Add a GRU layer. (#688)

* Add a GRU layer.

* Fix the n gate computation.

candle-nn/tests/rnn.rs

@@ -55,3 +55,47 @@ fn lstm() -> Result<()> {
     assert_eq!(to_vec2_round(c, 4)?, &[[5.725, 0.4458, -0.2908]]);
     Ok(())
 }
+
+/* The following test can be verified against PyTorch using the following snippet.
+import torch
+from torch import nn
+gru = nn.GRU(2, 3, 1)
+gru.weight_ih_l0 = torch.nn.Parameter(torch.arange(0., 18.).reshape(9, 2).cos())
+gru.weight_hh_l0 = torch.nn.Parameter(torch.arange(0., 27.).reshape(9, 3).sin())
+gru.bias_ih_l0 = torch.nn.Parameter(torch.tensor([-1., 1., -0.5, 2, -1, 1, -0.5, 2, -1]))
+gru.bias_hh_l0 = torch.nn.Parameter(torch.tensor([-1., 1., -0.5, 2, -1, 1, -0.5, 2, -1]).cos())
+state = torch.zeros((1, 3))
+for inp in [3., 1., 4., 1., 5., 9., 2.]:
+    inp = torch.tensor([[inp, inp * 0.5]])
+    _out, state = gru(inp, state)
+print(state)
+# tensor([[ 0.0579,  0.8836, -0.9991]], grad_fn=<SqueezeBackward1>)
+*/
+#[test]
+fn gru() -> Result<()> {
+    let cpu = &Device::Cpu;
+    let w_ih = Tensor::arange(0f32, 18f32, cpu)?.reshape((9, 2))?;
+    let w_ih = w_ih.cos()?;
+    let w_hh = Tensor::arange(0f32, 27f32, cpu)?.reshape((9, 3))?;
+    let w_hh = w_hh.sin()?;
+    let b_ih = Tensor::new(&[-1f32, 1., -0.5, 2., -1., 1., -0.5, 2., -1.], cpu)?;
+    let b_hh = b_ih.cos()?;
+    let tensors: std::collections::HashMap<_, _> = [
+        ("weight_ih_l0".to_string(), w_ih),
+        ("weight_hh_l0".to_string(), w_hh),
+        ("bias_ih_l0".to_string(), b_ih),
+        ("bias_hh_l0".to_string(), b_hh),
+    ]
+    .into_iter()
+    .collect();
+    let vb = candle_nn::VarBuilder::from_tensors(tensors, DType::F32, cpu);
+    let gru = candle_nn::gru(2, 3, Default::default(), vb)?;
+    let mut state = gru.zero_state(1)?;
+    for inp in [3f32, 1., 4., 1., 5., 9., 2.] {
+        let inp = Tensor::new(&[[inp, inp * 0.5]], cpu)?;
+        state = gru.step(&inp, &state)?
+    }
+    let h = state.h();
+    assert_eq!(to_vec2_round(h, 4)?, &[[0.0579, 0.8836, -0.9991]]);
+    Ok(())
+}