[RLlib] Fix time dimension shaping for PyTorch RNN models. (#21735)

rllib/policy/rnn_sequencing.py

@@ -206,11 +206,13 @@ def add_time_dimension(
 
         # Dynamically reshape the padded batch to introduce a time dimension.
         new_batch_size = padded_batch_size // max_seq_len
+        batch_major_shape = (new_batch_size, max_seq_len) + padded_inputs.shape[1:]
+        padded_outputs = padded_inputs.view(batch_major_shape)
+
         if time_major:
-            new_shape = (max_seq_len, new_batch_size) + padded_inputs.shape[1:]
+            # Swap the batch and time dimensions
-        else:
+            padded_outputs = padded_outputs.transpose(0, 1)
-            new_shape = (new_batch_size, max_seq_len) + padded_inputs.shape[1:]
+        return padded_outputs
-        return torch.reshape(padded_inputs, new_shape)
 
 
 @DeveloperAPI

rllib/policy/tests/test_rnn_sequencing.py

@@ -2,12 +2,20 @@ import numpy as np
 import unittest
 
 import ray
-from ray.rllib.policy.rnn_sequencing import pad_batch_to_sequences_of_same_size
+from ray.rllib.policy.rnn_sequencing import (
+    pad_batch_to_sequences_of_same_size,
+    add_time_dimension,
+)
 from ray.rllib.policy.sample_batch import SampleBatch
 from ray.rllib.policy.view_requirement import ViewRequirement
+from ray.rllib.utils.framework import try_import_tf, try_import_torch
 from ray.rllib.utils.test_utils import check
 
 
+tf1, tf, tfv = try_import_tf()
+torch, nn = try_import_torch()
+
+
 class TestRNNSequencing(unittest.TestCase):
     @classmethod
     def setUpClass(cls) -> None:
@@ -89,6 +97,52 @@ class TestRNNSequencing(unittest.TestCase):
         check(s1["a"].shape[0], max_seq_len * num_seqs)
         check(s1["b"].shape[0], max_seq_len * num_seqs)
 
+    def test_add_time_dimension(self):
+        """Test add_time_dimension gives sequential data along the time dimension"""
+
+        B, T, F = np.random.choice(
+            np.asarray(list(range(8, 32)), dtype=np.int32),  # use int32 for seq_lens
+            size=3,
+            replace=False,
+        )
+
+        inputs_numpy = np.repeat(
+            np.arange(B * T)[:, np.newaxis], repeats=F, axis=-1
+        ).astype(np.int32)
+        check(inputs_numpy.shape, (B * T, F))
+
+        time_shift_diff_batch_major = np.ones(shape=(B, T - 1, F), dtype=np.int32)
+        time_shift_diff_time_major = np.ones(shape=(T - 1, B, F), dtype=np.int32)
+
+        if tf is not None:
+            # Test tensorflow batch-major
+            padded_inputs = tf.constant(inputs_numpy)
+            batch_major_outputs = add_time_dimension(
+                padded_inputs, max_seq_len=T, framework="tf", time_major=False
+            )
+            check(batch_major_outputs.shape.as_list(), [B, T, F])
+            time_shift_diff = batch_major_outputs[:, 1:] - batch_major_outputs[:, :-1]
+            check(time_shift_diff, time_shift_diff_batch_major)
+
+        if torch is not None:
+            # Test torch batch-major
+            padded_inputs = torch.from_numpy(inputs_numpy)
+            batch_major_outputs = add_time_dimension(
+                padded_inputs, max_seq_len=T, framework="torch", time_major=False
+            )
+            check(batch_major_outputs.shape, (B, T, F))
+            time_shift_diff = batch_major_outputs[:, 1:] - batch_major_outputs[:, :-1]
+            check(time_shift_diff, time_shift_diff_batch_major)
+
+            # Test torch time-major
+            padded_inputs = torch.from_numpy(inputs_numpy)
+            time_major_outputs = add_time_dimension(
+                padded_inputs, max_seq_len=T, framework="torch", time_major=True
+            )
+            check(time_major_outputs.shape, (T, B, F))
+            time_shift_diff = time_major_outputs[1:] - time_major_outputs[:-1]
+            check(time_shift_diff, time_shift_diff_time_major)
+
 
 if __name__ == "__main__":
     import pytest