use functions to store constants so it can be read by C++

Signed-off-by: Jinzhe Zeng <[email protected]>
njzjz · Nov 3, 2024 · 8e216f5 · 8e216f5
1 parent 980b4a9
commit 8e216f5
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Showing 2 changed files with 45 additions and 27 deletions.
diff --git a/deepmd/jax/jax2tf/serialization.py b/deepmd/jax/jax2tf/serialization.py
@@ -71,24 +71,42 @@ def call_lower_with_fixed_do_atomic_virial(
         tf_model.call_lower_atomic_virial = exported_whether_do_atomic_virial(
             do_atomic_virial=True
         )
-        # set other attributes
-        tf_model.type_map = tf.Variable(model.get_type_map(), dtype=tf.string)
-        tf_model.rcut = tf.Variable(model.get_rcut(), dtype=tf.double)
-        tf_model.dim_fparam = tf.Variable(model.get_dim_fparam(), dtype=tf.int64)
-        tf_model.dim_aparam = tf.Variable(model.get_dim_aparam(), dtype=tf.int64)
-        tf_model.sel_type = tf.Variable(model.get_sel_type(), dtype=tf.int64)
-        tf_model.is_aparam_nall = tf.Variable(model.is_aparam_nall(), dtype=tf.bool)
-        tf_model.model_output_type = tf.Variable(
-            model.model_output_type(), dtype=tf.string
+        # set functions to export other attributes
+        tf_model.get_type_map = tf.function(
+            lambda: tf.constant(model.get_type_map(), dtype=tf.string)
+        )
+        tf_model.get_rcut = tf.function(
+            lambda: tf.constant(model.get_rcut(), dtype=tf.double)
+        )
+        tf_model.get_dim_fparam = tf.function(
+            lambda: tf.constant(model.get_dim_fparam(), dtype=tf.int64)
+        )
+        tf_model.get_dim_aparam = tf.function(
+            lambda: tf.constant(model.get_dim_aparam(), dtype=tf.int64)
+        )
+        tf_model.get_sel_type = tf.function(
+            lambda: tf.constant(model.get_sel_type(), dtype=tf.int64)
+        )
+        tf_model.is_aparam_nall = tf.function(
+            lambda: tf.constant(model.is_aparam_nall(), dtype=tf.bool)
+        )
+        tf_model.model_output_type = tf.function(
+            lambda: tf.constant(model.model_output_type(), dtype=tf.string)
+        )
+        tf_model.mixed_types = tf.function(
+            lambda: tf.constant(model.mixed_types(), dtype=tf.bool)
         )
-        tf_model.mixed_types = tf.Variable(model.mixed_types(), dtype=tf.bool)
         if model.get_min_nbor_dist() is not None:
-            tf_model.min_nbor_dist = tf.Variable(
-                model.get_min_nbor_dist(), dtype=tf.double
+            tf_model.get_min_nbor_dist = tf.function(
+                lambda: tf.constant(model.get_min_nbor_dist(), dtype=tf.double)
+            )
+        tf_model.get_sel = tf.function(
+            lambda: tf.constant(model.get_sel(), dtype=tf.int64)
+        )
+        tf_model.get_model_def_script = tf.function(
+            lambda: tf.constant(
+                json.dumps(model_def_script, separators=(",", ":")), dtype=tf.string
             )
-        tf_model.sel = tf.Variable(model.get_sel(), dtype=tf.int64)
-        tf_model.model_def_script = tf.Variable(
-            json.dumps(model_def_script, separators=(",", ":")), dtype=tf.string
         )
         tf.saved_model.save(
             tf_model,

diff --git a/deepmd/jax/jax2tf/tfmodel.py b/deepmd/jax/jax2tf/tfmodel.py
@@ -55,22 +55,22 @@ def __init__(
         self._call_lower_atomic_virial = jax2tf.call_tf(
             self.model.call_lower_atomic_virial
         )
-        self.type_map = decode_list_of_bytes(self.model.type_map.numpy().tolist())
-        self.rcut = self.model.rcut.numpy().item()
-        self.dim_fparam = self.model.dim_fparam.numpy().item()
-        self.dim_aparam = self.model.dim_aparam.numpy().item()
-        self.sel_type = self.model.sel_type.numpy().tolist()
-        self._is_aparam_nall = self.model.is_aparam_nall.numpy().item()
+        self.type_map = decode_list_of_bytes(self.model.get_type_map().numpy().tolist())
+        self.rcut = self.model.get_rcut().numpy().item()
+        self.dim_fparam = self.model.get_dim_fparam().numpy().item()
+        self.dim_aparam = self.model.get_dim_aparam().numpy().item()
+        self.sel_type = self.model.get_sel_type().numpy().tolist()
+        self._is_aparam_nall = self.model.is_aparam_nall().numpy().item()
         self._model_output_type = decode_list_of_bytes(
-            self.model.model_output_type.numpy().tolist()
+            self.model.model_output_type().numpy().tolist()
         )
-        self._mixed_types = self.model.mixed_types.numpy().item()
-        if hasattr(self.model, "min_nbor_dist"):
-            self.min_nbor_dist = self.model.min_nbor_dist.numpy().item()
+        self._mixed_types = self.model.mixed_types().numpy().item()
+        if hasattr(self.model, "get_min_nbor_dist"):
+            self.min_nbor_dist = self.model.get_min_nbor_dist().numpy().item()
         else:
             self.min_nbor_dist = None
-        self.sel = self.model.sel.numpy().tolist()
-        self.model_def_script = self.model.model_def_script.numpy().decode()
+        self.sel = self.model.get_sel().numpy().tolist()
+        self.model_def_script = self.model.get_model_def_script().numpy().decode()
 
     def __call__(
         self,