chore: import upstream snapshot with attribution

python/ray/data/tests/test_iter_jax.py

@@ -0,0 +1,125 @@
+import sys
+from unittest.mock import patch
+
+import numpy as np
+import pandas as pd
+import pytest
+
+import ray
+from ray.data.tests.conftest import *  # noqa
+from ray.tests.conftest import *  # noqa
+
+pytest.importorskip("jax")
+
+
+def test_iter_jax_batches(ray_start_regular_shared):
+    df1 = pd.DataFrame(
+        {"one": [1, 2, 3], "two": [1.0, 2.0, 3.0], "label": [1.0, 2.0, 3.0]}
+    )
+    df2 = pd.DataFrame(
+        {"one": [4, 5, 6], "two": [4.0, 5.0, 6.0], "label": [4.0, 5.0, 6.0]}
+    )
+    df3 = pd.DataFrame({"one": [7, 8], "two": [7.0, 8.0], "label": [7.0, 8.0]})
+    df = pd.concat([df1, df2, df3])
+    ds = ray.data.from_pandas([df1, df2, df3])
+
+    iterations = []
+    for batch in ds.iter_jax_batches(batch_size=3, paddings=-1):
+        iterations.append(
+            np.stack((batch["one"], batch["two"], batch["label"]), axis=1)
+        )
+    combined_iterations = np.concatenate(iterations)
+    # 8 rows total, batch_size 3, padding=-1 -> 3 batches of size 3 (9 rows total)
+    assert len(combined_iterations) == 9
+    expected_values = np.concatenate([df.values, [[-1.0, -1.0, -1.0]]])
+    np.testing.assert_array_equal(
+        np.sort(combined_iterations, axis=0),
+        np.sort(expected_values, axis=0),
+    )
+
+
+def test_iter_jax_batches_with_collate_fn(ray_start_regular_shared):
+    from ray.data.collate_fn import NumpyBatchCollateFn
+
+    class CustomCollateFn(NumpyBatchCollateFn):
+        def __call__(self, batch):
+            # Combine "one" and "two" columns into a single "features" tensor
+            return np.stack((batch["one"], batch["two"]), axis=1)
+
+    ds = ray.data.from_items([{"one": i, "two": i + 1} for i in range(10)])
+    iterations = []
+    for batch in ds.iter_jax_batches(batch_size=2, collate_fn=CustomCollateFn()):
+        # The output of collate_fn is now a single numpy array (sharded as jax.Array)
+        iterations.append(batch)
+
+    combined_iterations = np.concatenate(iterations)
+    # Expected shape: (10, 2)
+    expected = np.stack((np.arange(10), np.arange(10) + 1), axis=1)
+    np.testing.assert_array_equal(
+        np.sort(expected, axis=0),
+        np.sort(combined_iterations, axis=0),
+    )
+
+
+def test_iter_jax_batches_with_dtypes(ray_start_regular_shared):
+    import jax.numpy as jnp
+
+    ds = ray.data.from_items([{"one": i, "two": i + 0.5} for i in range(10)])
+
+    # Test single dtype
+    for batch in ds.iter_jax_batches(batch_size=2, dtypes=jnp.float32):
+        assert batch["one"].dtype == jnp.float32
+        assert batch["two"].dtype == jnp.float32
+
+    # Test dict of dtypes
+    dtypes = {"one": jnp.int32, "two": jnp.float16}
+    for batch in ds.iter_jax_batches(batch_size=2, dtypes=dtypes):
+        assert batch["one"].dtype == jnp.int32
+        assert batch["two"].dtype == jnp.float16
+
+    # Test padding with dtypes
+    # ds has 10 rows, batch_size=4 -> 3 batches (4, 4, 2)
+    # Without drop_last, 3rd batch is padded to 4.
+    for batch in ds.iter_jax_batches(
+        batch_size=4, dtypes=jnp.float16, paddings=-1, drop_last=False
+    ):
+        assert batch["one"].dtype == jnp.float16
+        assert batch["two"].dtype == jnp.float16
+
+
+def test_iter_jax_batches_with_dict_padding(ray_start_regular_shared):
+
+    ds = ray.data.from_items([{"one": i, "two": i + 0.5} for i in range(10)])
+
+    # ds has 10 rows, batch_size=4 -> 3 batches (4, 4, 2)
+    # Total 12 rows after padding.
+    paddings = {"one": -1, "two": -0.5}
+    batches = list(ds.iter_jax_batches(batch_size=4, paddings=paddings))
+    assert len(batches) == 3
+
+    combined_one = np.concatenate([batch["one"] for batch in batches])
+    combined_two = np.concatenate([batch["two"] for batch in batches])
+
+    assert len(combined_one) == 12
+    assert len(combined_two) == 12
+
+    expected_one = np.concatenate([np.arange(10), [-1, -1]])
+    expected_two = np.concatenate([np.arange(10) + 0.5, [-0.5, -0.5]])
+
+    np.testing.assert_array_equal(np.sort(combined_one), np.sort(expected_one))
+    np.testing.assert_array_equal(np.sort(combined_two), np.sort(expected_two))
+
+
+def test_iter_jax_batches_batch_size_divisibility_fail(ray_start_regular_shared):
+    with patch("jax.local_device_count", return_value=2):
+        ds = ray.data.range(10)
+        # batch_size must be divisible by num_local_devices=2
+        with pytest.raises(
+            ValueError,
+            match="evenly divisible by the number of local JAX devices",
+        ):
+            list(ds.iter_jax_batches(batch_size=3))
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main(["-v", __file__]))