feat(jax/array-api): DOS fitting (deepmodeling#4218)

<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->
## Summary by CodeRabbit

- **New Features**
- Introduced the `DOSFittingNet` class for enhanced fitting
capabilities.
- Added methods to evaluate different backends (JAX and Array API
Strict) for computing density of states.
- Enhanced testing framework to conditionally include tests based on
library availability.

- **Bug Fixes**
- Improved serialization of the `bias_atom_e` variable to ensure
consistent data representation.

- **Tests**
- Expanded the `TestDOS` class with new attributes and methods for
better backend evaluation.
<!-- end of auto-generated comment: release notes by coderabbit.ai -->

---------

Signed-off-by: Jinzhe Zeng <jinzhe.zeng@rutgers.edu>
Co-authored-by: coderabbitai[bot] <136622811+coderabbitai[bot]@users.noreply.github.com>

deepmd/dpmodel/fitting/dos_fitting.py

@@ -10,6 +10,7 @@
 
 from deepmd.dpmodel.common import (
     DEFAULT_PRECISION,
+    to_numpy_array,
 )
 from deepmd.dpmodel.fitting.invar_fitting import (
     InvarFitting,
@@ -89,6 +90,6 @@ def serialize(self) -> dict:
             **super().serialize(),
             "type": "dos",
         }
-        dd["@variables"]["bias_atom_e"] = self.bias_atom_e
+        dd["@variables"]["bias_atom_e"] = to_numpy_array(self.bias_atom_e)
 
         return dd

deepmd/jax/fitting/fitting.py

@@ -3,6 +3,7 @@
     Any,
 )
 
+from deepmd.dpmodel.fitting.dos_fitting import DOSFittingNet as DOSFittingNetDP
 from deepmd.dpmodel.fitting.ener_fitting import EnergyFittingNet as EnergyFittingNetDP
 from deepmd.jax.common import (
     flax_module,
@@ -37,3 +38,10 @@ class EnergyFittingNet(EnergyFittingNetDP):
     def __setattr__(self, name: str, value: Any) -> None:
         value = setattr_for_general_fitting(name, value)
         return super().__setattr__(name, value)
+
+
+@flax_module
+class DOSFittingNet(DOSFittingNetDP):
+    def __setattr__(self, name: str, value: Any) -> None:
+        value = setattr_for_general_fitting(name, value)
+        return super().__setattr__(name, value)

source/tests/array_api_strict/fitting/fitting.py

@@ -3,6 +3,7 @@
     Any,
 )
 
+from deepmd.dpmodel.fitting.dos_fitting import DOSFittingNet as DOSFittingNetDP
 from deepmd.dpmodel.fitting.ener_fitting import EnergyFittingNet as EnergyFittingNetDP
 
 from ..common import (
@@ -36,3 +37,9 @@ class EnergyFittingNet(EnergyFittingNetDP):
     def __setattr__(self, name: str, value: Any) -> None:
         value = setattr_for_general_fitting(name, value)
         return super().__setattr__(name, value)
+
+
+class DOSFittingNet(DOSFittingNetDP):
+    def __setattr__(self, name: str, value: Any) -> None:
+        value = setattr_for_general_fitting(name, value)
+        return super().__setattr__(name, value)

source/tests/consistent/fitting/test_dos.py

@@ -12,6 +12,8 @@
 )
 
 from ..common import (
+    INSTALLED_ARRAY_API_STRICT,
+    INSTALLED_JAX,
     INSTALLED_PT,
     INSTALLED_TF,
     CommonTest,
@@ -36,6 +38,20 @@
     fitting_dos,
 )
 
+if INSTALLED_JAX:
+    from deepmd.jax.env import (
+        jnp,
+    )
+    from deepmd.jax.fitting.fitting import DOSFittingNet as DOSFittingJAX
+else:
+    DOSFittingJAX = object
+if INSTALLED_ARRAY_API_STRICT:
+    import array_api_strict
+
+    from ...array_api_strict.fitting.fitting import DOSFittingNet as DOSFittingStrict
+else:
+    DOSFittingStrict = object
+
 
 @parameterized(
     (True, False),  # resnet_dt
@@ -74,9 +90,19 @@ def skip_pt(self) -> bool:
         ) = self.param
         return CommonTest.skip_pt
 
+    @property
+    def skip_jax(self) -> bool:
+        return not INSTALLED_JAX
+
+    @property
+    def skip_array_api_strict(self) -> bool:
+        return not INSTALLED_ARRAY_API_STRICT
+
     tf_class = DOSFittingTF
     dp_class = DOSFittingDP
     pt_class = DOSFittingPT
+    jax_class = DOSFittingJAX
+    array_api_strict_class = DOSFittingStrict
     args = fitting_dos()
 
     def setUp(self):
@@ -157,6 +183,39 @@ def eval_dp(self, dp_obj: Any) -> Any:
             fparam=self.fparam if numb_fparam else None,
         )["dos"]
 
+    def eval_jax(self, jax_obj: Any) -> Any:
+        (
+            resnet_dt,
+            precision,
+            mixed_types,
+            numb_fparam,
+            numb_dos,
+        ) = self.param
+        return np.asarray(
+            jax_obj(
+                jnp.asarray(self.inputs),
+                jnp.asarray(self.atype.reshape(1, -1)),
+                fparam=jnp.asarray(self.fparam) if numb_fparam else None,
+            )["dos"]
+        )
+
+    def eval_array_api_strict(self, array_api_strict_obj: Any) -> Any:
+        array_api_strict.set_array_api_strict_flags(api_version="2023.12")
+        (
+            resnet_dt,
+            precision,
+            mixed_types,
+            numb_fparam,
+            numb_dos,
+        ) = self.param
+        return np.asarray(
+            array_api_strict_obj(
+                array_api_strict.asarray(self.inputs),
+                array_api_strict.asarray(self.atype.reshape(1, -1)),
+                fparam=array_api_strict.asarray(self.fparam) if numb_fparam else None,
+            )["dos"]
+        )
+
     def extract_ret(self, ret: Any, backend) -> tuple[np.ndarray, ...]:
         if backend == self.RefBackend.TF:
             # shape is not same