Fix compatibility with flax-basic_example.ipynb after JAX update

dali/python/nvidia/dali/plugin/jax/fn/_function_transform.py

@@ -29,7 +29,7 @@ def gpu_to_dlpack(tensor: jax.Array, stream):
             f"The function returned array residing on the device of "
             f"kind `{devices[0].platform}`, expected `gpu`."
         )
-    return jax.dlpack.to_dlpack(tensor, stream=stream)
+    return tensor.__dlpack__(stream=stream)
 
 
 def cpu_to_dlpack(tensor: jax.Array):
@@ -44,7 +44,7 @@ def cpu_to_dlpack(tensor: jax.Array):
             f"The function returned array residing on the device of "
             f"kind `{devices[0].platform}`, expected `cpu`."
         )
-    return jax.dlpack.to_dlpack(tensor)
+    return tensor.__dlpack__()
 
 
 def with_gpu_dl_tensors_as_arrays(callback):

dali/python/nvidia/dali/plugin/jax/iterator.py

@@ -118,6 +118,7 @@ def __init__(
         last_batch_policy: LastBatchPolicy = LastBatchPolicy.FILL,
         prepare_first_batch: bool = True,
         sharding: Optional[Sharding] = None,
+        pmap_compatible: Optional[bool] = None,
     ):
         # check the assert first as _DaliBaseIterator would run the prefetch
         if len(set(output_map)) != len(output_map):
@@ -131,6 +132,10 @@ def __init__(
             ), "`sharding` should be an instance of `jax.sharding.Sharding`"
         self._sharding = sharding
 
+        # When pmap_compatible is None (default), auto-infer: False for single-pipeline
+        # iterators. _data_iterator_impl sets True automatically when devices are provided.
+        self._pmap_compatible = pmap_compatible if pmap_compatible is not None else False
+
         assert (
             last_batch_policy != LastBatchPolicy.PARTIAL
         ), "JAX iterator does not support partial last batch policy."
@@ -170,7 +175,7 @@ def _next_impl(self):
         for category_id, category_name in enumerate(self.output_map):
             category_outputs = self._gather_outputs_for_category(pipelines_outputs, category_id)
 
-            if self._num_gpus == 1 and self._sharding is None:
+            if self._num_gpus == 1 and self._sharding is None and not self._pmap_compatible:
                 next_output[category_name] = category_outputs[0]
             else:
                 self._assert_shards_shapes(category_outputs)
@@ -276,6 +281,7 @@ def _data_iterator_impl(
     prepare_first_batch: bool = True,
     sharding: Optional[Sharding] = None,
     devices: Optional[List[jax.Device]] = None,
+    pmap_compatible: Optional[bool] = None,
 ):
     """Implementation of the data_iterator decorator. It is extracted to a separate function
     to be reused by the peekable iterator decorator.
@@ -309,6 +315,7 @@ def create_iterator(*args, checkpoints=None, **wrapper_kwargs):
                     last_batch_padded=last_batch_padded,
                     last_batch_policy=last_batch_policy,
                     prepare_first_batch=prepare_first_batch,
+                    pmap_compatible=pmap_compatible,
                 )
             else:
                 pipelines = []
@@ -363,8 +370,14 @@ def create_iterator(*args, checkpoints=None, **wrapper_kwargs):
                         last_batch_policy=last_batch_policy,
                         prepare_first_batch=prepare_first_batch,
                         sharding=sharding,
+                        pmap_compatible=pmap_compatible,
                     )
                 elif devices is not None:
+                    # Auto-enable pmap_compatible when devices are provided, unless the user
+                    # explicitly overrode it.
+                    effective_pmap_compatible = (
+                        pmap_compatible if pmap_compatible is not None else True
+                    )
                     return iterator_type(
                         pipelines=pipelines,
                         output_map=output_map,
@@ -374,6 +387,7 @@ def create_iterator(*args, checkpoints=None, **wrapper_kwargs):
                         last_batch_padded=last_batch_padded,
                         last_batch_policy=last_batch_policy,
                         prepare_first_batch=prepare_first_batch,
+                        pmap_compatible=effective_pmap_compatible,
                     )
 
                 raise AssertionError(
@@ -396,6 +410,7 @@ def data_iterator(
     prepare_first_batch: bool = True,
     sharding: Optional[Sharding] = None,
     devices: Optional[List[jax.Device]] = None,
+    pmap_compatible: Optional[bool] = None,
 ):
     """Decorator for DALI iterator for JAX. Decorated function when called returns DALI
     iterator for JAX.
@@ -471,6 +486,14 @@ def data_iterator(
                 return outputs compatible with pmapped JAX functions.
                 This argument is  mutually exclusive with `sharding` argument. If `sharding`
                 is provided, `devices` should be set to None.
+    pmap_compatible : bool, optional, default = None
+                Controls whether the iterator produces outputs with a leading device axis
+                compatible with ``jax.pmap``. When ``None`` (default), it is inferred
+                automatically: ``True`` when ``devices`` is provided, ``False`` otherwise.
+                Set to ``True`` explicitly to force pmap-compatible output (shape
+                ``[num_devices, batch_per_device, ...]``) without using the ``devices``
+                argument. Set to ``False`` to suppress the device axis even when ``devices``
+                is provided.
     checkpoints : list of str, optional, default = None
                 Checkpoints obtained with `.checkpoints()` method of the iterator.
                 If provided, they will be used to restore the state of the pipelines.
@@ -506,4 +529,5 @@ def data_iterator(
         prepare_first_batch,
         sharding,
         devices,
+        pmap_compatible,
     )

qa/setup_packages.py

@@ -654,7 +654,7 @@ def get_pyvers_name(self, url, cuda_version):
         "flax",
         [
             PckgVer(
-                "0.7.4",
+                "0.10.0",
                 # Free-threaded Python build is incompatible with numpy<2.
                 python_free_threaded=False,
             ),