Add default arg values to JAX decorator

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

@@ -228,6 +228,20 @@ def _assert_shards_shapes(self, category_outputs):
                 "Shards shapes have to be the same."
 
 
+def default_num_threads_value():
+    """Returns default value for num_threads argument of DALI iterator decorator.
+
+.. note::
+    This value should not be considered as optimized for any particular workload. For best
+        performance, it is recommended to set this value manually.
+
+.. note::
+    This value is subject to change in the future.
+"""
+
+    return 4
+
+
 def data_iterator_impl(
         iterator_type,
         pipeline_fn=None,
@@ -246,7 +260,15 @@ def data_iterator_decorator(func):
         def create_iterator(*args, **wrapper_kwargs):
             pipeline_def_fn = pipeline_def(func)
 
+            if 'num_threads' not in wrapper_kwargs:
+                wrapper_kwargs['num_threads'] = default_num_threads_value()
+
             if sharding is None:
+                if 'device_id' not in wrapper_kwargs:
+                    # Due to https://github.com/google/jax/issues/16024 the best we can do is to
+                    # assume that the first device is the one we want to use.
+                    wrapper_kwargs['device_id'] = 0
+
                 pipelines = [pipeline_def_fn(*args, **wrapper_kwargs)]
             else:
                 pipelines = []
@@ -301,6 +323,8 @@ def data_iterator(
     passes them to the iterator constructor. It also accepts all arguments of
     :meth:`nvidia.dali.pipeline.pipeline_def` and passes them to the pipeline definition
     function.
+    If no `device_id` argument is passed to the decorated function, it is assumed that
+    the first device is the one we want to use and `device_id` is set to 0.
     If the same argument is passed to the decorator and the decorated function,
     exception is raised.
 

dali/test/python/jax_plugin/test_iterator_decorator.py

@@ -55,6 +55,21 @@ def iterator_function():
     run_and_assert_sequential_iterator(iter)
 
 
+def test_dali_iterator_decorator_declarative_with_default_args():
+    # given
+    @data_iterator(
+        output_map=['data'],
+        reader_name='reader')
+    def iterator_function():
+        return iterator_function_def()
+
+    iter = iterator_function(
+        batch_size=batch_size)
+
+    # then
+    run_and_assert_sequential_iterator(iter)
+
+
 def test_dali_iterator_decorator_declarative_pipeline_fn_with_argument():
     # given
     @data_iterator(

dali/test/python/jax_plugin/test_multigpu.py

@@ -381,3 +381,23 @@ def iterator_function(shard_id, num_shards):
 
     # then
     run_sharded_iterator_test(data_iterator_instance)
+
+
+def test_dali_sequential_iterator_decorator_non_default_device():
+    # given
+    @data_iterator(
+        output_map=['data'],
+        reader_name='reader')
+    def iterator_function():
+        return iterator_function_def()
+
+    # when
+    iter = iterator_function(
+        num_threads=4,
+        device_id=1,
+        batch_size=batch_size)
+
+    batch = next(iter)
+
+    # then
+    assert batch['data'].device_buffers[0].device() == jax.devices()[1]