[dlpack] Support more DLPack dtypes now that we target DLPack 1.1

I did not update `jax.dlpack.SUPPORTED_DTYPES` because neither NumPy nor
TensorFlow currently support importing DLPack arrays with the new dtypes.

PiperOrigin-RevId: 736882904

tests/array_interoperability_test.py

@@ -22,6 +22,7 @@ import jax.numpy as jnp
 from jax.sharding import PartitionSpec as P
 from jax._src import config
 from jax._src import test_util as jtu
+from jax._src.lib import version as jaxlib_version
 
 import numpy as np
 
@@ -42,6 +43,27 @@ except ImportError:
 
 dlpack_dtypes = sorted(jax.dlpack.SUPPORTED_DTYPES, key=lambda x: x.__name__)
 
+# These dtypes are not supported by neither NumPy nor TensorFlow, therefore
+# we list them separately from ``jax.dlpack.SUPPORTED_DTYPES``.
+extra_dlpack_dtypes = []
+if jaxlib_version >= (0, 5, 3):
+  extra_dlpack_dtypes = [
+      jnp.float8_e4m3b11fnuz,
+      jnp.float8_e4m3fn,
+      jnp.float8_e4m3fnuz,
+      jnp.float8_e5m2,
+      jnp.float8_e5m2fnuz,
+  ] + [
+      dtype
+      for name in [
+          "float4_e2m1fn",
+          "float8_e3m4",
+          "float8_e4m3",
+          "float8_e8m0fnu",
+      ]
+      if (dtype := getattr(jnp, name, None))
+  ]
+
 numpy_dtypes = sorted(
     [dt for dt in jax.dlpack.SUPPORTED_DTYPES if dt != jnp.bfloat16],
     key=lambda x: x.__name__)
@@ -63,14 +85,16 @@ class DLPackTest(jtu.JaxTestCase):
       self.skipTest(f"DLPack not supported on {jtu.device_under_test()}")
 
   @jtu.sample_product(
-    shape=all_shapes,
-    dtype=dlpack_dtypes,
-    copy=[False, True, None],
-    use_stream=[False, True],
+      shape=all_shapes,
+      dtype=dlpack_dtypes + extra_dlpack_dtypes,
+      copy=[False, True, None],
+      use_stream=[False, True],
   )
   @jtu.run_on_devices("gpu")
-  @jtu.ignore_warning(message="Calling from_dlpack with a DLPack tensor",
-                      category=DeprecationWarning)
+  @jtu.ignore_warning(
+      message="Calling from_dlpack with a DLPack tensor",
+      category=DeprecationWarning,
+  )
   def testJaxRoundTrip(self, shape, dtype, copy, use_stream):
     rng = jtu.rand_default(self.rng())
     np = rng(shape, dtype)