implement bidirectional lstm

burn-book/src/building-blocks/module.md

@@ -147,7 +147,7 @@ Burn comes with built-in modules that you can use to build your own modules.
 | Burn API         | PyTorch Equivalent     |
 |------------------|------------------------|
 | `Gru`            | `nn.GRU`               |
-| `Lstm`           | `nn.LSTM`              |
+| `Lstm`/`BiLstm`  | `nn.LSTM`              |
 | `GateController` | _No direct equivalent_ |
 
 ### Transformer

crates/burn-core/src/nn/rnn/gate_controller.rs

@@ -1,24 +1,23 @@
 use crate as burn;
 
 use crate::module::Module;
-use crate::nn::Initializer;
-use crate::nn::Linear;
-use crate::nn::LinearConfig;
-use burn_tensor::backend::Backend;
+use crate::nn::{Initializer, Linear, LinearConfig};
+use burn_tensor::{backend::Backend, Tensor};
 
 /// A GateController represents a gate in an LSTM cell. An
 /// LSTM cell generally contains three gates: an input gate,
-/// forget gate, and cell gate.
+/// forget gate, and output gate. Additionally, cell gate
+/// is just used to compute the cell state.
 ///
 /// An Lstm gate is modeled as two linear transformations.
 /// The results of these transformations are used to calculate
 /// the gate's output.
 #[derive(Module, Debug)]
 pub struct GateController<B: Backend> {
     /// Represents the affine transformation applied to input vector
-    pub(crate) input_transform: Linear<B>,
+    pub input_transform: Linear<B>,
     /// Represents the affine transformation applied to the hidden state
-    pub(crate) hidden_transform: Linear<B>,
+    pub hidden_transform: Linear<B>,
 }
 
 impl<B: Backend> GateController<B> {
@@ -48,6 +47,19 @@ impl<B: Backend> GateController<B> {
         }
     }
 
+    /// Helper function for performing weighted matrix product for a gate and adds
+    /// bias, if any.
+    ///
+    ///  Mathematically, performs `Wx*X + Wh*H + b`, where:
+    ///     Wx = weight matrix for the connection to input vector X
+    ///     Wh = weight matrix for the connection to hidden state H
+    ///     X = input vector
+    ///     H = hidden state
+    ///     b = bias terms
+    pub fn gate_product(&self, input: Tensor<B, 2>, hidden: Tensor<B, 2>) -> Tensor<B, 2> {
+        self.input_transform.forward(input) + self.hidden_transform.forward(hidden)
+    }
+
     /// Used to initialize a gate controller with known weight layers,
     /// allowing for predictable behavior. Used only for testing in
     /// lstm.