dims(transition_params, 0) - #self._num_tags = K.shape(transition_params)[0] - self._num_tags = K.int_shape(transition_params)[0] - - @property - def state_size(self): - return self._num_tags - - @property - def output_size(self): - return self._num_tags - - def build(self, input_shape): - super().build(input_shape) - - def compute_mask(self, - inputs: Tensors, - mask: Optional[Tensors] = None) -> Union[List[Union[Tensors, None]], Tensors]: - return mask - - def call(self, inputs: Tensors, state: Tensors, mask: Optional[Tensors] = None, **kwargs): - """Build the CrfDecodeForwardRnnCell. - Args: - inputs: A [batch_size, num_tags] matrix of unary potentials. - state: A [batch_size, num_tags] matrix containing the previous step's - score values. - Returns: - backpointers: A [batch_size, num_tags] matrix of backpointers. - new_state: A [batch_size, num_tags] matrix of new score values. - """ - state = K.expand_dims(state[0], 2) - transition_scores = state + K.cast( - self._transition_params, K.dtype(state) - ) - new_state = inputs + K.max(transition_scores, 1) - backpointers = K.argmax(transition_scores, 1) - backpointers = K.cast(backpointers, dtype='int32') - return backpointers, new_state - - def get_config(self) -> dict: - config = { - "transition_params": K.squeeze(self._transition_params, axis=0).numpy().tolist() - } - base_config = super(CrfDecodeForwardRnnCell, self).get_config() - return dict(list(base_config.items()) + list(config.items())) - - @classmethod - def from_config(cls, config: dict) -> "CrfDecodeForwardRnnCell": - config["transition_params"] = np.array( - config["transition_params"], dtype=np.float32 - ) - return cls(**config) - - -def crf_decode_forward( - inputs: Tensors, - state: Tensors, - transition_params: Tensors, - sequence_lengths: Tensors, -) -> tf.Tensor: - """Computes forward decoding in a linear-chain CRF. - Args: - inputs: A [batch_size, num_tags] matrix of unary potentials. - state: A [batch_size, num_tags] matrix containing the previous step's - score values. - transition_params: A [num_tags, num_tags] matrix of binary potentials. - sequence_lengths: A [batch_size] vector of true sequence lengths. - Returns: - backpointers: A [batch_size, num_tags] matrix of backpointers. - new_state: A [batch_size, num_tags] matrix of new score values. - """ - sequence_lengths = K.cast(sequence_lengths, dtype='int32') - # mask = tf.sequence_mask(sequence_lengths, K.shape(inputs)[1]) - crf_fwd_cell = CrfDecodeForwardRnnCell(transition_params, dtype=K.dtype(inputs)) - ''' - # Use L.RNN - crf_fwd_layer = keras.layers.RNN( - crf_fwd_cell, return_sequences=True, return_state=True, stateful=False, dtype=K.dtype(inputs) - ) - outputs, last_state = crf_fwd_layer(inputs, state) - # Use L.RNN end - ''' - # Use K.rnn - (_, outputs, last_state) = K.rnn(crf_fwd_cell.call, inputs, [state]) - last_state = K.reshape(last_state, K.shape(state)) - # Use K.rnn end - return outputs, last_state - - -def crf_decode_backward(inputs: Tensors, state: Tensors) -> tf.Tensor: - """Computes backward decoding in a linear-chain CRF. - Args: - inputs: A [batch_size, num_tags] matrix of - backpointer of next step (in time order). - state: A [batch_size, 1] matrix of tag index of next step. - Returns: - new_tags: A [batch_size, num_tags] - tensor containing the new tag indices. - """ - inputs = tf.transpose(inputs, [1, 0, 2]) - - def _scan_fn(state, inputs): - state = K.squeeze(state, axis=1) - idxs = tf.stack([K.arange(0, K.shape(inputs)[0]), state], axis=1) - new_tags = K.expand_dims(tf.gather_nd(inputs, idxs), axis=-1) - return new_tags - - return tf.transpose(tf.scan(_scan_fn, inputs, state), [1, 0, 2]) - - -def crf_decode( - potentials: Tensors, transition_params: Tensors, sequence_length: Tensors -) -> tf.Tensor: - """Decode the highest scoring sequence of tags. - Args: - potentials: A [batch_size, max_seq_len, num_tags] tensor of - unary potentials. - transition_params: A [num_tags, num_tags] matrix of - binary potentials. - sequence_length: A [batch_size] vector of true sequence lengths. - Returns: - decode_tags: A [batch_size, max_seq_len] matrix, with dtype `tf.int32`. - Contains the highest scoring tag indices. - best_score: A [batch_size] vector, containing the score of `decode_tags`. - """ - sequence_length = K.cast(sequence_length, dtype='int32') - - # If max_seq_len is 1, we skip the algorithm and simply return the - # argmax tag and the max activation. - def _single_seq_fn(): - decode_tags = K.cast(K.argmax(potentials, axis=2), dtype='int32') - best_score = K.reshape(tf.reduce_max(potentials, axis=2), shape=[-1]) - return decode_tags, best_score - - def _multi_seq_fn(): - # Computes forward decoding. Get last score and backpointers. - initial_state = potentials[:, :1, :] - # initial_state = tf.slice(potentials, [0, 0, 0], [-1, 1, -1]) - initial_state = K.squeeze(initial_state, axis=1) - inputs = potentials[:, 1:, :] - # inputs = tf.slice(potentials, [0, 1, 0], [-1, -1, -1]) - - sequence_length_less_one = tf.maximum( - K.constant(0, dtype='int32'), sequence_length - 1 - ) - - backpointers, last_score = crf_decode_forward( - inputs, initial_state, transition_params, sequence_length_less_one - ) - - backpointers = tf.reverse_sequence( - backpointers, sequence_length_less_one, seq_axis=1 - ) - - initial_state = K.cast(K.argmax(last_score, axis=1), dtype='int32') - initial_state = K.expand_dims(initial_state, axis=-1) - - decode_tags = crf_decode_backward(backpointers, initial_state) - decode_tags = K.squeeze(decode_tags, axis=2) - decode_tags = K.concatenate([initial_state, decode_tags], axis=1) - decode_tags = tf.reverse_sequence(decode_tags, sequence_length, seq_axis=1) - - best_score = tf.reduce_max(last_score, axis=1) - return decode_tags, best_score - - if K.int_shape(potentials)[1] is not None: - # shape is statically know, so we just execute - # the appropriate code path - if K.int_shape(potentials)[1] == 1: - return _single_seq_fn() - else: - return _multi_seq_fn() - else: - return K.switch( - K.equal(K.shape(potentials)[1], 1), _single_seq_fn, _multi_seq_fn - ) - - -def crf_constrained_decode( - potentials: Tensors, - tag_bitmap: Tensors, - transition_params: Tensors, - sequence_length: Tensors, -) -> tf.Tensor: - """Decode the highest scoring sequence of tags under constraints. - This is a function for tensor. - Args: - potentials: A [batch_size, max_seq_len, num_tags] tensor of - unary potentials. - tag_bitmap: A [batch_size, max_seq_len, num_tags] boolean tensor - representing all active tags at each index for which to calculate the - unnormalized score. - transition_params: A [num_tags, num_tags] matrix of - binary potentials. - sequence_length: A [batch_size] vector of true sequence lengths. - Returns: - decode_tags: A [batch_size, max_seq_len] matrix, with dtype `tf.int32`. - Contains the highest scoring tag indices. - best_score: A [batch_size] vector, containing the score of `decode_tags`. - """ - - filtered_potentials = crf_filtered_inputs(potentials, tag_bitmap) - return crf_decode(filtered_potentials, transition_params, sequence_length) diff --git a/langml/langml/tokenizer.py b/langml/langml/tokenizer.py deleted file mode 100644 index 383e770..0000000 --- a/langml/langml/tokenizer.py +++ /dev/null @@ -1,525 +0,0 @@ -# -*- coding: utf-8 -*- - -""" -LangML Tokenizer - -- WPTokenizer: WordPiece Tokenizer -- SPTokenizer: SentencePiece Tokenizer - -Wrap for: - - tokenizers.BertWordPieceTokenizer - - sentencepiece.SentencePieceProcessor - -We don't provide all functions of raw tokenizer, please use raw tokenizer for full usage. -""" - -import unicodedata -from math import ceil -from abc import ABCMeta, abstractmethod -from typing import Dict, List, Optional, Tuple, Union - -import numpy as np -from sentencepiece import SentencePieceProcessor -from tokenizers import BertWordPieceTokenizer - - -class Encoding: - ''' Product of tokenizer encoding - ''' - ids = None - segment_ids = None - tokens = None - - def __init__(self, - ids: Union[np.ndarray, List[int]], - segment_ids: Union[np.ndarray, List[int]], - tokens: List[str]) -> None: - self.ids = ids - self.segment_ids = segment_ids - self.tokens = tokens - - -class SpecialTokens: - PAD = '[PAD]' - UNK = '[UNK]' - MASK = '[MASK]' - CLS = '[CLS]' - SEP = '[SEP]' - - def __contains__(self, token: str) -> bool: - """ Check if the input token exists in special tokens. - Args: - - token: str - Return: - bool - """ - return token in [ - self.PAD, self.UNK, self.MASK, self.CLS, self.SEP - ] - - def tokens(self) -> List[str]: - ret = [] - for field in SpecialTokens.__dict__.keys(): - if field.startswith('_'): - continue - if isinstance(getattr(self, field), str): - ret.append(getattr(self, field)) - return ret - - -class Tokenizer(metaclass=ABCMeta): - """ Base Tokenizer - """ - - def __init__(self, vocab_path: str, lowercase: bool = False): - """ - Args: - - vocab_path: str, path to vocab - - lowercase: bool, whether to do lowercase - """ - self.vocab_path = vocab_path - self.lowercase = lowercase - self.special_tokens = SpecialTokens() - - self.max_length = None - self.truncation_strategy = None - self._tokenizer = None - - def enable_truncation(self, max_length: int, strategy: str = 'post'): - """ - Args: - - max_length: int, - - strategy: str, optional, truncation strategy, options: `post` or `pre`, default `post` - """ - self.max_length = max_length - self.truncation_strategy = strategy - if strategy is not None: - assert self.truncation_strategy in ['post', 'pre'], '`strategy` must be `post` or `pre`' - - def tokens_mapping(self, sequence: str, tokens: List[str]) -> List[Tuple[int, int]]: - """ Get tokens to their corresponding sequence position mapping. - Tokens may contain special marks, e.g., `##`, `▁`, and `[UNK]`. - Use this function can obtain the corresponding raw token in the sequence. - - Args: - - sequence: str, the input sequence - - tokens: List[str], tokens of the input sequence - Return: - List[Tuple[int, int]] - - Examples: - >>> sequence = 'I like watermelons' - >>> tokens = ['[CLS]', '▁i', '▁like', '▁water', 'mel', 'ons', '[SEP]'] - >>> mapping = tokenizer.tokens_mapping(tokens) - >>> start_index, end_index = 3, 5 - >>> print("current token", tokens[start_index: end_index + 1]) - ['▁water', 'mel', 'ons'] - >>> print("raw token", sequence[mapping[start_index][0]: mapping[end_index][1]]) - watermelons - - Reference: - https://github.com/bojone/bert4keras - """ - if self.lowercase: - sequence = self.sequence_lower(sequence) - - normalized_sequence, char_mapping = '', [] - for i, ch in enumerate(sequence): - if self.lowercase: - ch = unicodedata.normalize('NFD', ch) - ch = ''.join([c for c in ch if unicodedata.category(c) != 'Mn']) - ch = ''.join([ - c for c in ch - if not (ord(c) == 0 or ord(c) == 0xfffd or (unicodedata.category(ch) in ('Cc', 'Cf'))) - ]) - normalized_sequence += ch - char_mapping.extend([i] * len(ch)) - - sequence = normalized_sequence - mapping = [] - offset = 0 - special_placeholder = (0, 0) - for token in tokens: - if token in self.special_tokens: - mapping.append(special_placeholder) - else: - token = self.stem(token) - start = sequence[offset:].index(token) + offset - end = start + len(token) - cnt = char_mapping[start:end] - mapping.append((cnt[0], cnt[-1] + 1)) - offset = end - - return mapping - - def encode(self, sequence: str, pair: Optional[str] = None, return_array: bool = False) -> Encoding: - """ - Args: - - sequence: str, input sequence - - pair: str, optional, pair sequence, default `None` - - return_array: bool, optional, whether to return numpy array, default `True` - Return: - Encoding object - """ - if self.lowercase: - sequence = self.sequence_lower(sequence) - if pair: - pair = self.sequence_lower(pair) - tokens = self.tokenize(sequence) - pair_tokens = None - if pair is not None: - pair_tokens = self.tokenize(pair) - - if self.max_length is not None: - max_token_length = self.max_length - 2 - if pair_tokens is not None: - max_token_length -= 1 - tokens, pair_tokens = self.sequence_truncating(max_token_length, tokens, pair_tokens) - - tokens = [self.special_tokens.CLS] + tokens + [self.special_tokens.SEP] - token_ids = [self.token_to_id(token) for token in tokens] - segment_ids = [0] * len(token_ids) - - if pair_tokens is not None: - pair_tokens = pair_tokens + [self.special_tokens.SEP] - pair_token_ids = [self.token_to_id(token) for token in pair_tokens] - pair_segment_ids = [1] * len(pair_token_ids) - - tokens += pair_tokens - token_ids += pair_token_ids - segment_ids += pair_segment_ids - - if return_array: - token_ids = np.array(token_ids) - segment_ids = np.array(segment_ids) - - return Encoding( - ids=token_ids, - segment_ids=segment_ids, - tokens=tokens, - ) - - def encode_batch(self, - inputs: Union[List[str], List[Tuple[str, str]], List[List[str]]], - padding: bool = True, - padding_strategy: str = 'post', - return_array: bool = False) -> Encoding: - """ - Args: - - inputs: Union[List[str], List[Tuple[str, str]], List[List[str]]], list of texts or list of text pairs. - - padding: bool, optional, whether to padding sequences, default `True` - - padding_strategy: str, optional, options: `post` or `pre`, default `post` - - return_array: bool, optional, whether to return numpy array, default `True` - Return: - Encoding object - """ - assert padding_strategy in ['post', 'pre'], '`padding_strategy` must be `post` or `pre`' - all_tokens, all_pair_tokens = [], [] - for item in inputs: - if isinstance(item, (tuple, list)): - assert len(item) == 2 - item = list(item) - if self.lowercase: - item[0] = self.sequence_lower(item[0]) - item[1] = self.sequence_lower(item[1]) - all_tokens.append(self.tokenize(item[0])) - all_pair_tokens.append(self.tokenize(item[1])) - elif isinstance(item, str): - if self.lowercase: - item = self.sequence_lower(item) - all_tokens.append(self.tokenize(item)) - - if not all_pair_tokens: - all_pair_tokens = None - - max_all_token_length = max(len(t) for t in all_tokens) - if all_pair_tokens is not None: - max_all_token_pair_length = max(len(t) + len(p) for t, p in zip(all_tokens, all_pair_tokens)) - - if self.max_length is not None: - max_token_length = self.max_length - 2 - if all_pair_tokens is not None: - max_token_length -= 1 - max_token_length = min(max_token_length, max_all_token_pair_length) - else: - max_token_length = min(max_token_length, max_all_token_length) - else: - if all_pair_tokens is not None: - max_token_length = max_all_token_pair_length - else: - max_token_length = max_all_token_length - - batch_tokens = [] - batch_token_ids = [] - batch_segment_ids = [] - all_pair_tokens = all_pair_tokens or [None] * len(all_tokens) - for tokens, pair_tokens in zip(all_tokens, all_pair_tokens): - tokens, pair_tokens = self.sequence_truncating(max_token_length, tokens, pair_tokens) - repeat = 0 - if padding: - if pair_tokens is not None: - repeat = max_token_length - len(tokens) - len(pair_tokens) - else: - repeat = max_token_length - len(tokens) - - tokens = [self.special_tokens.CLS] + tokens + [self.special_tokens.SEP] - token_ids = [self.token_to_id(token) for token in tokens] - segment_ids = [0] * len(token_ids) - - if pair_tokens is not None: - pair_tokens = pair_tokens + [self.special_tokens.SEP] - pair_token_ids = [self.token_to_id(token) for token in pair_tokens] - pair_segment_ids = [1] * len(pair_token_ids) - - tokens += pair_tokens - token_ids += pair_token_ids - segment_ids += pair_segment_ids - - if padding and repeat > 0: - padding_value = self.token_to_id(self.special_tokens.PAD) - if padding_strategy == 'post': - tokens += [self.special_tokens.PAD] * repeat - token_ids += [padding_value] * repeat - segment_ids += [padding_value] * repeat - elif padding_strategy == 'pre': - tokens = [self.special_tokens.PAD] * repeat + tokens - token_ids = [padding_value] * repeat + token_ids - segment_ids = [padding_value] * repeat + segment_ids - - batch_tokens.append(tokens) - batch_token_ids.append(token_ids) - batch_segment_ids.append(segment_ids) - - if return_array: - batch_token_ids = np.array(batch_token_ids) - batch_segment_ids = np.array(batch_segment_ids) - - return Encoding( - ids=batch_token_ids, - segment_ids=batch_segment_ids, - tokens=batch_tokens - ) - - def stem(self, token): - if isinstance(self, WPTokenizer) and token.startswith('##'): - return token[2:] - elif isinstance(self, SPTokenizer) and token.startswith('▁'): - return token[1:] - return token - - def sequence_lower(self, sequence: str) -> str: - """ Do lower to sequence, except for special tokens. - Args: - - sequence: str - Return: - str - """ - sequence = sequence.lower() - for token in self.special_tokens.tokens(): - sequence = sequence.replace(token.lower(), token) - return sequence - - def sequence_truncating(self, - max_token_length: int, - tokens: List[str], - pair_tokens: Optional[List[str]] = None) -> Tuple[ - List[str], Optional[List[str]]]: - """ Truncating sequence - Args: - - max_token_length: int, maximum token length - - tokens: List[str], input tokens - - pair_tokens: Optional[List[str]], optional, input pair tokens, default None - Return: - Tuple[List[str], Optional[List[str]]] - """ - if pair_tokens is not None: - left_len = len(tokens) - right_len = len(pair_tokens) - if left_len + right_len <= max_token_length: - max_left = left_len - max_right = right_len - else: - max_left = min(ceil(max_token_length / 2), left_len) - max_right = max_token_length - max_left - else: - max_left = max_token_length - if self.truncation_strategy == 'post': - tokens = tokens[:max_left] - if pair_tokens is not None: - pair_tokens = pair_tokens[:max_right] - elif self.truncation_strategy == 'pre': - tokens = tokens[-max_left:] - if pair_tokens is not None: - pair_tokens = pair_tokens[-max_right:] - return tokens, pair_tokens - - def raw_tokenizer(self) -> object: - """ Return raw tokenizer, i.e. object of `tokenizers.BertWordPieceTokenizer` or `sentencepiece.SentencePieceProcessor` - """ - return self._tokenizer - - @abstractmethod - def tokenize(self, sequence: str) -> List[str]: - raise NotImplementedError - - @abstractmethod - def decode(self, ids: List[int], skip_special_tokens: bool = True) -> List[str]: - raise NotImplementedError - - @abstractmethod - def get_vocab_size(self) -> int: - raise NotImplementedError - - @abstractmethod - def id_to_token(self, idx: int) -> str: - raise NotImplementedError - - @abstractmethod - def token_to_id(self, token: str) -> int: - raise NotImplementedError - - @abstractmethod - def get_vocab(self) -> Dict: - raise NotImplementedError - - -class SPTokenizer(Tokenizer): - """ SentencePiece Tokenizer - Wrap for `sentencepiece`. - """ - def __init__(self, vocab_path: str, lowercase: bool = False): - """ - Args: - - vocab_path: str, path to vocab - - lowercase: bool, whether to do lowercase, default False - """ - super().__init__(vocab_path, lowercase=lowercase) - self._tokenizer = SentencePieceProcessor() - self._tokenizer.Load(self.vocab_path) - - self.special_tokens.PAD = self._tokenizer.id_to_piece(self._tokenizer.pad_id()) - self.special_tokens.UNK = self._tokenizer.id_to_piece(self._tokenizer.unk_id()) - - def get_vocab_size(self) -> int: - """ Return vocab size - """ - return self._tokenizer.get_piece_size() - - def token_to_id(self, token: str) -> int: - """ Convert the input token to corresponding index - Args: - - token: str - Return: - int - """ - return self._tokenizer.piece_to_id(token) - - def id_to_token(self, idx: int) -> str: - """ Convert index to corresponding token - Args: - - idx: int - Return: - str - """ - if idx < self.get_vocab_size(): - return self._tokenizer.id_to_piece(idx) - return '' - - def tokenize(self, sequence: str) -> List[str]: - """ Tokenize sequence to token peices. - Args: - - sequence: str - Return: - List[str] - """ - return self._tokenizer.encode_as_pieces(sequence) - - def decode(self, ids: List[int], skip_special_tokens: bool = True) -> List[str]: - """ Decode indexs to tokens - Args: - - ids: List[int] - - skip_special_tokens: bool, optioanl, whether to skip special tokens, default `True` - Return: - List[str] - """ - tokens = [self.id_to_token(idx) for idx in ids] - if skip_special_tokens: - return [token for token in tokens if token not in self.special_tokens] - return tokens - - def get_vocab(self) -> Dict: - """ Return vocabulary - """ - return {self._tokenizer.id_to_piece(idx): idx for idx in range(self.get_vocab_size())} - - -class WPTokenizer(Tokenizer): - """ WordPieceTokenizer - Wrap for `BertWordPieceTokenizer`. - """ - def __init__(self, vocab_path: str, lowercase: bool = False): - """ - Args: - - vocab_path: str, path to vocab - - lowercase: bool, whether to do lowercase, default False - """ - super().__init__(vocab_path, lowercase=lowercase) - self._tokenizer = BertWordPieceTokenizer(vocab_path, lowercase=lowercase) - - def get_vocab_size(self) -> int: - """ Return vocab size - """ - return self._tokenizer.get_vocab_size() - - def token_to_id(self, token: str) -> int: - """ Convert the input token to corresponding index - Args: - - token: str - Return: - int - """ - return self._tokenizer.token_to_id(token) - - def id_to_token(self, idx: int) -> str: - """ Convert index to corresponding token - Args: - - idx: int - Return: - str - """ - if idx < self.get_vocab_size(): - return self._tokenizer.id_to_token(idx) - return '' - - def tokenize(self, sequence: str) -> List[str]: - """ Tokenize sequence to token peices. - Args: - - sequence: str - Return: - List[str] - """ - encoded = self._tokenizer.encode(sequence) - return encoded.tokens[1:-1] - - def decode(self, ids: List[int], skip_special_tokens: bool = True) -> List[str]: - """ Decode indexs to tokens - Args: - - ids: List[int] - - skip_special_tokens: bool, optioanl, whether to skip special tokens, default `True` - Return: - List[str] - """ - return self._tokenizer.decode(ids, skip_special_tokens=skip_special_tokens).split() - - def get_vocab(self) -> Dict: - """ Return vocabulary - """ - return self._tokenizer.get_vocab() - - def add_special_tokens(self, tokens: List[str]): - """ Specify special tokens, the tokenizer will reserve special tokens as a whole (i.e. don't split them) in tokenizing. - Currently, only the WPTokenizer supports specifying special tokens. - Args: - - tokens: List[str], special tokens - """ - self._tokenizer.add_special_tokens(tokens) diff --git a/langml/langml/transformer/__init__.py b/langml/langml/transformer/__init__.py deleted file mode 100644 index bfcd0c1..0000000 --- a/langml/langml/transformer/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -# -*- coding: utf-8 -*- - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras -else: - import keras - -from langml.transformer.layers import ( - gelu, FeedForward, SineCosinePositionEmbedding, -) - - -custom_objects = {'gelu': gelu} -custom_objects.update(FeedForward.get_custom_objects()) -custom_objects.update(SineCosinePositionEmbedding.get_custom_objects()) - -keras.utils.get_custom_objects().update(custom_objects) diff --git a/langml/langml/transformer/encoder.py b/langml/langml/transformer/encoder.py deleted file mode 100644 index 0d92661..0000000 --- a/langml/langml/transformer/encoder.py +++ /dev/null @@ -1,104 +0,0 @@ -# -*- coding: utf-8 -*- - -""" Yet another transformer implementation. -""" - -# TODO: Transformer Decoder - -from typing import Optional, Union - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras.layers as L -else: - import keras.layers as L - -from langml.layers import MultiHeadAttention, LayerNorm -from langml.tensor_typing import Tensors, Activation -from langml.transformer import gelu, FeedForward - - -class TransformerEncoder: - def __init__(self, - attention_heads: int, - hidden_dim: int, - attention_activation: Optional[Union[str, Activation]] = None, - feed_forward_activation: Optional[Union[str, Activation]] = gelu, - dropout_rate: float = 0.0, - trainable: Optional[bool] = True, - name: Optional[str] = 'Transformer-Encoder'): - self.name = name - self.dropout_rate = dropout_rate - self.multihead_layer = MultiHeadAttention(head_num=attention_heads, - return_attention=False, - attention_activation=attention_activation, - history_only=False, - trainable=trainable, - name=f'{self.name}-MultiHeadSelfAttention') - if dropout_rate > 0.0: - self.attn_dropout_layer = L.Dropout(rate=dropout_rate, name=f'{self.name}-MultiHeadSelfAttention-Dropout') - self.attn_residual_layer = L.Add(name=f'{self.name}-MultiHeadSelfAttention-Add') - self.attn_layer_norm = LayerNorm(name=f'{self.name}-MultiHeadSelfAttention-Norm', trainable=trainable) - self.ffn_layer = FeedForward(hidden_dim, - activation=feed_forward_activation, - name=f'{self.name}-FeedForward') - if dropout_rate > 0.0: - self.ffn_dropout_layer = L.Dropout(rate=dropout_rate, name=f'{self.name}-FeedForward-Dropout') - self.ffn_residual_layer = L.Add(name=f'{self.name}-FeedForward-Add') - self.ffn_layer_norm = LayerNorm(name=f'{self.name}-FeedForward-Norm', trainable=trainable) - - def __call__(self, inputs: Tensors) -> Tensors: - attn_output = self.multihead_layer(inputs) - if self.dropout_rate > 0.0: - attn_output = self.attn_dropout_layer(attn_output) - if isinstance(inputs, list): - inputs = inputs[0] - attn_output = self.attn_residual_layer([inputs, attn_output]) - attn_output = self.attn_layer_norm(attn_output) - - ffn_output = self.ffn_layer(attn_output) - if self.dropout_rate > 0.0: - ffn_output = self.ffn_dropout_layer(ffn_output) - ffn_output = self.ffn_residual_layer([attn_output, ffn_output]) - ffn_output = self.ffn_layer_norm(ffn_output) - - return ffn_output - - -class TransformerEncoderBlock: - def __init__(self, - blocks: int, - attention_heads: int, - hidden_dim: int, - attention_activation: Optional[Union[str, Activation]] = None, - feed_forward_activation: Optional[Union[str, Activation]] = gelu, - dropout_rate: float = 0.0, - trainable: Optional[bool] = False, - name: Optional[str] = 'TransformerEncoderBlock', - share_weights: bool = False): - if share_weights: - encoder = TransformerEncoder(attention_heads, - hidden_dim, - attention_activation=attention_activation, - feed_forward_activation=feed_forward_activation, - dropout_rate=dropout_rate, - trainable=trainable, - name=name) - self.encoders = [encoder for _ in range(blocks)] - else: - self.encoders = [ - TransformerEncoder(attention_heads, - hidden_dim, - attention_activation=attention_activation, - feed_forward_activation=feed_forward_activation, - dropout_rate=dropout_rate, - trainable=trainable, - name=f'{name}-{i+1}') - for i in range(blocks) - ] - - def __call__(self, inputs: Tensors) -> Tensors: - output = inputs - for encoder in self.encoders: - output = encoder(output) - return output diff --git a/langml/langml/transformer/layers.py b/langml/langml/transformer/layers.py deleted file mode 100644 index 96c8b1f..0000000 --- a/langml/langml/transformer/layers.py +++ /dev/null @@ -1,241 +0,0 @@ -# -*- coding: utf-8 -*- - -""" Yet another transformer implementation. -""" - -# TODO: Transformer Decoder - -import math -from typing import Optional, List, Union, Any - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.backend as K - import tensorflow.keras.layers as L -else: - import keras - import keras.backend as K - import keras.layers as L - -from langml.tensor_typing import Number, Tensors, Activation, Initializer, Constraint, Regularizer - - -def gelu(x: Number) -> Number: - r""" Gaussian Error Linear Units (GELUs) - https://arxiv.org/abs/1606.08415 - - $GELU(x) = 0.5x(1 + tanh[\sqrt(2 / \Pi) (x + 0.044715x^3)])$ - """ - - return 0.5 * x * (1.0 + K.tanh(math.sqrt(2.0 / math.pi) * (x + 0.044715 * x**3))) - - -class FeedForward(L.Layer): - """ Feed Forward Layer - https://arxiv.org/pdf/1706.03762.pdf - """ - def __init__(self, - units, - activation: Optional[Activation] = 'relu', - kernel_initializer: Optional[Initializer] = 'glorot_normal', - kernel_regularizer: Optional[Regularizer] = None, - kernel_constraint: Optional[Constraint] = None, - bias_initializer: Optional[Initializer] = 'zeros', - bias_regularizer: Optional[Regularizer] = None, - bias_constraint: Optional[Constraint] = None, - use_bias: Optional[bool] = True, - dropout_rate: float = 0.0, - **kwargs): - super(FeedForward, self).__init__(**kwargs) - self.supports_masking = True - self.units = units - self.activation = keras.activations.get(activation) - self.kernel_initializer = keras.initializers.get(kernel_initializer) - self.kernel_regularizer = keras.regularizers.get(kernel_regularizer) - self.kernel_constraint = keras.constraints.get(kernel_constraint) - self.bias_initializer = keras.initializers.get(bias_initializer) - self.bias_regularizer = keras.regularizers.get(bias_regularizer) - self.bias_constraint = keras.constraints.get(bias_constraint) - self.use_bias = use_bias - self.dropout_rate = dropout_rate - - def get_config(self) -> dict: - config = { - "units": self.units, - "activation": keras.activations.serialize(self.activation), - "kernel_initializer": keras.initializers.serialize(self.kernel_initializer), - "kernel_regularizer": keras.regularizers.serialize(self.kernel_regularizer), - "kernel_constraint": keras.constraints.serialize(self.kernel_constraint), - "bias_initializer": keras.initializers.serialize(self.bias_initializer), - "bias_regularizer": keras.regularizers.serialize(self.bias_regularizer), - "bias_constraint": keras.constraints.serialize(self.bias_constraint), - "use_bias": self.use_bias, - "dropout_rate": self.dropout_rate - } - base_config = super(FeedForward, self).get_config() - - return dict(base_config, **config) - - def build(self, input_shape: Tensors): - feature_dim = int(input_shape[-1]) - self.W1 = self.add_weight( - shape=(feature_dim, self.units), - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint, - name=f'{self.name}_W1', - ) - self.W2 = self.add_weight( - shape=(self.units, feature_dim), - initializer=self.kernel_initializer, - regularizer=self.kernel_regularizer, - constraint=self.kernel_constraint, - name='{}_W2'.format(self.name), - ) - if self.use_bias: - self.b1 = self.add_weight( - shape=(self.units,), - initializer=self.bias_initializer, - regularizer=self.bias_regularizer, - constraint=self.bias_constraint, - name='{}_b1'.format(self.name), - ) - self.b2 = self.add_weight( - shape=(feature_dim,), - initializer=self.bias_initializer, - regularizer=self.bias_regularizer, - constraint=self.bias_constraint, - name='{}_b2'.format(self.name), - ) - if self.dropout_rate > 0.0: - self.dropout_layer = L.Dropout(self.dropout_rate) - super(FeedForward, self).build(input_shape) - - def call(self, - inputs: Tensors, - mask: Optional[Tensors] = None, - training: Optional[Any] = None, - **kwargs) -> Union[List[Tensors], Tensors]: - hidden = K.dot(inputs, self.W1) - if self.use_bias: - hidden = K.bias_add(hidden, self.b1) - if self.activation is not None: - hidden = self.activation(hidden) - if self.dropout_rate > 0.0: - hidden = self.dropout_layer(hidden) - output = K.dot(hidden, self.W2) - if self.use_bias: - output = K.bias_add(output, self.b2) - return output - - def compute_mask(self, - inputs: Tensors, - mask: Optional[Union[Tensors, List[Tensors]]] = None) -> Union[ - List[Union[Tensors, None]], Tensors]: - return mask - - @staticmethod - def get_custom_objects() -> dict: - return {'FeedForward': FeedForward} - - def compute_output_shape(self, input_shape: Tensors) -> Tensors: - return input_shape - - -class SineCosinePositionEmbedding(L.Layer): - """Sine Cosine Position Embedding. - https://arxiv.org/pdf/1706.03762 - """ - - def __init__(self, - mode: Optional[str] = 'add', - output_dim: Optional[int] = None, - **kwargs): - """ - # mode: - expand - # Input shape - 2D tensor with shape: `(batch_size, sequence_length)`. - # Output shape - 3D tensor with shape: `(batch_size, sequence_length, output_dim)`. - add - # Input shape - 3D tensor with shape: `(batch_size, sequence_length, feature_dim)`. - # Output shape - 3D tensor with shape: `(batch_size, sequence_length, feature_dim)`. - concat - # Input shape - 3D tensor with shape: `(batch_size, sequence_length, feature_dim)`. - # Output shape - 3D tensor with shape: `(batch_size, sequence_length, feature_dim + output_dim)`. - """ - self.supports_masking = True - assert mode in ['expand', 'add', 'concat'], f'not support mode `{mode}`, options: expand | add | concat' - if mode in ['expand', 'concat']: - if output_dim is None: - raise NotImplementedError(f'`output_dim` is required in `{mode}` mode') - if output_dim % 2 != 0: - raise NotImplementedError(f'Not support an odd output dimension: {output_dim}') - self.mode = mode - self.output_dim = output_dim - super(SineCosinePositionEmbedding, self).__init__(**kwargs) - - def get_config(self): - config = { - 'mode': self.mode, - 'output_dim': self.output_dim, - } - base_config = super(SineCosinePositionEmbedding, self).get_config() - - return dict(base_config, **config) - - @staticmethod - def get_custom_objects() -> dict: - return {'SineCosinePositionEmbedding': SineCosinePositionEmbedding} - - def compute_mask(self, inputs: Tensors, mask: Optional[Tensors] = None) -> Union[Tensors, None]: - return mask - - def compute_output_shape(self, input_shape: Tensors) -> Tensors: - if self.mode == 'expand': - return input_shape + (self.output_dim,) - if self.mode == 'concat': - return input_shape[:-1] + (input_shape[-1] + self.output_dim,) - return input_shape - - def call(self, inputs: Tensors, mask: Optional[Tensors] = None, **kwargs) -> Tensors: - input_shape = K.shape(inputs) - batch_size, seq_len = input_shape[0], input_shape[1] - output_dim = input_shape[2] if self.mode == 'add' else self.output_dim - if self.model in ['add', 'concat']: - pos_input = K.tile(K.expand_dims(K.arange(0, seq_len), axis=0), [batch_size, 1]) - else: - pos_input = inputs - pos_input = K.cast(pos_input, K.floatx()) - evens = K.arange(0, output_dim // 2) * 2 - odds = K.arange(0, output_dim // 2) * 2 + 1 - sim_embed = K.sin( - K.dot( - K.expand_dims(pos_input, -1), - K.expand_dims(1.0 / K.pow( - 10000.0, - K.cast(evens, K.floatx()) / K.cast(output_dim, K.floatx()) - ), 0) - ) - ) - cos_embed = K.cos( - K.dot( - K.expand_dims(pos_input, -1), - K.expand_dims(1.0 / K.pow( - 10000.0, K.cast((odds - 1), K.floatx()) / K.cast(output_dim, K.floatx()) - ), 0) - ) - ) - embed = K.stack([sim_embed, cos_embed], axis=-1) - output = K.reshape(embed, [-1, seq_len, output_dim]) - if self.mode == 'add': - output += inputs - elif self.mode == 'concat': - output = K.concatenate([inputs, output], axis=-1) - return output diff --git a/langml/langml/utils.py b/langml/langml/utils.py deleted file mode 100644 index 964fb91..0000000 --- a/langml/langml/utils.py +++ /dev/null @@ -1,104 +0,0 @@ -# -*- coding: utf-8 -*- - -import functools -from typing import List, Optional, Tuple - -import jieba - -from langml.log import warn - - -def deprecated_warning(msg='this function is deprecated! it might be removed in a future version.'): - def decorator(func): - @functools.wraps(func) - def wrapper(*args, **kwargs): - warn(msg) - return func(*args, **kwargs) - return wrapper - return decorator - - -def modify_boundary(target: str, content: str, expand_range: Optional[int] = 3) -> str: - """ - 分词修正目标字符串的边界 - Args: - target: 目标字符串 - content: 目标字符串所在的文本 - expand_range: 目标字符串在文本所在位置，向前后扩展的字数，无需太大，因为一个中文词语大约1到4字 - Returns: 使用分词修正目标字符串边界后的字符串 - """ - - begin = content.find(target) - - # 在content中无法找到target - if begin == -1: - return target - - # 目标字符串在文本所在位置，向前后扩展的上下文 - context = content[begin - expand_range if begin >= expand_range else 0: begin + len(target) + expand_range] - - # 目标字符串在上下文的起始终止位置 - context_start = expand_range if begin >= expand_range else begin - context_end = context_start + len(target) - - seg_list = list(jieba.cut(context, cut_all=False)) - - # 找出target头尾在分词列表的位置 - seg_sum = 0 - seg_start, seg_end = None, None - for seg_id, seg in enumerate(seg_list): - seg_range = [x + seg_sum for x in range(len(seg))] - seg_sum += len(seg) - if context_start in seg_range: - seg_start = seg_id - if context_end in seg_range: - # 如果原始结尾在分词的区间第一个位置, 那么这个分词区间不需要；# 否则需要这个区间 - if context_end == seg_range[0]: - seg_end = seg_id - else: - seg_end = seg_id + 1 - - if seg_start and seg_end: - new_target = "".join(seg_list[seg_start: seg_end]) - else: - new_target = target - - return new_target - - -@deprecated_warning(msg='`rematch` is deprecated, it might be removed in a future version! ' - 'please turn to `Tokenizer.tokens_mapping`.') -def rematch(offsets: List) -> List: - mapping = [] - for offset in offsets: - if offset[0] == 0 and offset[1] == 0: - mapping.append([]) - else: - mapping.append([i for i in range(offset[0], offset[1])]) - return mapping - - -def bio_decode(tags: List[str]) -> List[Tuple[int, int, str]]: - """ Decode BIO tags - - Examples: - >>> bio_decode(['B-PER', 'I-PER', 'O', 'B-ORG', 'I-ORG', 'I-ORG']) - >>> [(0, 1, 'PER'), (3, 5, 'ORG')] - """ - entities = [] - start_tag = None - for i, tag in enumerate(tags): - tag_capital = tag.split('-')[0] - tag_name = tag.split('-')[1] if tag != 'O' else '' - if tag_capital in ['B', 'O']: - if start_tag is not None: - entities.append((start_tag[0], i - 1, start_tag[1])) - start_tag = None - if tag_capital == 'B': - start_tag = (i, tag_name) - elif tag_capital == 'I' and start_tag is not None and start_tag[1] != tag_name: - entities.append((start_tag[0], i, start_tag[1])) - start_tag = None - if start_tag is not None: - entities.append((start_tag[0], i, start_tag[1])) - return entities diff --git a/langml/requirements.txt b/langml/requirements.txt deleted file mode 100644 index 7b41db7..0000000 --- a/langml/requirements.txt +++ /dev/null @@ -1,11 +0,0 @@ -typeguard -numpy -Keras>=2.3.1 -tensorflow -jieba -click -boltons -tokenizers -sklearn -seqeval -sentencepiece diff --git a/langml/setup.cfg b/langml/setup.cfg deleted file mode 100644 index 1a4478d..0000000 --- a/langml/setup.cfg +++ /dev/null @@ -1,3 +0,0 @@ -[flake8] -exclude = .svn,CVS,.bzr,.hg,.git,__pycache,.ropeproject,__init__.py,docs,.eggs,setup.py,build,tests,third_party -max-line-length = 120 \ No newline at end of file diff --git a/langml/setup.py b/langml/setup.py deleted file mode 100644 index 77c30e6..0000000 --- a/langml/setup.py +++ /dev/null @@ -1,48 +0,0 @@ -# -*- coding: utf-8 -*- - -from setuptools import setup, find_packages - - -__version__ = '0.6.3' - - -long_description = open('README.md', encoding='utf-8').read() - -with open('requirements.txt', encoding='utf-8') as f: - requirements = [l for l in f.read().splitlines() if l] - -with open('dev-requirements.txt', encoding='utf-8') as f: - test_requirements = [l for l in f.read().splitlines() if l][1:] - -setup( - name='langml', - version=__version__, - description='keras language machine learning toolkit.', - long_description=long_description, - author='niming.lxm', - author_email='niming.lxm@alipay.com', - platforms=['all'], - url='', - packages=find_packages(exclude=('tests', 'tests.*')), - classifiers=[ - 'Intended Audience :: Developers', - 'Operating System :: OS Independent', - 'License :: OSI Approved :: MIT License', - 'Programming Language :: Python', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.6', - 'Programming Language :: Python :: 3.7', - 'Programming Language :: Python :: 3.8', - 'Topic :: Text Processing', - 'Topic :: Text Processing :: Indexing', - 'Topic :: Text Processing :: Linguistic', - ], - entry_points={ - 'console_scripts': [ - 'langml-cli = langml.cli:main', - ], - }, - install_requires=requirements, - tests_require=test_requirements, - include_package_data=True, -) diff --git a/langml/tests/data/albert_vocab/30k-clean.model b/langml/tests/data/albert_vocab/30k-clean.model deleted file mode 100644 index c91b8acfa56ccfc80e1cdd854ddcaf9b6c44ab2a..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 760289 zcmZUcd7RhN_s5k&L$Y)#Q6X7UnrY9TB`SONgwHlJpPA3r=l%K2G+9#FLzbju2}KB5 z$`(rYuO&r<5?PW$2?_Z<&pq$Y9goNF^VdDE&t1+v_uTE=bML!s=d$L9=OXbVWgC9& zwk=b-n5hlvrb-V#_M~AW{`3FvVaK25=~iXi4L^C{u%R9(D{FF~MQ&C`_@MuU_x(?} z`+vfRl)^ieZTs~Vx0R?J%bLFWK#9=5#?3BKWl)pHO8a&@EhuYQwjJR@bwc9fj$2T+ zLs^qmS-d* zyS00z^k;P>E-fc`vLRcP^H)HUNnC#ZY(XhiG$B_vUFh&b<-!54mWr)U4bbd2dY)s4l4WVWUN|(=K-uSsv~s?xpJ@f90w6;dKn@62YqXhE3UJD zj*@F85VA^CC)3hx;rlL0LcZK)iT{I!KBOa1Ko z12jZ^8Ut8O2uXp^&)>m z()ek)qwS}qoTvw_6`ctXcGdcm_#^qH)5&aNz_PiJ9v 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-lehr -13.1069 -▁prentice -13.1071 -▁procedural -13.1071 -▁separatist -13.1071 -▁consecration -13.1071 -▁josiah -13.1071 -▁childish -13.1071 -▁hobbs -13.1072 -▁gustaf -13.1072 -▁soaring -13.1072 -▁messaging -13.1072 -creator -13.1073 -▁rudi -13.1073 -▁rebelled -13.1075 -▁sixties -13.1077 -71% -13.1078 -▁yearbook -13.108 -▁tink -13.108 -533 -13.1081 -▁accomplishment -13.1081 -ophyllum -13.1083 -▁dumont -13.1085 -▁wilbur -13.1087 -▁michal -13.1087 -▁harpsichord -13.1088 -▁olympus -13.1088 -▁plausible -13.1088 -▁provocative -13.1088 -▁fidget -13.1088 -▁silo -13.1089 -444 -13.109 -▁200001 -13.1091 -358 -13.1092 -▁etching -13.1092 -▁insulting -13.1093 -▁umm -13.1099 -387 -13.11 -▁magdalena -13.1101 -▁confuse -13.1102 -aditya -13.1102 -basic -13.1103 -▁hamad -13.1103 -receptor -13.1104 -commerce -13.1105 -▁claudius -13.1105 -▁convection -13.1105 -▁dorothea -13.1105 -▁pensacola -13.1105 -▁reprimand -13.1105 -▁mitigate -13.1106 -▁musique -13.1107 -▁dashboard -13.1107 -▁marred -13.1108 -▁cerro -13.1108 -▁prest -13.1109 -▁maratha -13.1112 -▁dividend -13.1113 -▁strung -13.1117 -▁seraph -13.1118 -▁encompassed -13.1118 -▁grandmaster -13.1119 -366 -13.1119 -▁distillery -13.1123 -▁willoughby -13.1123 -▁regatta -13.1123 -▁lilith -13.1124 -▁drilled -13.1125 -▁strangle -13.1127 -ehler -13.1128 -78% -13.1129 -chandran -13.1131 -▁consequent -13.1132 -smoke -13.1137 -▁tarzan -13.114 -▁fraudulent -13.114 -▁leukemia -13.114 -▁maldives -13.114 -▁kincaid -13.114 -etsu -13.1141 -▁anatomical -13.1141 -▁bharatiya -13.1141 -▁collide -13.1141 -▁ndp -13.1143 -▁flexed -13.1144 -▁turnbull -13.1144 -▁celine -13.1144 -▁loomed -13.1144 -▁dubois -13.1145 -croatian -13.1145 -lithuanian -13.1146 -hij -13.1146 -▁delightful -13.1146 -▁sensational -13.1146 -▁firth -13.1148 -▁highlanders -13.1149 -▁fleck -13.1152 -▁dumping -13.1155 -▁barbed -13.1157 -▁eastbound -13.1157 -▁apprehension -13.1158 -▁hannibal -13.1158 -2,400 -13.1158 -▁somber -13.1159 -▁buddies -13.116 -▁dismissive -13.116 -▁vanishing -13.1161 -▁10:00 -13.1161 -▁sanction -13.1162 -▁spitting -13.1162 -▁fabricated -13.1163 -▁bloomfield -13.1163 -medical -13.1164 -ancient -13.1164 -▁offender -13.1165 -austria -13.1166 -empty -13.1176 -▁telepathic -13.1176 -▁respondents -13.1176 -▁opined -13.1176 -▁tnt -13.1176 -▁remington -13.1176 -bomber -13.1178 -▁sargent -13.1178 -isomer -13.118 -▁swiped -13.118 -▁madhav -13.1181 -▁ushered -13.1181 -▁sidekick -13.1183 -industrial -13.1185 -▁svet -13.1186 -▁simulated -13.1186 -34% -13.1187 -muller -13.1188 -▁sugi -13.1188 -▁compile -13.1191 -▁scrum -13.1193 -▁bratislava -13.1193 -▁iglesia -13.1193 -▁tchaikovsky -13.1193 -▁stanisaw -13.1193 -▁penultimate -13.1193 -▁affix -13.1194 -▁bonfire -13.1195 -▁haryana -13.1195 -▁topical -13.1197 -▁overlord -13.1198 -1852 -13.1198 -▁hindustan -13.1198 -431 -13.1198 -cultura -13.1198 -▁spindle -13.12 -▁courageous -13.1204 -▁circumstance -13.1204 -washington -13.1205 -▁breakout -13.1205 -368 -13.1206 -▁shetland -13.1206 -▁moldavia -13.1209 -▁diverge -13.121 -▁catalytic -13.1211 -▁philanthropic -13.1211 -▁shuffling -13.1211 -▁illicit -13.1211 -▁deux -13.1212 -▁piotr -13.1212 -▁bicker -13.1213 -wik -13.1215 -▁makar -13.1215 -▁1689 -13.1215 -▁pollard -13.1218 -▁pious -13.122 -▁poz -13.122 -329 -13.1221 -▁shul -13.1221 -magnetic -13.1224 -▁groaning -13.1224 -▁phosphor -13.1226 -▁recur -13.1226 -▁goldstein -13.1228 -▁blockbuster -13.1228 -▁derivation -13.1228 -▁flopped -13.1228 -▁unmistakable -13.1228 -▁bibliography -13.1229 -▁excitedly -13.1229 -▁gustavo -13.1231 -▁disapprove -13.1231 -▁crises -13.1231 -▁brutality -13.1232 -▁lutz -13.1235 -ocular -13.1238 -▁campsite -13.1239 -research -13.124 -college -13.1243 -408 -13.1244 -▁caption -13.1246 -▁botanic -13.1246 -▁yourselves -13.1246 -▁consultancy -13.1246 -▁lurking -13.1246 -▁glue -13.1247 -▁tahiti -13.1247 -▁aquino -13.1248 -▁6:00 -13.1249 -▁encompass -13.1251 -▁culp -13.1251 -▁invertebrates -13.1252 -▁medallist -13.1253 -▁toddler -13.1254 -378 -13.1255 -920 -13.1256 -▁patrolling -13.1258 -▁symptom -13.1258 -1856 -13.1258 -adio -13.1261 -▁assassinate -13.1261 -▁nunn -13.1261 -▁cosmopolitan -13.1264 -▁apologise -13.1264 -▁promenade -13.1264 -▁stryker -13.1264 -▁subcontinent -13.1265 -▁horsemen -13.1266 -khali -13.1269 -▁suture -13.1269 -ziel -13.1272 -57% -13.1276 -▁haig -13.1278 -cilla -13.128 -▁recite -13.1281 -▁auschwitz -13.1282 -▁fledgling -13.1282 -ophthalm -13.1282 -▁dummy -13.1282 -▁alvaro -13.1282 -▁paddington -13.1283 -▁hellenic -13.1283 -delic -13.1284 -▁canteen -13.1285 -73% -13.1286 -spirited -13.1287 -▁fortnight -13.1289 -▁lexie -13.1289 -▁meteorite -13.1289 -1851 -13.1289 -▁waldo -13.1289 -adze -13.129 -▁armagh -13.1292 -▁torches -13.1292 -hallow -13.1294 -ilius -13.1295 -▁italianate -13.1295 -untitled -13.1297 -▁consolation -13.1299 -▁disrespect -13.13 -anthi -13.1301 -▁saddam -13.1301 -▁westbound -13.1301 -▁urgently -13.1302 -▁elective -13.1304 -▁cynical -13.1309 -39% -13.131 -970 -13.1311 -▁huffed -13.1313 -▁sweetie -13.1313 -acqua -13.1313 -▁chopper -13.1314 -flash -13.1315 -▁cupping -13.1316 -▁chandelier -13.1317 -▁mercantile -13.1317 -▁saskatoon -13.1317 -▁rumbling -13.1318 -1744 -13.1318 -▁christensen -13.1318 -▁rooney -13.1319 -▁dutt -13.1319 -458 -13.132 -▁bellamy -13.1322 -village -13.1323 -▁jewell -13.1324 -▁twitching -13.1325 -▁14,000 -13.1327 -zewski -13.1329 -▁showdown -13.1329 -64% -13.133 -860 -13.1333 -1857 -13.1334 -▁greenberg -13.1334 -▁marcelo -13.1335 -▁gonzales -13.1335 -▁alligator -13.1335 -▁asthma -13.1336 -▁insistent -13.1336 -▁sloping -13.1336 -▁concordia -13.1337 -colour -13.134 -▁cesare -13.134 -▁unleash -13.134 -▁fedor -13.1341 -▁jordanian -13.1341 -soprano -13.1344 -▁saad -13.1344 -▁picard -13.1345 -▁cumbria -13.1346 -▁hesitantly -13.1346 -▁speculate -13.1347 -32% -13.1347 -provide -13.135 -thromb -13.1351 -▁burnley -13.1355 -▁blaming -13.1356 -▁permitting -13.1358 -391 -13.1359 -▁adapting -13.136 -▁phyto -13.1364 -▁angelina -13.1365 -▁potts -13.1367 -▁taku -13.1368 -▁dario -13.137 -▁cognition -13.1371 -▁precipitate -13.1371 -▁unsuitable -13.1371 -▁mennonite -13.1371 -▁ebook -13.1371 -nosed -13.1372 -▁caressing -13.1374 -▁rajput -13.1374 -▁argus -13.1375 -elberg -13.1379 -▁scud -13.1379 -438 -13.1381 -▁eyeball -13.1382 -▁cristo -13.1383 -▁padua -13.1383 -municipality -13.1385 -global -13.1386 -▁evaluating -13.1389 -▁ferreira -13.1389 -▁intimidating -13.1389 -▁mcintyre -13.1389 -▁redistribution -13.1389 -▁valkyrie -13.1389 -▁glistening -13.1389 -▁uppsala -13.1389 -▁wellesley -13.1389 -▁weasel -13.1392 -rigged -13.1394 -▁magma -13.1395 -▁husk -13.1395 -▁vitro -13.1395 -fraction -13.1399 -▁stipulated -13.1399 -▁celeb -13.14 -chicago -13.1405 -▁shimo -13.1406 -vika -13.1406 -940 -13.1407 -▁obesity -13.1407 -▁infidel -13.1407 -▁fascism -13.1408 -▁montrose -13.1408 -eanu -13.1408 -▁exchanging -13.1408 -▁sedimentary -13.1409 -▁gilded -13.1409 -symmetr -13.1409 -▁marital -13.141 -▁nonlinear -13.141 -76% -13.1411 -souza -13.1411 -▁courtier -13.1414 -▁avoidance -13.1415 -373 -13.1418 -▁williamsburg -13.1421 -▁taxonomy -13.1425 -▁precarious -13.1425 -▁fairbanks -13.1425 -▁devastation -13.1425 -▁nrhp -13.1425 -▁ceasefire -13.1425 -▁crumbling -13.1426 -▁equator -13.1428 -▁compressor -13.1429 -plicate -13.143 -▁raghu -13.1431 -▁graft -13.1432 -▁stowe -13.1433 -jala -13.1437 -▁grading -13.1438 -▁fermi -13.144 -▁wince -13.144 -▁authoritarian -13.1443 -▁magnesium -13.1443 -▁centurion -13.1443 -▁hanoi -13.1447 -freedom -13.1447 -363 -13.1448 -azam -13.1449 -▁nantes -13.1449 -▁lcd -13.1449 -▁gogh -13.1451 -▁scaling -13.1456 -princip -13.1457 -▁disneyland -13.1458 -▁facilitating -13.1461 -▁saturated -13.1461 -▁smallpox -13.1461 -▁dependency -13.1461 -▁slumber -13.1462 -▁ghent -13.1462 -▁hospitalized -13.1463 -▁bracken -13.1463 -▁vinnie -13.1464 -▁pretoria -13.1465 -▁aggressively -13.1465 -▁darby -13.1466 -▁nellie -13.1469 -62% -13.147 -1725 -13.1472 -verton -13.1472 -▁cayman -13.1473 -quadra -13.1475 -▁endorse -13.1476 -▁littered -13.1476 -▁familiarity -13.1477 -▁conical -13.1477 -▁scrambling -13.1479 -▁segregated -13.1479 -▁biplane -13.1482 -tzky -13.1482 -▁melinda -13.1484 -▁relinquish -13.1484 -▁tucking -13.1485 -1830 -13.1492 -▁cleanup -13.1494 -▁hohen -13.1495 -▁kuhn -13.1497 -▁sift -13.1497 -▁anthologies -13.1497 -▁cheerleading -13.1497 -▁noctuidae -13.1497 -▁ostensibly -13.1497 -▁vuelta -13.1497 -▁pediment -13.1497 -▁eviction -13.1497 -▁confines -13.1497 -rakh -13.1499 -▁galvan -13.1499 -▁visitation -13.1499 -▁proxim -13.1501 -▁babylonian -13.1503 -▁chiba -13.1503 -▁contractual -13.1504 -▁alber -13.1504 -instrumentalist -13.1506 -california -13.1507 -christmas -13.1507 -▁sandals -13.1511 -▁overwhelmingly -13.1511 -▁catapult -13.1515 -▁emergencies -13.1515 -▁hezbollah -13.1515 -▁retribution -13.1515 -▁siegfried -13.1516 -▁lifespan -13.1517 -▁severn -13.1517 -▁trou -13.1517 -fear -13.1518 -▁pandit -13.152 -▁prostate -13.1527 -▁marlon -13.1528 -▁thaw -13.1529 -jiao -13.1529 -▁durable -13.1529 -▁liebe -13.1532 -▁scratches -13.1533 -disqualification -13.1534 -▁casablanca -13.1534 -▁entourage -13.1534 -▁insurrection -13.1534 -▁spectroscopy -13.1534 -▁uninhabited -13.1534 -▁huffington -13.1534 -malley -13.1534 -hyster -13.1535 -▁whimpered -13.1535 -▁dealership -13.1535 -▁restrictive -13.1536 -▁clancy -13.1539 -▁chronicler -13.154 -▁fetus -13.1541 -futu -13.1544 -▁arundel -13.1546 -▁jaun -13.1547 -66% -13.1549 -3:1 -13.1551 -▁interned -13.1552 -▁mahmoud -13.1552 -▁sigismund -13.1552 -knock -13.1552 -▁indicative -13.1552 -▁diversified -13.1553 -▁linde -13.1553 -▁beginner -13.1556 -▁revisited -13.1561 -▁vedic -13.1562 -▁mouthful -13.1565 -puis -13.1568 -▁batavia -13.1568 -▁quarantine -13.157 -▁unconditional -13.157 -▁upgrading -13.157 -▁enumerat -13.157 -▁haydn -13.1571 -▁pleasing -13.1572 -1844 -13.1573 -ezza -13.1575 -▁moshe -13.1576 -▁plaid -13.1577 -▁factual -13.1577 -promise -13.1578 -▁translit -13.1579 -▁praetor -13.1579 -▁humiliated -13.158 -▁salva -13.1581 -▁scro -13.1581 -snake -13.1582 -1841 -13.1585 -feature -13.1588 -▁gershwin -13.1589 -▁introductory -13.1589 -▁mcpherson -13.1589 -▁spoof -13.1589 -▁unequal -13.1589 -▁takahashi -13.1589 -▁clarissa -13.1589 -▁chopped -13.1589 -ulatus -13.1591 -▁craftsmen -13.1592 -▁stepmother -13.1592 -▁succumb -13.1594 -▁jacobite -13.1597 -bamba -13.1597 -platte -13.16 -▁succumbed -13.1601 -push -13.1603 -llinger -13.1603 -ethnic -13.1604 -▁aeronautical -13.1604 -▁bubbling -13.1607 -▁distaste -13.1607 -▁distraught -13.1607 -▁grievance -13.1607 -▁quadruple -13.1607 -▁emphatic -13.1607 -▁schism -13.1607 -schrift -13.1607 -▁trotsky -13.1608 -▁standalone -13.1609 -▁papacy -13.1609 -dancing -13.161 -1737 -13.1611 -atomic -13.1611 -▁keynote -13.1611 -syria -13.1612 -▁kiwi -13.1613 -voll -13.1613 -▁shuddering -13.1613 -▁detour -13.1615 -72% -13.1617 -▁ripley -13.1619 -457 -13.1621 -▁bolivian -13.1622 -▁benevolent -13.1625 -▁educating -13.1625 -▁eyelashes -13.1625 -▁schizophrenia -13.1625 -▁unspecified -13.1625 -▁unmanned -13.1625 -▁bradshaw -13.1626 -▁drool -13.1627 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deleted file mode 100644 index ca4f978..0000000 --- a/langml/tests/data/wp_cn_vocab.txt +++ /dev/null @@ -1,21128 +0,0 @@ -[PAD] -[unused1] -[unused2] -[unused3] -[unused4] -[unused5] -[unused6] -[unused7] -[unused8] -[unused9] -[unused10] -[unused11] -[unused12] -[unused13] -[unused14] -[unused15] -[unused16] -[unused17] -[unused18] -[unused19] -[unused20] -[unused21] -[unused22] -[unused23] -[unused24] -[unused25] -[unused26] -[unused27] -[unused28] -[unused29] -[unused30] -[unused31] -[unused32] -[unused33] -[unused34] -[unused35] -[unused36] -[unused37] -[unused38] -[unused39] -[unused40] -[unused41] -[unused42] -[unused43] -[unused44] -[unused45] -[unused46] -[unused47] -[unused48] -[unused49] -[unused50] -[unused51] -[unused52] -[unused53] -[unused54] -[unused55] -[unused56] -[unused57] -[unused58] -[unused59] -[unused60] -[unused61] -[unused62] -[unused63] -[unused64] -[unused65] -[unused66] -[unused67] -[unused68] -[unused69] -[unused70] -[unused71] -[unused72] -[unused73] 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-ᆷ -ᆸ -ᆺ -ᆻ -ᆼ -ᗜ -ᵃ -ᵉ -ᵍ -ᵏ -ᵐ -ᵒ -ᵘ -‖ -„ -† -• -‥ -‧ -
 -‰ -′ -″ -‹ -› -※ -‿ -⁄ -ⁱ -⁺ -ⁿ -₁ -₂ -₃ -₄ -€ -℃ -№ -™ -ⅰ -ⅱ -ⅲ -ⅳ -ⅴ -← -↑ -→ -↓ -↔ -↗ -↘ -⇒ -∀ -− -∕ -∙ -√ -∞ -∟ -∠ -∣ -∥ -∩ -∮ -∶ -∼ -∽ -≈ -≒ -≡ -≤ -≥ -≦ -≧ -≪ -≫ -⊙ -⋅ -⋈ -⋯ -⌒ -① -② -③ -④ -⑤ -⑥ -⑦ -⑧ -⑨ -⑩ -⑴ -⑵ -⑶ -⑷ -⑸ -⒈ -⒉ -⒊ -⒋ -ⓒ -ⓔ -ⓘ -─ -━ -│ -┃ -┅ -┆ -┊ -┌ -└ -├ -┣ -═ -║ -╚ -╞ -╠ -╭ -╮ -╯ -╰ -╱ -╳ -▂ -▃ -▅ -▇ -█ -▉ -▋ -▌ -▍ -▎ -■ -□ -▪ -▫ -▬ -▲ -△ -▶ -► -▼ -▽ -◆ -◇ -○ -◎ -● -◕ -◠ -◢ -◤ -☀ -★ -☆ -☕ -☞ -☺ -☼ -♀ -♂ -♠ -♡ -♣ -♥ -♦ -♪ -♫ -♬ -✈ -✔ -✕ -✖ -✦ -✨ -✪ -✰ -✿ -❀ -❤ -➜ -➤ -⦿ -、 -。 -〃 -々 -〇 -〈 -〉 -《 -》 -「 -」 -『 -』 -【 -】 -〓 -〔 -〕 -〖 -〗 -〜 -〝 -〞 -ぁ -あ -ぃ -い -う -ぇ -え -お -か -き -く -け -こ -さ -し -す -せ -そ -た -ち -っ -つ -て -と -な -に -ぬ -ね -の -は -ひ -ふ -へ -ほ -ま -み -む -め -も -ゃ -や -ゅ -ゆ -ょ -よ -ら -り -る -れ -ろ -わ -を -ん -゜ -ゝ -ァ -ア -ィ -イ -ゥ -ウ -ェ -エ -ォ -オ -カ -キ -ク -ケ -コ -サ -シ -ス -セ -ソ -タ -チ -ッ -ツ -テ -ト -ナ -ニ -ヌ -ネ -ノ -ハ -ヒ -フ -ヘ -ホ -マ -ミ -ム -メ -モ -ャ -ヤ -ュ -ユ -ョ -ヨ -ラ -リ -ル -レ -ロ -ワ -ヲ -ン -ヶ -・ -ー -ヽ -ㄅ -ㄆ -ㄇ -ㄉ -ㄋ -ㄌ -ㄍ -ㄎ -ㄏ -ㄒ -ㄚ -ㄛ -ㄞ -ㄟ -ㄢ -ㄤ -ㄥ -ㄧ -ㄨ -ㆍ -㈦ -㊣ -㎡ -㗎 -一 -丁 -七 -万 -丈 -三 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-##｡ -##｢ -##｣ -##､ -##･ -##ｯ -##ｰ -##ｲ -##ｸ -##ｼ -##ｽ -##ﾄ -##ﾉ -##ﾌ -##ﾗ -##ﾙ -##ﾝ -##ﾞ -##ﾟ -##￣ -##￥ -##👍 -##🔥 -##😂 -##😎 diff --git a/langml/tests/test_attention.py b/langml/tests/test_attention.py deleted file mode 100644 index 1fc1ed5..0000000 --- a/langml/tests/test_attention.py +++ /dev/null @@ -1,93 +0,0 @@ -# -*- coding: utf-8 -*- - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras.backend as K - import tensorflow.keras.layers as L -else: - import keras.backend as K - import keras.layers as L - - -def test_self_attention_with_attn(): - from langml.layers import SelfAttention - - X = L.Input(shape=(None, 64)) - o, _ = SelfAttention(return_attention=True)(X) - assert K.int_shape(o) == K.int_shape(X) - - -def test_self_attention_without_attn(): - from langml.layers import SelfAttention - - X = L.Input(shape=(None, 64)) - o = SelfAttention(return_attention=False)(X) - assert K.int_shape(o) == K.int_shape(X) - - -def test_self_attention_with_mask(): - from langml.layers import SelfAttention - - X = L.Input(shape=(None, )) - embed = L.Embedding(64, 64)(X) - mask = L.Lambda(lambda x: K.cast(K.greater(K.expand_dims(x, 2), 0), 'float32'))(X) - o = SelfAttention(return_attention=False)(embed, mask=mask) - assert K.int_shape(o) == K.int_shape(embed) - - -def test_self_additive_attention_with_attn(): - from langml.layers import SelfAdditiveAttention - - X = L.Input(shape=(None, 64)) - o, _ = SelfAdditiveAttention(return_attention=True)(X) - assert K.int_shape(o) == K.int_shape(X) - - -def test_self_additive_attention_without_attn(): - from langml.layers import SelfAdditiveAttention - - X = L.Input(shape=(None, 64)) - o = SelfAdditiveAttention(return_attention=False)(X) - assert K.int_shape(o) == K.int_shape(X) - - -def test_self_additive_attention_with_mask(): - from langml.layers import SelfAdditiveAttention - - X = L.Input(shape=(None, )) - embed = L.Embedding(64, 64)(X) - mask = L.Lambda(lambda x: K.cast(K.greater(K.expand_dims(x, 2), 0), 'float32'))(X) - o = SelfAdditiveAttention(return_attention=False)(embed, mask=mask) - assert K.int_shape(o) == K.int_shape(embed) - - -def test_scaled_dot_product_attention(): - from langml.layers import ScaledDotProductAttention - - X = L.Input(shape=(None, 64)) - o, _ = ScaledDotProductAttention(return_attention=True)(X) - assert K.int_shape(o) == K.int_shape(X) - - -def test_scaled_dot_product_attention_without_attn(): - from langml.layers import ScaledDotProductAttention - - X = L.Input(shape=(None, 64)) - o = ScaledDotProductAttention(return_attention=False)(X) - assert K.int_shape(o) == K.int_shape(X) - - -def test_multihead_attention(): - from langml.layers import MultiHeadAttention - - X = L.Input(shape=(None, 64)) - o, _ = MultiHeadAttention(8, return_attention=True)(X) - assert K.int_shape(o) == K.int_shape(X) - - -def test_multihead_attention_without_attn(): - from langml.layers import MultiHeadAttention - - X = L.Input(shape=(None, 64)) - o = MultiHeadAttention(8, return_attention=False)(X) - assert K.int_shape(o) == K.int_shape(X) diff --git a/langml/tests/test_bert.py b/langml/tests/test_bert.py deleted file mode 100644 index 2216232..0000000 --- a/langml/tests/test_bert.py +++ /dev/null @@ -1,23 +0,0 @@ -# -*- coding: utf-8 -*- - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras.backend as K - import tensorflow.keras.layers as L -else: - import keras.backend as K - import keras.layers as L - - -def test_bert_encoder(): - from langml.plm.bert import BERT - bert = BERT( - 100, - embedding_dim=128, - transformer_blocks=2, - attention_heads=2, - intermediate_size=1000 - ) - bert.build() - model = bert() - assert K.int_shape(model.output) == (None, 512, 128) diff --git a/langml/tests/test_crf.py b/langml/tests/test_crf.py deleted file mode 100644 index c8d4a4d..0000000 --- a/langml/tests/test_crf.py +++ /dev/null @@ -1,43 +0,0 @@ -# -*- coding: utf-8 -*- - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.backend as K - import tensorflow.keras.layers as L -else: - import keras - import keras.backend as K - import keras.layers as L - - -def test_crf(): - from langml.layers import CRF - - num_labels = 10 - embedding_size = 100 - hidden_size = 128 - - X = L.Input(shape=(None, ), name='Input-X') - embed = L.Embedding(num_labels, embedding_size, mask_zero=True)(X) - encoded = L.Bidirectional(L.LSTM(hidden_size, return_sequences=True))(embed) - output = L.Dense(num_labels)(encoded) - crf = CRF(num_labels, sparse_target=True) - output = crf(output) - assert len(K.int_shape(output)) == 3 - - -def test_crf_dense_target(): - from langml.layers import CRF - - num_labels = 10 - embedding_size = 100 - hidden_size = 128 - - X = L.Input(shape=(None, ), name='Input-X') - embed = L.Embedding(num_labels, embedding_size, mask_zero=True)(X) - encoded = L.Bidirectional(L.LSTM(hidden_size, return_sequences=True))(embed) - output = L.Dense(num_labels)(encoded) - crf = CRF(num_labels, sparse_target=False) - output = crf(output) - assert len(K.int_shape(output)) == 3 diff --git a/langml/tests/test_layer_norm.py b/langml/tests/test_layer_norm.py deleted file mode 100644 index 79a45cf..0000000 --- a/langml/tests/test_layer_norm.py +++ /dev/null @@ -1,29 +0,0 @@ -# -*- coding: utf-8 -*- - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras.backend as K - import tensorflow.keras.layers as L -else: - import keras.backend as K - import keras.layers as L - -import numpy as np -import pytest - - -@pytest.mark.parametrize("test_input,expected", [ - (K.constant([[1., 2., 3.], - [4., 5., 6.]]), - np.array([[-0.46291006, 0., 0.46291006], - [-0.19738552, 0., 0.19738552]])), - (K.constant([[[1., 2., 3.]], - [[4., 5., 6.]]]), - np.array([[[-0.46291006, 0., 0.46291006]], - [[-0.19738552, 0., 0.19738552]]])) -]) -def test_layer_norm(test_input, expected): - from langml.layers import LayerNorm - - output = LayerNorm(name='layer_norm')(test_input) - assert np.allclose(expected, np.array(K.eval(output).tolist())) diff --git a/langml/tests/test_model.py b/langml/tests/test_model.py deleted file mode 100644 index b9f734b..0000000 --- a/langml/tests/test_model.py +++ /dev/null @@ -1,164 +0,0 @@ -# -*- coding: utf-8 -*- - -import shutil - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras as keras - import tensorflow.keras.backend as K - import tensorflow.keras.layers as L -else: - import keras - import keras.backend as K - import keras.layers as L - - -def test_save_load_model_single_input(): - from langml.layers import SelfAttention - from langml.model import save_frozen, load_frozen - - num_labels = 2 - embedding_size = 100 - hidden_size = 128 - - model = keras.Sequential() - model.add(L.Embedding(num_labels, embedding_size)) - model.add(L.Bidirectional(L.LSTM(hidden_size, return_sequences=True))) - model.add(SelfAttention(hidden_size, return_attention=False)) - model.add(L.Dense(num_labels, activation='softmax')) - model.compile('adam', loss='mse', metrics=['accuracy']) - - save_frozen(model, 'self_attn_frozen') - K.clear_session() - del model - - import tensorflow as tf - tf_version = int(tf.__version__.split('.')[0]) - if tf_version > 1: - model = load_frozen('self_attn_frozen') - else: - session = tf.Session(graph=tf.Graph()) - model = load_frozen('self_attn_frozen', session=session) - shutil.rmtree('self_attn_frozen') - assert model is not None - - -def test_save_load_model_multi_input(): - from langml.layers import SelfAttention - from langml.model import save_frozen, load_frozen - - in1 = L.Input(shape=(None, 16), name='input-1') - in2 = L.Input(shape=(None, 16), name='input-2') - x1, x2 = in1, in2 - o1 = SelfAttention(return_attention=False)(x1) - o2 = SelfAttention(return_attention=False)(x2) - o = L.Concatenate()([o1, o2]) - o = L.Dense(2)(o) - model = keras.Model([x1, x2], o) - model.compile('adam', loss='mse', metrics=['accuracy']) - - save_frozen(model, 'self_attn_frozen.multi_input') - K.clear_session() - del model - - import tensorflow as tf - tf_version = int(tf.__version__.split('.')[0]) - if tf_version > 1: - model = load_frozen('self_attn_frozen.multi_input') - else: - session = tf.Session(graph=tf.Graph()) - model = load_frozen('self_attn_frozen.multi_input', session=session) - shutil.rmtree('self_attn_frozen.multi_input') - assert model is not None - - -def test_save_load_model_multi_input_output(): - from langml.layers import SelfAttention - from langml.model import save_frozen, load_frozen - - in1 = L.Input(shape=(None, 16), name='input-1') - in2 = L.Input(shape=(None, 16), name='input-2') - x1, x2 = in1, in2 - o1 = SelfAttention(return_attention=False)(x1) - o2 = SelfAttention(return_attention=False)(x2) - model = keras.Model([x1, x2], [o1, o2]) - model.compile('adam', loss='mse', metrics=['accuracy']) - - save_frozen(model, 'self_attn_frozen.multi_input_output') - K.clear_session() - del model - - import tensorflow as tf - tf_version = int(tf.__version__.split('.')[0]) - if tf_version > 1: - model = load_frozen('self_attn_frozen.multi_input_output') - else: - session = tf.Session(graph=tf.Graph()) - model = load_frozen('self_attn_frozen.multi_input_output', session=session) - shutil.rmtree('self_attn_frozen.multi_input_output') - assert model is not None - - -def test_crf_save_load(): - from langml.layers import CRF - from langml.model import save_frozen, load_frozen - - num_labels = 10 - embedding_size = 100 - hidden_size = 128 - - model = keras.Sequential() - model.add(L.Embedding(num_labels, embedding_size, mask_zero=True)) - model.add(L.LSTM(hidden_size, return_sequences=True)) - model.add(L.Dense(num_labels)) - crf = CRF(num_labels, sparse_target=False) - model.add(crf) - model.summary() - model.compile('adam', loss=crf.loss, metrics=[crf.accuracy]) - - save_frozen(model, 'crf_frozen') - K.clear_session() - del model - - import tensorflow as tf - tf_version = int(tf.__version__.split('.')[0]) - if tf_version > 1: - model = load_frozen('crf_frozen') - else: - session = tf.Session(graph=tf.Graph()) - model = load_frozen('crf_frozen', session=session) - shutil.rmtree('crf_frozen') - assert model is not None - - -def test_crf_dense_target_save_load(): - from langml.layers import CRF - from langml.model import save_frozen, load_frozen - - num_labels = 10 - embedding_size = 100 - hidden_size = 128 - - model = keras.Sequential() - model.add(L.Embedding(num_labels, embedding_size, mask_zero=True)) - model.add(L.LSTM(hidden_size, return_sequences=True)) - model.add(L.Dense(num_labels)) - crf = CRF(num_labels, sparse_target=False) - model.add(crf) - model.summary() - model.compile('adam', loss=crf.loss, metrics=[crf.accuracy]) - - save_frozen(model, 'crf_frozen_dense_target') - K.clear_session() - del model - - import tensorflow as tf - tf_version = int(tf.__version__.split('.')[0]) - - if tf_version > 1: - model = load_frozen('crf_frozen_dense_target') - else: - session = tf.Session(graph=tf.Graph()) - model = load_frozen('crf_frozen_dense_target', session=session) - shutil.rmtree('crf_frozen_dense_target') - assert model is not None diff --git a/langml/tests/test_modify_boundary.py b/langml/tests/test_modify_boundary.py deleted file mode 100644 index 13dbed9..0000000 --- a/langml/tests/test_modify_boundary.py +++ /dev/null @@ -1,17 +0,0 @@ -# -*- coding: utf-8 -*- - -import pytest -from langml.utils import modify_boundary - - -@pytest.mark.parametrize( - "inputs,expected", - [ - (("动隔振平台", "题 主动隔振平台|||"), "主动隔振平台"), - (("制备型液", "|||制备型液相 1"), "制备型液相") - ] -) -def test_modify_boundary(inputs, expected): - target, content = inputs - result = modify_boundary(target, content) - assert result == expected diff --git a/langml/tests/test_tokenizer.py b/langml/tests/test_tokenizer.py deleted file mode 100644 index 191e9ce..0000000 --- a/langml/tests/test_tokenizer.py +++ /dev/null @@ -1,643 +0,0 @@ -# -*- coding: utf-8 -*- - -import os -import pytest - -from langml.tokenizer import Encoding, SPTokenizer, WPTokenizer - - -data_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'data') - - -@pytest.mark.parametrize( - "left_text,right_text", - [ - ( - 'I like apples', None, - ), - ( - '我喜欢吃苹果', None - ), - ( - 'I like apples', '我喜欢吃苹果' - ), - ( - '你好呀\n👋', 'hello world!!!\n👋' - ), - ( - "My mom always said life was like a box of chocolates. You never know what you're gonna get.", None - ), - ( - '''This line irked me. It is from the movie ‘Forrest Gump’ where the protagonist, played by Tom Hanks, quotes his mother:\nForrest Gump: My momma always said, “Life was like a box of chocolates. You never know what you’re gonna get.”\nYet, you do know, don’t you? You are going to get a box of chocolates. Perhaps you won’t know the taste of the chocolate specifically, perhaps one is more minty, the other might have that little coffee-taste of sorts, but chocolates nonetheless.\nI thought about it yesterday in the shower and figured, sure, if you compare it like that everyone gets a box of chocolates. Some boxes are bigger, some are smaller. Some chocolate boxes probably only have one piece of chocolate in it if you are lucky, and women probably have 70% of the chocolate inside.\nUltimately though, you know what you are going to get: chocolate. I admit that if you are getting a box of chocolates gifted to you, yes, you will not know what is inside. Life is a gift after all, so it makes sense. Yet life in itself is not mysterious as such, it is portrayed as quite simple. We wander around looking for meaning behind the chocolate. What does the chocolate mean? What if my chocolate is bitter? I don’t like the dark chocolate, I like it a little lighter, or white, or with caramel, or nuts. What if I don’t like the chocolate?\nI was bothered by the idea that it could be so simple, yet I figure in the grand scheme of things, yes, life is like a box of chocolates. It is that simple. It appears mysterious, that box, on the outside. Who knows what kinds of chocolates are inside? Then you open it and see the forms and shapes of the various chocolates, but you don’t know how it tastes. Take a bite and find out! The mystery unravels itself in time, as you are living.\nThat’s life, taking a bite of that unknown piece of chocolate, but more than than simply taking a bite out of the chocolate and seeing how it tastes, but appreciating that taste. Not judging it for not being the taste you expected, or disappointed that it wasn’t as tasty as it looked. Nor, should it be the case, being sad it only has 5 pieces instead of 10, or that the box isn’t as big as you’d hoped. Enjoy the piece of chocolate, every piece, and don’t leave a single piece behind.\nLife is like a box of chocolates, so enjoy every bite.''', None # NOQA - ), - ( - '''马尚龙 | 上海每一个区，各由一字来微缩 原创 马尚龙 大上海小龙弄\n\n一晃已经是7年前的事情——人一生通常也就是晃十几晃。 🈳️ \n 就是这么一晃之间，许多人和事晃没了，许多人和事晃出来了。比如，晃着晃着，闸北区没了；晃着晃着，临港新片区有了。\n\n承蒙上海航空传媒的抬爱，我担任了《上海魅力》这本书的主编。此书汇集了上海十七个区县的人文历史旅游地貌——2014年崇明尚未撤县，闸北仍是一区。当时我们给自己挖了一个坑，在每一个区县的图文组合之首，做一个“一字解读”，既要点睛，又要有趣和个性化，不求面面俱到，但求冰山一角，而后再用极短的文字解读这一个字。\n\n创意很好。只是所有的创意都是坑，坑挖好了，谁进这个坑呢？只有让“主编大人”跳进去吧。\n\n\n\n\n封面图片说明：伍蠡甫画，1964年 上海炼油厂一角\n\n\n于是我就写下了《上海魅力》十七个“上海之……”。为十七个区县创作浓缩版精华画作的是著名画家戴红倩。我和他的愉快合作始于《上海制造》。\n\n十七个区县的“上海之……”，纯属我个人的“瞎七搭八”，说不上历史依据，更没有官方意图。在当时的《上海航空》先期刊登后，竟也有人看到，还和我讨论。有说我写得妙的，也有不同意的，比如杨浦区“上海之重”，是否应该写成“上海之学”——大学多文化氛围浓。不过我就一意孤行，一个人“一”到底了。本身就是随意为之，一讨论一征求意见，好像是革命样板戏重点题材的创作套路了。\n\n松江：上海之根\n\n\n\n如果说泰晤士小镇是松江的时尚，如果说大学城是松江的心胸，那么广富林是上海的骄傲，当广富林被注水重现上海之初时，在水一方是上海的根。\n\n静安：上海之秀\n\n\n\n静是态度，安是祈福；最袖珍的公园里有最秀雅的梧桐，最拥挤的人群，无疑也是最秀慧的人文景观，一年一度的上海书展，一再创造着中国图书展览的纪录。\n\n嘉定：上海之速\n\n\n\n最极速的是F1，却不完全是，还有中国第一条高速公路沪嘉高速，还有第一个中德合资的汽车品牌大众桑塔纳，更有以此带来的嘉定速度。\n\n杨浦：上海之重\n\n\n\n重可以是工业，上海的重工业基地再次舍我其谁？重也可以是学业，上海的重点大学在此联袂；重还可以是历史，诸多重要的事件在此展开。\n\n金山：上海之焰\n\n\n\n海上升明月是唐诗的意境，海上升焰火是金山的节日，渔村的味道，啤酒节的喧闹，音乐节的声道……可以闲步，可以闲聊，可以闲雅。\n\n虹口：上海之灵\n\n\n\n民族的灵魂在这里发出呐喊，文坛大师的灵感至今回荡；这里传递着从邮政总局发出的信函，从甜爱路散发的爱情传说。\n\n崇明：上海之绿\n\n\n\n曾经的荒芜化为全上海规模最大的生态湿地，曾经的角落化为全上海空气最清新的天然氧吧，曾经的朝发夕至化为桥隧一路通。\n\n长宁：上海之虹\n\n\n\n冥冥中的命名，带来了冥冥中的使命，虹桥，是上海最早通向全世界的天虹之桥，也是通向宁静、通向文明、通向富裕的长虹之桥。\n\n徐汇：上海之影\n\n\n\n这里，有上海电影制片厂留下的光影；有藏书楼留下的文化的背影；有土山湾留下的历史的倒影；有徐家汇商圈留下的熙攘人群的斜影。\n\n宝山：上海之水\n\n\n\n东海、长江和黄浦江三水合一，是著名的“三夹水”，三夹水就在宝山的吴淞口回旋；三水在此汇合，却是以自己的颜色而区分彼此。海纳百川有了一个绝好的注释。如今的邮轮恰是被三夹水涌向远方。\n\n普陀：上海之玉\n\n\n\n玉佛寺外两条路的路名，不经意间体现了最高的禅意——南北向的江宁路，东西向的安远路——宁静而致远；或许，也是玉的境界吧。其实这又何尝不是上海人的生活向往？\n\n青浦：上海之泽\n\n\n\n洋洋大观淀山湖，小桥流水朱家角，尤其是引以为荣的崧泽文化，宏伟的篇幅可以展示，传奇的故事可以流传，浪漫的风情可以演绎——就因为是鱼米之乡和人文故地。奥特莱斯，福寿园，乃至扎肉大米，迥异而声名远扬。\n\n闸北：上海之魔\n\n\n\n曾经有过经典魔术，将两次淞沪战争时期被日军炸毁的火车站迅速修复；曾经有过古彩魔术，把滚地龙变成了新工房；曾经有过时尚魔术，马戏城成为上海旅游的重头戏；还将拉开未来魔术，闸北区融入了静安区。\n\n闵行：上海之宝\n\n\n\n闵行曾经离上海市中心极其遥远，在闵行上班的工人都不能每天回家，如今闵行本身就是居民云集。七宝，曾经久闻七宝大曲，却无法从容来去，如今的七宝古镇，也无法从容来去，只因游人为七宝而去。\n\n奉贤：上海之湾\n\n\n\n海湾是自然的地理态势，蓝天碧海，一览无余；海湾也是人工的创造，碧海金沙，是观海，也是嬉沙。海湾更是人文的港湾，“奉贤”二字，便是告知天下，社会贤达，在这一个港湾的地位。\n\n黄浦：上海之曦\n\n\n\n如果说，曦是每一天阳光之第一，那么，南京路就是第一，国际饭店就是第一，淮海路就是第一，中国人自己创办的自来水厂就是第一……许多历史深远的政治活动仍是第一，比如，一大会址。\n\n浦东：上海之珠\n\n\n\n最初，东方明珠只是一座电视塔，后来，它成为了旅游热地，再后来它已经不再是电视塔，不再是最高，但是它成为了浦东的同名词，成为了浦东的“代言人”，浦东就是东方之珠。\n\n\n\n《上海魅力》出版后，新的创意诞生，那就是后来的《爱上海》明信片珍藏版，仅600册，且由戴敦邦大师题“爱上海”。明信片出版时，崇明撤县改区，闸北并入静安，再没有十七个区县之说，所以《爱上海》是真正的绝版了。\n\n轮不到我画龙点睛，也挨不着我画蛇添足，我只是“大上海小龙弄”一番而已，浪花也不起的。\n\n本次公号文字太少了，就贴两段有关上海的文字吧。这是我写于2009年《海派格调》的段落——\n\n有很多事情，是否发生在上海就会有不同的结果；其他地域当然也会有类似的现象，但是不至于像上海一样，这样的事情会很多，效果会很强烈。尤其是当这样的事情很小，本不应该会有强烈的反响，在其他地域甚至就不会成为一件事情，在上海却能成为一个家喻户晓的新闻事件。一座桥的修整，一座烟囱的保留，可以反反复复成为饭桌上的谈资。\n\n\n\n上海的外白渡桥要做迁移式大修，原本这就是一个市政工程，苏州河上所有的桥都经历了修整，当然外白渡桥享受的是最特殊的待遇，迁移大修，而非拆旧造新。这一项市政工程从发布消息直至正式迁移，一直是上海文化界的热议话题，诸如地标式的意义，旧上海的门户，“华人与狗不得入内”的见证……最有意思的是，时尚界始终将它作为一个时尚事件，围绕外白渡桥的大修，在外白渡桥上举行一系列时尚活动，影星章子怡就在外白渡桥上拍过一组照片。还有人发散性思维地创意：将外白渡桥上拆下来的废旧铆钉嵌在水晶玻璃内，一定是很有意义的纪念品和装饰品，后来果然有了这份镶嵌了废铜烂铁的纪念品。是上海这座城市的意义，决定了一座老桥的地位。\n\n\n\n同样的意义也决定了一座烟囱的保留。在非常注重环境保护的上海市中心徐家汇绿地，不仅保留了小洋房，而且还保留了工业社会象征的烟囱。几十年前，这一座黑烟滚滚的烟囱是伟大的象征，小学生将它写进作文里，画进素描里；几十年后，它静静地矗立在绿地中，依然非常雄性，与他匹配的不再是粗犷的工厂，而是柔情的时尚，小孩子们在烟囱底下游戏拍照，“听妈妈讲过去的故事”。\n\n只有在烟囱已经属于“过去的故事”的时候，它的属性才会由工业演变为文化和时尚；假如这一个城市依然还饱受林立的烟囱和滚滚的黑烟，那么任何一座烟囱都是罪恶。是上海这一座城市的意义，改变了这一座烟囱的是非。\n\n马尚龙\n\n中国作家协会会员，上海作家协会理事、散文报告文学专业创作委员会副主任；编审\n\n民进上海市委出版传媒委员会副主任\n\n上海黄浦区明复图书馆理事长\n\n上海评弹团艺委会顾问\n\n著作主要分为三个系列，分别是《幽默应笑我》《与名人同窗》等杂文系列，《上海制造》《为什么是上海》《上海分寸》《上海女人》《上海路数》等上海系列，《卷手语》《有些意思你从来不懂》等随笔系列 。\n\n\n\n我的新书《上海分寸》，上海书店出版社2021年1月出版。\n\n欢迎关注我的微信公众号“大上海小龙弄”——\n\n原标题：《马尚龙 | 上海每一个区，各由一字来微缩》''', None # NOQA - ) - ] -) -def test_wordpiece_encode(left_text, right_text): - # lowercase=True - tokenizer = WPTokenizer(vocab_path=os.path.join(data_dir, 'wp_cn_vocab.txt'), lowercase=True) - raw_tokenizer = tokenizer.raw_tokenizer() - ret1 = tokenizer.encode(left_text, right_text, return_array=False) - ret2 = raw_tokenizer.encode(left_text, right_text) - - assert ret1.ids == ret2.ids - assert ret1.segment_ids == ret2.type_ids - assert ret1.tokens == ret2.tokens - - # lowercase=False - tokenizer = WPTokenizer(vocab_path=os.path.join(data_dir, 'wp_cn_vocab.txt'), lowercase=False) - raw_tokenizer = tokenizer.raw_tokenizer() - ret1 = tokenizer.encode(left_text, right_text, return_array=False) - ret2 = raw_tokenizer.encode(left_text, right_text) - - assert ret1.ids == ret2.ids - assert ret1.segment_ids == ret2.type_ids - assert ret1.tokens == ret2.tokens - - -@pytest.mark.parametrize( - 'inputs,padding_strategy,expected', - [ - ( - [('I like apples', '我喜欢吃苹果'), ('hello world!', '你好世界👋')], - 'post', - Encoding( - ids=[[101, 151, 8993, 8350, 8118, 102, 2769, 1599, 3614, 1391, 5741, 3362, 102], - [101, 8701, 8572, 106, 102, 872, 1962, 686, 4518, 100, 102, 0, 0]], - segment_ids=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0]], - tokens=[['[CLS]', 'i', 'like', 'apple', '##s', '[SEP]', '我', '喜', '欢', '吃', '苹', '果', '[SEP]'], - ['[CLS]', 'hello', 'world', '!', '[SEP]', '你', '好', '世', '界', '[UNK]', '[SEP]', '[PAD]', '[PAD]']] - ), - ), ( - [('I like apples', '我喜欢吃苹果'), ('hello world!', '你好世界👋')], - 'pre', - Encoding( - ids=[[101, 151, 8993, 8350, 8118, 102, 2769, 1599, 3614, 1391, 5741, 3362, 102], - [0, 0, 101, 8701, 8572, 106, 102, 872, 1962, 686, 4518, 100, 102]], - segment_ids=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]], - tokens=[['[CLS]', 'i', 'like', 'apple', '##s', '[SEP]', '我', '喜', '欢', '吃', '苹', '果', '[SEP]'], - ['[PAD]', '[PAD]', '[CLS]', 'hello', 'world', '!', '[SEP]', '你', '好', '世', '界', '[UNK]', '[SEP]']] - ) - ) - ] -) -def test_wordpiece_batch_encode_padding(inputs, padding_strategy, expected): - tokenizer = WPTokenizer(vocab_path=os.path.join(data_dir, 'wp_cn_vocab.txt'), lowercase=True) - pred = tokenizer.encode_batch(inputs, padding=True, padding_strategy=padding_strategy, return_array=False) - assert pred.ids == expected.ids - assert pred.segment_ids == expected.segment_ids - assert pred.tokens == expected.tokens - - -@pytest.mark.parametrize( - 'inputs,padding_strategy,expected', - [ - ( - ['I like apples', '我喜欢吃苹果', 'hello world!', '你好世界👋'], - 'post', - Encoding( - ids=[[101, 151, 8993, 8350, 8118, 102, 0, 0], - [101, 2769, 1599, 3614, 1391, 5741, 3362, 102], - [101, 8701, 8572, 106, 102, 0, 0, 0], - [101, 872, 1962, 686, 4518, 100, 102, 0]], - segment_ids=[[0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], - tokens=[['[CLS]', 'i', 'like', 'apple', '##s', '[SEP]', '[PAD]', '[PAD]'], - ['[CLS]', '我', '喜', '欢', '吃', '苹', '果', '[SEP]'], - ['[CLS]', 'hello', 'world', '!', '[SEP]', '[PAD]', '[PAD]', '[PAD]'], - ['[CLS]', '你', '好', '世', '界', '[UNK]', '[SEP]', '[PAD]']] - ), - ), ( - ['I like apples', '我喜欢吃苹果', 'hello world!', '你好世界👋'], - 'pre', - Encoding( - ids=[[0, 0, 101, 151, 8993, 8350, 8118, 102], - [101, 2769, 1599, 3614, 1391, 5741, 3362, 102], - [0, 0, 0, 101, 8701, 8572, 106, 102], - [0, 101, 872, 1962, 686, 4518, 100, 102]], - segment_ids=[[0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], - tokens=[['[PAD]', '[PAD]', '[CLS]', 'i', 'like', 'apple', '##s', '[SEP]'], - ['[CLS]', '我', '喜', '欢', '吃', '苹', '果', '[SEP]'], - ['[PAD]', '[PAD]', '[PAD]', '[CLS]', 'hello', 'world', '!', '[SEP]'], - ['[PAD]', '[CLS]', '你', '好', '世', '界', '[UNK]', '[SEP]']] - ) - ) - ] -) -def test_wordpiece_batch_encode_single(inputs, padding_strategy, expected): - tokenizer = WPTokenizer(vocab_path=os.path.join(data_dir, 'wp_cn_vocab.txt'), lowercase=True) - pred = tokenizer.encode_batch(inputs, padding=True, padding_strategy=padding_strategy, return_array=False) - assert pred.ids == expected.ids - assert pred.segment_ids == expected.segment_ids - assert pred.tokens == expected.tokens - - -@pytest.mark.parametrize( - 'inputs,expected', - [ - ( - [('I like apples', '我喜欢吃苹果'), ('hello world!', '你好世界👋')], - Encoding( - ids=[[101, 151, 8993, 8350, 8118, 102, 2769, 1599, 3614, 1391, 5741, 3362, 102], - [101, 8701, 8572, 106, 102, 872, 1962, 686, 4518, 100, 102]], - segment_ids=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]], - tokens=[['[CLS]', 'i', 'like', 'apple', '##s', '[SEP]', '我', '喜', '欢', '吃', '苹', '果', '[SEP]'], - ['[CLS]', 'hello', 'world', '!', '[SEP]', '你', '好', '世', '界', '[UNK]', '[SEP]']] - ), - ) - ] -) -def test_wordpiece_batch_encode_no_padding(inputs, expected): - tokenizer = WPTokenizer(vocab_path=os.path.join(data_dir, 'wp_cn_vocab.txt'), lowercase=True) - pred = tokenizer.encode_batch(inputs, padding=False, return_array=False) - assert pred.ids == expected.ids - assert pred.segment_ids == expected.segment_ids - assert pred.tokens == expected.tokens - - -@pytest.mark.parametrize( - 'inputs,padding_strategy,expected', - [ - ( - [('I like apples', '我喜欢吃苹果'), ('hello world!', '你好世界👋')], - 'post', - Encoding( - ids=[[101, 151, 8993, 8350, 8118, 102, 2769, 1599, 3614, 102], - [101, 8701, 8572, 106, 102, 872, 1962, 686, 4518, 102]], - segment_ids=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]], - tokens=[['[CLS]', 'i', 'like', 'apple', '##s', '[SEP]', '我', '喜', '欢', '[SEP]'], - ['[CLS]', 'hello', 'world', '!', '[SEP]', '你', '好', '世', '界', '[SEP]']] - ), - ), - ( - [('I like apples', '我喜欢吃苹果'), ('hello world!', '你好世界👋')], - 'pre', - Encoding( - ids=[[101, 151, 8993, 8350, 8118, 102, 2769, 1599, 3614, 102], - [101, 8701, 8572, 106, 102, 872, 1962, 686, 4518, 102]], - segment_ids=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 1, 1, 1, 1, 1]], - tokens=[['[CLS]', 'i', 'like', 'apple', '##s', '[SEP]', '我', '喜', '欢', '[SEP]'], - ['[CLS]', 'hello', 'world', '!', '[SEP]', '你', '好', '世', '界', '[SEP]']] - ), - ) - ] -) -def test_wordpiece_batch_encode_truncation(inputs, padding_strategy, expected): - tokenizer = WPTokenizer(vocab_path=os.path.join(data_dir, 'wp_cn_vocab.txt'), lowercase=True) - tokenizer.enable_truncation(max_length=10) - pred = tokenizer.encode_batch(inputs, padding_strategy=padding_strategy, return_array=False) - assert pred.ids == expected.ids - assert pred.segment_ids == expected.segment_ids - assert pred.tokens == expected.tokens - - -@pytest.mark.parametrize( - "test_input,skip_special_tokens,expected", - [ - ([101, 872, 1962, 686, 4518, 100, 102], True, ['你', '好', '世', '界']), - ([101, 872, 1962, 686, 4518, 100, 102], False, ['[CLS]', '你', '好', '世', '界', '[UNK]', '[SEP]']), - ([101, 8701, 8572, 102], True, ['hello', 'world']), - ([101, 8701, 8572, 102], False, ['[CLS]', 'hello', 'world', '[SEP]']) - ] -) -def test_wordpiece_decode(test_input, skip_special_tokens, expected): - tokenizer = WPTokenizer(vocab_path=os.path.join(data_dir, 'wp_cn_vocab.txt'), lowercase=True) - - assert tokenizer.decode(test_input, skip_special_tokens=skip_special_tokens) == expected - - -@pytest.mark.parametrize( - "left_text,right_text,expected", - [ - ( - ''.join(['头'] + ['[UNK]'] * 2), - ''.join(['头'] + ['[UNK]'] * 2), - Encoding( - ids=[101, 1928, 100, 100, 102, 1928, 100, 100, 102], - segment_ids=[0, 0, 0, 0, 0, 1, 1, 1, 1], - tokens=['[CLS]', '头', '[UNK]', '[UNK]', '[SEP]', '头', '[UNK]', '[UNK]', '[SEP]'], - ) - ), - ( - ''.join(['头'] + ['[UNK]'] * 2), - ''.join(['头'] + ['[UNK]'] * 8), - Encoding( - ids=[101, 1928, 100, 100, 102, 1928, 100, 100, 100, 102], - segment_ids=[0, 0, 0, 0, 0, 1, 1, 1, 1, 1], - tokens=['[CLS]', '头', '[UNK]', '[UNK]', '[SEP]', '头', '[UNK]', '[UNK]', '[UNK]', '[SEP]'] - ) - ), - ( - ''.join(['头'] + ['[UNK]'] * 8), - ''.join(['头'] + ['[UNK]'] * 2), - Encoding( - ids=[101, 1928, 100, 100, 100, 102, 1928, 100, 100, 102], - segment_ids=[0, 0, 0, 0, 0, 0, 1, 1, 1, 1], - tokens=['[CLS]', '头', '[UNK]', '[UNK]', '[UNK]', '[SEP]', '头', '[UNK]', '[UNK]', '[SEP]'] - ) - ), - ( - ''.join(['头'] + ['[UNK]'] * 8), - ''.join(['头'] + ['[UNK]'] * 8), - Encoding( - ids=[101, 1928, 100, 100, 100, 102, 1928, 100, 100, 102], - segment_ids=[0, 0, 0, 0, 0, 0, 1, 1, 1, 1], - tokens=['[CLS]', '头', '[UNK]', '[UNK]', '[UNK]', '[SEP]', '头', '[UNK]', '[UNK]', '[SEP]'], - ) - ), - ] -) -def test_wordpiece_truncation_post(left_text, right_text, expected): - tokenizer = WPTokenizer(vocab_path=os.path.join(data_dir, 'wp_cn_vocab.txt'), lowercase=True) - tokenizer.enable_truncation(10, strategy='post') - - ret = tokenizer.encode(left_text, right_text, return_array=False) - assert ret.ids == expected.ids - assert ret.segment_ids == expected.segment_ids - assert ret.tokens == expected.tokens - - -@pytest.mark.parametrize( - "left_text,right_text,expected", - [ - ( - ''.join(['[UNK]'] * 2 + ['尾']), - ''.join(['[UNK]'] * 2 + ['尾']), - Encoding( - ids=[101, 100, 100, 2227, 102, 100, 100, 2227, 102], - segment_ids=[0, 0, 0, 0, 0, 1, 1, 1, 1], - tokens=['[CLS]', '[UNK]', '[UNK]', '尾', '[SEP]', '[UNK]', '[UNK]', '尾', '[SEP]'], - ) - ), - ( - ''.join(['[UNK]'] * 2 + ['尾']), - ''.join(['[UNK]'] * 8 + ['尾']), - Encoding( - ids=[101, 100, 100, 2227, 102, 100, 100, 100, 2227, 102], - segment_ids=[0, 0, 0, 0, 0, 1, 1, 1, 1, 1], - tokens=['[CLS]', '[UNK]', '[UNK]', '尾', '[SEP]', '[UNK]', '[UNK]', '[UNK]', '尾', '[SEP]'] - ) - ), - ( - ''.join(['[UNK]'] * 8 + ['尾']), - ''.join(['[UNK]'] * 2 + ['尾']), - Encoding( - ids=[101, 100, 100, 100, 2227, 102, 100, 100, 2227, 102], - segment_ids=[0, 0, 0, 0, 0, 0, 1, 1, 1, 1], - tokens=['[CLS]', '[UNK]', '[UNK]', '[UNK]', '尾', '[SEP]', '[UNK]', '[UNK]', '尾', '[SEP]'] - ) - ), - ( - ''.join(['[UNK]'] * 8 + ['尾']), - ''.join(['[UNK]'] * 8 + ['尾']), - Encoding( - ids=[101, 100, 100, 100, 2227, 102, 100, 100, 2227, 102], - segment_ids=[0, 0, 0, 0, 0, 0, 1, 1, 1, 1], - tokens=['[CLS]', '[UNK]', '[UNK]', '[UNK]', '尾', '[SEP]', '[UNK]', '[UNK]', '尾', '[SEP]'], - ) - ), - ] -) -def test_wordpiece_truncation_pre(left_text, right_text, expected): - tokenizer = WPTokenizer(vocab_path=os.path.join(data_dir, 'wp_cn_vocab.txt'), lowercase=True) - tokenizer.enable_truncation(10, strategy='pre') - - ret = tokenizer.encode(left_text, right_text, return_array=False) - assert ret.ids == expected.ids - assert ret.segment_ids == expected.segment_ids - assert ret.tokens == expected.tokens - - -@pytest.mark.parametrize( - 'test_input,start_index,end_index,expected', - [ - ( - 'I like apples.', 3, 4, 'apples' - ), - ( - '我好菜啊!!', 1, 3, '我好菜' - ) - ] -) -def test_wordpiece_tokens_mapping(test_input, start_index, end_index, expected): - tokenizer = WPTokenizer(vocab_path=os.path.join(data_dir, 'wp_cn_vocab.txt'), lowercase=True) - pred = tokenizer.encode(test_input) - mapping = tokenizer.tokens_mapping(test_input, pred.tokens) - assert test_input[mapping[start_index][0]: mapping[end_index][1]] == expected - - -@pytest.mark.parametrize( - "left_text,right_text,expected", - [ - ( - 'I like apples', None, - Encoding( - [2, 31, 101, 4037, 18, 3], - [0, 0, 0, 0, 0, 0], - ['[CLS]', '▁i', '▁like', '▁apple', 's', '[SEP]'] - ) - ), - ( - 'hello world', 'hello world!!!\n👋', - Encoding( - [2, 10975, 126, 3, 10975, 126, 28116, 13, 1, 3], - [0, 0, 0, 0, 1, 1, 1, 1, 1, 1], - ['[CLS]', '▁hello', '▁world', '[SEP]', '▁hello', '▁world', '!!!', '▁', '👋', '[SEP]'] - ) - ), - ] -) -def test_sentencepiece_encode(left_text, right_text, expected): - tokenizer = SPTokenizer(vocab_path=os.path.join(data_dir, 'albert_vocab/30k-clean.model'), lowercase=True) - ret = tokenizer.encode(left_text, right_text, return_array=False) - - assert ret.ids == expected.ids - assert ret.segment_ids == expected.segment_ids - assert ret.tokens == expected.tokens - - -@pytest.mark.parametrize( - "test_input,skip_special_tokens,expected", - [ - ([2, 10975, 126, 3], True, ['▁hello', '▁world']), - ([2, 10975, 126, 3], False, ['[CLS]', '▁hello', '▁world', '[SEP]']), - ([2, 10975, 126, 28116, 13, 3], True, ['▁hello', '▁world', '!!!', '▁']), - ([2, 10975, 126, 28116, 13, 3], False, ['[CLS]', '▁hello', '▁world', '!!!', '▁', '[SEP]']) - ] -) -def test_sentencepiece_decode(test_input, skip_special_tokens, expected): - tokenizer = SPTokenizer(vocab_path=os.path.join(data_dir, 'albert_vocab/30k-clean.model')) - - assert tokenizer.decode(test_input, skip_special_tokens=skip_special_tokens) == expected - - -@pytest.mark.parametrize( - "left_text,right_text,expected", - [ - ( - ' '.join(['head'] + ['unknown'] * 2), - ' '.join(['head'] + ['unknown'] * 2), - Encoding( - ids=[2, 157, 2562, 2562, 3, 157, 2562, 2562, 3], - segment_ids=[0, 0, 0, 0, 0, 1, 1, 1, 1], - tokens=['[CLS]', '▁head', '▁unknown', '▁unknown', '[SEP]', '▁head', '▁unknown', '▁unknown', '[SEP]'], - ) - ), - ( - ' '.join(['head'] + ['unknown'] * 2), - ' '.join(['head'] + ['unknown'] * 8), - Encoding( - ids=[2, 157, 2562, 2562, 3, 157, 2562, 2562, 2562, 3], - segment_ids=[0, 0, 0, 0, 0, 1, 1, 1, 1, 1], - tokens=['[CLS]', '▁head', '▁unknown', '▁unknown', '[SEP]', '▁head', '▁unknown', '▁unknown', '▁unknown', '[SEP]'] - ) - ), - ( - ' '.join(['head'] + ['unknown'] * 8), - ' '.join(['head'] + ['unknown'] * 2), - Encoding( - ids=[2, 157, 2562, 2562, 2562, 3, 157, 2562, 2562, 3], - segment_ids=[0, 0, 0, 0, 0, 0, 1, 1, 1, 1], - tokens=['[CLS]', '▁head', '▁unknown', '▁unknown', '▁unknown', '[SEP]', '▁head', '▁unknown', '▁unknown', '[SEP]'] - ) - ), - ( - ' '.join(['head'] + ['unknown'] * 8), - ' '.join(['head'] + ['unknown'] * 8), - Encoding( - ids=[2, 157, 2562, 2562, 2562, 3, 157, 2562, 2562, 3], - segment_ids=[0, 0, 0, 0, 0, 0, 1, 1, 1, 1], - tokens=['[CLS]', '▁head', '▁unknown', '▁unknown', '▁unknown', '[SEP]', '▁head', '▁unknown', '▁unknown', '[SEP]'], - ) - ), - ] -) -def test_sentencepiece_truncation_post(left_text, right_text, expected): - tokenizer = SPTokenizer(vocab_path=os.path.join(data_dir, 'albert_vocab/30k-clean.model')) - tokenizer.enable_truncation(10, strategy='post') - - ret = tokenizer.encode(left_text, right_text, return_array=False) - assert ret.ids == expected.ids - assert ret.segment_ids == expected.segment_ids - assert ret.tokens == expected.tokens - - -@pytest.mark.parametrize( - "left_text,right_text,expected", - [ - ( - ' '.join(['unknown'] * 2 + ['tail']), - ' '.join(['unknown'] * 2 + ['tail']), - Encoding( - ids=[2, 2562, 2562, 3424, 3, 2562, 2562, 3424, 3], - segment_ids=[0, 0, 0, 0, 0, 1, 1, 1, 1], - tokens=['[CLS]', '▁unknown', '▁unknown', '▁tail', '[SEP]', '▁unknown', '▁unknown', '▁tail', '[SEP]'], - ) - ), - ( - ' '.join(['unknown'] * 2 + ['tail']), - ' '.join(['unknown'] * 8 + ['tail']), - Encoding( - ids=[2, 2562, 2562, 3424, 3, 2562, 2562, 2562, 3424, 3], - segment_ids=[0, 0, 0, 0, 0, 1, 1, 1, 1, 1], - tokens=['[CLS]', '▁unknown', '▁unknown', '▁tail', '[SEP]', '▁unknown', '▁unknown', '▁unknown', '▁tail', '[SEP]'] - ) - ), - ( - ' '.join(['unknown'] * 8 + ['tail']), - ' '.join(['unknown'] * 2 + ['tail']), - Encoding( - ids=[2, 2562, 2562, 2562, 3424, 3, 2562, 2562, 3424, 3], - segment_ids=[0, 0, 0, 0, 0, 0, 1, 1, 1, 1], - tokens=['[CLS]', '▁unknown', '▁unknown', '▁unknown', '▁tail', '[SEP]', '▁unknown', '▁unknown', '▁tail', '[SEP]'] - ) - ), - ( - ' '.join(['unknown'] * 8 + ['tail']), - ' '.join(['unknown'] * 8 + ['tail']), - Encoding( - ids=[2, 2562, 2562, 2562, 3424, 3, 2562, 2562, 3424, 3], - segment_ids=[0, 0, 0, 0, 0, 0, 1, 1, 1, 1], - tokens=['[CLS]', '▁unknown', '▁unknown', '▁unknown', '▁tail', '[SEP]', '▁unknown', '▁unknown', '▁tail', '[SEP]'], - ) - ), - ] -) -def test_sentencepiece_truncation_pre(left_text, right_text, expected): - tokenizer = SPTokenizer(vocab_path=os.path.join(data_dir, 'albert_vocab/30k-clean.model')) - tokenizer.enable_truncation(10, strategy='pre') - - ret = tokenizer.encode(left_text, right_text, return_array=False) - assert ret.ids == expected.ids - assert ret.segment_ids == expected.segment_ids - assert ret.tokens == expected.tokens - - -@pytest.mark.parametrize( - 'inputs,padding_strategy,expected', - [ - ( - [('I like apples', 'I like watermelones'), ('hello world!', 'hello world')], - 'post', - Encoding( - ids=[[2, 13, 1, 101, 4037, 18, 3, 13, 1, 101, 308, 21008, 2696, 3], - [2, 10975, 126, 187, 3, 10975, 126, 3, 0, 0, 0, 0, 0, 0]], - segment_ids=[[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0]], - tokens=[['[CLS]', '▁', 'I', '▁like', '▁apple', 's', '[SEP]', '▁', 'I', '▁like', '▁water', 'melo', 'nes', '[SEP]'], - ['[CLS]', '▁hello', '▁world', '!', '[SEP]', '▁hello', '▁world', '[SEP]', '', '', '', '', '', '']] - ), - ), ( - [('I like apples', 'I like watermelones'), ('hello world!', 'hello world')], - 'pre', - Encoding( - ids=[[2, 13, 1, 101, 4037, 18, 3, 13, 1, 101, 308, 21008, 2696, 3], - [0, 0, 0, 0, 0, 0, 2, 10975, 126, 187, 3, 10975, 126, 3]], - segment_ids=[[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1]], - tokens=[['[CLS]', '▁', 'I', '▁like', '▁apple', 's', '[SEP]', '▁', 'I', '▁like', '▁water', 'melo', 'nes', '[SEP]'], - ['', '', '', '', '', '', '[CLS]', '▁hello', '▁world', '!', '[SEP]', '▁hello', '▁world', '[SEP]']] - ) - ) - ] -) -def test_sentencepiece_batch_encode_padding(inputs, padding_strategy, expected): - tokenizer = SPTokenizer(vocab_path=os.path.join(data_dir, 'albert_vocab/30k-clean.model')) - pred = tokenizer.encode_batch(inputs, padding=True, padding_strategy=padding_strategy, return_array=False) - assert pred.ids == expected.ids - assert pred.segment_ids == expected.segment_ids - assert pred.tokens == expected.tokens - - -@pytest.mark.parametrize( - 'inputs,expected', - [ - ( - [('I like apples', 'I like watermelones'), ('hello world!', 'hello world')], - Encoding( - ids=[[2, 13, 1, 101, 4037, 18, 3, 13, 1, 101, 308, 21008, 2696, 3], - [2, 10975, 126, 187, 3, 10975, 126, 3]], - segment_ids=[[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 1, 1, 1]], - tokens=[['[CLS]', '▁', 'I', '▁like', '▁apple', 's', '[SEP]', '▁', 'I', '▁like', '▁water', 'melo', 'nes', '[SEP]'], - ['[CLS]', '▁hello', '▁world', '!', '[SEP]', '▁hello', '▁world', '[SEP]']] - ), - ) - ] -) -def test_sentencepiece_batch_encode_no_padding(inputs, expected): - tokenizer = SPTokenizer(vocab_path=os.path.join(data_dir, 'albert_vocab/30k-clean.model')) - pred = tokenizer.encode_batch(inputs, padding=False, return_array=False) - assert pred.ids == expected.ids - assert pred.segment_ids == expected.segment_ids - assert pred.tokens == expected.tokens - - -@pytest.mark.parametrize( - 'inputs,padding_strategy,expected', - [ - ( - [('I like apples', 'I like watermelones'), ('hello world!', 'hello world')], - 'post', - Encoding( - ids=[[2, 13, 1, 101, 4037, 3, 13, 1, 101, 3], - [2, 10975, 126, 187, 3, 10975, 126, 3, 0, 0]], - segment_ids=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 1, 1, 1, 0, 0]], - tokens=[['[CLS]', '▁', 'I', '▁like', '▁apple', '[SEP]', '▁', 'I', '▁like', '[SEP]'], - ['[CLS]', '▁hello', '▁world', '!', '[SEP]', '▁hello', '▁world', '[SEP]', '', '']] - ), - ), - ( - [('I like apples', 'I like watermelones'), ('hello world!', 'hello world')], - 'pre', - Encoding( - ids=[[2, 13, 1, 101, 4037, 3, 13, 1, 101, 3], - [0, 0, 2, 10975, 126, 187, 3, 10975, 126, 3]], - segment_ids=[[0, 0, 0, 0, 0, 0, 1, 1, 1, 1], - [0, 0, 0, 0, 0, 0, 0, 1, 1, 1]], - tokens=[['[CLS]', '▁', 'I', '▁like', '▁apple', '[SEP]', '▁', 'I', '▁like', '[SEP]'], - ['', '', '[CLS]', '▁hello', '▁world', '!', '[SEP]', '▁hello', '▁world', '[SEP]']] - ), - ) - ] -) -def test_sentencepiece_batch_encode_truncation(inputs, padding_strategy, expected): - tokenizer = SPTokenizer(vocab_path=os.path.join(data_dir, 'albert_vocab/30k-clean.model')) - tokenizer.enable_truncation(max_length=10) - pred = tokenizer.encode_batch(inputs, padding_strategy=padding_strategy, return_array=False) - assert pred.ids == expected.ids - assert pred.segment_ids == expected.segment_ids - assert pred.tokens == expected.tokens - - -@pytest.mark.parametrize( - 'inputs,padding_strategy,expected', - [ - ( - ['I like apples', 'I like watermelones', 'hello world!'], - 'post', - Encoding( - ids=[[2, 13, 1, 101, 4037, 18, 3, 0], - [2, 13, 1, 101, 308, 21008, 2696, 3], - [2, 10975, 126, 187, 3, 0, 0, 0]], - segment_ids=[[0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], - tokens=[['[CLS]', '▁', 'I', '▁like', '▁apple', 's', '[SEP]', ''], - ['[CLS]', '▁', 'I', '▁like', '▁water', 'melo', 'nes', '[SEP]'], - ['[CLS]', '▁hello', '▁world', '!', '[SEP]', '', '', '']] - ), - ), ( - ['I like apples', 'I like watermelones', 'hello world!'], - 'pre', - Encoding( - ids=[[0, 2, 13, 1, 101, 4037, 18, 3], - [2, 13, 1, 101, 308, 21008, 2696, 3], - [0, 0, 0, 2, 10975, 126, 187, 3]], - segment_ids=[[0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0]], - tokens=[['', '[CLS]', '▁', 'I', '▁like', '▁apple', 's', '[SEP]'], - ['[CLS]', '▁', 'I', '▁like', '▁water', 'melo', 'nes', '[SEP]'], - ['', '', '', '[CLS]', '▁hello', '▁world', '!', '[SEP]']] - ) - ) - ] -) -def test_sentencepiece_batch_encode_single(inputs, padding_strategy, expected): - tokenizer = SPTokenizer(vocab_path=os.path.join(data_dir, 'albert_vocab/30k-clean.model')) - pred = tokenizer.encode_batch(inputs, padding=True, padding_strategy=padding_strategy, return_array=False) - assert pred.ids == expected.ids - assert pred.segment_ids == expected.segment_ids - assert pred.tokens == expected.tokens - - -@pytest.mark.parametrize( - 'test_input,start_index,end_index,expected', - [ - ( - 'I like watermelons.', 3, 5, 'watermelons' - ), - ( - 'Hello world!!!', 1, 1, 'Hello' - ) - ] -) -def test_sentencepiece_tokens_mapping(test_input, start_index, end_index, expected): - tokenizer = SPTokenizer(vocab_path=os.path.join(data_dir, 'albert_vocab/30k-clean.model'), lowercase=True) - pred = tokenizer.encode(test_input) - mapping = tokenizer.tokens_mapping(test_input, pred.tokens) - assert test_input[mapping[start_index][0]: mapping[end_index][1]] == expected diff --git a/langml/tests/test_transformer.py b/langml/tests/test_transformer.py deleted file mode 100644 index 7e309ae..0000000 --- a/langml/tests/test_transformer.py +++ /dev/null @@ -1,25 +0,0 @@ -# -*- coding: utf-8 -*- - -from langml import TF_KERAS -if TF_KERAS: - import tensorflow.keras.backend as K - import tensorflow.keras.layers as L -else: - import keras.backend as K - import keras.layers as L - - -def test_transformer_encoder(): - from langml.transformer.encoder import TransformerEncoder - - X = L.Input(shape=(None, 64)) - o = TransformerEncoder(4, 64)(X) - assert K.int_shape(o) == K.int_shape(X) - - -def test_transformer_encoder_block(): - from langml.transformer.encoder import TransformerEncoderBlock - - X = L.Input(shape=(None, 64)) - o = TransformerEncoderBlock(2, 4, 64)(X) - assert K.int_shape(o) == K.int_shape(X) diff --git a/model.py b/model.py index 30b6749..7a986d3 100644 --- a/model.py +++ b/model.py @@ -1,152 +1,170 @@ # -*- coding:utf-8 -*- - -import os -from typing import List, Tuple - -import tensorflow as tf -import keras.layers as L -import keras.backend as K -from keras.models import Model -from keras.callbacks import Callback -from keras.optimizers import Adam -from langml.plm.bert import load_bert -from langml.layers import SelfAttention -from langml.tensor_typing import Models - +import torch +import torch.nn as nn +import torch.optim as optim +from transformers import BertModel from utils import compute_metrics - -def build_model(bert_dir: str, learning_rate: float, relation_size: int) -> Tuple[Models, Models, Models, Models]: - - def gather_span(x): - seq, idxs = x - idxs = K.cast(idxs, 'int32') - if len(K.int_shape(idxs)) == 3: - res = [] - for i in range(len(K.int_shape(idxs))): - batch_idxs = K.arange(0, K.shape(seq)[0]) - batch_idxs = K.expand_dims(batch_idxs, 1) - indices = K.concatenate([batch_idxs, idxs[:, i, :]], 1) - res.append(K.expand_dims(tf.gather_nd(seq, indices), 1)) - return K.concatenate(res, 1) - batch_idxs = K.arange(0, K.shape(seq)[0]) - batch_idxs = K.expand_dims(batch_idxs, 1) - idxs = K.concatenate([batch_idxs, idxs], 1) - return tf.gather_nd(seq, idxs) - - bert_model, _ = load_bert( - config_path=os.path.join(bert_dir, 'bert_config.json'), - checkpoint_path=os.path.join(bert_dir, 'bert_model.ckpt'), - ) - - # entities in - gold_entity_heads_in = L.Input(shape=(None, 2), name='gold_entity_heads') - gold_entity_tails_in = L.Input(shape=(None, 2), name='gold_entity_tails') - gold_rels_in = L.Input(shape=(relation_size, ), name='gold_rels') - # pos sample - sub_head_in = L.Input(shape=(1,), name='sample_subj_head') - sub_tail_in = L.Input(shape=(1,), name='sample_subj_tail') - rel_in = L.Input(shape=(1,), name='sample_rel') - gold_obj_head_in = L.Input(shape=(None, ), name='sample_obj_heads') - - gold_entity_heads, gold_entity_tails, sub_head, sub_tail, rel, gold_rels, gold_obj_head = gold_entity_heads_in, gold_entity_tails_in, sub_head_in, sub_tail_in, rel_in, gold_rels_in, gold_obj_head_in - mask = L.Lambda(lambda x: K.cast(K.greater(K.expand_dims(x, 2), 0), 'float32'))(bert_model.input[0]) - - tokens_feature = bert_model.output - - # predict relations - pred_rels = L.Lambda(lambda x: x[:, 0])(tokens_feature) - pred_rels = L.Dense(relation_size, activation='sigmoid', name='pred_rel')(pred_rels) - rel_model = Model(bert_model.input, [pred_rels]) - - # predict entity - pred_entity_heads = L.Dense(2, activation='sigmoid', name='entity_heads')(tokens_feature) - pred_entity_tails = L.Dense(2, activation='sigmoid', name='entity_tails')(tokens_feature) - entity_model = Model(bert_model.input, [pred_entity_heads, pred_entity_tails]) - - # predict object - tokens_feature_size = K.int_shape(tokens_feature)[-1] - sub_head_feature = L.Lambda(gather_span)([tokens_feature, sub_head]) - sub_head_feature = L.Lambda(lambda x: K.expand_dims(x, 1))(sub_head_feature) - sub_tail_feature = L.Lambda(gather_span)([tokens_feature, sub_tail]) - sub_tail_feature = L.Lambda(lambda x: K.expand_dims(x, 1))(sub_tail_feature) - sub_feature = L.Average()([sub_head_feature, sub_tail_feature]) - - rel_feature = L.Embedding(relation_size, tokens_feature_size)(rel) - rel_feature = L.Dense(tokens_feature_size, activation='relu')(rel_feature) - - obj_feature = L.Add()([tokens_feature, sub_feature, rel_feature]) - - value = SelfAttention(is_residual=True, attention_activation='relu')(obj_feature) - - pred_obj_head = L.Dense(1, activation='sigmoid', name='pred_obj_head')(value) - - translate_model = Model((*bert_model.input, sub_head_in, sub_tail_in, rel_in), [pred_obj_head]) - - train_model = Model(inputs=(*bert_model.input, gold_entity_heads_in, gold_entity_tails_in, gold_rels_in, sub_head_in, sub_tail_in, rel_in, gold_obj_head_in), - outputs=[pred_entity_heads, pred_entity_tails, pred_rels, pred_obj_head]) - - # entity loss - entity_heads_loss = K.sum(K.binary_crossentropy(gold_entity_heads, pred_entity_heads), 2, keepdims=True) - entity_heads_loss = K.sum(entity_heads_loss * mask) / K.sum(mask) - entity_tails_loss = K.sum(K.binary_crossentropy(gold_entity_tails, pred_entity_tails), 2, keepdims=True) - entity_tails_loss = K.sum(entity_tails_loss * mask) / K.sum(mask) - - # rel loss - rel_loss = K.mean(K.binary_crossentropy(gold_rels, pred_rels)) - - # obj loss - gold_obj_head = K.expand_dims(gold_obj_head, 2) - obj_head_loss = K.binary_crossentropy(gold_obj_head, pred_obj_head) - obj_head_loss = K.sum(obj_head_loss * mask) / K.sum(mask) - - # joint loss - loss = (entity_heads_loss + entity_tails_loss) + rel_loss + 5.0 * obj_head_loss - - train_model.add_loss(loss) - train_model.compile(optimizer=Adam(learning_rate)) - train_model.summary() - - return entity_model, rel_model, translate_model, train_model - - -class Evaluator(Callback): - def __init__(self, - infer: object, - train_model: Models, - dev_data: List, - save_weights_path: str, - model_name: str, - learning_rate: float = 5e-5, - min_learning_rate: float = 5e-6): +class SelfAttention(nn.Module): + def __init__(self, hidden_dim): + super(SelfAttention, self).__init__() + self.hidden_dim = hidden_dim + self.attention = nn.MultiheadAttention(hidden_dim, num_heads=8, batch_first=True) + + def forward(self, x): + attn_output, _ = self.attention(x, x, x) + return attn_output + x # residual connection + +class EntityModel(nn.Module): + def __init__(self, bert_model_name): + super(EntityModel, self).__init__() + self.bert = BertModel.from_pretrained(bert_model_name) + self.pred_entity_heads = nn.Linear(self.bert.config.hidden_size, 2) + self.pred_entity_tails = nn.Linear(self.bert.config.hidden_size, 2) + + def forward(self, input_ids, attention_mask, token_type_ids): + input_ids = input_ids.to(self.bert.device) + attention_mask = attention_mask.to(self.bert.device) + token_type_ids = token_type_ids.to(self.bert.device) + + bert_outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) + tokens_feature = bert_outputs.last_hidden_state + pred_entity_heads = torch.sigmoid(self.pred_entity_heads(tokens_feature)) + pred_entity_tails = torch.sigmoid(self.pred_entity_tails(tokens_feature)) + return pred_entity_heads, pred_entity_tails + +class RelationModel(nn.Module): + def __init__(self, bert_model_name, relation_size): + super(RelationModel, self).__init__() + self.bert = BertModel.from_pretrained(bert_model_name) + self.pred_rels = nn.Linear(self.bert.config.hidden_size, relation_size) + + def forward(self, input_ids, attention_mask, token_type_ids): + input_ids = input_ids.to(self.bert.device) + attention_mask = attention_mask.to(self.bert.device) + token_type_ids = token_type_ids.to(self.bert.device) + + bert_outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) + tokens_feature = bert_outputs.last_hidden_state + pred_rels = torch.sigmoid(self.pred_rels(tokens_feature[:, 0, :])) + return pred_rels + +class TranslateModel(nn.Module): + def __init__(self, bert_model_name, relation_size, hidden_size): + super(TranslateModel, self).__init__() + self.bert = BertModel.from_pretrained(bert_model_name) + self.rel_embedding = nn.Embedding(relation_size, hidden_size) + self.rel_dense = nn.Linear(hidden_size, hidden_size) + self.self_attention = SelfAttention(hidden_size) + self.pred_obj_head = nn.Linear(hidden_size, 1) + + def forward(self, input_ids, attention_mask, token_type_ids, sub_head, sub_tail, rel): + input_ids = input_ids.to(self.bert.device) + attention_mask = attention_mask.to(self.bert.device) + token_type_ids = token_type_ids.to(self.bert.device) + sub_head = sub_head.to(self.bert.device) + sub_tail = sub_tail.to(self.bert.device) + rel = rel.to(self.bert.device) + + bert_outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) + tokens_feature = bert_outputs.last_hidden_state # (batch_size, seq_len, hidden_size) + + sub_head_feature = tokens_feature[torch.arange(tokens_feature.size(0)), sub_head.squeeze()] # (batch_size, hidden_size) + sub_tail_feature = tokens_feature[torch.arange(tokens_feature.size(0)), sub_tail.squeeze()] # (batch_size, hidden_size) + sub_feature = (sub_head_feature + sub_tail_feature) / 2 # (batch_size, hidden_size) + + rel_feature = torch.relu(self.rel_dense(self.rel_embedding(rel).squeeze(1))) # (batch_size, hidden_size) + + # Ensure dimensions match for broadcasting + sub_feature = sub_feature.unsqueeze(1).expand(-1, tokens_feature.size(1), -1) # (batch_size, seq_len, hidden_size) + rel_feature = rel_feature.unsqueeze(1).expand(-1, tokens_feature.size(1), -1) # (batch_size, seq_len, hidden_size) + + obj_feature = tokens_feature + sub_feature + rel_feature # (batch_size, seq_len, hidden_size) + obj_feature = self.self_attention(obj_feature) # (batch_size, seq_len, hidden_size) + pred_obj_head = torch.sigmoid(self.pred_obj_head(obj_feature)) # (batch_size, seq_len, 1) + + return pred_obj_head.squeeze(-1) # (batch_size, seq_len) + +class TDEERModel(nn.Module): + def __init__(self, entity_model, rel_model, translate_model): + super(TDEERModel, self).__init__() + self.entity_model = entity_model + self.rel_model = rel_model + self.translate_model = translate_model + + def forward(self, input_ids, attention_mask, token_type_ids, gold_entity_heads, gold_entity_tails, gold_rels, sub_head, sub_tail, rel, gold_obj_head): + input_ids = input_ids.to(self.entity_model.bert.device) + attention_mask = attention_mask.to(self.entity_model.bert.device) + token_type_ids = token_type_ids.to(self.entity_model.bert.device) + gold_entity_heads = gold_entity_heads.to(self.entity_model.bert.device) + gold_entity_tails = gold_entity_tails.to(self.entity_model.bert.device) + gold_rels = gold_rels.to(self.entity_model.bert.device) + sub_head = sub_head.to(self.entity_model.bert.device) + sub_tail = sub_tail.to(self.entity_model.bert.device) + rel = rel.to(self.entity_model.bert.device) + gold_obj_head = gold_obj_head.to(self.entity_model.bert.device) + + pred_entity_heads, pred_entity_tails = self.entity_model(input_ids, attention_mask, token_type_ids) + pred_rels = self.rel_model(input_ids, attention_mask, token_type_ids) + pred_obj_head = self.translate_model(input_ids, attention_mask, token_type_ids, sub_head, sub_tail, rel) + + loss = self.compute_loss(pred_entity_heads, gold_entity_heads, pred_entity_tails, gold_entity_tails, pred_rels, gold_rels, pred_obj_head, gold_obj_head) + return loss + + def compute_loss(self, pred_entity_heads, gold_entity_heads, pred_entity_tails, gold_entity_tails, pred_rels, gold_rels, pred_obj_head, gold_obj_head): + entity_heads_loss = nn.functional.binary_cross_entropy(pred_entity_heads, gold_entity_heads, reduction='mean') + entity_tails_loss = nn.functional.binary_cross_entropy(pred_entity_tails, gold_entity_tails, reduction='mean') + rel_loss = nn.functional.binary_cross_entropy(pred_rels, gold_rels, reduction='mean') + pred_obj_head = pred_obj_head.squeeze(-1) + obj_head_loss = nn.functional.binary_cross_entropy(pred_obj_head, gold_obj_head, reduction='mean') + + total_loss = entity_heads_loss + entity_tails_loss + rel_loss + 5.0 * obj_head_loss + return total_loss + +def build_model(bert_model_name: str, learning_rate: float, relation_size: int, device): + hidden_size = BertModel.from_pretrained(bert_model_name).config.hidden_size + entity_model = EntityModel(bert_model_name).to(device) + rel_model = RelationModel(bert_model_name, relation_size).to(device) + translate_model = TranslateModel(bert_model_name, relation_size, hidden_size).to(device) + train_model = TDEERModel(entity_model, rel_model, translate_model).to(device) + + optimizer = optim.AdamW(train_model.parameters(), lr=learning_rate) + return entity_model, rel_model, translate_model, train_model, optimizer + +class Evaluator: + def __init__(self, infer, model, dev_data, save_weights_path, model_name, optimizer, device, learning_rate=5e-5, min_learning_rate=5e-6): self.infer = infer - self.train_model = train_model + self.model = model self.dev_data = dev_data self.save_weights_path = save_weights_path self.model_name = model_name + self.optimizer = optimizer + self.device = device self.learning_rate = learning_rate self.min_learning_rate = min_learning_rate - self.passed = 0 - self.is_exact_match = True if self.model_name.startswith('NYT11-HRL') else False - - def on_train_begin(self, logs=None): self.best = float('-inf') + self.passed = 0 + self.is_exact_match = model_name.startswith('NYT11-HRL') - def on_batch_begin(self, batch, logs=None): - if self.passed < self.params['steps']: - lr = (self.passed + 1.) / self.params['steps'] * self.learning_rate - K.set_value(self.train_model.optimizer.lr, lr) - self.passed += 1 - elif self.params['steps'] <= self.passed < self.params['steps'] * 2: - lr = (2 - (self.passed + 1.) / self.params['steps']) * (self.learning_rate - self.min_learning_rate) - lr += self.min_learning_rate - K.set_value(self.train_model.optimizer.lr, lr) - self.passed += 1 - - def on_epoch_end(self, epoch, logs=None): + def evaluate(self): + self.model.eval() precision, recall, f1 = compute_metrics(self.infer, self.dev_data, exact_match=self.is_exact_match, model_name=self.model_name) if f1 > self.best: self.best = f1 - self.train_model.save_weights(self.save_weights_path) - print("new best result!") - print('f1: %.4f, precision: %.4f, recall: %.4f, best f1: %.4f\n' % (f1, precision, recall, self.best)) + torch.save(self.model.state_dict(), self.save_weights_path) + print("New best result!") + print(f'f1: {f1:.4f}, precision: {precision:.4f}, recall: {recall:.4f}, best f1: {self.best:.4f}') + self.model.train() + + def adjust_learning_rate(self, step, total_steps): + if step < total_steps: + lr = (step + 1) / total_steps * self.learning_rate + else: + lr = (2 - (step + 1) / total_steps) * (self.learning_rate - self.min_learning_rate) + self.min_learning_rate + for param_group in self.optimizer.param_groups: + param_group['lr'] = lr + +# Example of usage: +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') +entity_model, rel_model, translate_model, train_model, optimizer = build_model('bert-base-uncased', 2e-5, 10, device) +evaluator = Evaluator(None, train_model, None, 'model.pth', 'NYT11-HRL', optimizer, device) diff --git a/pretrained-bert/README.md b/pretrained-bert/README.md deleted file mode 100644 index c23a203..0000000 --- a/pretrained-bert/README.md +++ /dev/null @@ -1 +0,0 @@ -Click 👉[BERT-Base-Cased](https://storage.googleapis.com/bert_models/2018_10_18/cased_L-12_H-768_A-12.zip) to download the pretrained model and then decompress to this folder. \ No newline at end of file diff --git a/requirements.txt b/requirements.txt deleted file mode 100644 index f7a5fc4..0000000 --- a/requirements.txt +++ /dev/null @@ -1,6 +0,0 @@ -tokenizers -boltons -Keras==2.3.1 -tqdm -click -langml<0.2.0 diff --git a/run.py b/run.py index 1678ffb..bced37e 100644 --- a/run.py +++ b/run.py @@ -1,15 +1,15 @@ -#! -*- coding:utf-8 -*- +# -*- coding:utf-8 -*- import os import argparse - -from tokenizers import BertWordPieceTokenizer - -from dataloader import DataGenerator, load_data, load_rel +import torch +from torch.utils.data import DataLoader +from transformers import BertTokenizerFast +from tqdm import tqdm +from dataloader import DataGenerator, load_data, load_rel, collate_fn from model import build_model, Evaluator from utils import Infer, compute_metrics - parser = argparse.ArgumentParser(description='TDEER cli') parser.add_argument('--do_train', action='store_true', help='to train TDEER, plz specify --do_train') parser.add_argument('--do_test', action='store_true', help='specify --do_test to evaluate') @@ -18,7 +18,7 @@ parser.add_argument('--train_path', type=str, help='specify the train path') parser.add_argument('--dev_path', type=str, help='specify the dev path') parser.add_argument('--test_path', type=str, help='specify the test path') -parser.add_argument('--bert_dir', type=str, help='specify the pre-trained bert model') +parser.add_argument('--bert_model_name', type=str, default='bert-base-cased', help='specify the pre-trained bert model') parser.add_argument('--save_path', default=None, type=str, help='specify the save path to save model [training phase]') parser.add_argument('--ckpt_path', default=None, type=str, help='specify the ckpt path [test phase]') parser.add_argument('--learning_rate', default=2e-5, type=float, help='specify the learning rate') @@ -30,44 +30,76 @@ parser.add_argument('--verbose', default=2, type=int, help='specify verbose: 0 = silent, 1 = progress bar, 2 = one line per epoch') args = parser.parse_args() - print("Argument:", args) +device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + id2rel, rel2id, all_rels = load_rel(args.rel_path) -tokenizer = BertWordPieceTokenizer(os.path.join(args.bert_dir, 'vocab.txt'), lowercase=False) -tokenizer.enable_truncation(max_length=args.max_len) -entity_model, rel_model, translate_model, train_model = build_model(args.bert_dir, args.learning_rate, len(all_rels)) +tokenizer = BertTokenizerFast.from_pretrained(args.bert_model_name) +entity_model, rel_model, translate_model, train_model, optimizer = build_model(args.bert_model_name, args.learning_rate, len(all_rels), device) +train_model.to(device) if args.do_train: - assert args.save_path is not None, "please specify --save_path in traning phase" - # check save - train_model.save_weights(args.save_path) - + assert args.save_path is not None, "please specify --save_path in training phase" + + # 加载训练数据、验证数据和测试数据 train_data = load_data(args.train_path, rel2id, is_train=True) dev_data = load_data(args.dev_path, rel2id, is_train=False) - if args.test_path is not None: - test_data = load_data(args.test_path, rel2id, is_train=False) - else: - test_data = None - generator = DataGenerator( - train_data, tokenizer, rel2id, all_rels, - args.max_len, args.batch_size, args.max_sample_triples, args.neg_samples - ) + test_data = load_data(args.test_path, rel2id, is_train=False) if args.test_path is not None else None + + # 创建数据生成器和数据加载器 + train_generator = DataGenerator(train_data, tokenizer, rel2id, all_rels, args.max_len, args.max_sample_triples, args.neg_samples) + train_loader = DataLoader(train_generator, batch_size=args.batch_size, shuffle=True, collate_fn=collate_fn) + + # 创建优化器和评估器 + # optimizer = torch.optim.AdamW(train_model.parameters(), lr=args.learning_rate) infer = Infer(entity_model, rel_model, translate_model, tokenizer, id2rel) - evaluator = Evaluator(infer, train_model, dev_data, args.save_path, args.model_name, - learning_rate=args.learning_rate) - train_model.fit( - generator.forfit(random=True), - steps_per_epoch=len(generator), - epochs=args.epoch, - callbacks=[evaluator], - verbose=args.verbose - ) + evaluator = Evaluator(infer, train_model, dev_data, args.save_path, args.model_name, optimizer, device) + + # 训练模型 + for epoch in range(args.epoch): + train_model.train() + with tqdm(train_loader, desc=f'Epoch {epoch+1}/{args.epoch}', unit='batch') as t_loader: + for batch in t_loader: + batch = {k: v.to(device).float() if v.dtype == torch.float64 else v.to(device) for k, v in batch.items()} + # print(batch) + inputs = { + 'input_ids': batch['token_ids'], # Changed from 'input_ids' to 'token_ids' + 'attention_mask': batch['attention_mask'], # Assuming this key is correct + 'token_type_ids': batch['segment_ids'], # Changed from 'token_type_ids' to 'segment_ids' + 'gold_entity_heads': batch['entity_heads'], # Assuming this key is correct + 'gold_entity_tails': batch['entity_tails'], # Assuming this key is correct + 'gold_rels': batch['rels'], # Assuming this key is correct + 'sub_head': batch['sample_subj_head'], # Assuming this key is correct + 'sub_tail': batch['sample_subj_tail'], # Assuming this key is correct + 'rel': batch['sample_rel'], # Assuming this key is correct + 'gold_obj_head': batch['sample_obj_heads'] # Assuming this key is correct + } + + + optimizer.zero_grad() + loss = train_model(**inputs) + loss.backward() + optimizer.step() + t_loader.set_postfix({'loss': loss.item()}) + t_loader.update() + + # 每个 epoch 结束后进行评估 + evaluator.evaluate() + + # 保存训练好的模型 + torch.save(train_model.state_dict(), args.save_path) if args.do_test: assert args.ckpt_path is not None, "please specify --ckpt_path in test phase" + + # 加载测试数据和模型 test_data = load_data(args.test_path, rel2id, is_train=False) - train_model.load_weights(args.ckpt_path) + train_model.load_state_dict(torch.load(args.ckpt_path, map_location=device)) + train_model.to(device) + train_model.eval() + + # 进行测试并计算评估指标 infer = Infer(entity_model, rel_model, translate_model, tokenizer, id2rel) precision, recall, f1_score = compute_metrics(infer, test_data, model_name=args.model_name) print(f'precision: {precision}, recall: {recall}, f1: {f1_score}') diff --git a/utils.py b/utils.py index bfbdf9d..e21cba7 100644 --- a/utils.py +++ b/utils.py @@ -1,12 +1,10 @@ -#! -*- coding:utf-8 -*- - import json import time from typing import Dict, List, Set +import torch import numpy as np from tqdm import tqdm -from langml.tensor_typing import Models def rematch(offsets: List) -> List: @@ -15,77 +13,120 @@ def rematch(offsets: List) -> List: if offset[0] == 0 and offset[1] == 0: mapping.append([]) else: - mapping.append([i for i in range(offset[0], offset[1])]) + mapping.append(list(range(offset[0], offset[1]))) return mapping +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') class Infer: - def __init__(self, entity_model: Models, rel_model: Models, translate_mdoel: Models, + def __init__(self, entity_model: torch.nn.Module, rel_model: torch.nn.Module, translate_model: torch.nn.Module, tokenizer: object, id2rel: Dict): - self.entity_model = entity_model - self.rel_model = rel_model - self.translate_model = translate_mdoel + self.entity_model = entity_model.to(device) + self.rel_model = rel_model.to(device) + self.translate_model = translate_model.to(device) self.tokenizer = tokenizer self.id2rel = id2rel def decode_entity(self, text: str, mapping: List, start: int, end: int): - s = mapping[start] - e = mapping[end] - s = 0 if not s else s[0] - e = len(text) - 1 if not e else e[-1] + # print(f"Input text: {text}") + # print(f"Mapping: {mapping}") + # print(f"Start index: {start}, End index: {end}") + + if start >= len(mapping) or end >= len(mapping): + print("Start or end index out of range.") + return "" + + s = mapping[start] if start < len(mapping) else [] + e = mapping[end] if end < len(mapping) else [] + # print(f"Raw start mapping: {s}, Raw end mapping: {e}") + + # 取s和e的第一个和最后一个值来确保提取正确的子串 + s = s[0] if s else 0 + e = e[-1] if e else len(text) - 1 + # print(f"Adjusted start: {s}, Adjusted end: {e}") + + s = max(0, s) + e = min(len(text) - 1, e) + entity = text[s: e + 1] + # print(f"Extracted entity: {entity}") return entity + def __call__(self, text: str, threshold: float = 0.5) -> Set: - tokened = self.tokenizer.encode(text) - token_ids, segment_ids = np.array([tokened.ids]), np.array([tokened.type_ids]) - mapping = rematch(tokened.offsets) - entity_heads_logits, entity_tails_logits = self.entity_model.predict([token_ids, segment_ids]) - entity_heads, entity_tails = np.where(entity_heads_logits[0] > threshold), np.where(entity_tails_logits[0] > threshold) + # Tokenize text and prepare input tensors + tokened = self.tokenizer.encode_plus(text, add_special_tokens=True, return_offsets_mapping=True) # 确保添加了特殊标记 + token_ids = torch.tensor([tokened.input_ids], dtype=torch.long).to(device) + segment_ids = torch.tensor([tokened.token_type_ids], dtype=torch.long).to(device) + attention_mask = torch.tensor([tokened.attention_mask], dtype=torch.long).to(device) + mapping = rematch(tokened.offset_mapping) + + # Run entity model to get logits for entity heads and tails + with torch.no_grad(): + entity_heads_logits, entity_tails_logits = self.entity_model(token_ids, attention_mask, segment_ids) + + # Apply threshold to get entity indices + entity_heads = (entity_heads_logits > threshold).nonzero(as_tuple=True) + entity_tails = (entity_tails_logits > threshold).nonzero(as_tuple=True) subjects = [] entity_map = {} - for head, head_type in zip(*entity_heads): - for tail, tail_type in zip(*entity_tails): - if head <= tail and head_type == tail_type: - entity = self.decode_entity(text, mapping, head, tail) - if head_type == 0: - subjects.append((entity, head, tail)) + + # print(f"entity_heads_logits shape: {entity_heads_logits.shape}") + # print(f"entity_tails_logits shape: {entity_tails_logits.shape}") + # print(f"entity_heads: {entity_heads}") + # print(f"entity_tails: {entity_tails}") + + # Generate potential subjects and entities from heads and tails + for head_idx, head_type_idx in zip(entity_heads[1], entity_heads[2]): + for tail_idx, tail_type_idx in zip(entity_tails[1], entity_tails[2]): + if head_idx <= tail_idx and head_type_idx == tail_type_idx: + entity = self.decode_entity(text, mapping, head_idx.item(), tail_idx.item()) + # print(f"Extracted entity: {entity} from indices {head_idx.item()} to {tail_idx.item()}") + if head_type_idx == 0: # Assuming type 0 are subjects + subjects.append((entity, head_idx.item(), tail_idx.item())) else: - entity_map[head] = entity - break + entity_map[head_idx.item()] = entity + + print(f"Subjects: {subjects}") + print(f"Entity map: {entity_map}") triple_set = set() if subjects: - # translating decoding - relations_logits = self.rel_model.predict([token_ids, segment_ids]) - relations = np.where(relations_logits[0] > threshold)[0].tolist() - if relations: - batch_sub_heads = [] - batch_sub_tails = [] - batch_rels = [] - batch_sub_entities = [] - batch_rel_types = [] - for (sub, sub_head, sub_tail) in subjects: - for rel in relations: - batch_sub_heads.append([sub_head]) - batch_sub_tails.append([sub_tail]) - batch_rels.append([rel]) - batch_sub_entities.append(sub) - batch_rel_types.append(self.id2rel[rel]) - - batch_token_ids = np.repeat(token_ids, len(subjects) * len(relations), 0) - batch_segment_ids = np.zeros_like(batch_token_ids) - obj_head_logits = self.translate_model.predict_on_batch([ - batch_token_ids, batch_segment_ids, np.array(batch_sub_heads), np.array(batch_sub_tails), np.array(batch_rels) - ]) - for sub, rel, obj_head_logit in zip(batch_sub_entities, batch_rel_types, obj_head_logits): - for h in np.where(obj_head_logit > threshold)[0].tolist(): - if h in entity_map: - obj = entity_map[h] - triple_set.add((sub, rel, obj)) + with torch.no_grad(): + relations_logits = self.rel_model(token_ids, attention_mask=attention_mask, token_type_ids=segment_ids) + relations = (relations_logits > threshold).nonzero(as_tuple=True) + if relations[0].numel() > 0: + batch_size = len(subjects) + for sub, sub_head, sub_tail in subjects: + for rel_idx in relations[0]: + rel = self.id2rel[rel_idx.item()] + batch_token_ids = token_ids.expand(batch_size, -1) + batch_segment_ids = segment_ids.expand(batch_size, -1) + batch_attention_mask = attention_mask.expand(batch_size, -1) + batch_subj_head = torch.tensor([sub_head], dtype=torch.long).expand(batch_size, 1).to(device) + batch_subj_tail = torch.tensor([sub_tail], dtype=torch.long).expand(batch_size, 1).to(device) + batch_rels = torch.tensor([rel_idx.item()], dtype=torch.long).expand(batch_size, 1).to(device) + + # Debugging shapes + # print(f"batch_token_ids shape: {batch_token_ids.shape}") + # print(f"batch_segment_ids shape: {batch_segment_ids.shape}") + # print(f"batch_attention_mask shape: {batch_attention_mask.shape}") + # print(f"batch_subj_head shape: {batch_subj_head.shape}") + # print(f"batch_subj_tail shape: {batch_subj_tail.shape}") + # print(f"batch_rels shape: {batch_rels.shape}") + + obj_head_logits = self.translate_model(batch_token_ids, batch_attention_mask, batch_segment_ids, batch_subj_head, batch_subj_tail, batch_rels) + for obj_head_idx in obj_head_logits.argmax(dim=1).tolist(): + if obj_head_idx in entity_map: + obj = entity_map[obj_head_idx] + triple_set.add((sub, rel, obj)) + return triple_set + + + def partial_match(pred_set, gold_set): pred = {(i[0].split(' ')[0] if len(i[0].split(' ')) > 0 else i[0], i[1], i[2].split(' ')[0] if len(i[2].split(' ')) > 0 else i[2]) for i in pred_set} gold = {(i[0].split(' ')[0] if len(i[0].split(' ')) > 0 else i[0], i[1], i[2].split(' ')[0] if len(i[2].split(' ')) > 0 else i[2]) for i in gold_set} @@ -104,11 +145,15 @@ def compute_metrics(infer, dev_data, exact_match=False, model_name='tmp'): orders = ['subject', 'relation', 'object'] correct_num, predict_num, gold_num = 1e-10, 1e-10, 1e-10 infer_times = [] + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + for line in tqdm(iter(dev_data)): start_time = time.time() pred_triples = infer(line['text']) infer_times.append(time.time() - start_time) gold_triples = set(line['triple_list']) + # print(f"Predicted triples: {pred_triples}") + # print(f"Gold triples: {gold_triples}") if exact_match: gold_triples = remove_space(gold_triples) @@ -116,6 +161,8 @@ def compute_metrics(infer, dev_data, exact_match=False, model_name='tmp'): pred_triples_eval, gold_triples_eval = partial_match(pred_triples, gold_triples) if not exact_match else (pred_triples, gold_triples) + print(f"Predicted triples (eval): {pred_triples_eval}") + print(f"Gold triples (eval): {gold_triples_eval}") correct_num += len(pred_triples_eval & gold_triples_eval) predict_num += len(pred_triples_eval) gold_num += len(gold_triples_eval) @@ -147,3 +194,4 @@ def compute_metrics(infer, dev_data, exact_match=False, model_name='tmp'): print(f'correct_num:{correct_num}\npredict_num:{predict_num}\ngold_num:{gold_num}') print("avg infer time:", sum(infer_times) / len(infer_times)) return precision, recall, f1_score + From 49695baabaccc239e5a0d6d8d0ec56bfcfc80a23 Mon Sep 17 00:00:00 2001 From: innovation64 Date: Sat, 27 Jul 2024 18:58:43 +0800 Subject: [PATCH 19/20] refactor: refactor the code to Pytorch --- README.md | 115 ++-------------------- dataloader.py | 265 ++++++++++++++++++++------------------------------ model .py | 117 ++++++++++++++++++++++ model.py | 170 -------------------------------- run.py | 176 ++++++++++++++++++++------------- utils.py | 186 ++++++++++++++--------------------- 6 files changed, 410 insertions(+), 619 deletions(-) create mode 100644 model .py delete mode 100644 model.py diff --git a/README.md b/README.md index bfd2d96..2184d52 100644 --- a/README.md +++ b/README.md @@ -45,127 +45,26 @@ Click 👉[BERT-Base-Cased](https://storage.googleapis.com/bert_models/2018_10_1 ### 4. Train & Eval -You can use `run.py` with `--do_train` to train the model. After training, you can also use `run.py` with `--do_test` to evaluate data. +we have already refactor this code to pytorch version, you could download the pretrain model directly from [huggingface](https://huggingface.co/bert-base-cased/tree/main). -Our training and evaluating commands are as follows: -1\. NYT - -train: - -```bash -CUDA_VISIBLE_DEVICES=0 nohup python -u run.py \ ---do_train \ ---model_name NYT \ ---rel_path data/NYT/rel2id.json \ ---train_path data/NYT/train_triples.json \ ---dev_path data/NYT/test_triples.json \ ---bert_dir pretrained-bert/cased_L-12_H-768_A-12 \ ---save_path ckpts/nyt.model \ ---learning_rate 0.00005 \ ---neg_samples 2 \ ---epoch 200 \ ---verbose 2 > nyt.log & -``` - -evaluate: +if you want to fast vaild the code -``` -CUDA_VISIBLE_DEVICES=0 python run.py \ ---do_test \ ---model_name NYT \ ---rel_path data/NYT/rel2id.json \ ---test_path data/NYT/test_triples.json \ ---bert_dir pretrained-bert/cased_L-12_H-768_A-12 \ ---ckpt_path ckpts/nyt.model \ ---max_len 512 \ ---verbose 1 -``` +please excuete the following command: -You can evaluate other data by specifying `--test_path`. - -2\. WebNLG - -train: +#### for training ```bash -CUDA_VISIBLE_DEVICES=0 nohup python -u run.py \ ---do_train \ ---model_name WebNLG \ ---rel_path data/WebNLG/rel2id.json \ ---train_path data/WebNLG/train_triples.json \ ---dev_path data/WebNLG/test_triples.json \ ---bert_dir pretrained-bert/cased_L-12_H-768_A-12 \ ---save_path ckpts/webnlg.model \ ---max_sample_triples 5 \ ---neg_samples 5 \ ---learning_rate 0.00005 \ ---epoch 300 \ ---verbose 2 > webnlg.log & -``` -evaluate: - -```bash -CUDA_VISIBLE_DEVICES=0 python run.py \ ---do_test \ ---model_name WebNLG \ ---rel_path data/WebNLG/rel2id.json \ ---test_path data/WebNLG/test_triples.json \ ---bert_dir pretrained-bert/cased_L-12_H-768_A-12 \ ---ckpt_path ckpts/webnlg.model \ ---max_len 512 \ ---verbose 1 +python run.py --do_train --model_name NYT_quick_test --rel_path data/data/NYT/rel2id.json --train_path data/data/NYT/train_triples.json --dev_path data/data/NYT/test_triples.json --bert_model bert-base-cased --save_path ckpts/nyt_quick_test.model --learning_rate 0.00005 --neg_samples 2 --epoch 5 --batch_size 32 --max_len 120 --max_sample_triples 100 --eval_steps 100 --num_workers 4 --use_amp --subset_size 1000 ``` -You can evaluate other data by specifying `--test_path`. - - -3\. NYT11-HRL - -train: +#### for testing ```bash -CUDA_VISIBLE_DEVICES=0 nohup python -u run.py \ ---do_train \ ---model_name NYT11-HRL \ ---rel_path data/NYT11-HRL/rel2id.json \ ---train_path data/NYT11-HRL/train_triples.json \ ---dev_path data/NYT11-HRL/test_triples.json \ ---bert_dir pretrained-bert/cased_L-12_H-768_A-12 \ ---save_path ckpts/nyt11hrl.model \ ---learning_rate 0.00005 \ ---neg_samples 1 \ ---epoch 100 \ ---verbose 2 > nyt11hrl.log & -``` - -evaluate: - -``` -CUDA_VISIBLE_DEVICES=0 python run.py \ ---do_test \ ---model_name NYT11-HRL \ ---rel_path data/NYT/rel2id.json \ ---test_path data/NYT11-HRL/test_triples.json \ ---bert_dir pretrained-bert/cased_L-12_H-768_A-12 \ ---ckpt_path ckpts/nyt11hrl.model \ ---max_len 512 \ ---verbose 1 +python run.py --do_test --model_name NYT_full_test --rel_path data/data/NYT/rel2id.json --test_path data/data/NYT/test_triples.json --bert_model bert-base-cased --ckpt_path ckpts/nyt_full_train.model --batch_size 32 --max_len 120 ``` - -### Pre-trained Models - -We released our pre-trained models for NYT, WebNLG, and NYT11-HRL datasets, and uploaded them to this repository via git-lfs. - -You can download pre-trained models and then decompress them (`ckpts.zip`) to the `ckpts` folder. - -To use the pre-trained models, you need to download our processed datasets and specify `--rel_path` to our processed `rel2id.json`. - -To evaluate by the pre-trained models, you can use above commands and specify `--ckpt_path` to specific model. - - In our setting, NYT, WebNLG, and NYT11-HRL achieve the best result on Epoch 86, 174, and 23 respectively. 1\. NYT diff --git a/dataloader.py b/dataloader.py index b49fd3a..1d32903 100644 --- a/dataloader.py +++ b/dataloader.py @@ -1,5 +1,3 @@ -# -*- coding:utf-8 -*- - import json from typing import Dict, List, Optional, Tuple from collections import defaultdict @@ -9,41 +7,35 @@ from torch.utils.data import Dataset, DataLoader from transformers import BertTokenizer -import log - -def find_entity(source, target): - """ Find the start index of the target sequence in the source sequence """ +def find_entity(source: List[int], target: List[int]) -> int: target_len = len(target) - for i in range(len(source) - target_len + 1): - if source[i:i+target_len] == target: + for i in range(len(source)): + if source[i: i + target_len] == target: return i return -1 -def to_tuple(sent): - """ Convert lists to tuples in place """ - sent['triple_list'] = [tuple(triple) for triple in sent['triple_list']] +def to_tuple(sent: dict): + """ list to tuple (inplace operation) + """ + triple_list = [] + for triple in sent['triple_list']: + triple_list.append(tuple(triple)) + sent['triple_list'] = triple_list -def filter_data(fpath: str, rel2id: dict): +def filter_data(fpath: str, rel2id: Dict): filtered_data = [] - try: - with open(fpath, 'r') as file: - data = json.load(file) - except FileNotFoundError: - print("File not found:", fpath) - return filtered_data - except json.JSONDecodeError: - print("Error decoding JSON from:", fpath) - return filtered_data - - for obj in data: - if 'NYT11-HRL' in fpath and len(obj.get('triple_list', [])) != 1: + for obj in json.load(open(fpath)): + filtered_triples = [] + if 'NYT11-HRL' in fpath and len(obj['triple_list']) != 1: continue - filtered_triples = [triple for triple in obj.get('triple_list', []) if triple[1] in rel2id] + for triple in obj['triple_list']: + if triple[1] not in rel2id: + continue + filtered_triples.append(triple) if not filtered_triples: continue obj['triple_list'] = filtered_triples filtered_data.append(obj) - return filtered_data def load_rel(rel_path: str) -> Tuple[Dict, Dict, List]: @@ -52,26 +44,24 @@ def load_rel(rel_path: str) -> Tuple[Dict, Dict, List]: id2rel = {int(i): j for i, j in id2rel.items()} return id2rel, rel2id, all_rels - def load_data(fpath: str, rel2id: Dict, is_train: bool = False) -> List: data = filter_data(fpath, rel2id) if is_train: text_lens = [len(obj['text'].split()) for obj in data] - log.info("train text insight") - log.info(f" max len: {max(text_lens)}") - log.info(f" min len: {min(text_lens)}") - log.info(f" avg len: {sum(text_lens) / len(text_lens)}") + print("train text insight") + print(f" max len: {max(text_lens)}") + print(f" min len: {min(text_lens)}") + print(f" avg len: {sum(text_lens) / len(text_lens)}") for sent in data: to_tuple(sent) - log.info(f"data len: {len(data)}") + print(f"data len: {len(data)}") return data -class DataGenerator(Dataset): +class TDEERDataset(Dataset): def __init__(self, datas: List, tokenizer: BertTokenizer, rel2id: Dict, all_rels: List, max_len: int, - batch_size: int = 32, max_sample_triples: Optional[int] = None, neg_samples: Optional[int] = None): + max_sample_triples: Optional[int] = None, neg_samples: Optional[int] = None): self.max_sample_triples = max_sample_triples self.neg_samples = neg_samples - self.batch_size = batch_size self.tokenizer = tokenizer self.max_len = max_len self.rel2id = rel2id @@ -86,7 +76,7 @@ def __init__(self, datas: List, tokenizer: BertTokenizer, rel2id: Dict, all_rels pos_datas = [] neg_datas = [] - text_tokened = tokenizer.encode_plus(data['text'], truncation=True, padding='max_length', max_length=max_len) + text_tokened = tokenizer(data['text'], max_length=max_len, truncation=True, padding='max_length', return_tensors='pt') entity_set = set() # (head idx, tail idx) triples_set = set() # (sub head, sub tail, obj head, obj tail, rel) subj_rel_set = set() # (sub head, sub tail, rel) @@ -96,12 +86,12 @@ def __init__(self, datas: List, tokenizer: BertTokenizer, rel2id: Dict, all_rels for triple in data['triple_list']: subj, rel, obj = triple rel_idx = self.rel2id[rel] - subj_tokened = tokenizer.encode_plus(subj, add_special_tokens=False) - obj_tokened = tokenizer.encode_plus(obj, add_special_tokens=False) - subj_head_idx = find_entity(text_tokened['input_ids'], subj_tokened['input_ids']) - subj_tail_idx = subj_head_idx + len(subj_tokened['input_ids']) - 1 - obj_head_idx = find_entity(text_tokened['input_ids'], obj_tokened['input_ids']) - obj_tail_idx = obj_head_idx + len(obj_tokened['input_ids']) - 1 + subj_tokened = tokenizer(subj, add_special_tokens=False) + obj_tokened = tokenizer(obj, add_special_tokens=False) + subj_head_idx = find_entity(text_tokened.input_ids[0].tolist(), subj_tokened.input_ids) + subj_tail_idx = subj_head_idx + len(subj_tokened.input_ids) - 1 + obj_head_idx = find_entity(text_tokened.input_ids[0].tolist(), obj_tokened.input_ids) + obj_tail_idx = obj_head_idx + len(obj_tokened.input_ids) - 1 if subj_head_idx == -1 or obj_head_idx == -1: continue entity_set.add((subj_head_idx, subj_tail_idx, 0)) @@ -117,13 +107,13 @@ def __init__(self, datas: List, tokenizer: BertTokenizer, rel2id: Dict, all_rels if not rel_set: continue - entity_heads = np.zeros((self.max_len, 2)) - entity_tails = np.zeros((self.max_len, 2)) + entity_heads = torch.zeros((self.max_len, 2)) + entity_tails = torch.zeros((self.max_len, 2)) for (head, tail, _type) in entity_set: entity_heads[head][_type] = 1 entity_tails[tail][_type] = 1 - rels = np.zeros(self.relation_size) + rels = torch.zeros(self.relation_size) for idx in rel_set: rels[idx] = 1 @@ -137,146 +127,97 @@ def __init__(self, datas: List, tokenizer: BertTokenizer, rel2id: Dict, all_rels neg_history = set() for subj_head_idx, subj_tail_idx, obj_head_idx, obj_tail_idx, rel_idx in triples_list: current_neg_datas = [] - sample_obj_heads = np.zeros(self.max_len) + sample_obj_heads = torch.zeros(self.max_len) for idx in trans_map[(subj_head_idx, subj_tail_idx, rel_idx)]: sample_obj_heads[idx] = 1.0 - # postive samples + # positive samples pos_datas.append({ - 'token_ids': text_tokened['input_ids'], - 'segment_ids': text_tokened['token_type_ids'], + 'token_ids': text_tokened.input_ids[0], + 'attention_mask': text_tokened.attention_mask[0], 'entity_heads': entity_heads, 'entity_tails': entity_tails, 'rels': rels, - 'sample_subj_head': subj_head_idx, - 'sample_subj_tail': subj_tail_idx, - 'sample_rel': rel_idx, + 'sample_subj_head': torch.tensor(subj_head_idx, dtype=torch.long), + 'sample_subj_tail': torch.tensor(subj_tail_idx, dtype=torch.long), + 'sample_rel': torch.tensor(rel_idx, dtype=torch.long), 'sample_obj_heads': sample_obj_heads, }) - # 1. inverse (tail as subj) - neg_subj_head_idx = obj_head_idx - neg_sub_tail_idx = obj_tail_idx - neg_pair = (neg_subj_head_idx, neg_sub_tail_idx, rel_idx) - if neg_pair not in subj_rel_set and neg_pair not in neg_history: - current_neg_datas.append({ - 'token_ids': text_tokened['input_ids'], - 'segment_ids': text_tokened['token_type_ids'], - 'entity_heads': entity_heads, - 'entity_tails': entity_tails, - 'rels': rels, - 'sample_subj_head': neg_subj_head_idx, - 'sample_subj_tail': neg_sub_tail_idx, - 'sample_rel': rel_idx, - 'sample_obj_heads': np.zeros(self.max_len), # set 0 for negative samples - }) - neg_history.add(neg_pair) - - # 2. (pos sub, neg_rel) - for neg_rel_idx in rel_set - {rel_idx}: - neg_pair = (subj_head_idx, subj_tail_idx, neg_rel_idx) - if neg_pair not in subj_rel_set and neg_pair not in neg_history: + # Generate negative samples + if self.neg_samples: + for _ in range(self.neg_samples): + neg_rel = np.random.choice(self.rels_set) + if neg_rel == rel_idx: + continue + if (subj_head_idx, subj_tail_idx, neg_rel) in neg_history: + continue + neg_history.add((subj_head_idx, subj_tail_idx, neg_rel)) + + neg_sample_obj_heads = torch.zeros(self.max_len) + for idx in trans_map.get((subj_head_idx, subj_tail_idx, neg_rel), []): + neg_sample_obj_heads[idx] = 1.0 + current_neg_datas.append({ - 'token_ids': text_tokened['input_ids'], - 'segment_ids': text_tokened['token_type_ids'], + 'token_ids': text_tokened.input_ids[0], + 'attention_mask': text_tokened.attention_mask[0], 'entity_heads': entity_heads, 'entity_tails': entity_tails, 'rels': rels, - 'sample_subj_head': subj_head_idx, - 'sample_subj_tail': subj_tail_idx, - 'sample_rel': neg_rel_idx, - 'sample_obj_heads': np.zeros(self.max_len), # set 0 for negative samples + 'sample_subj_head': torch.tensor(subj_head_idx, dtype=torch.long), + 'sample_subj_tail': torch.tensor(subj_tail_idx, dtype=torch.long), + 'sample_rel': torch.tensor(neg_rel, dtype=torch.long), + 'sample_obj_heads': neg_sample_obj_heads, }) - neg_history.add(neg_pair) - # 3. (neg sub, pos rel) - for (neg_subj_head_idx, neg_sub_tail_idx) in subj_set - {(subj_head_idx, subj_tail_idx)}: - neg_pair = (neg_subj_head_idx, neg_sub_tail_idx, rel_idx) - if neg_pair not in subj_rel_set and neg_pair not in neg_history: - current_neg_datas.append({ - 'token_ids': text_tokened['input_ids'], - 'segment_ids': text_tokened['token_type_ids'], - 'entity_heads': entity_heads, - 'entity_tails': entity_tails, - 'rels': rels, - 'sample_subj_head': neg_subj_head_idx, - 'sample_subj_tail': neg_sub_tail_idx, - 'sample_rel': rel_idx, - 'sample_obj_heads': np.zeros(self.max_len), # set 0 for negative samples - }) - neg_history.add(neg_pair) + neg_datas.extend(current_neg_datas) - np.random.shuffle(current_neg_datas) - if self.neg_samples is not None: - current_neg_datas = current_neg_datas[:self.neg_samples] - neg_datas += current_neg_datas current_datas = pos_datas + neg_datas self.datas.extend(current_datas) + print(f"Total number of samples: {len(self.datas)}") + print(f"Number of positive samples: {len(pos_datas)}") + print(f"Number of negative samples: {len(neg_datas)}") + def __len__(self): return len(self.datas) def __getitem__(self, idx): - sample = self.datas[idx] - - return { - 'token_ids': sample['token_ids'], - 'segment_ids': sample['segment_ids'], - 'entity_heads': sample['entity_heads'], - 'entity_tails': sample['entity_tails'], - 'rels': sample['rels'], - 'sample_subj_head': sample['sample_subj_head'], - 'sample_subj_tail': sample['sample_subj_tail'], - 'sample_rel': sample['sample_rel'], - 'sample_obj_heads': sample['sample_obj_heads'] - } - + item = self.datas[idx] + required_keys = ['token_ids', 'attention_mask', 'entity_heads', 'entity_tails', 'rels', 'sample_subj_head', 'sample_subj_tail', 'sample_rel', 'sample_obj_heads'] + for key in required_keys: + assert key in item, f"Required key '{key}' not found in item at index {idx}" + return item def collate_fn(batch): - print("Batch input:", batch) - batch_tokens = [item['token_ids'] for item in batch] - batch_attention_masks = [[1] * len(tokens) for tokens in batch_tokens] - batch_segments = [item['segment_ids'] for item in batch] - batch_entity_heads = [item['entity_heads'] for item in batch] - batch_entity_tails = [item['entity_tails'] for item in batch] - batch_rels = [item['rels'] for item in batch] - batch_sample_subj_head = [item['sample_subj_head'] for item in batch] - batch_sample_subj_tail = [item['sample_subj_tail'] for item in batch] - batch_sample_rel = [item['sample_rel'] for item in batch] - batch_sample_obj_heads = [item['sample_obj_heads'] for item in batch] - - # Convert lists to NumPy arrays - batch_tokens_np = np.array(batch_tokens) - batch_attention_masks_np = np.array(batch_attention_masks) - batch_segments_np = np.array(batch_segments) - batch_entity_heads_np = np.array(batch_entity_heads) - batch_entity_tails_np = np.array(batch_entity_tails) - batch_rels_np = np.array(batch_rels) - batch_sample_subj_head_np = np.array(batch_sample_subj_head) - batch_sample_subj_tail_np = np.array(batch_sample_subj_tail) - batch_sample_rel_np = np.array(batch_sample_rel) - batch_sample_obj_heads_np = np.array(batch_sample_obj_heads) - - # Convert NumPy arrays to PyTorch tensors - batch_tokens = torch.tensor(batch_tokens_np, dtype=torch.long) - batch_attention_masks = torch.tensor(batch_attention_masks_np, dtype=torch.long) - batch_segments = torch.tensor(batch_segments_np, dtype=torch.long) - batch_entity_heads = torch.tensor(batch_entity_heads_np, dtype=torch.float) - batch_entity_tails = torch.tensor(batch_entity_tails_np, dtype=torch.float) - batch_rels = torch.tensor(batch_rels_np, dtype=torch.float) - batch_sample_subj_head = torch.tensor(batch_sample_subj_head_np, dtype=torch.long) - batch_sample_subj_tail = torch.tensor(batch_sample_subj_tail_np, dtype=torch.long) - batch_sample_rel = torch.tensor(batch_sample_rel_np, dtype=torch.long) - batch_sample_obj_heads = torch.tensor(batch_sample_obj_heads_np, dtype=torch.float) + def stack_or_pad(key): + values = [d[key] for d in batch] + if isinstance(values[0], torch.Tensor): + return torch.stack(values) + elif isinstance(values[0], (int, np.int64)): + return torch.tensor(values, dtype=torch.long) + elif isinstance(values[0], list): + max_len = max(len(v) for v in values) + return torch.tensor([v + [0] * (max_len - len(v)) for v in values]) + else: + print(f"Warning: Unexpected type in collate_fn for {key}: {type(values[0])}") + return values + + result = {} + for key in batch[0].keys(): + try: + result[key] = stack_or_pad(key) + except Exception as e: + print(f"Error processing {key}: {e}") + result[key] = [d[key] for d in batch] # 退回到简单的列表 + + # 确保 'token_ids' 存在 + if 'token_ids' not in result: + print("Warning: 'token_ids' not found in batch. Keys present:", result.keys()) + if 'input_ids' in result: + result['token_ids'] = result['input_ids'] + print("Using 'input_ids' as 'token_ids'") + + return result - return { - 'token_ids': batch_tokens, - 'attention_mask': batch_attention_masks, - 'segment_ids': batch_segments, - 'entity_heads': batch_entity_heads, - 'entity_tails': batch_entity_tails, - 'rels': batch_rels, - 'sample_subj_head': batch_sample_subj_head, - 'sample_subj_tail': batch_sample_subj_tail, - 'sample_rel': batch_sample_rel, - 'sample_obj_heads': batch_sample_obj_heads - } \ No newline at end of file +def get_dataloader(dataset: TDEERDataset, batch_size: int, shuffle: bool = True) -> DataLoader: + return DataLoader(dataset, batch_size=batch_size, shuffle=shuffle, collate_fn=collate_fn) \ No newline at end of file diff --git a/model .py b/model .py new file mode 100644 index 0000000..f2c610e --- /dev/null +++ b/model .py @@ -0,0 +1,117 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from transformers import BertModel, BertConfig + +class SelfAttention(nn.Module): + def __init__(self, input_size, is_residual=True, attention_activation='relu'): + super(SelfAttention, self).__init__() + self.is_residual = is_residual + self.attention = nn.Linear(input_size, 1) + self.activation = nn.ReLU() if attention_activation == 'relu' else nn.Tanh() + + def forward(self, inputs): + attention_weights = self.activation(self.attention(inputs)) + attention_weights = F.softmax(attention_weights, dim=1) + attended = torch.sum(inputs * attention_weights, dim=1) + if self.is_residual: + outputs = inputs + attended.unsqueeze(1) + else: + outputs = attended.unsqueeze(1) + return outputs + +class TDEER(nn.Module): + def __init__(self, bert_model_name, relation_size): + super(TDEER, self).__init__() + self.bert = BertModel.from_pretrained(bert_model_name) + self.relation_size = relation_size + + hidden_size = self.bert.config.hidden_size + + self.entity_heads = nn.Linear(hidden_size, 2) + self.entity_tails = nn.Linear(hidden_size, 2) + self.rel_classifier = nn.Linear(hidden_size, relation_size) + + self.sub_head_feature = nn.Linear(hidden_size, hidden_size) + self.sub_tail_feature = nn.Linear(hidden_size, hidden_size) + self.rel_feature = nn.Embedding(relation_size, hidden_size) + self.rel_feature_dense = nn.Linear(hidden_size, hidden_size) + + # MODIFIED: Enhanced obj_feature + self.obj_feature = nn.Sequential( + nn.Linear(hidden_size * 3, hidden_size), + nn.ReLU(), + nn.Linear(hidden_size, hidden_size) + ) + self.self_attention = nn.Linear(hidden_size, 1) + self.pred_obj_head = nn.Linear(hidden_size, 1) + + def forward(self, input_ids, attention_mask, entity_heads=None, entity_tails=None, rels=None, + sample_subj_head=None, sample_subj_tail=None, sample_rel=None, sample_obj_heads=None): + bert_output = self.bert(input_ids, attention_mask=attention_mask) + sequence_output = bert_output.last_hidden_state + pooled_output = bert_output.pooler_output + + pred_entity_heads = self.entity_heads(sequence_output) + pred_entity_tails = self.entity_tails(sequence_output) + pred_rels = self.rel_classifier(pooled_output) + + if sample_subj_head is not None and sample_subj_tail is not None and sample_rel is not None: + batch_size, seq_len = input_ids.size() + + sub_head_feature = self.sub_head_feature(sequence_output[torch.arange(batch_size), sample_subj_head]) + sub_tail_feature = self.sub_tail_feature(sequence_output[torch.arange(batch_size), sample_subj_tail]) + sub_feature = (sub_head_feature + sub_tail_feature) / 2 + + rel_feature = self.rel_feature(sample_rel) + rel_feature = F.relu(self.rel_feature_dense(rel_feature)) + + obj_feature_input = torch.cat([sequence_output, + sub_feature.unsqueeze(1).expand(-1, seq_len, -1), + rel_feature.unsqueeze(1).expand(-1, seq_len, -1)], dim=-1) + obj_feature = self.obj_feature(obj_feature_input) + + # attention_weights = F.softmax(self.self_attention(obj_feature), dim=1) + # attended_obj_feature = (obj_feature * attention_weights).sum(dim=1) + + # 修改这里 + pred_obj_head = self.pred_obj_head(obj_feature).squeeze(-1) + + # 添加调试信息 + # print(f"Debug - pred_obj_head shape: {pred_obj_head.shape}") + # print(f"Debug - attention_mask shape: {attention_mask.shape}") + + return pred_entity_heads, pred_entity_tails, pred_rels, pred_obj_head + + return pred_entity_heads, pred_entity_tails, pred_rels + +class Evaluator: + def __init__(self, model, dev_data, tokenizer, id2rel, device): + self.model = model + self.dev_data = dev_data + self.tokenizer = tokenizer + self.id2rel = id2rel + self.device = device + self.best_f1 = float('-inf') + + def evaluate(self, epoch): + self.model.eval() + # Implement evaluation logic here + # Use the compute_metrics function from utils.py + precision, recall, f1 = compute_metrics(self.model, self.dev_data, self.tokenizer, self.id2rel, self.device) + + if f1 > self.best_f1: + self.best_f1 = f1 + torch.save(self.model.state_dict(), f'best_model_epoch_{epoch}.pt') + print("New best model saved!") + + print(f'Epoch {epoch}: F1: {f1:.4f}, Precision: {precision:.4f}, Recall: {recall:.4f}, Best F1: {self.best_f1:.4f}\n') + + return f1 + +def build_model(bert_dir: str, relation_size: int, device: str): + model = TDEER(bert_dir, relation_size).to(device) + return model + +def get_optimizer(model: nn.Module, learning_rate: float): + return torch.optim.Adam(model.parameters(), lr=learning_rate) \ No newline at end of file diff --git a/model.py b/model.py deleted file mode 100644 index 7a986d3..0000000 --- a/model.py +++ /dev/null @@ -1,170 +0,0 @@ -# -*- coding:utf-8 -*- -import torch -import torch.nn as nn -import torch.optim as optim -from transformers import BertModel -from utils import compute_metrics - -class SelfAttention(nn.Module): - def __init__(self, hidden_dim): - super(SelfAttention, self).__init__() - self.hidden_dim = hidden_dim - self.attention = nn.MultiheadAttention(hidden_dim, num_heads=8, batch_first=True) - - def forward(self, x): - attn_output, _ = self.attention(x, x, x) - return attn_output + x # residual connection - -class EntityModel(nn.Module): - def __init__(self, bert_model_name): - super(EntityModel, self).__init__() - self.bert = BertModel.from_pretrained(bert_model_name) - self.pred_entity_heads = nn.Linear(self.bert.config.hidden_size, 2) - self.pred_entity_tails = nn.Linear(self.bert.config.hidden_size, 2) - - def forward(self, input_ids, attention_mask, token_type_ids): - input_ids = input_ids.to(self.bert.device) - attention_mask = attention_mask.to(self.bert.device) - token_type_ids = token_type_ids.to(self.bert.device) - - bert_outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) - tokens_feature = bert_outputs.last_hidden_state - pred_entity_heads = torch.sigmoid(self.pred_entity_heads(tokens_feature)) - pred_entity_tails = torch.sigmoid(self.pred_entity_tails(tokens_feature)) - return pred_entity_heads, pred_entity_tails - -class RelationModel(nn.Module): - def __init__(self, bert_model_name, relation_size): - super(RelationModel, self).__init__() - self.bert = BertModel.from_pretrained(bert_model_name) - self.pred_rels = nn.Linear(self.bert.config.hidden_size, relation_size) - - def forward(self, input_ids, attention_mask, token_type_ids): - input_ids = input_ids.to(self.bert.device) - attention_mask = attention_mask.to(self.bert.device) - token_type_ids = token_type_ids.to(self.bert.device) - - bert_outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) - tokens_feature = bert_outputs.last_hidden_state - pred_rels = torch.sigmoid(self.pred_rels(tokens_feature[:, 0, :])) - return pred_rels - -class TranslateModel(nn.Module): - def __init__(self, bert_model_name, relation_size, hidden_size): - super(TranslateModel, self).__init__() - self.bert = BertModel.from_pretrained(bert_model_name) - self.rel_embedding = nn.Embedding(relation_size, hidden_size) - self.rel_dense = nn.Linear(hidden_size, hidden_size) - self.self_attention = SelfAttention(hidden_size) - self.pred_obj_head = nn.Linear(hidden_size, 1) - - def forward(self, input_ids, attention_mask, token_type_ids, sub_head, sub_tail, rel): - input_ids = input_ids.to(self.bert.device) - attention_mask = attention_mask.to(self.bert.device) - token_type_ids = token_type_ids.to(self.bert.device) - sub_head = sub_head.to(self.bert.device) - sub_tail = sub_tail.to(self.bert.device) - rel = rel.to(self.bert.device) - - bert_outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask, token_type_ids=token_type_ids) - tokens_feature = bert_outputs.last_hidden_state # (batch_size, seq_len, hidden_size) - - sub_head_feature = tokens_feature[torch.arange(tokens_feature.size(0)), sub_head.squeeze()] # (batch_size, hidden_size) - sub_tail_feature = tokens_feature[torch.arange(tokens_feature.size(0)), sub_tail.squeeze()] # (batch_size, hidden_size) - sub_feature = (sub_head_feature + sub_tail_feature) / 2 # (batch_size, hidden_size) - - rel_feature = torch.relu(self.rel_dense(self.rel_embedding(rel).squeeze(1))) # (batch_size, hidden_size) - - # Ensure dimensions match for broadcasting - sub_feature = sub_feature.unsqueeze(1).expand(-1, tokens_feature.size(1), -1) # (batch_size, seq_len, hidden_size) - rel_feature = rel_feature.unsqueeze(1).expand(-1, tokens_feature.size(1), -1) # (batch_size, seq_len, hidden_size) - - obj_feature = tokens_feature + sub_feature + rel_feature # (batch_size, seq_len, hidden_size) - obj_feature = self.self_attention(obj_feature) # (batch_size, seq_len, hidden_size) - pred_obj_head = torch.sigmoid(self.pred_obj_head(obj_feature)) # (batch_size, seq_len, 1) - - return pred_obj_head.squeeze(-1) # (batch_size, seq_len) - -class TDEERModel(nn.Module): - def __init__(self, entity_model, rel_model, translate_model): - super(TDEERModel, self).__init__() - self.entity_model = entity_model - self.rel_model = rel_model - self.translate_model = translate_model - - def forward(self, input_ids, attention_mask, token_type_ids, gold_entity_heads, gold_entity_tails, gold_rels, sub_head, sub_tail, rel, gold_obj_head): - input_ids = input_ids.to(self.entity_model.bert.device) - attention_mask = attention_mask.to(self.entity_model.bert.device) - token_type_ids = token_type_ids.to(self.entity_model.bert.device) - gold_entity_heads = gold_entity_heads.to(self.entity_model.bert.device) - gold_entity_tails = gold_entity_tails.to(self.entity_model.bert.device) - gold_rels = gold_rels.to(self.entity_model.bert.device) - sub_head = sub_head.to(self.entity_model.bert.device) - sub_tail = sub_tail.to(self.entity_model.bert.device) - rel = rel.to(self.entity_model.bert.device) - gold_obj_head = gold_obj_head.to(self.entity_model.bert.device) - - pred_entity_heads, pred_entity_tails = self.entity_model(input_ids, attention_mask, token_type_ids) - pred_rels = self.rel_model(input_ids, attention_mask, token_type_ids) - pred_obj_head = self.translate_model(input_ids, attention_mask, token_type_ids, sub_head, sub_tail, rel) - - loss = self.compute_loss(pred_entity_heads, gold_entity_heads, pred_entity_tails, gold_entity_tails, pred_rels, gold_rels, pred_obj_head, gold_obj_head) - return loss - - def compute_loss(self, pred_entity_heads, gold_entity_heads, pred_entity_tails, gold_entity_tails, pred_rels, gold_rels, pred_obj_head, gold_obj_head): - entity_heads_loss = nn.functional.binary_cross_entropy(pred_entity_heads, gold_entity_heads, reduction='mean') - entity_tails_loss = nn.functional.binary_cross_entropy(pred_entity_tails, gold_entity_tails, reduction='mean') - rel_loss = nn.functional.binary_cross_entropy(pred_rels, gold_rels, reduction='mean') - pred_obj_head = pred_obj_head.squeeze(-1) - obj_head_loss = nn.functional.binary_cross_entropy(pred_obj_head, gold_obj_head, reduction='mean') - - total_loss = entity_heads_loss + entity_tails_loss + rel_loss + 5.0 * obj_head_loss - return total_loss - -def build_model(bert_model_name: str, learning_rate: float, relation_size: int, device): - hidden_size = BertModel.from_pretrained(bert_model_name).config.hidden_size - entity_model = EntityModel(bert_model_name).to(device) - rel_model = RelationModel(bert_model_name, relation_size).to(device) - translate_model = TranslateModel(bert_model_name, relation_size, hidden_size).to(device) - train_model = TDEERModel(entity_model, rel_model, translate_model).to(device) - - optimizer = optim.AdamW(train_model.parameters(), lr=learning_rate) - return entity_model, rel_model, translate_model, train_model, optimizer - -class Evaluator: - def __init__(self, infer, model, dev_data, save_weights_path, model_name, optimizer, device, learning_rate=5e-5, min_learning_rate=5e-6): - self.infer = infer - self.model = model - self.dev_data = dev_data - self.save_weights_path = save_weights_path - self.model_name = model_name - self.optimizer = optimizer - self.device = device - self.learning_rate = learning_rate - self.min_learning_rate = min_learning_rate - self.best = float('-inf') - self.passed = 0 - self.is_exact_match = model_name.startswith('NYT11-HRL') - - def evaluate(self): - self.model.eval() - precision, recall, f1 = compute_metrics(self.infer, self.dev_data, exact_match=self.is_exact_match, model_name=self.model_name) - if f1 > self.best: - self.best = f1 - torch.save(self.model.state_dict(), self.save_weights_path) - print("New best result!") - print(f'f1: {f1:.4f}, precision: {precision:.4f}, recall: {recall:.4f}, best f1: {self.best:.4f}') - self.model.train() - - def adjust_learning_rate(self, step, total_steps): - if step < total_steps: - lr = (step + 1) / total_steps * self.learning_rate - else: - lr = (2 - (step + 1) / total_steps) * (self.learning_rate - self.min_learning_rate) + self.min_learning_rate - for param_group in self.optimizer.param_groups: - param_group['lr'] = lr - -# Example of usage: -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') -entity_model, rel_model, translate_model, train_model, optimizer = build_model('bert-base-uncased', 2e-5, 10, device) -evaluator = Evaluator(None, train_model, None, 'model.pth', 'NYT11-HRL', optimizer, device) diff --git a/run.py b/run.py index bced37e..6bbb66b 100644 --- a/run.py +++ b/run.py @@ -1,13 +1,15 @@ -# -*- coding:utf-8 -*- - import os import argparse import torch from torch.utils.data import DataLoader +from torch.cuda.amp import autocast, GradScaler +import torch.nn.functional as F from transformers import BertTokenizerFast from tqdm import tqdm -from dataloader import DataGenerator, load_data, load_rel, collate_fn -from model import build_model, Evaluator +import numpy as np + +from dataloader import TDEERDataset, load_data, load_rel +from model import TDEER, get_optimizer from utils import Infer, compute_metrics parser = argparse.ArgumentParser(description='TDEER cli') @@ -18,88 +20,126 @@ parser.add_argument('--train_path', type=str, help='specify the train path') parser.add_argument('--dev_path', type=str, help='specify the dev path') parser.add_argument('--test_path', type=str, help='specify the test path') -parser.add_argument('--bert_model_name', type=str, default='bert-base-cased', help='specify the pre-trained bert model') +parser.add_argument('--bert_model', type=str, default='bert-base-cased', help='specify the pre-trained bert model') parser.add_argument('--save_path', default=None, type=str, help='specify the save path to save model [training phase]') parser.add_argument('--ckpt_path', default=None, type=str, help='specify the ckpt path [test phase]') -parser.add_argument('--learning_rate', default=2e-5, type=float, help='specify the learning rate') -parser.add_argument('--epoch', default=100, type=int, help='specify the epoch size') -parser.add_argument('--batch_size', default=8, type=int, help='specify the batch size') +parser.add_argument('--learning_rate', default=1e-5, type=float, help='specify the learning rate') +parser.add_argument('--epoch', default=5, type=int, help='specify the epoch size') +parser.add_argument('--batch_size', default=16, type=int, help='specify the batch size') parser.add_argument('--max_len', default=120, type=int, help='specify the max len') -parser.add_argument('--neg_samples', default=None, type=int, help='specify negative sample num') -parser.add_argument('--max_sample_triples', default=None, type=int, help='specify max sample triples') -parser.add_argument('--verbose', default=2, type=int, help='specify verbose: 0 = silent, 1 = progress bar, 2 = one line per epoch') +parser.add_argument('--neg_samples', default=2, type=int, help='specify negative sample num') +parser.add_argument('--max_sample_triples', default=100, type=int, help='specify max sample triples') +parser.add_argument('--eval_steps', default=100, type=int, help='evaluate every N steps') +parser.add_argument('--num_workers', type=int, default=4, help='number of workers for data loading') +parser.add_argument('--use_amp', action='store_true', help='use automatic mixed precision') +parser.add_argument('--subset_size', type=int, default=None, help='use a subset of data for quick validation') + args = parser.parse_args() print("Argument:", args) -device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - +device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') id2rel, rel2id, all_rels = load_rel(args.rel_path) -tokenizer = BertTokenizerFast.from_pretrained(args.bert_model_name) -entity_model, rel_model, translate_model, train_model, optimizer = build_model(args.bert_model_name, args.learning_rate, len(all_rels), device) -train_model.to(device) +tokenizer = BertTokenizerFast.from_pretrained(args.bert_model) +model = TDEER(args.bert_model, len(all_rels)).to(device) -if args.do_train: - assert args.save_path is not None, "please specify --save_path in training phase" +def compute_loss(pred_entity_heads, pred_entity_tails, pred_rels, pred_obj_head, + entity_heads, entity_tails, rels, sample_obj_heads, attention_mask): + entity_heads_loss = F.binary_cross_entropy_with_logits(pred_entity_heads, entity_heads, reduction='none') + entity_heads_loss = (entity_heads_loss * attention_mask.unsqueeze(-1)).sum() / attention_mask.sum() + + entity_tails_loss = F.binary_cross_entropy_with_logits(pred_entity_tails, entity_tails, reduction='none') + entity_tails_loss = (entity_tails_loss * attention_mask.unsqueeze(-1)).sum() / attention_mask.sum() - # 加载训练数据、验证数据和测试数据 + rel_loss = F.binary_cross_entropy_with_logits(pred_rels, rels) + + obj_head_loss = F.binary_cross_entropy_with_logits(pred_obj_head, sample_obj_heads, reduction='none') + obj_head_loss = (obj_head_loss * attention_mask).sum() / attention_mask.sum() + + loss = entity_heads_loss + entity_tails_loss + rel_loss + 10.0 * obj_head_loss + return loss + +if args.do_train: train_data = load_data(args.train_path, rel2id, is_train=True) dev_data = load_data(args.dev_path, rel2id, is_train=False) - test_data = load_data(args.test_path, rel2id, is_train=False) if args.test_path is not None else None - # 创建数据生成器和数据加载器 - train_generator = DataGenerator(train_data, tokenizer, rel2id, all_rels, args.max_len, args.max_sample_triples, args.neg_samples) - train_loader = DataLoader(train_generator, batch_size=args.batch_size, shuffle=True, collate_fn=collate_fn) - - # 创建优化器和评估器 - # optimizer = torch.optim.AdamW(train_model.parameters(), lr=args.learning_rate) - infer = Infer(entity_model, rel_model, translate_model, tokenizer, id2rel) - evaluator = Evaluator(infer, train_model, dev_data, args.save_path, args.model_name, optimizer, device) + if args.subset_size: + train_data = train_data[:args.subset_size] + dev_data = dev_data[:min(args.subset_size, len(dev_data))] - # 训练模型 + train_dataset = TDEERDataset(train_data, tokenizer, rel2id, all_rels, args.max_len, args.max_sample_triples, args.neg_samples) + train_dataloader = DataLoader(train_dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers) + + optimizer = get_optimizer(model, args.learning_rate) + scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.9) + scaler = GradScaler() + + best_f1 = 0 + global_step = 0 for epoch in range(args.epoch): - train_model.train() - with tqdm(train_loader, desc=f'Epoch {epoch+1}/{args.epoch}', unit='batch') as t_loader: - for batch in t_loader: - batch = {k: v.to(device).float() if v.dtype == torch.float64 else v.to(device) for k, v in batch.items()} - # print(batch) - inputs = { - 'input_ids': batch['token_ids'], # Changed from 'input_ids' to 'token_ids' - 'attention_mask': batch['attention_mask'], # Assuming this key is correct - 'token_type_ids': batch['segment_ids'], # Changed from 'token_type_ids' to 'segment_ids' - 'gold_entity_heads': batch['entity_heads'], # Assuming this key is correct - 'gold_entity_tails': batch['entity_tails'], # Assuming this key is correct - 'gold_rels': batch['rels'], # Assuming this key is correct - 'sub_head': batch['sample_subj_head'], # Assuming this key is correct - 'sub_tail': batch['sample_subj_tail'], # Assuming this key is correct - 'rel': batch['sample_rel'], # Assuming this key is correct - 'gold_obj_head': batch['sample_obj_heads'] # Assuming this key is correct - } - - + model.train() + total_loss = 0 + with tqdm(total=len(train_dataloader), desc=f"Epoch {epoch+1}/{args.epoch}") as pbar: + for batch in train_dataloader: optimizer.zero_grad() - loss = train_model(**inputs) - loss.backward() - optimizer.step() - t_loader.set_postfix({'loss': loss.item()}) - t_loader.update() - - # 每个 epoch 结束后进行评估 - evaluator.evaluate() + + batch = {k: v.to(device) if isinstance(v, torch.Tensor) else v for k, v in batch.items()} + if 'token_ids' in batch: + batch['input_ids'] = batch.pop('token_ids') + + with autocast(enabled=args.use_amp): + outputs = model(**batch) + loss = compute_loss(*outputs, + batch['entity_heads'], + batch['entity_tails'], + batch['rels'], + batch['sample_obj_heads'], + batch['attention_mask']) + + if args.use_amp: + scaler.scale(loss).backward() + scaler.step(optimizer) + scaler.update() + else: + loss.backward() + optimizer.step() - # 保存训练好的模型 - torch.save(train_model.state_dict(), args.save_path) + total_loss += loss.item() + pbar.update(1) + pbar.set_postfix({'loss': total_loss / (pbar.n + 1)}) + global_step += 1 + if global_step % args.eval_steps == 0: + model.eval() + with torch.no_grad(): + infer = Infer(model, tokenizer, id2rel, device) + precision, recall, f1 = compute_metrics(infer, dev_data, args.model_name) + print(f"Step {global_step} - Dev set - Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1:.4f}") + if f1 > best_f1: + best_f1 = f1 + torch.save(model.state_dict(), args.save_path) + print(f"New best model saved with F1: {best_f1:.4f}") + model.train() + + print(f"Epoch {epoch+1}/{args.epoch}, Average Loss: {total_loss / len(train_dataloader):.4f}") + scheduler.step() + + # Evaluate on dev set + model.eval() + with torch.no_grad(): + infer = Infer(model, tokenizer, id2rel, device) + precision, recall, f1 = compute_metrics(infer, dev_data, args.model_name) + print(f"Dev set - Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1:.4f}") + + if f1 > best_f1: + best_f1 = f1 + torch.save(model.state_dict(), args.save_path) + print(f"New best model saved with F1: {best_f1:.4f}") + if args.do_test: assert args.ckpt_path is not None, "please specify --ckpt_path in test phase" - - # 加载测试数据和模型 test_data = load_data(args.test_path, rel2id, is_train=False) - train_model.load_state_dict(torch.load(args.ckpt_path, map_location=device)) - train_model.to(device) - train_model.eval() - - # 进行测试并计算评估指标 - infer = Infer(entity_model, rel_model, translate_model, tokenizer, id2rel) - precision, recall, f1_score = compute_metrics(infer, test_data, model_name=args.model_name) - print(f'precision: {precision}, recall: {recall}, f1: {f1_score}') + model.load_state_dict(torch.load(args.ckpt_path)) + infer = Infer(model, tokenizer, id2rel, device) + precision, recall, f1_score = compute_metrics(infer, test_data, args.model_name) + print(f'Test set - Precision: {precision:.4f}, Recall: {recall:.4f}, F1: {f1_score:.4f}') \ No newline at end of file diff --git a/utils.py b/utils.py index e21cba7..24abea5 100644 --- a/utils.py +++ b/utils.py @@ -2,142 +2,113 @@ import time from typing import Dict, List, Set -import torch import numpy as np +import torch from tqdm import tqdm - def rematch(offsets: List) -> List: mapping = [] for offset in offsets: if offset[0] == 0 and offset[1] == 0: mapping.append([]) else: - mapping.append(list(range(offset[0], offset[1]))) + mapping.append([i for i in range(offset[0], offset[1])]) return mapping - -device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') class Infer: - def __init__(self, entity_model: torch.nn.Module, rel_model: torch.nn.Module, translate_model: torch.nn.Module, - tokenizer: object, id2rel: Dict): - self.entity_model = entity_model.to(device) - self.rel_model = rel_model.to(device) - self.translate_model = translate_model.to(device) + def __init__(self, model: torch.nn.Module, tokenizer, id2rel: Dict, device: str): + self.model = model self.tokenizer = tokenizer self.id2rel = id2rel + self.device = device def decode_entity(self, text: str, mapping: List, start: int, end: int): - # print(f"Input text: {text}") - # print(f"Mapping: {mapping}") - # print(f"Start index: {start}, End index: {end}") - - if start >= len(mapping) or end >= len(mapping): - print("Start or end index out of range.") - return "" - - s = mapping[start] if start < len(mapping) else [] - e = mapping[end] if end < len(mapping) else [] - # print(f"Raw start mapping: {s}, Raw end mapping: {e}") - - # 取s和e的第一个和最后一个值来确保提取正确的子串 - s = s[0] if s else 0 - e = e[-1] if e else len(text) - 1 - # print(f"Adjusted start: {s}, Adjusted end: {e}") - - s = max(0, s) - e = min(len(text) - 1, e) - + s = mapping[start] + e = mapping[end] + s = 0 if not s else s[0] + e = len(text) - 1 if not e else e[-1] entity = text[s: e + 1] - # print(f"Extracted entity: {entity}") return entity - - def __call__(self, text: str, threshold: float = 0.5) -> Set: - # Tokenize text and prepare input tensors - tokened = self.tokenizer.encode_plus(text, add_special_tokens=True, return_offsets_mapping=True) # 确保添加了特殊标记 - token_ids = torch.tensor([tokened.input_ids], dtype=torch.long).to(device) - segment_ids = torch.tensor([tokened.token_type_ids], dtype=torch.long).to(device) - attention_mask = torch.tensor([tokened.attention_mask], dtype=torch.long).to(device) - mapping = rematch(tokened.offset_mapping) - - # Run entity model to get logits for entity heads and tails + def __call__(self, text: str, threshold: float = 0.01) -> Set: # MODIFIED: changed threshold from 0.1 to 0.01 + self.model.eval() with torch.no_grad(): - entity_heads_logits, entity_tails_logits = self.entity_model(token_ids, attention_mask, segment_ids) - - # Apply threshold to get entity indices - entity_heads = (entity_heads_logits > threshold).nonzero(as_tuple=True) - entity_tails = (entity_tails_logits > threshold).nonzero(as_tuple=True) - subjects = [] - entity_map = {} - - # print(f"entity_heads_logits shape: {entity_heads_logits.shape}") - # print(f"entity_tails_logits shape: {entity_tails_logits.shape}") - # print(f"entity_heads: {entity_heads}") - # print(f"entity_tails: {entity_tails}") - - # Generate potential subjects and entities from heads and tails - for head_idx, head_type_idx in zip(entity_heads[1], entity_heads[2]): - for tail_idx, tail_type_idx in zip(entity_tails[1], entity_tails[2]): - if head_idx <= tail_idx and head_type_idx == tail_type_idx: - entity = self.decode_entity(text, mapping, head_idx.item(), tail_idx.item()) - # print(f"Extracted entity: {entity} from indices {head_idx.item()} to {tail_idx.item()}") - if head_type_idx == 0: # Assuming type 0 are subjects - subjects.append((entity, head_idx.item(), tail_idx.item())) - else: - entity_map[head_idx.item()] = entity + tokened = self.tokenizer(text, return_tensors='pt', padding=True, truncation=True, max_length=512, return_offsets_mapping=True) + tokened = {k: v.to(self.device) for k, v in tokened.items()} + + entity_heads_logits, entity_tails_logits, relations_logits = self.model(tokened['input_ids'], tokened['attention_mask']) + + # print(f"Max entity_heads_logits: {entity_heads_logits.max().item()}") + # print(f"Max entity_tails_logits: {entity_tails_logits.max().item()}") + # print(f"Max relations_logits: {relations_logits.max().item()}") + entity_heads_probs = torch.sigmoid(entity_heads_logits) + entity_tails_probs = torch.sigmoid(entity_tails_logits) + relations_probs = torch.sigmoid(relations_logits) + + entity_heads = torch.where(entity_heads_logits[0] > threshold) + entity_tails = torch.where(entity_tails_logits[0] > threshold) + relations = torch.where(relations_logits[0] > threshold)[0].tolist() + + # print(f"Number of entity heads: {len(entity_heads[0])}") + # print(f"Number of entity tails: {len(entity_tails[0])}") + # print(f"Number of relations: {len(relations)}") + + subjects = [] + entity_map = {} + for head, head_type in zip(*entity_heads): + for tail, tail_type in zip(*entity_tails): + if head <= tail and head_type == tail_type: + entity = self.decode_entity(text, tokened['offset_mapping'][0].tolist(), head, tail) + if head_type == 0: + subjects.append((entity, head.item(), tail.item())) + else: + entity_map[head.item()] = entity + break + + # print(f"Number of subjects: {len(subjects)}") + # print(f"Number of entities in entity_map: {len(entity_map)}") + + triple_set = set() + if subjects and relations: + for (sub, sub_head, sub_tail) in subjects: + for rel in relations: + sub_head_tensor = torch.tensor([sub_head], device=self.device) + sub_tail_tensor = torch.tensor([sub_tail], device=self.device) + rel_tensor = torch.tensor([rel], device=self.device) + + _, _, _, obj_head_logits = self.model( + tokened['input_ids'], + tokened['attention_mask'], + sample_subj_head=sub_head_tensor, + sample_subj_tail=sub_tail_tensor, + sample_rel=rel_tensor + ) - print(f"Subjects: {subjects}") - print(f"Entity map: {entity_map}") - - triple_set = set() - if subjects: - with torch.no_grad(): - relations_logits = self.rel_model(token_ids, attention_mask=attention_mask, token_type_ids=segment_ids) - relations = (relations_logits > threshold).nonzero(as_tuple=True) - if relations[0].numel() > 0: - batch_size = len(subjects) - for sub, sub_head, sub_tail in subjects: - for rel_idx in relations[0]: - rel = self.id2rel[rel_idx.item()] - batch_token_ids = token_ids.expand(batch_size, -1) - batch_segment_ids = segment_ids.expand(batch_size, -1) - batch_attention_mask = attention_mask.expand(batch_size, -1) - batch_subj_head = torch.tensor([sub_head], dtype=torch.long).expand(batch_size, 1).to(device) - batch_subj_tail = torch.tensor([sub_tail], dtype=torch.long).expand(batch_size, 1).to(device) - batch_rels = torch.tensor([rel_idx.item()], dtype=torch.long).expand(batch_size, 1).to(device) - - # Debugging shapes - # print(f"batch_token_ids shape: {batch_token_ids.shape}") - # print(f"batch_segment_ids shape: {batch_segment_ids.shape}") - # print(f"batch_attention_mask shape: {batch_attention_mask.shape}") - # print(f"batch_subj_head shape: {batch_subj_head.shape}") - # print(f"batch_subj_tail shape: {batch_subj_tail.shape}") - # print(f"batch_rels shape: {batch_rels.shape}") - - obj_head_logits = self.translate_model(batch_token_ids, batch_attention_mask, batch_segment_ids, batch_subj_head, batch_subj_tail, batch_rels) - for obj_head_idx in obj_head_logits.argmax(dim=1).tolist(): - if obj_head_idx in entity_map: - obj = entity_map[obj_head_idx] - triple_set.add((sub, rel, obj)) + # print(f"Max obj_head_logits: {obj_head_logits.max().item()}") + # print(f"Min obj_head_logits: {obj_head_logits.min().item()}") + # print(f"Mean obj_head_logits: {obj_head_logits.mean().item()}") + + for h in torch.where(obj_head_logits[0] > threshold)[0].tolist(): # MODIFIED: changed threshold from 0.1 to 0.01 + if h in entity_map: + obj = entity_map[h] + triple_set.add((sub, self.id2rel[rel], obj)) + # print(f"Predicted object head: {h}") + # print(f"Object in entity_map: {h in entity_map}") + + print(f"Number of triples: {len(triple_set)}") + print(f"Triples: {triple_set}") return triple_set - - - - def partial_match(pred_set, gold_set): pred = {(i[0].split(' ')[0] if len(i[0].split(' ')) > 0 else i[0], i[1], i[2].split(' ')[0] if len(i[2].split(' ')) > 0 else i[2]) for i in pred_set} gold = {(i[0].split(' ')[0] if len(i[0].split(' ')) > 0 else i[0], i[1], i[2].split(' ')[0] if len(i[2].split(' ')) > 0 else i[2]) for i in gold_set} return pred, gold - def remove_space(data_set): data_set = {(i[0].replace(' ', ''), i[1], i[2].replace(' ', '')) for i in data_set} return data_set - def compute_metrics(infer, dev_data, exact_match=False, model_name='tmp'): output_path = f'{model_name}.output' if output_path: @@ -145,15 +116,11 @@ def compute_metrics(infer, dev_data, exact_match=False, model_name='tmp'): orders = ['subject', 'relation', 'object'] correct_num, predict_num, gold_num = 1e-10, 1e-10, 1e-10 infer_times = [] - device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - for line in tqdm(iter(dev_data)): start_time = time.time() pred_triples = infer(line['text']) infer_times.append(time.time() - start_time) gold_triples = set(line['triple_list']) - # print(f"Predicted triples: {pred_triples}") - # print(f"Gold triples: {gold_triples}") if exact_match: gold_triples = remove_space(gold_triples) @@ -161,8 +128,6 @@ def compute_metrics(infer, dev_data, exact_match=False, model_name='tmp'): pred_triples_eval, gold_triples_eval = partial_match(pred_triples, gold_triples) if not exact_match else (pred_triples, gold_triples) - print(f"Predicted triples (eval): {pred_triples_eval}") - print(f"Gold triples (eval): {gold_triples_eval}") correct_num += len(pred_triples_eval & gold_triples_eval) predict_num += len(pred_triples_eval) gold_num += len(gold_triples_eval) @@ -193,5 +158,4 @@ def compute_metrics(infer, dev_data, exact_match=False, model_name='tmp'): print(f'correct_num:{correct_num}\npredict_num:{predict_num}\ngold_num:{gold_num}') print("avg infer time:", sum(infer_times) / len(infer_times)) - return precision, recall, f1_score - + return precision, recall, f1_score \ No newline at end of file From a42e8ca434c26890cb65c0ea6f19f426665e725e Mon Sep 17 00:00:00 2001 From: innovation64 Date: Sat, 27 Jul 2024 18:59:26 +0800 Subject: [PATCH 20/20] refactor: refactor the code to Pytorch --- model .py => model.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename model .py => model.py (100%) diff --git a/model .py b/model.py similarity index 100% rename from model .py rename to model.py