flodl 0.3.0

floDl — a flow-graph deep learning framework built on libtorch
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
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234

use std::cell::RefCell;

use crate::autograd::Variable;
use crate::tensor::{Device, DType, Result, RnnParams, Tensor, TensorOptions};

use super::grucell::GRUCell;
use super::parameter::Parameter;
use super::Module;

/// Multi-layer GRU (Gated Recurrent Unit) sequence module.
///
/// Wraps multiple [`GRUCell`] layers and loops over timesteps, matching
/// the PyTorch `nn.GRU` interface. Each layer feeds its output sequence
/// as input to the next layer.
///
/// ```ignore
/// let gru = GRU::new(4, 8, 2)?;       // input=4, hidden=8, 2 layers
/// let x = Variable::new(Tensor::randn(&[10, 1, 4], opts)?, false); // [seq, batch, input]
/// let (output, h_n) = gru.forward_seq(&x, None)?;
/// // output: [10, 1, 8], h_n: [2, 1, 8]
/// ```
pub struct GRU {
    cells: Vec<GRUCell>,
    hidden_size: i64,
    num_layers: usize,
    batch_first: bool,
    /// Cached cuDNN params — lives on C++ side, zero per-forward overhead.
    rnn_params: RefCell<Option<RnnParams>>,
}

impl GRU {
    /// Create a multi-layer GRU on CPU.
    pub fn new(input_size: i64, hidden_size: i64, num_layers: usize) -> Result<Self> {
        Self::on_device(input_size, hidden_size, num_layers, false, Device::CPU)
    }

    /// Create a multi-layer GRU on a specific device.
    pub fn on_device(
        input_size: i64,
        hidden_size: i64,
        num_layers: usize,
        batch_first: bool,
        device: Device,
    ) -> Result<Self> {
        assert!(num_layers >= 1, "GRU requires at least 1 layer");
        let mut cells = Vec::with_capacity(num_layers);
        for layer in 0..num_layers {
            let in_size = if layer == 0 { input_size } else { hidden_size };
            cells.push(GRUCell::on_device(in_size, hidden_size, device)?);
        }
        Ok(GRU {
            cells,
            hidden_size,
            num_layers,
            batch_first,
            rnn_params: RefCell::new(None),
        })
    }

    /// Set batch_first mode. When true, input/output are `[batch, seq, features]`.
    pub fn batch_first(mut self, batch_first: bool) -> Self {
        self.batch_first = batch_first;
        self
    }

    /// Forward pass over a full sequence.
    ///
    /// - `input`: `[seq_len, batch, input_size]` (or `[batch, seq_len, input_size]` if batch_first)
    /// - `h_0`: optional initial hidden state `[num_layers, batch, hidden_size]`, or None for zeros
    ///
    /// Returns `(output, h_n)`:
    /// - `output`: `[seq_len, batch, hidden_size]` (or `[batch, seq_len, hidden_size]` if batch_first)
    /// - `h_n`: `[num_layers, batch, hidden_size]`
    pub fn forward_seq(
        &self,
        input: &Variable,
        h_0: Option<&Variable>,
    ) -> Result<(Variable, Variable)> {
        let shape = input.shape();
        let batch = if self.batch_first { shape[0] } else { shape[1] };
        let nl = self.num_layers as i64;
        let hs = self.hidden_size;
        let opts = TensorOptions {
            dtype: DType::Float32,
            device: self.cells[0].parameters()[0].variable.device(),
        };

        // Initial hidden state (zeros if not provided)
        let h0 = match h_0 {
            Some(h) => h.data(),
            None => Tensor::zeros(&[nl, batch, hs], opts)?,
        };

        // Lazily create C++ cached params on first forward (with cuDNN flatten).
        // Subsequent forwards pass the opaque handle directly — zero overhead.
        {
            let mut cache = self.rnn_params.borrow_mut();
            if cache.is_none() {
                let params: Vec<Tensor> = self.cells.iter()
                    .flat_map(|cell| cell.parameters().into_iter().map(|p| p.variable.data()))
                    .collect();
                *cache = Some(RnnParams::new(&params, 3, nl, self.batch_first, true)?);
            }
        }
        let cache = self.rnn_params.borrow();
        let (output, h_n) = input.data().gru_seq_cached(
            &h0, cache.as_ref().unwrap(), nl, self.batch_first,
        )?;

        Ok((Variable::wrap(output), Variable::wrap(h_n)))
    }
}

impl Module for GRU {
    fn name(&self) -> &str { "gru" }

    /// Module trait forward: runs the full sequence with zero-initialized hidden state.
    /// Returns only the output sequence (not h_n). Use [`forward_seq`](GRU::forward_seq)
    /// for explicit hidden state access.
    fn forward(&self, input: &Variable) -> Result<Variable> {
        let (output, _h_n) = self.forward_seq(input, None)?;
        Ok(output)
    }

    fn parameters(&self) -> Vec<Parameter> {
        self.cells.iter().flat_map(|c| c.parameters()).collect()
    }
}

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_gru_shapes() {
        let dev = crate::tensor::test_device();
        let opts = crate::tensor::test_opts();
        let gru = GRU::on_device(4, 8, 2, false, dev).unwrap();

        let x = Variable::new(Tensor::randn(&[5, 3, 4], opts).unwrap(), false);
        let (output, h_n) = gru.forward_seq(&x, None).unwrap();

        assert_eq!(output.shape(), vec![5, 3, 8]); // [seq, batch, hidden]
        assert_eq!(h_n.shape(), vec![2, 3, 8]);    // [layers, batch, hidden]
    }

    #[test]
    fn test_gru_batch_first() {
        let dev = crate::tensor::test_device();
        let opts = crate::tensor::test_opts();
        let gru = GRU::on_device(4, 8, 2, true, dev).unwrap();

        let x = Variable::new(Tensor::randn(&[3, 5, 4], opts).unwrap(), false);
        let (output, h_n) = gru.forward_seq(&x, None).unwrap();

        assert_eq!(output.shape(), vec![3, 5, 8]); // [batch, seq, hidden]
        assert_eq!(h_n.shape(), vec![2, 3, 8]);    // [layers, batch, hidden]
    }

    #[test]
    fn test_gru_with_initial_hidden() {
        let dev = crate::tensor::test_device();
        let opts = crate::tensor::test_opts();
        let gru = GRU::on_device(4, 8, 2, false, dev).unwrap();

        let x = Variable::new(Tensor::randn(&[5, 3, 4], opts).unwrap(), false);
        let h0 = Variable::new(Tensor::randn(&[2, 3, 8], opts).unwrap(), false);
        let (output, h_n) = gru.forward_seq(&x, Some(&h0)).unwrap();

        assert_eq!(output.shape(), vec![5, 3, 8]);
        assert_eq!(h_n.shape(), vec![2, 3, 8]);
    }

    #[test]
    fn test_gru_single_layer() {
        let dev = crate::tensor::test_device();
        let opts = crate::tensor::test_opts();
        let gru = GRU::on_device(4, 8, 1, false, dev).unwrap();

        let x = Variable::new(Tensor::randn(&[5, 3, 4], opts).unwrap(), false);
        let (output, h_n) = gru.forward_seq(&x, None).unwrap();

        assert_eq!(output.shape(), vec![5, 3, 8]);
        assert_eq!(h_n.shape(), vec![1, 3, 8]);
    }

    #[test]
    fn test_gru_gradient() {
        let dev = crate::tensor::test_device();
        let opts = crate::tensor::test_opts();
        let gru = GRU::on_device(4, 8, 2, false, dev).unwrap();

        let x = Variable::new(Tensor::randn(&[5, 3, 4], opts).unwrap(), true);
        let (output, _h_n) = gru.forward_seq(&x, None).unwrap();
        let loss = output.sum().unwrap();
        loss.backward().unwrap();

        for p in gru.parameters() {
            assert!(p.variable.grad().is_some(), "missing grad for {}", p.name);
        }
        assert!(x.grad().is_some());
    }

    #[test]
    fn test_gru_module_forward() {
        let dev = crate::tensor::test_device();
        let opts = crate::tensor::test_opts();
        let gru = GRU::on_device(4, 8, 2, false, dev).unwrap();

        let x = Variable::new(Tensor::randn(&[5, 3, 4], opts).unwrap(), false);
        let y = gru.forward(&x).unwrap();
        assert_eq!(y.shape(), vec![5, 3, 8]);
    }

    #[test]
    fn test_gru_parameters_count() {
        let dev = crate::tensor::test_device();
        let gru = GRU::on_device(4, 8, 2, false, dev).unwrap();

        // Layer 0: 4 params (w_ih, w_hh, b_ih, b_hh)
        // Layer 1: 4 params
        assert_eq!(gru.parameters().len(), 8);
    }

    #[test]
    fn test_gru_builder_pattern() {
        let dev = crate::tensor::test_device();
        let gru = GRU::on_device(4, 8, 1, false, dev).unwrap().batch_first(true);
        let opts = crate::tensor::test_opts();
        let x = Variable::new(Tensor::randn(&[3, 5, 4], opts).unwrap(), false);
        let (output, _) = gru.forward_seq(&x, None).unwrap();
        assert_eq!(output.shape(), vec![3, 5, 8]); // [batch, seq, hidden]
    }
}