Core Data Structures & Communication Primitives for Tensor Parallel for Keras

keras/src/backend/distributed/__init__.py

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+from .base import BaseDistributedBackend
+from .factory import get_distributed_backend
+
+__all__ = ["get_distributed_backend", "BaseDistributedBackend"]

keras/src/backend/distributed/base.py

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+from abc import ABC
+from abc import abstractmethod
+from typing import Any
+from typing import List
+
+
+class BaseDistributedBackend(ABC):
+    """
+    Abstract Base Class for a distributed backend.
+    """
+
+    @abstractmethod
+    def get_tensor_lib(self):
+        """Get the appropriate tensor library for the backend."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def convert_to_backend_tensor(self, tensor: Any) -> Any:
+        """Convert a tensor to the appropriate backend format."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def compute_gradients(
+        self, loss: Any, trainable_vars: List[Any]
+    ) -> List[Any]:
+        """Compute gradients using the backend's automatic differentiation."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def apply_gradients(
+        self,
+        gradients: List[Any],
+        trainable_vars: List[Any],
+        learning_rate: float = 0.001,
+    ) -> None:
+        """Apply gradients to trainable variables."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def create_optimizer(self, optimizer_class: str, **kwargs):
+        """Create an optimizer for the backend."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_device_info(self) -> dict:
+        """Get information about available devices."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def is_multi_device_capable(self) -> bool:
+        """Check if the backend supports multi-device operations."""
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_communication_ops(self) -> dict:
+        """Get collective communication operations for the backend."""
+        raise NotImplementedError

keras/src/backend/distributed/factory.py

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+import logging
+
+from keras.src.backend.distributed.base import BaseDistributedBackend
+
+logger = logging.getLogger(__name__)
+
+
+def get_distributed_backend(
+    backend_name: str = "auto",
+) -> BaseDistributedBackend:
+    """
+    Factory to get the best available or a specific distributed backend.
+    """
+    if backend_name == "auto":
+        try:
+            from keras.src.backend.jax.distributed_backend import (
+                JaxDistributedBackend,
+            )
+
+            logger.info("Auto-detected JAX for distributed backend.")
+            return JaxDistributedBackend()
+        except ImportError:
+            try:
+                from keras.src.backend.tensorflow.distributed_backend import (
+                    TensorflowDistributedBackend,
+                )
+
+                logger.info("Auto-detected TensorFlow for distributed backend.")
+                return TensorflowDistributedBackend()
+            except ImportError:
+                try:
+                    from keras.src.backend.torch.distributed_backend import (
+                        TorchDistributedBackend,
+                    )
+
+                    logger.info(
+                        "Auto-detected PyTorch for distributed backend."
+                    )
+                    return TorchDistributedBackend()
+                except ImportError:
+                    error_msg = (
+                        "Could not automatically detect a distributed backend "
+                        "(JAX, TensorFlow, or PyTorch). Please install them "
+                        "or explicitly specify a backend."
+                    )
+                    logger.error(error_msg)
+                    raise ImportError(error_msg)
+
+    elif backend_name == "jax":
+        from keras.src.backend.jax.distributed_backend import (
+            JaxDistributedBackend,
+        )
+
+        return JaxDistributedBackend()
+    elif backend_name == "tensorflow":
+        from keras.src.backend.tensorflow.distributed_backend import (
+            TensorflowDistributedBackend,
+        )
+
+        return TensorflowDistributedBackend()
+    elif backend_name == "torch":
+        from keras.src.backend.torch.distributed_backend import (
+            TorchDistributedBackend,
+        )
+
+        return TorchDistributedBackend()
+    elif backend_name == "numpy":
+        from keras.src.backend.numpy.distributed_backend import (
+            NumpyDistributedBackend,
+        )
+
+        logger.warning(
+            "Using explicitly requested NumPy distributed backend. "
+            "This backend is for simulation and does not support "
+            "multi-device computation."
+        )
+        return NumpyDistributedBackend()
+    else:
+        raise ValueError(f"Unknown distributed backend: {backend_name}")

keras/src/backend/jax/distributed_backend.py

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+import logging
+from typing import Any
+from typing import List
+
+import jax
+import jax.lax as lax
+import jax.numpy as jnp
+import optax
+
+import keras
+from keras.src.backend.distributed.base import BaseDistributedBackend
+
+logger = logging.getLogger(__name__)
+
+
+class JaxDistributedBackend(BaseDistributedBackend):
+    """JAX-specific implementation of distributed operations."""
+
+    def get_tensor_lib(self):
+        return jnp
+
+    def convert_to_backend_tensor(self, tensor: Any) -> Any:
+        if isinstance(tensor, jax.Array):
+            return tensor
+        return jnp.array(tensor)
+
+    def compute_gradients(
+        self, loss: Any, trainable_vars: List[Any]
+    ) -> List[Any]:
+        """
+        JAX backend doesn't support gradient computation with pre-computed loss.
+
+        This method returns zero gradients as a fallback. For JAX, gradient
+        computation must be done via `jax.grad` on a function that computes
+        the loss from the parameters, which requires a different architecture.
+        """
+        logger.warning(
+            "JAX backend `compute_gradients` is a fallback and returns "
+            "zero gradients. A functional `jax.grad` approach should be used "
+            "for training."
+        )
+        return [jnp.zeros_like(var) for var in trainable_vars]
+
+    def apply_gradients(
+        self,
+        gradients: List[Any],
+        trainable_vars: List[Any],
+        learning_rate: float = 0.001,
+    ) -> None:
+        for grad, var in zip(gradients, trainable_vars):
+            if grad is not None:
+                new_value = var - (learning_rate * grad)
+                if hasattr(var, "assign"):
+                    var.assign(new_value)
+                else:
+                    logger.warning(
+                        "Applying gradients to a standard JAX array has no "
+                        "effect as JAX arrays are immutable. This operation "
+                        "only works for mutable objects with an `.assign()` "
+                        "method."
+                    )
+
+    def create_optimizer(self, optimizer_class: str, **kwargs):
+        if optimizer_class.lower() == "adam":
+            return optax.adam(**kwargs)
+        elif optimizer_class.lower() == "sgd":
+            return optax.sgd(**kwargs)
+        else:
+            kwargs.setdefault("learning_rate", 0.001)
+            return optax.adam(**kwargs)
+
+    def get_device_info(self) -> dict:
+        info = {"backend": "jax", "devices": [], "device_count": 0}
+        try:
+            info["devices"] = [str(d) for d in jax.devices()]
+            info["device_count"] = jax.local_device_count()
+        except Exception as e:
+            logger.warning(f"Could not get device info for JAX: {e}")
+            info["devices"] = ["cpu"]
+            info["device_count"] = 1
+        return info
+
+    def is_multi_device_capable(self) -> bool:
+        return self.get_device_info()["device_count"] > 1
+
+    def get_communication_ops(self) -> dict:
+        try:
+            if not self.is_multi_device_capable():
+                raise RuntimeError("JAX is not running on multiple devices.")
+
+            logger.info("Using real JAX collective communication ops.")
+
+            def all_reduce_jax(x, op="sum", axis_name="data"):
+                if op == "sum":
+                    return lax.psum(x, axis_name=axis_name)
+                elif op == "mean":
+                    return lax.pmean(x, axis_name=axis_name)
+                raise ValueError(f"Unsupported all_reduce op: {op}")
+
+            def all_gather_jax(x, axis=0, axis_name="model"):
+                return lax.all_gather(x, axis_name=axis_name, axis=axis)
+
+            def broadcast_jax(x, root=0, axis_name="data"):
+                return lax.all_gather(x, axis_name=axis_name, axis=0)[root]
+
+            def scatter_jax(x, root=0):
+                logger.warning(
+                    "Scatter is not a native op in JAX pmap; returning the "
+                    "input tensor as a fallback."
+                )
+                return x
+
+            return {
+                "all_reduce": all_reduce_jax,
+                "all_gather": all_gather_jax,
+                "broadcast": broadcast_jax,
+                "scatter": scatter_jax,
+            }
+        except (ImportError, RuntimeError) as e:
+            logger.warning(
+                "JAX collective ops not available or multiple devices not "
+                f"configured: {e}. Using SIMULATED ops."
+            )
+
+            device_info = self.get_device_info()
+            simulated_world_size = device_info.get("device_count", 1)
+            if simulated_world_size == 0:
+                simulated_world_size = 1
+
+            logger.info(
+                f"Simulating with world_size={simulated_world_size} "
+                "based on available devices."
+            )
+
+            def all_reduce_simulated(x, op="sum"):
+                if simulated_world_size <= 1:
+                    return x
+                if op == "sum":
+                    return keras.ops.multiply(x, simulated_world_size)
+                elif op == "mean":
+                    return x
+                else:
+                    raise ValueError(f"Unsupported all_reduce op: {op}")
+
+            def all_gather_simulated(x, axis=0):
+                if simulated_world_size <= 1:
+                    return x
+                return keras.ops.concatenate(
+                    [x] * simulated_world_size, axis=axis
+                )
+
+            def broadcast_simulated(x, root=0):
+                return x
+
+            def scatter_simulated(x, root=0):
+                if simulated_world_size <= 1:
+                    return x
+                if keras.ops.shape(x)[0] % simulated_world_size != 0:
+                    raise ValueError(
+                        "For simulation, the first dimension of tensor must "
+                        f"be divisible by the simulated world size "
+                        f"({simulated_world_size})."
+                    )
+                chunks = keras.ops.split(x, simulated_world_size, axis=0)
+                return chunks[0]
+
+            return {
+                "all_reduce": all_reduce_simulated,
+                "all_gather": all_gather_simulated,
+                "broadcast": broadcast_simulated,
+                "scatter": scatter_simulated,
+            }