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
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191

use super::conv;
use crate::backend::Backend;

/// Gradient computed during the backward pass for each tensor used by [conv2d](ModuleOps::conv2d).
#[derive(new)]
pub struct Conv2dBackward<B: Backend> {
    pub x_grad: B::TensorPrimitive<4>,
    pub weights_grad: B::TensorPrimitive<4>,
    pub bias_grad: Option<B::TensorPrimitive<1>>,
}

/// Gradient computed during the backward pass for each tensor used by [max_pool2d](ModuleOps::max_pool2d).
#[derive(new)]
pub struct MaxPool2dBackward<B: Backend> {
    pub x_grad: B::TensorPrimitive<4>,
}

/// Results from [max_pool2d](ModuleOps::max_pool2d_with_indexes).
#[derive(new)]
pub struct MaxPool2dWithIndexes<B: Backend> {
    pub output: B::TensorPrimitive<4>,
    pub indexes: B::IntTensorPrimitive<4>,
}

/// Gradient computed during the backward pass for each tensor used by [conv1d](ModuleOps::conv1d).
#[derive(new)]
pub struct Conv1dBackward<B: Backend> {
    pub x_grad: B::TensorPrimitive<3>,
    pub weights_grad: B::TensorPrimitive<3>,
    pub bias_grad: Option<B::TensorPrimitive<1>>,
}

/// Convolution options.
#[derive(new, Debug, Clone)]
pub struct ConvOptions<const N: usize> {
    pub stride: [usize; N],
    pub padding: [usize; N],
    pub dilation: [usize; N],
    pub groups: usize,
}

/// Transposed convolution options.
#[derive(new, Debug, Clone)]
pub struct ConvTransposeOptions<const N: usize> {
    pub stride: [usize; N],
    pub padding: [usize; N],
    pub padding_out: [usize; N],
    pub dilation: [usize; N],
    pub groups: usize,
}

pub trait ModuleOps<B: Backend> {
    fn embedding(
        weights: B::TensorPrimitive<2>,
        indexes: B::IntTensorPrimitive<2>,
    ) -> B::TensorPrimitive<3>;
    fn embedding_backward(
        weights: B::TensorPrimitive<2>,
        output: B::TensorPrimitive<3>,
        indexes: B::IntTensorPrimitive<2>,
    ) -> B::TensorPrimitive<2>;
    /// Two dimensional convolution.
    ///
    /// # Shapes
    ///
    /// x:      [batch_size, channels_in, height, width],
    /// weight: [channels_out, channels_in, kernel_size_1, kernel_size_2],
    /// bias:   [channels_out],
    fn conv2d(
        x: B::TensorPrimitive<4>,
        weight: B::TensorPrimitive<4>,
        bias: Option<B::TensorPrimitive<1>>,
        options: ConvOptions<2>,
    ) -> B::TensorPrimitive<4>;
    /// Two dimensional transposed convolution.
    ///
    /// # Shapes
    ///
    /// x:      [batch_size, channels_in, height, width],
    /// weight: [channels_in, channels_out, kernel_size_1, kernel_size_2],
    /// bias:   [channels_out],
    fn conv_transpose2d(
        x: B::TensorPrimitive<4>,
        weight: B::TensorPrimitive<4>,
        bias: Option<B::TensorPrimitive<1>>,
        options: ConvTransposeOptions<2>,
    ) -> B::TensorPrimitive<4>;

    /// Backward pass for the [conv2d](ModuleOps::conv2d) operation.
    fn conv2d_backward(
        x: B::TensorPrimitive<4>,
        weight: B::TensorPrimitive<4>,
        bias: Option<B::TensorPrimitive<1>>,
        output_grad: B::TensorPrimitive<4>,
        options: ConvOptions<2>,
    ) -> Conv2dBackward<B> {
        conv::conv2d_backward(x, weight, bias, output_grad, options)
    }
    /// One dimensional convolution.
    ///
    /// # Shapes
    ///
    /// x:      [batch_size, channels_in, length],
    /// weight: [channels_out, channels_in, kernel_size],
    /// bias:   [channels_out],
    fn conv1d(
        x: B::TensorPrimitive<3>,
        weight: B::TensorPrimitive<3>,
        bias: Option<B::TensorPrimitive<1>>,
        options: ConvOptions<1>,
    ) -> B::TensorPrimitive<3> {
        conv::conv1d_from_conv2d::<B>(x, weight, bias, options)
    }
    /// One dimensional transposed convolution.
    ///
    /// # Shapes
    ///
    /// x:      [batch_size, channels_in, length],
    /// weight: [channels_in, channels_out, length],
    /// bias:   [channels_out],
    fn conv_transpose1d(
        x: B::TensorPrimitive<3>,
        weight: B::TensorPrimitive<3>,
        bias: Option<B::TensorPrimitive<1>>,
        options: ConvTransposeOptions<1>,
    ) -> B::TensorPrimitive<3> {
        conv::conv_transpose1d_from_conv_transpose2d::<B>(x, weight, bias, options)
    }
    /// Backward pass for the [conv1d](ModuleOps::conv1d) operation.
    fn conv1d_backward(
        x: B::TensorPrimitive<3>,
        weight: B::TensorPrimitive<3>,
        bias: Option<B::TensorPrimitive<1>>,
        output_grad: B::TensorPrimitive<3>,
        options: ConvOptions<1>,
    ) -> Conv1dBackward<B> {
        conv::conv1d_backward(x, weight, bias, output_grad, options)
    }
    /// Two dimensional avg pooling.
    ///
    /// # Shapes
    ///
    /// x: [batch_size, channels, height, width],
    fn avg_pool2d(
        x: B::TensorPrimitive<4>,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2],
    ) -> B::TensorPrimitive<4>;
    /// Backward pass for the [avg pooling 2d](ModuleOps::avg_pool2d) operation.
    fn avg_pool2d_backward(
        x: B::TensorPrimitive<4>,
        grad: B::TensorPrimitive<4>,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2],
    ) -> B::TensorPrimitive<4>;

    /// Two dimensional max pooling.
    ///
    /// # Shapes
    ///
    /// x: [batch_size, channels, height, width],
    fn max_pool2d(
        x: B::TensorPrimitive<4>,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2],
    ) -> B::TensorPrimitive<4>;

    /// Two dimensional max pooling with indexes.
    ///
    /// # Shapes
    ///
    /// x: [batch_size, channels, height, width],
    fn max_pool2d_with_indexes(
        x: B::TensorPrimitive<4>,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2],
    ) -> MaxPool2dWithIndexes<B>;
    /// Backward pass for the [max pooling 2d](ModuleOps::max_pool2d_with_indexes) operation.
    fn max_pool2d_with_indexes_backward(
        x: B::TensorPrimitive<4>,
        kernel_size: [usize; 2],
        stride: [usize; 2],
        padding: [usize; 2],
        output_grad: B::TensorPrimitive<4>,
        indexes: B::IntTensorPrimitive<4>,
    ) -> MaxPool2dBackward<B>;
}