Add sharding constraint for the output tensor in the model

keras/backend/jax/core.py

@@ -35,7 +35,7 @@ def _initialize(self, value):
 
     def _direct_assign(self, value):
         if getattr(self, "_layout", None) is not None:
-            value = distribution_lib.distribute_value(value, self._layout)
+            value = distribution_lib.distribute_variable(value, self._layout)
         self._value = value
 
     def _convert_to_tensor(self, value, dtype=None):

keras/backend/jax/distribution_lib.py

@@ -8,6 +8,8 @@
 import jax
 import numpy as np
 
+from keras.utils import jax_utils
+
 
 def list_devices(device_type=None):
     """Return all the available devices based on the device type.
@@ -27,8 +29,33 @@ def list_devices(device_type=None):
     return [f"{device.device_kind}:{device.id}" for device in jax_devices]
 
 
-def distribute_value(value, tensor_layout):
-    """Distribute the value based on the layout.
+def distribute_variable(value, tensor_layout):
+    """Create a distributed variable for JAX.
+
+    Since JAX doesn't have variable class, this will just return a jax.Array
+    with the corresponding layout/sharding specified.
+
+    Note that this function should be used in eager context, not in jitted
+    function.
+
+    Args:
+        value: the initial value of the variable.
+        tensor_layout: `TensorLayout` for the created variable, or a
+            `jax.sharding.Sharding` instance.
+
+    Returns:
+        jax.Array which is the distributed variable.
+    """
+    if not isinstance(tensor_layout, jax.sharding.Sharding):
+        tensor_layout = _to_jax_layout(tensor_layout)
+    return jax.device_put(value, tensor_layout)
+
+
+def distribute_tensor(tensor, tensor_layout):
+    """Distribute the tensor based on the layout.
+
+    Note that this function can be used both in eager context, or within a
+    jitted function.
 
     Args:
         value: `jax.Array` that need to be distributed.
@@ -40,7 +67,13 @@ def distribute_value(value, tensor_layout):
     """
     if not isinstance(tensor_layout, jax.sharding.Sharding):
         tensor_layout = _to_jax_layout(tensor_layout)
-    return jax.device_put(value, tensor_layout)
+
+    # TODO(scottzhu): This might not be a cheap check, we should consider
+    # have some proper JAX API for doing this check.
+    if jax_utils.is_in_jax_tracing_scope():
+        return jax.lax.with_sharding_constraint(tensor, tensor_layout)
+    else:
+        return jax.device_put(tensor, tensor_layout)
 
 
 def _to_jax_device(device_id):

keras/backend/jax/trainer.py

@@ -846,7 +846,7 @@ def _distribute_data(self, data):
 
             def distribute_single_value(d):
                 layout = distribution.get_data_layout(d.shape)
-                return jax_distribution_lib.distribute_value(d, layout)
+                return jax_distribution_lib.distribute_tensor(d, layout)
 
             return jax.tree_util.tree_map(distribute_single_value, data)
         else:

keras/distribution/distribution_lib.py

@@ -14,6 +14,7 @@
 import numpy as np
 
 from keras.api_export import keras_export
+from keras.backend import KerasTensor
 from keras.backend import distribution_lib
 from keras.backend.common import global_state
 
@@ -170,6 +171,7 @@ class Distribution:
 
     1. Distribute the model variables to a `DeviceMesh`.
     2. Distribute the input data to a `DeviceMesh`.
+    3. Distribute an intermediate state tensor in the model.
 
     It can create a context scope so that the framework to properly detect the
     `Distribution` and distribute the variable/data accordingly.
@@ -205,6 +207,19 @@ def get_variable_layout(self, variable):
         """
         raise NotImplementedError()
 
+    def get_tensor_layout(self, path):
+        """Retrieve the `TensorLayout` for the intermediate tensor.
+
+        Args:
+            path: a string path for the correspoding tensor.
+
+        return:
+            The `TensorLayout` for the intermediate tensor, which can be used
+            by `backend.relayout()` to reshard the tensor. Could also return
+            None.
+        """
+        raise NotImplementedError()
+
     @contextlib.contextmanager
     def scope(self):
         """Context manager to make the `Distribution` current."""
@@ -296,6 +311,10 @@ def get_variable_layout(self, variable):
         variable_shard_spec = [None] * len(variable.shape)
         return TensorLayout(variable_shard_spec, self.device_mesh)
 
+    def get_tensor_layout(self, path):
+        # For data parallel training, the intermediate state is not changed.
+        return None
+
 
 @keras_export("keras.distribution.ModelParallel")
 class ModelParallel(Distribution):
@@ -393,6 +412,9 @@ def get_variable_layout(self, variable):
         variable_shard_spec = [None] * len(variable.shape)
         return TensorLayout(variable_shard_spec, self.device_mesh)
 
+    def get_tensor_layout(self, path):
+        return self._layout_map[path]
+
 
 @keras_export("keras.distribution.LayoutMap")
 class LayoutMap(collections.abc.MutableMapping):
@@ -507,6 +529,28 @@ def _maybe_populate_device_mesh(self, layout):
 LayoutMap.get.__doc__ = LayoutMap.__getitem__.__doc__
 
 
+@keras_export("keras.distribution.distribute_tensor")
+def distribute_tensor(tensor, tensor_layout):
+    """Change the layout of a Tensor value in the jit function execution.
+
+    Note that this might not work outside of the jitted function for certain
+    backend. To change the layout of a value eagerly, please use
+    `backend.distribution_lib.distribute_value`.
+
+    Args:
+        tensor: a Tensor to change the layout.
+        tensor_layout: TensorLayout to be applied on the value.
+
+    Returns:
+        a new value with the specified tensor layout.
+    """
+    if isinstance(tensor, KerasTensor):
+        # keras tensor is only used for building functional model, and can't be
+        # used to alter layout/sharding.
+        return tensor
+    return distribution_lib.distribute_tensor(tensor, tensor_layout)
+
+
 @keras_export("keras.distribution.distribution")
 def distribution():
     """Retrieve the current distribution from global context."""

keras/distribution/distribution_lib_test.py

@@ -1,5 +1,6 @@
 """Test for distribution_lib.py."""
 
+import functools
 import os
 from unittest import mock
 
@@ -192,6 +193,15 @@ def test_get_variable_layout(self):
         self.assertIs(variable_layout.device_mesh, self.device_mesh)
         self.assertEqual(variable_layout.axes, (None,))
 
+    def test_get_tensor_layout(self):
+        distribution = distribution_lib.DataParallel(
+            device_mesh=self.device_mesh
+        )
+
+        path = "path/to/tensor"
+        tensor_layout = distribution.get_tensor_layout(path)
+        self.assertIsNone(tensor_layout)
+
 
 class ModelParallelDistributionTest(testing.TestCase):
     def setUp(self):
@@ -239,6 +249,22 @@ def test_distribute_data(self):
         self.assertIs(data_layout.device_mesh, self.device_mesh)
         self.assertEqual(data_layout.axes, ("data", None, None))
 
+    def test_get_tensor_layout(self):
+        layout_map = distribution_lib.LayoutMap(self.device_mesh)
+        layout_map[".*kernel"] = distribution_lib.TensorLayout([None, "model"])
+        layout_map[".*bias"] = distribution_lib.TensorLayout(["model"])
+        layout_map["/model/layer/tensor"] = ("data", None)
+
+        distribution = distribution_lib.ModelParallel(
+            self.device_mesh, layout_map, batch_dim_name="data"
+        )
+        layout = distribution.get_tensor_layout("/model/layer/tensor")
+        self.assertIs(layout.device_mesh, self.device_mesh)
+        self.assertEqual(layout.axes, ("data", None))
+
+        layout = distribution.get_tensor_layout("/model/layer/other_tensor")
+        self.assertIsNone(layout)
+
 
 class LayoutMapTest(testing.TestCase):
     def setUp(self):
@@ -362,6 +388,30 @@ def test_list_devices(self):
         self.assertEqual(len(distribution_lib.list_devices("cpu")), 8)
         self.assertEqual(len(distribution_lib.list_devices("cpu")), 8)
 
+    def test_distribute_tensor(self):
+        jax_mesh = jax.sharding.Mesh(
+            np.array(jax.devices()).reshape(2, 4), ("batch", "model")
+        )
+
+        inputs = jax.numpy.array(np.random.normal(size=(16, 8)))
+        target_layout = jax.sharding.NamedSharding(
+            jax_mesh, jax.sharding.PartitionSpec("batch", None)
+        )
+
+        @functools.partial(jax.jit, static_argnames="target_layout")
+        def test_function(inputs, target_layout):
+            return distribution_lib.distribute_tensor(inputs, target_layout)
+
+        result = test_function(inputs, target_layout)
+        # Note that the returned tensor has a different sharding implementation
+        # which is GSPMDSharding, but it should be equivalent as the target
+        # layout specified.
+        self.assertTrue(result.sharding.is_equivalent_to(target_layout, ndim=2))
+
+        # Test without jit
+        result = distribution_lib.distribute_tensor(inputs, target_layout)
+        self.assertTrue(result.sharding.is_equivalent_to(target_layout, ndim=2))
+
     def test_to_jax_mesh(self):
         devices = [f"cpu:{i}" for i in range(8)]
         shape = (4, 2)
@@ -499,6 +549,78 @@ def test_e2e_model_parallel_model(self):
             model.compile(loss="mse")
             model.fit(inputs, labels)
 
+    def test_e2e_model_parallel_with_output_sharding(self):
+        shape = (4, 2)
+        axis_names = ["batch", "model"]
+        device_mesh = distribution_lib.DeviceMesh(
+            shape, axis_names, backend_dlib.list_devices()
+        )
+
+        layout_map = distribution_lib.LayoutMap(device_mesh)
+        layout_map[".*dense.*kernel"] = distribution_lib.TensorLayout(
+            [None, "model"]
+        )
+        layout_map[".*dense.*bias"] = distribution_lib.TensorLayout(["model"])
+        # Force the dense layer output to be batch parallel only, and not
+        # sharded on model dimension.
+        layout_map[".*dense.*output"] = ("batch", None)
+
+        distribution = distribution_lib.ModelParallel(
+            device_mesh, layout_map, batch_dim_name="batch"
+        )
+        sharding_capture = ShardingCaptureLayer()
+        with distribution.scope():
+            inputs = layers.Input(shape=[28, 28, 1])
+            y = layers.Flatten()(inputs)
+            y = layers.Dense(units=200, use_bias=False, activation="relu")(y)
+            y = sharding_capture(y)
+            y = layers.Dropout(0.4)(y)
+            y = layers.Dense(units=10, activation="softmax")(y)
+            model = models.Model(inputs=inputs, outputs=y)
+
+        for weight in model.weights:
+            if "kernel" in weight.name:
+                self.assertEqual(weight._value.sharding.spec, (None, "model"))
+            elif "bias" in weight.name:
+                self.assertEqual(weight._value.sharding.spec, ("model",))
+            else:
+                self.assertTrue(weight._value.sharding.is_fully_replicated)
+
+        inputs = np.random.normal(size=(32, 28, 28, 1))
+        labels = np.random.normal(size=(32, 10))
+
+        with distribution.scope():
+            model.compile(loss="mse")
+            model.fit(inputs, labels)
+
+        # Note that the intermediate_tensor_layout is only captured during the
+        # actual training, and not at the model building time.
+        intermediate_tensor_layout = jax.sharding.NamedSharding(
+            backend_dlib._to_jax_mesh(distribution.device_mesh),
+            jax.sharding.PartitionSpec("batch", None),
+        )
+        self.assertTrue(
+            sharding_capture.captured_input_sharding.is_equivalent_to(
+                intermediate_tensor_layout, ndim=2
+            )
+        )
+
+
+class ShardingCaptureLayer(layers.Layer):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.captured_input_sharding = None
+        self.supports_masking = True
+
+    def call(self, inputs):
+        jax.debug.inspect_array_sharding(
+            inputs, callback=lambda x: self.capture_input_sharding(x)
+        )
+        return inputs
+
+    def capture_input_sharding(self, sharding):
+        self.captured_input_sharding = sharding
+
 
 # @pytest.mark.skipif(
 #     backend.backend() != "tensorflow",

keras/layers/layer.py

@@ -30,6 +30,8 @@
 from keras.api_export import keras_export
 from keras.backend import KerasTensor
 from keras.backend.common import global_state
+from keras.backend.common.name_scope import current_path
+from keras.distribution import distribution_lib
 from keras.layers import input_spec
 from keras.metrics.metric import Metric
 from keras.ops.operation import Operation
@@ -808,6 +810,19 @@ def maybe_convert(x):
                         outputs = super().__call__(*args, **kwargs)
                 else:
                     outputs = super().__call__(*args, **kwargs)
+                # Change the layout for the layer output if needed.
+                # This is useful for relayout intermediate tensor in the model
+                # to achieve the optimal performance.
+                distribution = distribution_lib.distribution()
+                if distribution is not None:
+                    current_layer_path = current_path()
+                    current_layer_path += "/output"
+                    layout = distribution.get_tensor_layout(current_layer_path)
+                    if layout:
+                        outputs = distribution_lib.distribute_tensor(
+                            outputs, layout
+                        )
+
                 if not self.built:
                     self.built = True
                 # Record activity regularizer loss.