Fix OOM Issue

Author: praveenhosdrug123

keras/src/backend/jax/distribution_lib.py

@@ -260,3 +260,80 @@ def _to_backend_layout(tensor_layout):
     partition_spec = jax.sharding.PartitionSpec(*tensor_layout.axes)
     jax_mesh = tensor_layout.device_mesh.backend_mesh
     return jax.sharding.NamedSharding(jax_mesh, partition_spec)
+
+
+def _distribute_initializer(
+    init_func=None, mean=0.0, stddev=1.0, seed=None, layout=None
+):
+    """
+    Distribution-aware token embedding initializer for JAX backend.
+
+    This function will create a Jax random array and
+    distribute it according to the current token embedding layout.
+
+    Args:
+        init_func: A functools.partial-wrapped object that takes the seed
+            as argument and returns a jax.Array. Must have shape and dtype
+            already bound via partial.
+        mean: Mean of distribution (applied to normal/truncated_normal).
+        stddev: Standard deviation of the distribution.
+        seed: Random seed for initialization.
+        layout: TensorLayout for the distributed tensor.
+
+    Returns:
+        A distributed jax array.
+
+    Raises:
+        ValueError: If init_func or seed is None.
+                    If init_func.func is not a supported random function.
+        TypeError: If init_func is not a functools.partial object.
+    """
+    import warnings
+    from functools import partial
+
+    # Validate all required arguments
+    if seed is None:
+        raise ValueError("seed cannot be None. Use keras.random.SeedGenerator.")
+
+    if init_func is None:
+        raise ValueError(
+            "init_func cannot be None. Shape and dtype info are required."
+        )
+
+    # Ensure init_func is a partial
+    if not isinstance(init_func, partial):
+        raise TypeError(
+            f"init_func must be functools.partial object, got {type(init_func)}"
+        )
+
+    # Shard based on tensor layout
+    if layout is None:
+        warnings.warn(
+            f"The layout is {layout}, sharding will default to single device"
+        )
+        sharding = None
+    else:
+        sharding = _to_backend_layout(layout)
+
+    # The init_func has static arguments baked in as per initializer.
+    compiled_init = jax.jit(
+        lambda seed: init_func(seed), out_shardings=sharding
+    )
+
+    sample = compiled_init(seed)
+
+    # Apply mean/stddev only for distributions where it makes sense
+    if init_func.func in (jax.random.normal, jax.random.truncated_normal):
+        return sample * stddev + mean
+    elif init_func.func == jax.random.uniform:
+        # Uniform doesn't use mean/stddev - warn
+        if mean != 0.0 or stddev != 1.0:
+            warnings.warn(
+                "mean and stddev are ignored for uniform distribution"
+            )
+        return sample
+    else:
+        raise ValueError(
+            f"Unsupported initializer: {init_func.func.__name__}. "
+            f"Supported: normal, truncated_normal, uniform"
+        )