We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Typo in keras.distribution.initialize()
Hi,

There is a typo when calling `keras.distribution.initialize` due to a typo in the jax backend. The function pass the `corrdinator_address` argument instead of `coordinator_address` to `jax.distributed.initialize`

```log
---> 13 keras.distribution.initialize()

File /usr/local/lib/python3.10/site-packages/keras/src/distribution/distribution_lib.py:131, in initialize(job_addresses, num_processes, proceed_id)
    129 if proceed_id is None and "KERAS_DISTRIBUTION_PROCESS_ID" in os.environ:
    130     proceed_id = int(os.environ["KERAS_DISTRIBUTION_PROCESS_ID"])
--> 131 distribution_lib.initialize(job_addresses, num_processes, proceed_id)

File /usr/local/lib/python3.10/site-packages/keras/src/backend/jax/distribution_lib.py:207, in initialize(job_addresses, num_processes, process_id)
    204 else:
    205     corrdinator_address = job_addresses
--> 207 jax.distributed.initialize(
    208     corrdinator_address=corrdinator_address,
    209     num_processes=num_processes,
    210     process_id=process_id,
    211 )

TypeError: initialize() got an unexpected keyword argument 'corrdinator_address'
```



--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `keras/backend/jax/distribution_lib.py`
Content:
```
1 """!!!DO NOT USE!!!
2 
3 Distribution related class for JAX backend.
4 
5 This is just a prototype and we might want to unify it
6 with other backends in the future.
7 """
8 
9 import jax
10 import numpy as np
11 
12 from keras.utils import jax_utils
13 
14 
15 def list_devices(device_type=None):
16     """Return all the available devices based on the device type.
17 
18     Note that this should return the global devices in a distributed setting.
19 
20     Args:
21         device_type: string of `"cpu"`, `"gpu"` or `"tpu"`. Defaults to `"gpu"`
22             or `"tpu"` if available when device_type is not provided. Otherwise
23             will return the `"cpu"` devices.
24 
25     Return:
26         List of devices that are available for distribute computation.
27     """
28     device_type = device_type.lower() if device_type else None
29     jax_devices = jax.devices(backend=device_type)
30     return [f"{device.platform}:{device.id}" for device in jax_devices]
31 
32 
33 def distribute_variable(value, layout):
34     """Create a distributed variable for JAX.
35 
36     Since JAX doesn't have a variable class, this will just return a `jax.Array`
37     with the corresponding layout/sharding specified.
38 
39     Note that this function should be used in eager context, not in jitted
40     function.
41 
42     Args:
43         value: the initial value of the variable.
44         layout: `TensorLayout` for the created variable, or a
45             `jax.sharding.Sharding` instance.
46 
47     Returns:
48         jax.Array which is the distributed variable.
49     """
50     if not isinstance(layout, jax.sharding.Sharding):
51         layout = _to_jax_layout(layout)
52     if isinstance(
53         value, (jax.Array, jax.numpy.ndarray)
54     ) and value.sharding.is_equivalent_to(layout, ndim=len(value.shape)):
55         # Skip the relayout if the value is already having the proper sharding
56         return value
57 
58     if layout.is_fully_addressable:
59         return jax.device_put(value, layout)
60     else:
61         # Need to only distribute the value to local addressible devices, and
62         # repack them back into global format.
63         mapping = layout.addressable_devices_indices_map(value.shape)
64         local_values = jax.device_put(
65             [value[i] for i in mapping.values()], list(mapping.keys())
66         )
67         global_value = jax.make_array_from_single_device_arrays(
68             value.shape, layout, local_values
69         )
70         return global_value
71 
72 
73 def distribute_tensor(tensor, layout):
74     """Distribute the tensor based on the layout.
75 
76     Note that this function can be used both in eager context, or within a
77     jitted function.
78 
79     Args:
80         tensor: `jax.Array` that need to be distributed.
81         layout: `TensorLayout` for the distribution information, or a
82             `jax.sharding.Sharding` instance.
83 
84     Returns:
85         Distributed value.
86     """
87     if not isinstance(layout, jax.sharding.Sharding):
88         layout = _to_jax_layout(layout)
89     # TODO(scottzhu): This might not be a cheap check, we should consider
90     # have some proper JAX API for doing this check.
91     if jax_utils.is_in_jax_tracing_scope():
92         return jax.lax.with_sharding_constraint(tensor, layout)
93 
94     if layout.is_fully_addressable:
95         return jax.device_put(tensor, layout)
96     else:
97         # Need to only distribute the value to local addressible devices, and
98         # repack them back into global format.
99         mapping = layout.addressable_devices_indices_map(tensor.shape)
100         local_values = jax.device_put(
101             [tensor[i] for i in mapping.values()], list(mapping.keys())
102         )
103         global_value = jax.make_array_from_single_device_arrays(
104             tensor.shape, layout, local_values
105         )
106         return global_value
107 
108 
109 def distribute_data_input(inputs, layout):
110     """Distribute the input data with the corresponding layout.
111 
112     Note that the inputs here is a local worker batch. Within the local worker,
113     the data need to be further partitioned to map to the each of the devices.
114 
115     Args:
116         inputs: `jax.Array` that is already sharded to a local process size.
117         layout: `TensorLayout` for the distribution information, or a
118             `jax.sharding.Sharding` instance.
119 
120     Returns:
121         Distributed inputs thats been properly put to local devices.
122     """
123     if not isinstance(layout, jax.sharding.Sharding):
124         layout = _to_jax_layout(layout)
125     if layout.is_fully_addressable:
126         return jax.device_put(inputs, layout)
127 
128     # We need the jax mesh information to determine how to place the data
129     # on to each of the worker.
130     jax_mesh = layout.mesh
131     mesh_rank = len(jax_mesh.shape)
132     per_process_batch_size = inputs.shape[0]
133     if mesh_rank == 1:
134         # This is data parallel mesh only. We will split the full data
135         # across the batch dim.
136         num_split = jax.local_device_count()
137         per_replica_batch_size = per_process_batch_size // num_split
138         if per_process_batch_size % per_replica_batch_size != 0:
139             raise ValueError(
140                 f"The local batch size {per_process_batch_size} is not"
141                 "divisible by the number of local replicas "
142                 f"{num_split}"
143             )
144         global_batch_size = per_process_batch_size * jax.process_count()
145         per_replica_batches = jax.numpy.split(inputs, num_split, axis=0)
146     elif mesh_rank == 2:
147         # Data+Model parallel
148         # In this case, we need to check if the mesh batch dim shape is large
149         # than number of local devices, so that we can decide whether a split
150         # is needed for the data, or a repeat/copy of the data is needed for
151         # each of the device.
152         # TODO(scottzhu): The mesh batch dim name is not available here, since
153         # we only have jax Mesh. We assume the first dim is for batch, and
154         # second dim is for model for now.
155         mesh_batch_dim_size = list(jax_mesh.shape.values())[0]
156         local_device_count = jax.local_device_count()
157         if mesh_batch_dim_size < local_device_count:
158             # No split needed, we only need to repeat here.
159             global_batch_size = per_process_batch_size
160             per_replica_batches = [inputs for _ in range(local_device_count)]
161         else:
162             # Note that global batch size is not simply per_process_batch_size *
163             # num_process. It actually depends on the model dim size.
164             global_batch_size = per_process_batch_size * (
165                 mesh_batch_dim_size // local_device_count
166             )
167             per_replica_batches = jax.numpy.split(
168                 inputs, local_device_count, axis=0
169             )
170     else:
171         raise ValueError(
172             "Only 1D or 2D mesh is supported at the moment. "
173             f"Received mesh shape = {jax_mesh.shape}"
174         )
175 
176     global_shape = (global_batch_size,) + inputs.shape[1:]
177     global_batch_array = jax.make_array_from_single_device_arrays(
178         global_shape,
179         layout,
180         arrays=[
181             jax.device_put(batch, device)
182             for batch, device in zip(
183                 per_replica_batches, layout.addressable_devices
184             )
185         ],
186     )
187     return global_batch_array
188 
189 
190 def initialize(job_addresses, num_processes, process_id):
191     if job_addresses and "," in job_addresses:
192         # When user provide all the job addresses, we will split and get the
193         # first one, which is the coordinator.
194         job_addresses = job_addresses.split(",")
195         # Do a sanity check to make sure the number of addresses also match
196         # the num_processes.
197         if num_processes is not None and num_processes != len(job_addresses):
198             raise ValueError(
199                 f"The provided job_addresses {job_addresses} has "
200                 f"{len(job_addresses)} jobs, but num_processes is "
201                 f"{num_processes}"
202             )
203         corrdinator_address = job_addresses[0]
204     else:
205         corrdinator_address = job_addresses
206 
207     jax.distributed.initialize(
208         corrdinator_address=corrdinator_address,
209         num_processes=num_processes,
210         process_id=process_id,
211     )
212 
213 
214 def num_processes():
215     """Return the number of processes for the current distribution setting."""
216     return jax.process_count()
217 
218 
219 def process_id():
220     """Return the current process ID for the distribution setting."""
221     return jax.process_index()
222 
223 
224 def _to_jax_device(device_id):
225     if isinstance(device_id, jax.Device):
226         return device_id
227     device_type, index = device_id.split(":")
228     index = int(index)
229     devices = jax.devices(backend=device_type)
230     if index >= len(devices):
231         raise ValueError(f"Unknown device: {device_id}")
232     return devices[index]
233 
234 
235 def _to_jax_mesh(device_mesh):
236     """Convert the DeviceMesh to JAX backend specific Mesh.
237 
238     Args:
239         device_mesh: DeviceMesh instance to convert.
240 
241     Returns:
242         A `jax.sharding.Mesh` instance.
243     """
244     shape = device_mesh.devices.shape
245     devices = [_to_jax_device(d) for d in device_mesh.devices.flatten()]
246     devices = np.array(devices).reshape(shape)
247     return jax.sharding.Mesh(devices, device_mesh.axis_names)
248 
249 
250 def _to_jax_layout(tensor_layout):
251     """Convert the TensorLayout to JAX backend specific Sharding.
252 
253     Args:
254         tensor_layout: TensorLayout instance to convert.
255 
256     Returns:
257         A `jax.sharding.NamedSharding` instance.
258     """
259     if tensor_layout.device_mesh is None:
260         raise ValueError(
261             "Cannot create sharding when device mesh is not set "
262             "for TensorLayout."
263         )
264     partition_spec = jax.sharding.PartitionSpec(*tensor_layout.axes)
265     jax_mesh = _to_jax_mesh(tensor_layout.device_mesh)
266     return jax.sharding.NamedSharding(jax_mesh, partition_spec)
267
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/keras/backend/jax/distribution_lib.py b/keras/backend/jax/distribution_lib.py
--- a/keras/backend/jax/distribution_lib.py
+++ b/keras/backend/jax/distribution_lib.py
@@ -200,12 +200,12 @@
                 f"{len(job_addresses)} jobs, but num_processes is "
                 f"{num_processes}"
             )
-        corrdinator_address = job_addresses[0]
+        coordinator_address = job_addresses[0]
     else:
-        corrdinator_address = job_addresses
+        coordinator_address = job_addresses
 
     jax.distributed.initialize(
-        corrdinator_address=corrdinator_address,
+        coordinator_address=coordinator_address,
         num_processes=num_processes,
         process_id=process_id,
     )

{"golden_diff": "diff --git a/keras/backend/jax/distribution_lib.py b/keras/backend/jax/distribution_lib.py\n--- a/keras/backend/jax/distribution_lib.py\n+++ b/keras/backend/jax/distribution_lib.py\n@@ -200,12 +200,12 @@\n                 f\"{len(job_addresses)} jobs, but num_processes is \"\n                 f\"{num_processes}\"\n             )\n-        corrdinator_address = job_addresses[0]\n+        coordinator_address = job_addresses[0]\n     else:\n-        corrdinator_address = job_addresses\n+        coordinator_address = job_addresses\n \n     jax.distributed.initialize(\n-        corrdinator_address=corrdinator_address,\n+        coordinator_address=coordinator_address,\n         num_processes=num_processes,\n         process_id=process_id,\n     )\n", "issue": "Typo in keras.distribution.initialize()\nHi,\r\n\r\nThere is a typo when calling `keras.distribution.initialize` due to a typo in the jax backend. The function pass the `corrdinator_address` argument instead of `coordinator_address` to `jax.distributed.initialize`\r\n\r\n```log\r\n---> 13 keras.distribution.initialize()\r\n\r\nFile /usr/local/lib/python3.10/site-packages/keras/src/distribution/distribution_lib.py:131, in initialize(job_addresses, num_processes, proceed_id)\r\n    129 if proceed_id is None and \"KERAS_DISTRIBUTION_PROCESS_ID\" in os.environ:\r\n    130     proceed_id = int(os.environ[\"KERAS_DISTRIBUTION_PROCESS_ID\"])\r\n--> 131 distribution_lib.initialize(job_addresses, num_processes, proceed_id)\r\n\r\nFile /usr/local/lib/python3.10/site-packages/keras/src/backend/jax/distribution_lib.py:207, in initialize(job_addresses, num_processes, process_id)\r\n    204 else:\r\n    205     corrdinator_address = job_addresses\r\n--> 207 jax.distributed.initialize(\r\n    208     corrdinator_address=corrdinator_address,\r\n    209     num_processes=num_processes,\r\n    210     process_id=process_id,\r\n    211 )\r\n\r\nTypeError: initialize() got an unexpected keyword argument 'corrdinator_address'\r\n```\r\n\r\n\n", "before_files": [{"content": "\"\"\"!!!DO NOT USE!!!\n\nDistribution related class for JAX backend.\n\nThis is just a prototype and we might want to unify it\nwith other backends in the future.\n\"\"\"\n\nimport jax\nimport numpy as np\n\nfrom keras.utils import jax_utils\n\n\ndef list_devices(device_type=None):\n    \"\"\"Return all the available devices based on the device type.\n\n    Note that this should return the global devices in a distributed setting.\n\n    Args:\n        device_type: string of `\"cpu\"`, `\"gpu\"` or `\"tpu\"`. Defaults to `\"gpu\"`\n            or `\"tpu\"` if available when device_type is not provided. Otherwise\n            will return the `\"cpu\"` devices.\n\n    Return:\n        List of devices that are available for distribute computation.\n    \"\"\"\n    device_type = device_type.lower() if device_type else None\n    jax_devices = jax.devices(backend=device_type)\n    return [f\"{device.platform}:{device.id}\" for device in jax_devices]\n\n\ndef distribute_variable(value, layout):\n    \"\"\"Create a distributed variable for JAX.\n\n    Since JAX doesn't have a variable class, this will just return a `jax.Array`\n    with the corresponding layout/sharding specified.\n\n    Note that this function should be used in eager context, not in jitted\n    function.\n\n    Args:\n        value: the initial value of the variable.\n        layout: `TensorLayout` for the created variable, or a\n            `jax.sharding.Sharding` instance.\n\n    Returns:\n        jax.Array which is the distributed variable.\n    \"\"\"\n    if not isinstance(layout, jax.sharding.Sharding):\n        layout = _to_jax_layout(layout)\n    if isinstance(\n        value, (jax.Array, jax.numpy.ndarray)\n    ) and value.sharding.is_equivalent_to(layout, ndim=len(value.shape)):\n        # Skip the relayout if the value is already having the proper sharding\n        return value\n\n    if layout.is_fully_addressable:\n        return jax.device_put(value, layout)\n    else:\n        # Need to only distribute the value to local addressible devices, and\n        # repack them back into global format.\n        mapping = layout.addressable_devices_indices_map(value.shape)\n        local_values = jax.device_put(\n            [value[i] for i in mapping.values()], list(mapping.keys())\n        )\n        global_value = jax.make_array_from_single_device_arrays(\n            value.shape, layout, local_values\n        )\n        return global_value\n\n\ndef distribute_tensor(tensor, layout):\n    \"\"\"Distribute the tensor based on the layout.\n\n    Note that this function can be used both in eager context, or within a\n    jitted function.\n\n    Args:\n        tensor: `jax.Array` that need to be distributed.\n        layout: `TensorLayout` for the distribution information, or a\n            `jax.sharding.Sharding` instance.\n\n    Returns:\n        Distributed value.\n    \"\"\"\n    if not isinstance(layout, jax.sharding.Sharding):\n        layout = _to_jax_layout(layout)\n    # TODO(scottzhu): This might not be a cheap check, we should consider\n    # have some proper JAX API for doing this check.\n    if jax_utils.is_in_jax_tracing_scope():\n        return jax.lax.with_sharding_constraint(tensor, layout)\n\n    if layout.is_fully_addressable:\n        return jax.device_put(tensor, layout)\n    else:\n        # Need to only distribute the value to local addressible devices, and\n        # repack them back into global format.\n        mapping = layout.addressable_devices_indices_map(tensor.shape)\n        local_values = jax.device_put(\n            [tensor[i] for i in mapping.values()], list(mapping.keys())\n        )\n        global_value = jax.make_array_from_single_device_arrays(\n            tensor.shape, layout, local_values\n        )\n        return global_value\n\n\ndef distribute_data_input(inputs, layout):\n    \"\"\"Distribute the input data with the corresponding layout.\n\n    Note that the inputs here is a local worker batch. Within the local worker,\n    the data need to be further partitioned to map to the each of the devices.\n\n    Args:\n        inputs: `jax.Array` that is already sharded to a local process size.\n        layout: `TensorLayout` for the distribution information, or a\n            `jax.sharding.Sharding` instance.\n\n    Returns:\n        Distributed inputs thats been properly put to local devices.\n    \"\"\"\n    if not isinstance(layout, jax.sharding.Sharding):\n        layout = _to_jax_layout(layout)\n    if layout.is_fully_addressable:\n        return jax.device_put(inputs, layout)\n\n    # We need the jax mesh information to determine how to place the data\n    # on to each of the worker.\n    jax_mesh = layout.mesh\n    mesh_rank = len(jax_mesh.shape)\n    per_process_batch_size = inputs.shape[0]\n    if mesh_rank == 1:\n        # This is data parallel mesh only. We will split the full data\n        # across the batch dim.\n        num_split = jax.local_device_count()\n        per_replica_batch_size = per_process_batch_size // num_split\n        if per_process_batch_size % per_replica_batch_size != 0:\n            raise ValueError(\n                f\"The local batch size {per_process_batch_size} is not\"\n                \"divisible by the number of local replicas \"\n                f\"{num_split}\"\n            )\n        global_batch_size = per_process_batch_size * jax.process_count()\n        per_replica_batches = jax.numpy.split(inputs, num_split, axis=0)\n    elif mesh_rank == 2:\n        # Data+Model parallel\n        # In this case, we need to check if the mesh batch dim shape is large\n        # than number of local devices, so that we can decide whether a split\n        # is needed for the data, or a repeat/copy of the data is needed for\n        # each of the device.\n        # TODO(scottzhu): The mesh batch dim name is not available here, since\n        # we only have jax Mesh. We assume the first dim is for batch, and\n        # second dim is for model for now.\n        mesh_batch_dim_size = list(jax_mesh.shape.values())[0]\n        local_device_count = jax.local_device_count()\n        if mesh_batch_dim_size < local_device_count:\n            # No split needed, we only need to repeat here.\n            global_batch_size = per_process_batch_size\n            per_replica_batches = [inputs for _ in range(local_device_count)]\n        else:\n            # Note that global batch size is not simply per_process_batch_size *\n            # num_process. It actually depends on the model dim size.\n            global_batch_size = per_process_batch_size * (\n                mesh_batch_dim_size // local_device_count\n            )\n            per_replica_batches = jax.numpy.split(\n                inputs, local_device_count, axis=0\n            )\n    else:\n        raise ValueError(\n            \"Only 1D or 2D mesh is supported at the moment. \"\n            f\"Received mesh shape = {jax_mesh.shape}\"\n        )\n\n    global_shape = (global_batch_size,) + inputs.shape[1:]\n    global_batch_array = jax.make_array_from_single_device_arrays(\n        global_shape,\n        layout,\n        arrays=[\n            jax.device_put(batch, device)\n            for batch, device in zip(\n                per_replica_batches, layout.addressable_devices\n            )\n        ],\n    )\n    return global_batch_array\n\n\ndef initialize(job_addresses, num_processes, process_id):\n    if job_addresses and \",\" in job_addresses:\n        # When user provide all the job addresses, we will split and get the\n        # first one, which is the coordinator.\n        job_addresses = job_addresses.split(\",\")\n        # Do a sanity check to make sure the number of addresses also match\n        # the num_processes.\n        if num_processes is not None and num_processes != len(job_addresses):\n            raise ValueError(\n                f\"The provided job_addresses {job_addresses} has \"\n                f\"{len(job_addresses)} jobs, but num_processes is \"\n                f\"{num_processes}\"\n            )\n        corrdinator_address = job_addresses[0]\n    else:\n        corrdinator_address = job_addresses\n\n    jax.distributed.initialize(\n        corrdinator_address=corrdinator_address,\n        num_processes=num_processes,\n        process_id=process_id,\n    )\n\n\ndef num_processes():\n    \"\"\"Return the number of processes for the current distribution setting.\"\"\"\n    return jax.process_count()\n\n\ndef process_id():\n    \"\"\"Return the current process ID for the distribution setting.\"\"\"\n    return jax.process_index()\n\n\ndef _to_jax_device(device_id):\n    if isinstance(device_id, jax.Device):\n        return device_id\n    device_type, index = device_id.split(\":\")\n    index = int(index)\n    devices = jax.devices(backend=device_type)\n    if index >= len(devices):\n        raise ValueError(f\"Unknown device: {device_id}\")\n    return devices[index]\n\n\ndef _to_jax_mesh(device_mesh):\n    \"\"\"Convert the DeviceMesh to JAX backend specific Mesh.\n\n    Args:\n        device_mesh: DeviceMesh instance to convert.\n\n    Returns:\n        A `jax.sharding.Mesh` instance.\n    \"\"\"\n    shape = device_mesh.devices.shape\n    devices = [_to_jax_device(d) for d in device_mesh.devices.flatten()]\n    devices = np.array(devices).reshape(shape)\n    return jax.sharding.Mesh(devices, device_mesh.axis_names)\n\n\ndef _to_jax_layout(tensor_layout):\n    \"\"\"Convert the TensorLayout to JAX backend specific Sharding.\n\n    Args:\n        tensor_layout: TensorLayout instance to convert.\n\n    Returns:\n        A `jax.sharding.NamedSharding` instance.\n    \"\"\"\n    if tensor_layout.device_mesh is None:\n        raise ValueError(\n            \"Cannot create sharding when device mesh is not set \"\n            \"for TensorLayout.\"\n        )\n    partition_spec = jax.sharding.PartitionSpec(*tensor_layout.axes)\n    jax_mesh = _to_jax_mesh(tensor_layout.device_mesh)\n    return jax.sharding.NamedSharding(jax_mesh, partition_spec)\n", "path": "keras/backend/jax/distribution_lib.py"}], "after_files": [{"content": "\"\"\"!!!DO NOT USE!!!\n\nDistribution related class for JAX backend.\n\nThis is just a prototype and we might want to unify it\nwith other backends in the future.\n\"\"\"\n\nimport jax\nimport numpy as np\n\nfrom keras.utils import jax_utils\n\n\ndef list_devices(device_type=None):\n    \"\"\"Return all the available devices based on the device type.\n\n    Note that this should return the global devices in a distributed setting.\n\n    Args:\n        device_type: string of `\"cpu\"`, `\"gpu\"` or `\"tpu\"`. Defaults to `\"gpu\"`\n            or `\"tpu\"` if available when device_type is not provided. Otherwise\n            will return the `\"cpu\"` devices.\n\n    Return:\n        List of devices that are available for distribute computation.\n    \"\"\"\n    device_type = device_type.lower() if device_type else None\n    jax_devices = jax.devices(backend=device_type)\n    return [f\"{device.platform}:{device.id}\" for device in jax_devices]\n\n\ndef distribute_variable(value, layout):\n    \"\"\"Create a distributed variable for JAX.\n\n    Since JAX doesn't have a variable class, this will just return a `jax.Array`\n    with the corresponding layout/sharding specified.\n\n    Note that this function should be used in eager context, not in jitted\n    function.\n\n    Args:\n        value: the initial value of the variable.\n        layout: `TensorLayout` for the created variable, or a\n            `jax.sharding.Sharding` instance.\n\n    Returns:\n        jax.Array which is the distributed variable.\n    \"\"\"\n    if not isinstance(layout, jax.sharding.Sharding):\n        layout = _to_jax_layout(layout)\n    if isinstance(\n        value, (jax.Array, jax.numpy.ndarray)\n    ) and value.sharding.is_equivalent_to(layout, ndim=len(value.shape)):\n        # Skip the relayout if the value is already having the proper sharding\n        return value\n\n    if layout.is_fully_addressable:\n        return jax.device_put(value, layout)\n    else:\n        # Need to only distribute the value to local addressible devices, and\n        # repack them back into global format.\n        mapping = layout.addressable_devices_indices_map(value.shape)\n        local_values = jax.device_put(\n            [value[i] for i in mapping.values()], list(mapping.keys())\n        )\n        global_value = jax.make_array_from_single_device_arrays(\n            value.shape, layout, local_values\n        )\n        return global_value\n\n\ndef distribute_tensor(tensor, layout):\n    \"\"\"Distribute the tensor based on the layout.\n\n    Note that this function can be used both in eager context, or within a\n    jitted function.\n\n    Args:\n        tensor: `jax.Array` that need to be distributed.\n        layout: `TensorLayout` for the distribution information, or a\n            `jax.sharding.Sharding` instance.\n\n    Returns:\n        Distributed value.\n    \"\"\"\n    if not isinstance(layout, jax.sharding.Sharding):\n        layout = _to_jax_layout(layout)\n    # TODO(scottzhu): This might not be a cheap check, we should consider\n    # have some proper JAX API for doing this check.\n    if jax_utils.is_in_jax_tracing_scope():\n        return jax.lax.with_sharding_constraint(tensor, layout)\n\n    if layout.is_fully_addressable:\n        return jax.device_put(tensor, layout)\n    else:\n        # Need to only distribute the value to local addressible devices, and\n        # repack them back into global format.\n        mapping = layout.addressable_devices_indices_map(tensor.shape)\n        local_values = jax.device_put(\n            [tensor[i] for i in mapping.values()], list(mapping.keys())\n        )\n        global_value = jax.make_array_from_single_device_arrays(\n            tensor.shape, layout, local_values\n        )\n        return global_value\n\n\ndef distribute_data_input(inputs, layout):\n    \"\"\"Distribute the input data with the corresponding layout.\n\n    Note that the inputs here is a local worker batch. Within the local worker,\n    the data need to be further partitioned to map to the each of the devices.\n\n    Args:\n        inputs: `jax.Array` that is already sharded to a local process size.\n        layout: `TensorLayout` for the distribution information, or a\n            `jax.sharding.Sharding` instance.\n\n    Returns:\n        Distributed inputs thats been properly put to local devices.\n    \"\"\"\n    if not isinstance(layout, jax.sharding.Sharding):\n        layout = _to_jax_layout(layout)\n    if layout.is_fully_addressable:\n        return jax.device_put(inputs, layout)\n\n    # We need the jax mesh information to determine how to place the data\n    # on to each of the worker.\n    jax_mesh = layout.mesh\n    mesh_rank = len(jax_mesh.shape)\n    per_process_batch_size = inputs.shape[0]\n    if mesh_rank == 1:\n        # This is data parallel mesh only. We will split the full data\n        # across the batch dim.\n        num_split = jax.local_device_count()\n        per_replica_batch_size = per_process_batch_size // num_split\n        if per_process_batch_size % per_replica_batch_size != 0:\n            raise ValueError(\n                f\"The local batch size {per_process_batch_size} is not\"\n                \"divisible by the number of local replicas \"\n                f\"{num_split}\"\n            )\n        global_batch_size = per_process_batch_size * jax.process_count()\n        per_replica_batches = jax.numpy.split(inputs, num_split, axis=0)\n    elif mesh_rank == 2:\n        # Data+Model parallel\n        # In this case, we need to check if the mesh batch dim shape is large\n        # than number of local devices, so that we can decide whether a split\n        # is needed for the data, or a repeat/copy of the data is needed for\n        # each of the device.\n        # TODO(scottzhu): The mesh batch dim name is not available here, since\n        # we only have jax Mesh. We assume the first dim is for batch, and\n        # second dim is for model for now.\n        mesh_batch_dim_size = list(jax_mesh.shape.values())[0]\n        local_device_count = jax.local_device_count()\n        if mesh_batch_dim_size < local_device_count:\n            # No split needed, we only need to repeat here.\n            global_batch_size = per_process_batch_size\n            per_replica_batches = [inputs for _ in range(local_device_count)]\n        else:\n            # Note that global batch size is not simply per_process_batch_size *\n            # num_process. It actually depends on the model dim size.\n            global_batch_size = per_process_batch_size * (\n                mesh_batch_dim_size // local_device_count\n            )\n            per_replica_batches = jax.numpy.split(\n                inputs, local_device_count, axis=0\n            )\n    else:\n        raise ValueError(\n            \"Only 1D or 2D mesh is supported at the moment. \"\n            f\"Received mesh shape = {jax_mesh.shape}\"\n        )\n\n    global_shape = (global_batch_size,) + inputs.shape[1:]\n    global_batch_array = jax.make_array_from_single_device_arrays(\n        global_shape,\n        layout,\n        arrays=[\n            jax.device_put(batch, device)\n            for batch, device in zip(\n                per_replica_batches, layout.addressable_devices\n            )\n        ],\n    )\n    return global_batch_array\n\n\ndef initialize(job_addresses, num_processes, process_id):\n    if job_addresses and \",\" in job_addresses:\n        # When user provide all the job addresses, we will split and get the\n        # first one, which is the coordinator.\n        job_addresses = job_addresses.split(\",\")\n        # Do a sanity check to make sure the number of addresses also match\n        # the num_processes.\n        if num_processes is not None and num_processes != len(job_addresses):\n            raise ValueError(\n                f\"The provided job_addresses {job_addresses} has \"\n                f\"{len(job_addresses)} jobs, but num_processes is \"\n                f\"{num_processes}\"\n            )\n        coordinator_address = job_addresses[0]\n    else:\n        coordinator_address = job_addresses\n\n    jax.distributed.initialize(\n        coordinator_address=coordinator_address,\n        num_processes=num_processes,\n        process_id=process_id,\n    )\n\n\ndef num_processes():\n    \"\"\"Return the number of processes for the current distribution setting.\"\"\"\n    return jax.process_count()\n\n\ndef process_id():\n    \"\"\"Return the current process ID for the distribution setting.\"\"\"\n    return jax.process_index()\n\n\ndef _to_jax_device(device_id):\n    if isinstance(device_id, jax.Device):\n        return device_id\n    device_type, index = device_id.split(\":\")\n    index = int(index)\n    devices = jax.devices(backend=device_type)\n    if index >= len(devices):\n        raise ValueError(f\"Unknown device: {device_id}\")\n    return devices[index]\n\n\ndef _to_jax_mesh(device_mesh):\n    \"\"\"Convert the DeviceMesh to JAX backend specific Mesh.\n\n    Args:\n        device_mesh: DeviceMesh instance to convert.\n\n    Returns:\n        A `jax.sharding.Mesh` instance.\n    \"\"\"\n    shape = device_mesh.devices.shape\n    devices = [_to_jax_device(d) for d in device_mesh.devices.flatten()]\n    devices = np.array(devices).reshape(shape)\n    return jax.sharding.Mesh(devices, device_mesh.axis_names)\n\n\ndef _to_jax_layout(tensor_layout):\n    \"\"\"Convert the TensorLayout to JAX backend specific Sharding.\n\n    Args:\n        tensor_layout: TensorLayout instance to convert.\n\n    Returns:\n        A `jax.sharding.NamedSharding` instance.\n    \"\"\"\n    if tensor_layout.device_mesh is None:\n        raise ValueError(\n            \"Cannot create sharding when device mesh is not set \"\n            \"for TensorLayout.\"\n        )\n    partition_spec = jax.sharding.PartitionSpec(*tensor_layout.axes)\n    jax_mesh = _to_jax_mesh(tensor_layout.device_mesh)\n    return jax.sharding.NamedSharding(jax_mesh, partition_spec)\n", "path": "keras/backend/jax/distribution_lib.py"}]}