feat(jax): export call_lower to SavedModel via jax2tf

deepmd/backend/jax.py

@@ -38,7 +38,7 @@ class JAXBackend(Backend):
         | Backend.Feature.NEIGHBOR_STAT
     )
     """The features of the backend."""
-    suffixes: ClassVar[list[str]] = [".hlo", ".jax"]
+    suffixes: ClassVar[list[str]] = [".hlo", ".jax", ".savedmodel"]
     """The suffixes of the backend."""
 
     def is_available(self) -> bool:

deepmd/jax/infer/deep_eval.py

@@ -90,15 +90,24 @@
         self.output_def = output_def
         self.model_path = model_file
 
-        model_data = load_dp_model(model_file)
-        self.dp = HLO(
-            stablehlo=model_data["@variables"]["stablehlo"].tobytes(),
-            stablehlo_atomic_virial=model_data["@variables"][
-                "stablehlo_atomic_virial"
-            ].tobytes(),
-            model_def_script=model_data["model_def_script"],
-            **model_data["constants"],
-        )
+        if model_file.endswith(".hlo"):
+            model_data = load_dp_model(model_file)
+            self.dp = HLO(
+                stablehlo=model_data["@variables"]["stablehlo"].tobytes(),
+                stablehlo_atomic_virial=model_data["@variables"][
+                    "stablehlo_atomic_virial"
+                ].tobytes(),
+                model_def_script=model_data["model_def_script"],
+                **model_data["constants"],
+            )
+        elif model_file.endswith(".savedmodel"):
+            from deepmd.jax.jax2tf.tfmodel import (
+                TFModelWrapper,
+            )
+
+            self.dp = TFModelWrapper(model_file)
+        else:
+            raise ValueError("Unsupported file extension")
         self.rcut = self.dp.get_rcut()
         self.type_map = self.dp.get_type_map()
         if isinstance(auto_batch_size, bool):

deepmd/jax/jax2tf/__init__.py

@@ -0,0 +1,11 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+import tensorflow as tf
+
+if not tf.executing_eagerly():
+    # TF disallow temporary eager execution
+    raise RuntimeError(
+        "Unfortunatly, jax2tf (requires eager execution) cannot be used with the "
+        "TensorFlow backend (disables eager execution). "
+        "If you are converting a model between different backends, "
+        "considering converting to the `.dp` format first."
+    )

deepmd/jax/jax2tf/serialization.py

@@ -0,0 +1,172 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+import json
+
+import tensorflow as tf
+from jax.experimental import (
+    jax2tf,
+)
+
+from deepmd.jax.model.base_model import (
+    BaseModel,
+)
+
+
+def deserialize_to_file(model_file: str, data: dict) -> None:
+    """Deserialize the dictionary to a model file.
+
+    Parameters
+    ----------
+    model_file : str
+        The model file to be saved.
+    data : dict
+        The dictionary to be deserialized.
+    """
+    if model_file.endswith(".savedmodel"):
+        model = BaseModel.deserialize(data["model"])
+        model_def_script = data["model_def_script"]
+        call_lower = model.call_lower
+
+        tf_model = tf.Module()
+
+        def exported_whether_do_atomic_virial(do_atomic_virial):
+            def call_lower_with_fixed_do_atomic_virial(
+                coord, atype, nlist, mapping, fparam, aparam
+            ):
+                return call_lower(
+                    coord,
+                    atype,
+                    nlist,
+                    mapping,
+                    fparam,
+                    aparam,
+                    do_atomic_virial=do_atomic_virial,
+                )
+
+            return jax2tf.convert(
+                call_lower_with_fixed_do_atomic_virial,
+                polymorphic_shapes=[
+                    "(nf, nloc + nghost, 3)",
+                    "(nf, nloc + nghost)",
+                    f"(nf, nloc, {model.get_nnei()})",
+                    "(nf, nloc + nghost)",
+                    f"(nf, {model.get_dim_fparam()})",
+                    f"(nf, nloc, {model.get_dim_aparam()})",
+                ],
+                with_gradient=True,
+            )
+
+        # Save a function that can take scalar inputs.
+        # We need to explicit set the function name, so C++ can find it.
+        @tf.function(
+            autograph=False,
+            input_signature=[
+                tf.TensorSpec([None, None, 3], tf.float64),
+                tf.TensorSpec([None, None], tf.int32),
+                tf.TensorSpec([None, None, model.get_nnei()], tf.int64),
+                tf.TensorSpec([None, None], tf.int64),
+                tf.TensorSpec([None, model.get_dim_fparam()], tf.float64),
+                tf.TensorSpec([None, None, model.get_dim_aparam()], tf.float64),
+            ],
+        )
+        def call_lower_without_atomic_virial(
+            coord, atype, nlist, mapping, fparam, aparam
+        ):
+            return exported_whether_do_atomic_virial(do_atomic_virial=False)(
+                coord, atype, nlist, mapping, fparam, aparam
+            )
+
+        tf_model.call_lower = call_lower_without_atomic_virial
+
+        @tf.function(
+            autograph=False,
+            input_signature=[
+                tf.TensorSpec([None, None, 3], tf.float64),
+                tf.TensorSpec([None, None], tf.int32),
+                tf.TensorSpec([None, None, model.get_nnei()], tf.int64),
+                tf.TensorSpec([None, None], tf.int64),
+                tf.TensorSpec([None, model.get_dim_fparam()], tf.float64),
+                tf.TensorSpec([None, None, model.get_dim_aparam()], tf.float64),
+            ],
+        )
+        def call_lower_with_atomic_virial(coord, atype, nlist, mapping, fparam, aparam):
+            return exported_whether_do_atomic_virial(do_atomic_virial=True)(
+                coord, atype, nlist, mapping, fparam, aparam
+            )
+
+        tf_model.call_lower_atomic_virial = call_lower_with_atomic_virial
+
+        # set functions to export other attributes
+        @tf.function
+        def get_type_map():
+            return tf.constant(model.get_type_map(), dtype=tf.string)
+
+        tf_model.get_type_map = get_type_map
+
+        @tf.function
+        def get_rcut():
+            return tf.constant(model.get_rcut(), dtype=tf.double)
+
+        tf_model.get_rcut = get_rcut
+
+        @tf.function
+        def get_dim_fparam():
+            return tf.constant(model.get_dim_fparam(), dtype=tf.int64)
+
+        tf_model.get_dim_fparam = get_dim_fparam
+
+        @tf.function
+        def get_dim_aparam():
+            return tf.constant(model.get_dim_aparam(), dtype=tf.int64)
+
+        tf_model.get_dim_aparam = get_dim_aparam
+
+        @tf.function
+        def get_sel_type():
+            return tf.constant(model.get_sel_type(), dtype=tf.int64)
+
+        tf_model.get_sel_type = get_sel_type
+
+        @tf.function
+        def is_aparam_nall():
+            return tf.constant(model.is_aparam_nall(), dtype=tf.bool)
+
+        tf_model.is_aparam_nall = is_aparam_nall
+
+        @tf.function
+        def model_output_type():
+            return tf.constant(model.model_output_type(), dtype=tf.string)
+
+        tf_model.model_output_type = model_output_type
+
+        @tf.function
+        def mixed_types():
+            return tf.constant(model.mixed_types(), dtype=tf.bool)
+
+        tf_model.mixed_types = mixed_types
+
+        if model.get_min_nbor_dist() is not None:
+
+            @tf.function
+            def get_min_nbor_dist():
+                return tf.constant(model.get_min_nbor_dist(), dtype=tf.double)
+
+            tf_model.get_min_nbor_dist = get_min_nbor_dist
+
+        @tf.function
+        def get_sel():
+            return tf.constant(model.get_sel(), dtype=tf.int64)
+
+        tf_model.get_sel = get_sel
+
+        @tf.function
+        def get_model_def_script():
+            return tf.constant(
+                json.dumps(model_def_script, separators=(",", ":")), dtype=tf.string
+            )
+
+        tf_model.get_model_def_script = get_model_def_script
+        tf.saved_model.save(
+            tf_model,
+            model_file,
+            options=tf.saved_model.SaveOptions(experimental_custom_gradients=True),
+        )