burn-import 0.14.0

Library for importing datamodels into the Burn framework
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
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152

use super::{Node, NodeCodegen, SerializationBackend};
use crate::burn::{BurnImports, OtherType, Scope, TensorType, Type};
use burn::{
    module::{ConstantRecord, Param, ParamId},
    nn::{PReluConfig, PReluRecord},
    record::{PrecisionSettings, Record},
    tensor::{Tensor, TensorData},
};
use proc_macro2::TokenStream;
use quote::quote;
use serde::Serialize;

#[derive(Clone, Debug)]
pub struct PReluNode {
    pub field: OtherType,
    pub input: TensorType,
    pub output: TensorType,
    pub alpha: TensorData,
    pub config: PReluConfig,
}

impl PReluNode {
    pub fn new<S: AsRef<str>>(
        name: S,
        input: TensorType,
        output: TensorType,
        alpha: TensorData,
        config: PReluConfig,
    ) -> Self {
        Self {
            field: OtherType::new(
                name,
                quote! {
                    PRelu<B>
                },
            ),
            input,
            output,
            alpha,
            config,
        }
    }
}

impl<PS: PrecisionSettings> NodeCodegen<PS> for PReluNode {
    fn input_types(&self) -> Vec<Type> {
        vec![Type::Tensor(self.input.clone())]
    }
    fn output_types(&self) -> Vec<Type> {
        vec![Type::Tensor(self.output.clone())]
    }
    fn field_type(&self) -> Option<Type> {
        Some(Type::Other(self.field.clone()))
    }

    fn field_init(&self) -> Option<TokenStream> {
        let name = &self.field.name;
        let tokens = quote! {
            let #name = PReluConfig::new()
                .init(device);
        };

        Some(tokens)
    }

    fn field_serialize<S: serde::Serializer>(&self, serializer: S) -> Result<S::Ok, S::Error> {
        let device = Default::default();
        let record = PReluRecord::<SerializationBackend> {
            alpha: Param::initialized(
                ParamId::new(),
                Tensor::from_data(self.alpha.clone().convert::<PS::FloatElem>(), &device),
            ),
            alpha_value: ConstantRecord,
        };

        let item = Record::into_item::<PS>(record);
        item.serialize(serializer)
    }

    fn forward(&self, scope: &mut Scope, node_position: usize) -> TokenStream {
        let input = scope.tensor_use_owned(&self.input, node_position);
        let output = &self.output.name;
        let field = &self.field.name;

        quote! {
            let #output = self.#field.forward(#input);
        }
    }
    fn register_imports(&self, imports: &mut BurnImports) {
        imports.register("burn::nn::PRelu");
        imports.register("burn::nn::PReluConfig");
    }

    fn into_node(self) -> Node<PS> {
        Node::PRelu(self)
    }
}

#[cfg(test)]
mod tests {
    use super::*;
    use crate::burn::{graph::BurnGraph, node::test::assert_tokens, TensorType};
    use burn::record::FullPrecisionSettings;

    #[test]
    fn test_codegen() {
        let mut graph = BurnGraph::<FullPrecisionSettings>::default();

        graph.register(PReluNode::new(
            "prelu",
            TensorType::new_float("input", 4),
            TensorType::new_float("output", 4),
            TensorData::from([2f32]),
            PReluConfig::new(),
        ));

        graph.register_input_output(vec!["input".to_string()], vec!["output".to_string()]);

        let expected = quote! {
        use burn::nn::PRelu;
        use burn::nn::PReluConfig;
        use burn::{
            module::Module,
            tensor::{backend::Backend, Tensor},
        };
        #[derive(Module, Debug)]
        pub struct Model<B: Backend> {
            prelu: PRelu<B>,
            phantom: core::marker::PhantomData<B>,
            device: burn::module::Ignored<B::Device>,
        }
        impl<B: Backend> Model<B> {
            #[allow(unused_variables)]
            pub fn new(device: &B::Device) -> Self {
                let prelu = PReluConfig::new().init(device);
                Self {
                    prelu,
                    phantom: core::marker::PhantomData,
                   device: burn::module::Ignored(device.clone()),
                }
            }
            #[allow(clippy::let_and_return, clippy::approx_constant)]
            pub fn forward(&self, input: Tensor<B, 4>) -> Tensor<B, 4> {
                let output = self.prelu.forward(input);
                output
            }
        }
        };

        assert_tokens(graph.codegen(), expected);
    }
}