feat: Add multi-layer, bidirectional RNN with tanh activation

ivy/functional/ivy/layers.py

@@ -2562,6 +2562,305 @@ def lstm(
 
     return output[:, -1], output, (h_outs, c_outs)
 
+@handle_exceptions
+@handle_nestable
+@handle_array_like_without_promotion
+@inputs_to_ivy_arrays
+@handle_array_function
+def rnn_tanh_update(
+    x: Union[ivy.Array, ivy.NativeArray],
+    init_h: Union[ivy.Array, ivy.NativeArray],
+    kernel: Union[ivy.Array, ivy.NativeArray],
+    recurrent_kernel: Union[ivy.Array, ivy.NativeArray],
+    /,
+    *,
+    bias: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
+    recurrent_bias: Optional[Union[ivy.Array, ivy.NativeArray]] = None,
+    time_major: bool = False,
+) -> Tuple[ivy.Array, ivy.Array]:
+    """Perform RNN-tanh update by unrolling time dimension of input array.
+
+    Parameters
+    ----------
+    x
+        input tensor of RNN layer *[batch_shape, t, in]* if time_major=False,
+        else *[t, batch_shape, in]*.
+    init_h
+        initial state tensor for the cell output *[batch_shape, out]*.
+    kernel
+        weights for cell kernel *[in, out]*.
+    recurrent_kernel
+        weights for cell recurrent kernel *[out, out]*.
+    bias
+        bias for cell kernel *[out]*. (Default value = None)
+    recurrent_bias
+        bias for cell recurrent kernel *[out]*. (Default value = None)
+    time_major
+        whether or not the input tensor `x` has the time dimension before batch dim.
+
+    Returns
+    -------
+    ret
+        hidden state for all timesteps of shape *[batch_shape,t,out]* if time_major
+        is False, else *[t, batch_shape, out]*, and the final hidden state of shape
+        *[batch_shape,out]*.
+    """
+    if time_major:
+        x = ivy.swapaxes(x, 0, 1)
+
+    # get shapes
+    x_shape = list(x.shape)
+    batch_shape = x_shape[:-2]
+    timesteps = x_shape[-2]
+    input_channels = x_shape[-1]
+    x_flat = ivy.reshape(x, (-1, input_channels))
+
+    # input kernel
+    Wx = kernel
+    Wx_x = ivy.reshape(
+        ivy.matmul(x_flat, Wx) + (bias if bias is not None else 0),
+        batch_shape + [timesteps, -1],
+    )
+
+    # recurrent kernel
+    Wh = recurrent_kernel
+
+    # rnn states
+    ht = init_h
+
+    # rnn outputs
+    hts_list = []
+
+    # unrolled time dimension with rnn steps
+    for Wx_xt in ivy.unstack(Wx_x, axis=-2):
+        htm1 = ht
+
+        Wh_htm1 = ivy.matmul(htm1, Wh) + (
+            recurrent_bias if recurrent_bias is not None else 0
+        )
+
+        ht = ivy.tanh(Wx_xt + Wh_htm1)
+
+        hts_list.append(ivy.expand_dims(ht, axis=-2))
+
+    ret = ivy.concat(hts_list, axis=-2)
+    if time_major:
+        ret = ivy.swapaxes(ret, 0, 1)
+
+    return ret, ht
+
+
+@handle_exceptions
+@handle_nestable
+@handle_array_like_without_promotion
+@inputs_to_ivy_arrays
+@handle_array_function
+def rnn_tanh(
+    input: ivy.Array,
+    initial_state: ivy.Array,
+    all_weights: Tuple[ivy.Array],
+    num_layers: int,
+    dropout: float,
+    train: bool,
+    bidirectional: bool,
+    batch_first: bool = False,
+    batch_sizes: Sequence = None,
+    weights_transposed: bool = False,
+    has_bias: bool = True,
+):
+    """Applies a multi-layer RNN-tanh to an input sequence.
+
+    Parameters
+    ----------
+    input
+        input array of shape (seq_len, batch, input_size) when `batch_first` is False
+        or (batch, seq_len, input_size) when `batch_first` is True.
+    initial_state
+        initial hidden state of shape (num_layers * num_directions, batch, hidden_size).
+    all_weights
+        tuple of arrays representing the learnable weights of the rnn, with each
+        layer having up to two arrays (w_ih, w_hh, b_ih, b_hh) representing the weights
+        and biases (if biases are being used).
+
+        w_ih: weight of shape (hidden_size, input_size)
+        w_hh: weight of shape (hidden_size, hidden_size)
+        b_ih: bias of shape (hidden_size,)
+        b_hh: bias of shape (hidden_size,)
+    num_layers
+        number of layers for the rnn to use.
+    dropout
+        dropout rate.
+    train
+        whether to run the rnn in train mode or eval mode.
+    bidirectional
+        whether the rnn is bidirectional or unidirectional.
+    batch_first
+        defines the data format of the input and output arrays.
+    batch_sizes
+        specifies the batch size at each timestep, when the input is a packed sequence.
+    weights_transposed
+        whether the weights are transposed compared to the format in which they are expected (input_size, hidden_size)
+        rather than (hidden_size, input_size).
+    has_bias
+        whether the `all_weights` argument includes biases.
+
+    Returns
+    -------
+    output
+        output array of shape (seq_len, batch, num_directions * hidden_size) or
+        (batch, seq_len, num_directions * hidden_size), depending on `batch_first`.
+    h_out
+        final hidden state of shape (num_layers * num_directions, batch, hidden_size).
+    """
+    if weights_transposed:
+        # transpose the weights if they are in the wrong format
+        all_weights = [
+            ivy.swapaxes(weight, 1, 0) if weight.dim() == 2 else weight
+            for weight in all_weights
+        ]
+    else:
+        all_weights = list(all_weights)
+
+    weights_per_layer = 2 if not has_bias else 4
+
+    assert len(all_weights) == num_layers * weights_per_layer * (1 + bidirectional)
+    layer_weights = [
+        all_weights[i : i + weights_per_layer]
+        for i in range(0, len(all_weights), weights_per_layer)
+    ]
+
+    if batch_sizes is not None:
+        input, batch_sizes = _pad_packed_sequence(input, batch_sizes)
+
+    if batch_first:
+        input = ivy.swapaxes(input, 0, 1)
+
+    if dropout and train:
+        raise ivy.utils.exceptions.IvyNotImplementedException()
+
+    unidirectional = not bidirectional
+
+    h0 = initial_state
+    h_outs = []
+
+    output = input
+    for i in range(num_layers):
+        if unidirectional:
+            if has_bias:
+                weight_ih, weight_hh, (bias_i, bias_h) = _transform_weights(
+                    layer_weights, i
+                )
+            else:
+                weight_ih, weight_hh = _transform_weights_no_bias(layer_weights, i)
+                bias_i = bias_h = None
+
+            state_indices = i, i + 1
+        else:
+            if has_bias:
+                weight_ih_f, weight_hh_f, (bias_i_f, bias_h_f) = _transform_weights(
+                    layer_weights, 2 * i
+                )
+                weight_ih_b, weight_hh_b, (bias_i_b, bias_h_b) = _transform_weights(
+                    layer_weights, 2 * i + 1
+                )
+            else:
+                weight_ih_f, weight_hh_f = _transform_weights_no_bias(
+                    layer_weights, 2 * i
+                )
+                weight_ih_b, weight_hh_b = _transform_weights_no_bias(
+                    layer_weights, 2 * i + 1
+                )
+                bias_i_f = bias_h_f = bias_i_b = bias_h_b = None
+
+            weight_ih = weight_ih_f, weight_ih_b
+            weight_hh = weight_hh_f, weight_hh_b
+            bias_i = bias_i_f, bias_i_b
+            bias_h = bias_h_f, bias_h_b
+
+            state_indices = 2 * i, 2 * i + 2
+
+        output, h_out = _rnn_tanh_layer(
+            output,
+            _retrieve_state(h0, *state_indices, num_layers),
+            (weight_ih, weight_hh),
+            (bias_i, bias_h),
+            bidirectional,
+            batch_first=False,
+            batch_sizes=batch_sizes,
+        )
+        h_outs.append(h_out)
+
+    if batch_first:
+        output = ivy.swapaxes(output, 0, 1)
+
+    h_outs = h_out if num_layers == 1 else ivy.concat(h_outs, axis=0)
+
+    if batch_sizes is not None:
+        output = _pack_padded_sequence(output, batch_sizes)[0]
+
+    return output[:, -1], output, h_outs
+
+
+def rnn_tanh_cell(
+    x,
+    init_h,
+    kernel,
+    recurrent_kernel,
+    bias,
+    recurrent_bias,
+    batch_first,
+    batch_sizes=None,
+):
+    if init_h.shape[0] == 1:
+        init_h = ivy.squeeze(init_h, axis=0)
+
+    out, ht = ivy.rnn_tanh_update(
+        x,
+        init_h,
+        kernel,
+        recurrent_kernel,
+        bias=bias,
+        recurrent_bias=recurrent_bias,
+        time_major=not batch_first,
+    )
+    return out, ivy.expand_dims(ht, axis=0)
+
+
+def _rnn_tanh_layer(
+    x,
+    initial_state,
+    all_weights,
+    all_biases,
+    bidirectional,
+    batch_first,
+    batch_sizes=None,
+):
+    out, ht = rnn_tanh_cell(
+        x,
+        initial_state,
+        *all_weights,
+        *all_biases,
+        batch_first=batch_first,
+        batch_sizes=batch_sizes,
+    )
+    if bidirectional:
+        x_rev = ivy.flip(x, axis=0)
+        initial_state_rev = ivy.flip(initial_state, axis=0)
+
+        out_rev, ht_rev = rnn_tanh_cell(
+            x_rev,
+            initial_state_rev,
+            *all_weights,
+            *all_biases,
+            batch_first=batch_first,
+            batch_sizes=batch_sizes,
+        )
+        out_rev = ivy.flip(out_rev, axis=0)
+        ht_rev = ivy.flip(ht_rev, axis=0)
+        return ivy.concat([out, out_rev], axis=-1), ivy.concat([ht, ht_rev], axis=0)
+
+    return out, ht
+
 
 # Helpers #