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FUXI
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yuyan-dialogue

Text Generation
PyTorch
Chinese
dialogue-generation
inference acceleration
gpt2
gpt3
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  1. README.md +113 -0
  2. checkpoint_weight_index.json +584 -0
  3. dict.txt +0 -0
  4. inference.py +205 -0
README.md CHANGED
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  ---
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  license: apache-2.0
 
 
 
 
 
 
 
 
 
 
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  ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  license: apache-2.0
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+
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+ language: zh
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+ inference: false
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+ tags:
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+ - text-generation
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+ - dialogue-generation
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+ - pytorch
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+ - inference acceleration
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+ - gpt2
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+ - gpt3
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  ---
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+ # YuYan-Dialogue
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+
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+ YuYan is a series of Chinese language models with different size, developed by Fuxi AI lab, Netease.Inc. They are trained on a large Chinese novel dataset of high quality.
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+
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+ YuYan is in the same family of decoder-only models like [GPT2 and GPT-3](https://arxiv.org/abs/2005.14165). As such, it was pretrained using the self-supervised causal language modedling objective.
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+
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+ YuYan-Dialogue is a dialogue model by fine-tuning the YuYan-11b on a large multi-turn dialogue dataset of high quality. It has very strong conversation generation capabilities.
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+
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+ ## Model Inference Acceleration
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+
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+ As the model size increases, the model inference time increases and more computational resources are required.
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+
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+ Therefore, we developed our own transformer model inference acceleration framework, [EET](https://github.com/NetEase-FuXi/EET.git). More details are in [Easy and Efficient Transformer: Scalable Inference Solution For Large NLP Model](https://aclanthology.org/2022.naacl-industry.8/).
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+
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+ We combine our language model with the EET inference framework to provide industrial-grade inference reasoning performance.
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+
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+ ## How to use
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+
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+ Our model is trained based on the [fairseq](https://github.com/facebookresearch/fairseq). As a result, the inference and finetuning depend on it.
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+
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+ For inference, we modify some parts of the original fairseq codes. Mainly
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+ > fairseq-0.12.2/fairseq/sequence_generator.py
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+
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+ We integrate the EET with sequence_generator. We replace the eos token to a token unlikely to be sampled to ensure the generated text length. The repetition penalty trick is also modified. You can change the penalty strength by adjusting the value of `self.ban_weight`.
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+
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+ Then, to keep the eos token in the final generated text, we change the line 75 `include_eos=False` to `include_eos=True` in
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+ > fairseq-0.12.2/fairseq/data/dictionary.py
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+
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+ Finally, to pass in parameters in python scripts, we remove the line 67 ~ line 69 in
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+ >fairseq-0.12.2/fairseq/dataclass/utils.py
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+
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+ Below are the install tutorial.
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+
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+ ```
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+ # install pytorch
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+ pip install torch==1.8.1 # install pytorch
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+
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+ # install fairseq
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+ unzip fairseq-0.12.2.zip
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+ cd fairseq-0.12.2
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+ pip install.
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+
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+ # install EET
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+ git clone https://github.com/NetEase-FuXi/EET.git
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+ cd EET
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+ pip install .
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+
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+ # install transformers (EET requirements)
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+ pip install transformers==4.23
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+
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+ # make a folder, move the dictionary file and model file into it.
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+ mkdir transformer_lm_gpt2_xxl_dialogue
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+ mv dict.txt transformer_lm_gpt2_xxl_dialogue/
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+ mv checkpoint_best_part_*.pt transformer_lm_gpt2_xxl_dialogue/
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+
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+ ```
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+ `inference.py` is a script to provide a interface to initialize the EET object and sequence_generator. It includes some pre-process and post-process functions for text input and output. You can modify the script according to your needs.
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+
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+ In addition, it provide a simple object to organize the dialogue generation and dialogue history.
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+
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+ After the environment is ready, several lines of codes can realize the inference.
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+
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+ ``` python
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+
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+ from inference import Inference
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+ model_path = "transformer_lm_gpt2_xxl_dialogue/checkpoint_best.pt"
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+ data_path = "transformer_lm_gpt2_xxl_dialogue"
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+ eet_batch_size = 10 # max inference batch size, adjust according to cuda memory, 40GB memory is necessary
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+ inference = Inference(model_path, data_path, eet_batch_size)
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+ dialogue_model = Dialogue(inference)
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+ dialogue_model.get_repsonse("你好啊")
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+ ```
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+ ## Citation
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+ If you find the technical report or resource is useful, please cite the following technical report in your paper.
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+ - https://aclanthology.org/2022.naacl-industry.8/
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+ ```
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+ @inproceedings{li-etal-2022-easy,
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+ title = "Easy and Efficient Transformer: Scalable Inference Solution For Large {NLP} Model",
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+ author = "Li, Gongzheng and
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+ Xi, Yadong and
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+ Ding, Jingzhen and
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+ Wang, Duan and
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+ Luo, Ziyang and
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+ Zhang, Rongsheng and
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+ Liu, Bai and
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+ Fan, Changjie and
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+ Mao, Xiaoxi and
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+ Zhao, Zeng",
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+ booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
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+ month = jul,
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+ year = "2022",
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+ address = "Hybrid: Seattle, Washington + Online",
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+ publisher = "Association for Computational Linguistics",
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+ url = "https://aclanthology.org/2022.naacl-industry.8",
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+ doi = "10.18653/v1/2022.naacl-industry.8",
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+ pages = "62--68"
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+ }
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+
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+ ```
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+ ## Contact Us
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+ You can also contact us by email:
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+
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+ [email protected], [email protected]
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570
+ "decoder.layers.34.final_layer_norm.weight": "checkpoint_best_part_3.pt",
571
+ "decoder.layers.35.final_layer_norm.weight": "checkpoint_best_part_3.pt",
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+ "decoder.layers.24.final_layer_norm.bias": "checkpoint_best_part_3.pt",
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+ "decoder.layers.25.final_layer_norm.bias": "checkpoint_best_part_3.pt",
574
+ "decoder.layers.26.final_layer_norm.bias": "checkpoint_best_part_3.pt",
575
+ "decoder.layers.27.final_layer_norm.bias": "checkpoint_best_part_3.pt",
576
+ "decoder.layers.28.final_layer_norm.bias": "checkpoint_best_part_3.pt",
577
+ "decoder.layers.29.final_layer_norm.bias": "checkpoint_best_part_3.pt",
578
+ "decoder.layers.30.final_layer_norm.bias": "checkpoint_best_part_3.pt",
579
+ "decoder.layers.31.final_layer_norm.bias": "checkpoint_best_part_3.pt",
580
+ "decoder.layers.32.final_layer_norm.bias": "checkpoint_best_part_3.pt",
581
+ "decoder.layers.33.final_layer_norm.bias": "checkpoint_best_part_3.pt",
582
+ "decoder.layers.34.final_layer_norm.bias": "checkpoint_best_part_3.pt",
583
+ "decoder.layers.35.final_layer_norm.bias": "checkpoint_best_part_3.pt"
584
+ }
dict.txt ADDED
The diff for this file is too large to render. See raw diff
 
inference.py ADDED
@@ -0,0 +1,205 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ #!/usr/bin/env python3 -u
2
+
3
+ from collections import namedtuple
4
+
5
+ import math
6
+ import torch
7
+ from torch.nn.utils.rnn import pad_sequence
8
+
9
+ from fairseq import checkpoint_utils, options, tasks, utils
10
+
11
+ Batch = namedtuple('Batch', 'ids src_tokens src_lengths')
12
+
13
+ def make_batches(lines, task, max_positions, encode_fn):
14
+
15
+ tokens = [task.source_dictionary.encode_line(encode_fn(line),
16
+ add_if_not_exist=False,
17
+ append_eos=False,
18
+ reverse_order=True).long()
19
+ for line in lines]
20
+ lengths = [t.numel() for t in tokens]
21
+ tokens = pad_sequence(tokens, batch_first=True,
22
+ padding_value=1).flip(dims=(1,))
23
+
24
+ return Batch(ids=torch.arange(len(tokens)),
25
+ src_tokens=tokens,
26
+ src_lengths=torch.tensor(lengths))
27
+
28
+ def encode_fn(x_str):
29
+ x_str = "</s> " + x_str
30
+ return x_str
31
+
32
+
33
+ def decode_fn(x):
34
+ x = x.replace(" ", "")
35
+ return x
36
+
37
+ def eos_token_filter(sent):
38
+ return True
39
+
40
+
41
+ def post_precess(line):
42
+
43
+ if "<" in line:
44
+ line = line.split("<")[0]
45
+ return line
46
+
47
+
48
+ class Inference(object):
49
+
50
+ def __init__(self, model_path, data_path, eet_batch_size):
51
+
52
+ parser = options.get_generation_parser(default_task="language_modeling")
53
+ args = options.parse_args_and_arch(parser)
54
+ args.data = data_path
55
+ args.path = model_path
56
+ self.args = args
57
+
58
+ # generate parameter
59
+ args.beam = 1 # don't change
60
+ args.min_len = 5
61
+ args.max_len_b = 30
62
+ args.lenpen = 1.0
63
+ args.sampling = True
64
+ # args.sampling_topp = 0.7
65
+ args.sampling_topk = 10
66
+ args.temperature = 0.8
67
+ args.no_repeat_ngram_size = 1
68
+ args.fp16 = True
69
+
70
+ # Setup task, e.g., translation
71
+ task = tasks.setup_task(args)
72
+ self.task = task
73
+ # Set dictionaries
74
+ self.src_dict = task.source_dictionary
75
+ self.tgt_dict = task.target_dictionary
76
+
77
+ use_cuda = torch.cuda.is_available() and not args.cpu
78
+ self.use_cuda = use_cuda
79
+
80
+ # Optimize ensemble for generation
81
+ state = torch.load(args.path, map_location=torch.device("cpu"))
82
+ cfg_args = eval(str(state["cfg"]))["model"]
83
+ del cfg_args["_name"]
84
+ keys_list = []
85
+ values_list = []
86
+ for key,value in cfg_args.items() :
87
+ keys_list.append(key)
88
+ values_list.append(value)
89
+ Model_args = namedtuple("Model_args", keys_list)
90
+ model_args = Model_args._make(values_list)
91
+ del state
92
+
93
+ eet_seq_len = 512 # max seqence length
94
+ eet_batch_size = eet_batch_size
95
+ data_type = torch.float16
96
+ eet_config = {"data_type":data_type,
97
+ "max_batch":eet_batch_size,
98
+ "full_seq_len":eet_seq_len}
99
+ print(model_args)
100
+ from eet.fairseq.transformer import EETTransformerDecoder
101
+ eet_model = EETTransformerDecoder.from_fairseq_pretrained(model_id_or_path = args.path,
102
+ dictionary = self.src_dict,args=model_args,
103
+ config = eet_config,
104
+ no_encoder_attn = True)
105
+ self.models = [eet_model]
106
+ # Initialize generator
107
+ self.generator = task.build_generator(self.models, args)
108
+
109
+ # Load alignment dictionary for unknown word replacement
110
+ # (None if no unknown word replacement, empty if no path to align dictionary)
111
+ self.align_dict = utils.load_align_dict(args.replace_unk)
112
+
113
+ self.max_positions = 1024
114
+ self.eos_index = self.tgt_dict.eos()
115
+ self.pad_index = self.tgt_dict.pad()
116
+
117
+ def __call__(self, inputs, append_right_eos=True):
118
+
119
+ results = []
120
+ start_id = 0
121
+
122
+ batch = make_batches(inputs, self.task, self.max_positions, encode_fn)
123
+ inputs_str = inputs
124
+
125
+ src_tokens = batch.src_tokens
126
+ src_lengths = batch.src_lengths
127
+ # a new paragraph always
128
+ if src_tokens[0][-1].item() != self.eos_index and append_right_eos:
129
+ src_tokens = torch.cat([src_tokens, src_tokens.new_ones(src_tokens.size(0), 1) * self.eos_index], dim=1)
130
+ src_lengths += 1
131
+ if self.use_cuda:
132
+ src_tokens = src_tokens.cuda()
133
+ src_lengths = src_lengths.cuda()
134
+ sample = {
135
+ 'net_input': {
136
+ 'src_tokens': src_tokens,
137
+ 'src_lengths': src_lengths,
138
+ },
139
+ }
140
+
141
+ translations = self.task.inference_step(self.generator, self.models, sample)
142
+
143
+ for i, (id, hypos) in enumerate(zip(batch.ids.tolist(), translations)):
144
+ results.append((start_id + id, src_tokens[i], hypos))
145
+
146
+ # sort output to match input order
147
+ final_results = []
148
+ for id, src_tokens, hypos in sorted(results, key=lambda x: x[0]):
149
+ # Process top predictions
150
+ tmp_res = []
151
+ for hypo in hypos[:min(len(hypos), self.args.nbest)]:
152
+ hypo_tokens, hypo_str, alignment = utils.post_process_prediction(
153
+ hypo_tokens=hypo['tokens'].int().cpu()[len(src_tokens)-1:],
154
+ src_str=None,
155
+ alignment=hypo['alignment'],
156
+ align_dict=self.align_dict,
157
+ tgt_dict=self.tgt_dict)
158
+
159
+ detok_hypo_str = decode_fn(hypo_str)
160
+ if eos_token_filter(detok_hypo_str):
161
+ detok_hypo_str = post_precess(detok_hypo_str)
162
+ score = hypo['score'] / math.log(2) # convert to base 2
163
+ tmp_res.append([detok_hypo_str, score])
164
+ final_results.append(tmp_res)
165
+ return final_results
166
+
167
+
168
+
169
+
170
+ class Dialogue(object):
171
+ def __init__(self, inference_model=None, max_dialogue_history=6):
172
+
173
+ self.inference_model = inference_model
174
+ self.max_dialogue_history = max_dialogue_history
175
+ self.dialogue_history = []
176
+
177
+ def get_repsonse(self, input_text):
178
+ self.dialogue_history.append(input_text.strip())
179
+ model_inp = ""
180
+ for idx, x in enumerate(self.dialogue_history[-self.max_dialogue_history:]):
181
+ if idx % 2 == 0:
182
+ model_inp += " <0> " + " ".join(list(x))
183
+ else:
184
+ model_inp += " <1> " + " ".join(list(x))
185
+ if idx % 2 == 0:
186
+ model_inp += " <1>"
187
+ else:
188
+ model_inp += " <0>"
189
+ # generate 5 candidates
190
+ text = self.inference_model([model_inp]*5, append_right_eos=False)
191
+ response = [x[0][0] for x in text]
192
+ # response rank according to length
193
+ response = sorted(response, key=lambda x:len(set(x)))
194
+ # overlap-score
195
+ overlap = [[len(set(x) & set(model_inp)) * len(x), x] for x in response[-4:-1]]
196
+ overlap = sorted(overlap, key=lambda x:x[0])
197
+ final_response = overlap[-2][1]
198
+ self.dialogue_history.append(final_response)
199
+ return final_response
200
+
201
+ def clear_dialogue_history(self):
202
+ self.dialogue_history = []
203
+
204
+
205
+