Introduces support for exporting SavedModel in the torch backend using torch-xla

.github/workflows/actions.yml

@@ -54,7 +54,6 @@ jobs:
           fi
           pip install -r $REQUIREMENTS_FILE --progress-bar off --upgrade
           pip uninstall -y keras keras-nightly
-          pip install tf_keras==2.16.0 --progress-bar off --upgrade
           pip install -e "." --progress-bar off --upgrade
       - name: Test applications with pytest
         if: ${{ steps.filter.outputs.applications == 'true' }}

.kokoro/github/ubuntu/gpu/build.sh

@@ -72,7 +72,9 @@ then
    # Raise error if GPU is not detected.
    python3 -c 'import torch;assert torch.cuda.is_available()'
 
+   # TODO: keras/src/export/export_lib_test.py update LD_LIBRARY_PATH 
    pytest keras --ignore keras/src/applications \
+               --ignore keras/src/export/export_lib_test.py \
                --cov=keras \
                --cov-config=pyproject.toml
 

keras/src/backend/torch/export.py

@@ -1,35 +1,142 @@
-from keras.src import layers
+import copy
+import warnings
+
+import torch
+
+from keras.src import backend
+from keras.src import ops
 from keras.src import tree
+from keras.src.utils.module_utils import tensorflow as tf
+from keras.src.utils.module_utils import torch_xla
 
 
 class TorchExportArchive:
     def track(self, resource):
-        if not isinstance(resource, layers.Layer):
-            raise ValueError(
-                "Invalid resource type. Expected an instance of a "
-                "JAX-based Keras `Layer` or `Model`. "
-                f"Received instead an object of type '{type(resource)}'. "
-                f"Object received: {resource}"
-            )
+        raise NotImplementedError(
+            "`track` is not implemented in the torch backend. Use"
+            "`track_and_add_endpoint` instead."
+        )
 
-        if isinstance(resource, layers.Layer):
-            # Variables in the lists below are actually part of the trackables
-            # that get saved, because the lists are created in __init__.
-            variables = resource.variables
-            trainable_variables = resource.trainable_variables
-            non_trainable_variables = resource.non_trainable_variables
-            self._tf_trackable.variables += tree.map_structure(
-                self._convert_to_tf_variable, variables
-            )
-            self._tf_trackable.trainable_variables += tree.map_structure(
-                self._convert_to_tf_variable, trainable_variables
+    def add_endpoint(self, name, fn, input_signature, **kwargs):
+        raise NotImplementedError(
+            "`add_endpoint` is not implemented in the torch backend. Use"
+            "`track_and_add_endpoint` instead."
+        )
+
+    def track_and_add_endpoint(self, name, resource, input_signature, **kwargs):
+        # Disable false alarms related to lifting parameters.
+        warnings.filterwarnings("ignore", message=".*created when tracing.*")
+        warnings.filterwarnings(
+            "ignore", message=".*Unable to find the path of the module.*"
+        )
+
+        if not isinstance(resource, torch.nn.Module):
+            raise TypeError(
+                "`resource` must be an instance of `torch.nn.Module`. "
+                f"Received: resource={resource} (of type {type(resource)})"
             )
-            self._tf_trackable.non_trainable_variables += tree.map_structure(
-                self._convert_to_tf_variable, non_trainable_variables
+
+        def _check_input_signature(input_spec):
+            for s in tree.flatten(input_spec.shape):
+                if s is None:
+                    raise ValueError(
+                        "The shape in the `input_spec` must be fully "
+                        f"specified. Received: input_spec={input_spec}"
+                    )
+
+        def _to_torch_tensor(x, replace_none_number=1):
+            shape = backend.standardize_shape(x.shape)
+            shape = tuple(
+                s if s is not None else replace_none_number for s in shape
             )
+            return ops.ones(shape, x.dtype)
 
-    def add_endpoint(self, name, fn, input_signature=None, **kwargs):
-        # TODO: torch-xla?
-        raise NotImplementedError(
-            "`add_endpoint` is not implemented in the torch backend."
+        tree.map_structure(_check_input_signature, input_signature)
+        sample_inputs = tree.map_structure(_to_torch_tensor, input_signature)
+        sample_inputs = tuple(sample_inputs)
+
+        # Ref: torch_xla.tf_saved_model_integration
+        # TODO: Utilize `dynamic_shapes`
+        exported = torch.export.export(
+            resource, sample_inputs, dynamic_shapes=None, strict=False
+        )
+        options = torch_xla.stablehlo.StableHLOExportOptions(
+            override_tracing_arguments=sample_inputs
+        )
+        stablehlo_model = torch_xla.stablehlo.exported_program_to_stablehlo(
+            exported, options
+        )
+        state_dict_keys = list(stablehlo_model._bundle.state_dict.keys())
+
+        # Remove unused variables.
+        for k in state_dict_keys:
+            if "lifted" not in k:
+                stablehlo_model._bundle.state_dict.pop(k)
+
+        bundle = copy.deepcopy(stablehlo_model._bundle)
+        bundle.state_dict = {
+            k: tf.Variable(v, trainable=False, name=k)
+            for k, v in bundle.state_dict.items()
+        }
+        bundle.additional_constants = [
+            tf.Variable(v, trainable=False) for v in bundle.additional_constants
+        ]
+
+        # Track variables in `bundle` for `write_out`.
+        self._tf_trackable.variables += (
+            list(bundle.state_dict.values()) + bundle.additional_constants
+        )
+
+        # Ref: torch_xla.tf_saved_model_integration.save_stablehlo_graph_as_tf
+        def make_tf_function(func, bundle):
+            from tensorflow.compiler.tf2xla.python import xla as tfxla
+
+            def _get_shape_with_dynamic(signature):
+                shape = copy.copy(signature.shape)
+                for i in signature.dynamic_dims:
+                    shape[i] = None
+                return shape
+
+            def _extract_call_parameters(args, meta, bundle):
+                call_args = []
+                if meta.input_pytree_spec is not None:
+                    args = tree.flatten(args)
+                for loc in meta.input_locations:
+                    if loc.type_ == torch_xla.stablehlo.VariableType.PARAMETER:
+                        call_args.append(bundle.state_dict[loc.name])
+                    elif loc.type_ == torch_xla.stablehlo.VariableType.CONSTANT:
+                        call_args.append(
+                            bundle.additional_constants[loc.position]
+                        )
+                    else:
+                        call_args.append(args[loc.position])
+                return call_args
+
+            def inner(*args):
+                Touts = [sig.dtype for sig in func.meta.output_signature]
+                Souts = [
+                    _get_shape_with_dynamic(sig)
+                    for sig in func.meta.output_signature
+                ]
+                call_args = _extract_call_parameters(args, func.meta, bundle)
+                results = tfxla.call_module(
+                    tuple(call_args),
+                    version=5,
+                    Tout=Touts,  # dtype information
+                    Sout=Souts,  # Shape information
+                    function_list=[],
+                    module=func.bytecode,
+                )
+                if len(Souts) == 1:
+                    results = results[0]
+                return results
+
+            return inner
+
+        decorated_fn = tf.function(
+            make_tf_function(
+                stablehlo_model._bundle.stablehlo_funcs[0], bundle
+            ),
+            input_signature=input_signature,
         )
+        return decorated_fn

keras/src/export/export_lib.py

@@ -91,7 +91,7 @@ class ExportArchive(BackendExportArchive):
 
     **Note on resource tracking:**
 
-    `ExportArchive` is able to automatically track all `tf.Variables` used
+    `ExportArchive` is able to automatically track all `keras.Variables` used
     by its endpoints, so most of the time calling `.track(model)`
     is not strictly required. However, if your model uses lookup layers such
     as `IntegerLookup`, `StringLookup`, or `TextVectorization`,
@@ -104,9 +104,10 @@ class ExportArchive(BackendExportArchive):
 
     def __init__(self):
         super().__init__()
-        if backend.backend() not in ("tensorflow", "jax"):
+        if backend.backend() not in ("tensorflow", "jax", "torch"):
             raise NotImplementedError(
-                "The export API is only compatible with JAX and TF backends."
+                "`ExportArchive` is only compatible with TensorFlow, JAX and "
+                "Torch backends."
             )
 
         self._endpoint_names = []
@@ -141,8 +142,8 @@ def track(self, resource):
         (`TextVectorization`, `IntegerLookup`, `StringLookup`)
         are automatically tracked in `add_endpoint()`.
 
-        Arguments:
-            resource: A trackable TensorFlow resource.
+        Args:
+            resource: A trackable Keras resource, such as a layer or model.
         """
         if isinstance(resource, layers.Layer) and not resource.built:
             raise ValueError(
@@ -334,12 +335,78 @@ def serving_fn(x):
         self._endpoint_names.append(name)
         return decorated_fn
 
+    def track_and_add_endpoint(self, name, resource, input_signature, **kwargs):
+        """Track the variables and register a new serving endpoint.
+
+        This function combines the functionality of `track` and `add_endpoint`.
+        It tracks the variables of the `resource` (either a layer or a model)
+        and registers a serving endpoint using `resource.__call__`.
+
+        Args:
+            name: `str`. The name of the endpoint.
+            resource: A trackable Keras resource, such as a layer or model.
+            input_signature: Optional. Specifies the shape and dtype of `fn`.
+                Can be a structure of `keras.InputSpec`, `tf.TensorSpec`,
+                `backend.KerasTensor`, or backend tensor (see below for an
+                example showing a `Functional` model with 2 input arguments). If
+                not provided, `fn` must be a `tf.function` that has been called
+                at least once. Defaults to `None`.
+            **kwargs: Additional keyword arguments:
+                - Specific to the JAX backend:
+                    - `is_static`: Optional `bool`. Indicates whether `fn` is
+                        static. Set to `False` if `fn` involves state updates
+                        (e.g., RNG seeds).
+                    - `jax2tf_kwargs`: Optional `dict`. Arguments for
+                        `jax2tf.convert`. See [`jax2tf.convert`](
+                            https://github.com/google/jax/blob/main/jax/experimental/jax2tf/README.md).
+                        If `native_serialization` and `polymorphic_shapes` are
+                        not provided, they are automatically computed.
+
+        """
+        if name in self._endpoint_names:
+            raise ValueError(f"Endpoint name '{name}' is already taken.")
+        if not isinstance(resource, layers.Layer):
+            raise ValueError(
+                "Invalid resource type. Expected an instance of a Keras "
+                "`Layer` or `Model`. "
+                f"Received: resource={resource} (of type {type(resource)})"
+            )
+        if not resource.built:
+            raise ValueError(
+                "The layer provided has not yet been built. "
+                "It must be built before export."
+            )
+        if backend.backend() != "jax":
+            if "jax2tf_kwargs" in kwargs or "is_static" in kwargs:
+                raise ValueError(
+                    "'jax2tf_kwargs' and 'is_static' are only supported with "
+                    f"the jax backend. Current backend: {backend.backend()}"
+                )
+
+        input_signature = tree.map_structure(_make_tensor_spec, input_signature)
+
+        if not hasattr(BackendExportArchive, "track_and_add_endpoint"):
+            # Default behavior.
+            self.track(resource)
+            return self.add_endpoint(
+                name, resource.__call__, input_signature, **kwargs
+            )
+        else:
+            # Special case for the torch backend.
+            decorated_fn = BackendExportArchive.track_and_add_endpoint(
+                self, name, resource, input_signature, **kwargs
+            )
+            self._endpoint_signatures[name] = input_signature
+            setattr(self._tf_trackable, name, decorated_fn)
+            self._endpoint_names.append(name)
+            return decorated_fn
+
     def add_variable_collection(self, name, variables):
         """Register a set of variables to be retrieved after reloading.
 
         Arguments:
             name: The string name for the collection.
-            variables: A tuple/list/set of `tf.Variable` instances.
+            variables: A tuple/list/set of `keras.Variable` instances.
 
         Example:
 
@@ -496,9 +563,6 @@ def export_saved_model(
 ):
     """Export the model as a TensorFlow SavedModel artifact for inference.
 
-    **Note:** This feature is currently supported only with TensorFlow and
-    JAX backends.
-
     This method lets you export a model to a lightweight SavedModel artifact
     that contains the model's forward pass only (its `call()` method)
     and can be served via e.g. TensorFlow Serving. The forward pass is
@@ -527,6 +591,14 @@ def export_saved_model(
                     If `native_serialization` and `polymorphic_shapes` are not
                     provided, they are automatically computed.
 
+    **Note:** This feature is currently supported only with TensorFlow, JAX and
+    Torch backends. Support for the Torch backend is experimental.
+
+    **Note:** The dynamic shape feature is not yet supported with Torch
+    backend. As a result, you must fully define the shapes of the inputs using
+    `input_signature`. If `input_signature` is not provided, all instances of
+    `None` (such as the batch size) will be replaced with `1`.
+
     Example:
 
     ```python
@@ -543,28 +615,29 @@ def export_saved_model(
     `export()` method relies on `ExportArchive` internally.
     """
     export_archive = ExportArchive()
-    export_archive.track(model)
-    if isinstance(model, (Functional, Sequential)):
-        if input_signature is None:
+    if input_signature is None:
+        if not model.built:
+            raise ValueError(
+                "The layer provided has not yet been built. "
+                "It must be built before export."
+            )
+        if isinstance(model, (Functional, Sequential)):
             input_signature = tree.map_structure(
                 _make_tensor_spec, model.inputs
             )
-        if isinstance(input_signature, list) and len(input_signature) > 1:
-            input_signature = [input_signature]
-        export_archive.add_endpoint(
-            "serve", model.__call__, input_signature, **kwargs
-        )
-    else:
-        if input_signature is None:
+            if isinstance(input_signature, list) and len(input_signature) > 1:
+                input_signature = [input_signature]
+        else:
             input_signature = _get_input_signature(model)
-        if not input_signature or not model._called:
-            raise ValueError(
-                "The model provided has never called. "
-                "It must be called at least once before export."
-            )
-        export_archive.add_endpoint(
-            "serve", model.__call__, input_signature, **kwargs
-        )
+            if not input_signature or not model._called:
+                raise ValueError(
+                    "The model provided has never called. "
+                    "It must be called at least once before export."
+                )
+
+    export_archive.track_and_add_endpoint(
+        "serve", model, input_signature, **kwargs
+    )
     export_archive.write_out(filepath, verbose=verbose)