Add a LSTM test. (#681)

* Add a LSTM test.

* Clippy.

candle-nn/src/rnn.rs

@@ -110,7 +110,7 @@ pub fn lstm(
         config.w_ih_init,
     )?;
     let w_hh = vb.get_with_hints(
-        (4 * hidden_dim, in_dim),
+        (4 * hidden_dim, hidden_dim),
         "weight_hh_l0", // Only a single layer is supported.
         config.w_hh_init,
     )?;

candle-nn/tests/rnn.rs

@@ -0,0 +1,42 @@
+#[cfg(feature = "mkl")]
+extern crate intel_mkl_src;
+
+#[cfg(feature = "accelerate")]
+extern crate accelerate_src;
+
+use candle::{test_utils::to_vec2_round, DType, Device, Result, Tensor};
+use candle_nn::RNN;
+
+#[test]
+fn lstm() -> Result<()> {
+    let cpu = &Device::Cpu;
+    let w_ih = Tensor::arange(0f32, 24f32, cpu)?.reshape((12, 2))?;
+    let w_ih = w_ih.cos()?;
+    let w_hh = Tensor::arange(0f32, 36f32, cpu)?.reshape((12, 3))?;
+    let w_hh = w_hh.sin()?;
+    let b_ih = Tensor::new(
+        &[-1f32, 1., -0.5, 2., -1., 1., -0.5, 2., -1., 1., -0.5, 2.],
+        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 lstm = candle_nn::lstm(2, 3, Default::default(), vb)?;
+    let mut state = lstm.zero_state(1)?;
+    for inp in [3f32, 1., 4., 1., 5., 9., 2.] {
+        let inp = Tensor::new(&[[inp, inp * 0.5]], cpu)?;
+        state = lstm.step(&inp, &state)?
+    }
+    let h = state.h();
+    let c = state.c();
+    assert_eq!(to_vec2_round(h, 4)?, &[[0.9919, 0.1738, -0.1451]]);
+    assert_eq!(to_vec2_round(c, 4)?, &[[5.725, 0.4458, -0.2908]]);
+    Ok(())
+}