rust-bert 0.7.2

Ready-to-use NLP pipelines and transformer-based models (BERT, DistilBERT, GPT2,...)
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80

// Copyright 2018-present, the HuggingFace Inc. team
// Copyright 2018-present, The OpenAI Team Authors
// Copyright (c) 2018, NVIDIA CORPORATION.  All rights reserved.
// Copyright 2019 Guillaume Becquin
// 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.

use crate::gpt2::attention::{GPTConv1D, Attention};
use tch::{Tensor, nn};
use crate::common::dropout::Dropout;
use crate::gpt2::gpt2::{Gpt2Config, GptActivation};
use crate::common::activations::{_gelu_new, _relu, _swish};

pub struct MLP {
    c_fc: GPTConv1D,
    c_proj: GPTConv1D,
    activation: Box<dyn Fn(&Tensor) -> Tensor>,
    dropout: Dropout,
}

impl MLP {
    pub fn new(p: &nn::Path, config: &Gpt2Config) -> MLP {
        let c_fc = GPTConv1D::new(&(p / "c_fc"), config.n_embd * 4, config.n_embd);
        let c_proj = GPTConv1D::new(&(p / "c_proj"), config.n_embd, config.n_embd * 4);
        let activation = Box::new(match &config.afn {
            Some(activation_enum) => match activation_enum {
                GptActivation::gelu => _gelu_new,
                GptActivation::relu => _relu,
                GptActivation::swish => _swish,
            },
            None => _gelu_new
        });
        let resid_pdrop = match config.resid_pdrop {
            Some(value) => value,
            None => 0.1
        };
        let dropout = Dropout::new(resid_pdrop);
        MLP { c_fc, c_proj, activation, dropout }
    }

    pub fn forward_t(&self, x: &Tensor, train: bool) -> Tensor {
        let h = (self.activation)(&x.apply(&self.c_fc));
        h.apply(&self.c_proj).apply_t(&self.dropout, train)
    }
}

pub struct Block {
    ln_1: nn::LayerNorm,
    attn: Attention,
    ln_2: nn::LayerNorm,
    mlp: MLP,
}

impl Block {
    pub fn new(p: &nn::Path, config: &Gpt2Config, scale: bool) -> Block {
        let layer_norm_config = nn::LayerNormConfig { eps: config.layer_norm_epsilon, ..Default::default() };
        let ln_1 = nn::layer_norm(p / "ln_1", vec![config.n_embd], layer_norm_config);
        let ln_2 = nn::layer_norm(p / "ln_2", vec![config.n_embd], layer_norm_config);
        let attn = Attention::new(&(p / "attn"), config, scale);
        let mlp = MLP::new(&(p / "mlp"), config);

        Block { ln_1, attn, ln_2, mlp }
    }

    pub fn forward_t(&self, x: &Tensor, layer_past: &Option<Tensor>, attention_mask: &Option<Tensor>, train: bool)
                     -> (Tensor, Tensor, Option<Tensor>) {
        let (output, present, attentions) = self.attn.forward_t(&x.apply(&self.ln_1), layer_past, attention_mask, train);
        let x = x + output;
        let m = self.mlp.forward_t(&x.apply(&self.ln_2), train);
        let x = x + m;
        (x, present, attentions)
    }
}