Fix JAX RNN backend issue.

keras_core/backend/jax/rnn.py

@@ -1,8 +1,10 @@
+import contextlib
+
 import tree
 from jax import lax
 from jax import numpy as jnp
 
-from keras_core.backend.common.stateless_scope import StatelessScope
+from keras_core.backend.common import stateless_scope
 from keras_core.utils.nest import pack_sequence_as
 
 
@@ -181,7 +183,12 @@ def _step(states, current_input):
 
             scan_xs = inputs
 
-        with StatelessScope():
+        if stateless_scope.in_stateless_scope():
+            # Reuse the existing parent stateless scope.
+            scope = contextlib.nullcontext()
+        else:
+            scope = stateless_scope.StatelessScope()
+        with scope:
             # We must use a stateless scope because `scan` will involve
             # JAX tracing -- any variable update at this stage would
             # be a leak.

keras_core/layers/__init__.py

@@ -121,9 +121,12 @@
 from keras_core.layers.rnn.conv_lstm2d import ConvLSTM2D
 from keras_core.layers.rnn.conv_lstm3d import ConvLSTM3D
 from keras_core.layers.rnn.gru import GRU
+from keras_core.layers.rnn.gru import GRUCell
 from keras_core.layers.rnn.lstm import LSTM
+from keras_core.layers.rnn.lstm import LSTMCell
 from keras_core.layers.rnn.rnn import RNN
 from keras_core.layers.rnn.simple_rnn import SimpleRNN
+from keras_core.layers.rnn.simple_rnn import SimpleRNNCell
 from keras_core.layers.rnn.stacked_rnn_cells import StackedRNNCells
 from keras_core.layers.rnn.time_distributed import TimeDistributed
 from keras_core.saving import serialization_lib

keras_core/layers/rnn/rnn.py

@@ -390,6 +390,10 @@ def call(
             initial_state,
         )
 
+        # Prepopulate the dropout state so that the inner_loop is stateless
+        # this is particularly important for JAX backend.
+        self._maybe_config_dropout_masks(self.cell, sequences, initial_state)
+
         last_output, outputs, states = self.inner_loop(
             sequences=sequences,
             initial_state=initial_state,
@@ -421,6 +425,20 @@ def call(
             return output, *states
         return output
 
+    def _maybe_config_dropout_masks(self, cell, input_sequence, input_state):
+        step_input = input_sequence[:, 0, :]
+        state = (
+            input_state[0]
+            if isinstance(input_state, (list, tuple))
+            else input_state
+        )
+        if isinstance(cell, DropoutRNNCell):
+            cell.get_dropout_mask(step_input)
+            cell.get_recurrent_dropout_mask(state)
+        if isinstance(cell, StackedRNNCells):
+            for c, s in zip(cell.cells, input_state):
+                self._maybe_config_dropout_masks(c, input_sequence, s)
+
     def _maybe_reset_dropout_masks(self, cell):
         if isinstance(cell, DropoutRNNCell):
             cell.reset_dropout_mask()

keras_core/layers/rnn/stacked_rnn_cells.py

@@ -89,14 +89,15 @@ def call(self, inputs, states, training=False, **kwargs):
         # Call the cells in order and store the returned states.
         new_states = []
         for cell, states in zip(self.cells, states):
+            state_is_list = tree.is_nested(states)
             states = list(states) if tree.is_nested(states) else [states]
             if isinstance(cell, Layer) and cell._call_has_training_arg:
                 kwargs["training"] = training
             else:
                 kwargs.pop("training", None)
             cell_call_fn = cell.__call__ if callable(cell) else cell.call
             inputs, states = cell_call_fn(inputs, states, **kwargs)
-            if len(states) == 1:
+            if len(states) == 1 and not state_is_list:
                 states = states[0]
             new_states.append(states)
 

keras_core/layers/rnn/stacked_rnn_cells_test.py

@@ -81,6 +81,57 @@ def test_basics(self):
             supports_masking=True,
             custom_objects={"TwoStatesRNNCell": TwoStatesRNNCell},
         )
+        self.run_layer_test(
+            layers.RNN,
+            init_kwargs={
+                "cell": [
+                    layers.SimpleRNNCell(3, dropout=0.1, recurrent_dropout=0.1),
+                    layers.SimpleRNNCell(4, dropout=0.1, recurrent_dropout=0.1),
+                    layers.SimpleRNNCell(5, dropout=0.1, recurrent_dropout=0.1),
+                ],
+                "return_sequences": True,
+            },
+            input_shape=(2, 3, 4),
+            expected_output_shape=(2, 3, 5),
+            expected_num_trainable_weights=9,
+            expected_num_non_trainable_weights=0,
+            expected_num_seed_generators=0,
+            supports_masking=True,
+        )
+        self.run_layer_test(
+            layers.RNN,
+            init_kwargs={
+                "cell": [
+                    layers.GRUCell(3, dropout=0.1, recurrent_dropout=0.1),
+                    layers.GRUCell(4, dropout=0.1, recurrent_dropout=0.1),
+                    layers.GRUCell(5, dropout=0.1, recurrent_dropout=0.1),
+                ],
+                "return_sequences": True,
+            },
+            input_shape=(2, 3, 4),
+            expected_output_shape=(2, 3, 5),
+            expected_num_trainable_weights=9,
+            expected_num_non_trainable_weights=0,
+            expected_num_seed_generators=0,
+            supports_masking=True,
+        )
+        self.run_layer_test(
+            layers.RNN,
+            init_kwargs={
+                "cell": [
+                    layers.LSTMCell(3, dropout=0.1, recurrent_dropout=0.1),
+                    layers.LSTMCell(4, dropout=0.1, recurrent_dropout=0.1),
+                    layers.LSTMCell(5, dropout=0.1, recurrent_dropout=0.1),
+                ],
+                "return_sequences": True,
+            },
+            input_shape=(2, 3, 4),
+            expected_output_shape=(2, 3, 5),
+            expected_num_trainable_weights=9,
+            expected_num_non_trainable_weights=0,
+            expected_num_seed_generators=0,
+            supports_masking=True,
+        )
 
     def test_correctness_single_state_stack(self):
         sequence = np.arange(24).reshape((2, 3, 4)).astype("float32")