feat(jax): force & virial

deepmd/dpmodel/model/make_model.py

@@ -222,22 +222,42 @@ def call_lower(
                 extended_coord, fparam=fparam, aparam=aparam
             )
             del extended_coord, fparam, aparam
-            atomic_ret = self.atomic_model.forward_common_atomic(
+            model_predict = self.forward_common_atomic(
                 cc_ext,
                 extended_atype,
                 nlist,
                 mapping=mapping,
                 fparam=fp,
                 aparam=ap,
+                do_atomic_virial=do_atomic_virial,
+            )
+            model_predict = self.output_type_cast(model_predict, input_prec)
+            return model_predict
+
+        def forward_common_atomic(
+            self,
+            extended_coord: np.ndarray,
+            extended_atype: np.ndarray,
+            nlist: np.ndarray,
+            mapping: Optional[np.ndarray] = None,
+            fparam: Optional[np.ndarray] = None,
+            aparam: Optional[np.ndarray] = None,
+            do_atomic_virial: bool = False,
+        ):
+            atomic_ret = self.atomic_model.forward_common_atomic(
+                extended_coord,
+                extended_atype,
+                nlist,
+                mapping=mapping,
+                fparam=fparam,
+                aparam=aparam,
             )
-            model_predict = fit_output_to_model_output(
+            return fit_output_to_model_output(
                 atomic_ret,
                 self.atomic_output_def(),
-                cc_ext,
+                extended_coord,
                 do_atomic_virial=do_atomic_virial,
             )
-            model_predict = self.output_type_cast(model_predict, input_prec)
-            return model_predict
 
         forward_lower = call_lower
 

deepmd/dpmodel/model/transform_output.py

@@ -9,6 +9,7 @@
 from deepmd.dpmodel.output_def import (
     FittingOutputDef,
     ModelOutputDef,
+    OutputVariableDef,
     get_deriv_name,
     get_reduce_name,
 )
@@ -47,6 +48,28 @@
     return model_ret
 
 
+def get_leading_dims(
+    vv: np.ndarray,
+    vdef: OutputVariableDef,
+):
+    """Get the dimensions of nf x nloc.
+
+    Parameters
+    ----------
+    vv : np.ndarray
+        The input array from which to compute the leading dimensions.
+    vdef : OutputVariableDef
+        The output variable definition containing the shape to exclude from `vv`.
+
+    Returns
+    -------
+    list
+        A list of leading dimensions of `vv`, excluding the last `len(vdef.shape)` dimensions.
+    """
+    vshape = vv.shape
+    return list(vshape[: (len(vshape) - len(vdef.shape))])
+
+
 def communicate_extended_output(
     model_ret: dict[str, np.ndarray],
     model_output_def: ModelOutputDef,
@@ -57,6 +80,7 @@
     local and ghost (extended) atoms to local atoms.
 
     """
+    xp = array_api_compat.get_namespace(mapping)
     new_ret = {}
     for kk in model_output_def.keys_outp():
         vv = model_ret[kk]
@@ -65,15 +89,64 @@
         if vdef.reducible:
             kk_redu = get_reduce_name(kk)
             new_ret[kk_redu] = model_ret[kk_redu]
+            kk_derv_r, kk_derv_c = get_deriv_name(kk)
+            mldims = list(mapping.shape)
+            vldims = get_leading_dims(vv, vdef)
             if vdef.r_differentiable:
-                kk_derv_r, kk_derv_c = get_deriv_name(kk)
-                # name holders
-                new_ret[kk_derv_r] = None
+                if model_ret[kk_derv_r] is not None:
+                    derv_r_ext_dims = list(vdef.shape) + [3]  # noqa:RUF005
+                    mapping = xp.reshape(mapping, (mldims + [1] * len(derv_r_ext_dims)))
+                    mapping = xp.tile(mapping, [1] * len(mldims) + derv_r_ext_dims)
+                    force = xp.zeros(
+                        vldims + derv_r_ext_dims, dtype=vv.dtype, device=vv.device
+                    )
+                    # jax only
+                    if array_api_compat.is_jax_array(force):
+                        from deepmd.jax.common import (
+                            scatter_sum,
+                        )
+
+                        force = scatter_sum(
+                            force,
+                            1,
+                            mapping,
+                            model_ret[kk_derv_r],
+                        )
+                    else:
+                        raise NotImplementedError("Only JAX arrays are supported.")
+                    new_ret[kk_derv_r] = force
+                else:
+                    # name holders
+                    new_ret[kk_derv_r] = None
             if vdef.c_differentiable:
                 assert vdef.r_differentiable
-                kk_derv_r, kk_derv_c = get_deriv_name(kk)
-                new_ret[kk_derv_c] = None
-                new_ret[kk_derv_c + "_redu"] = None
+                if model_ret[kk_derv_c] is not None:
+                    derv_c_ext_dims = list(vdef.shape) + [9]  # noqa:RUF005
+                    mapping = xp.tile(
+                        mapping, [1] * (len(mldims) + len(vdef.shape)) + [3]
+                    )
+                    virial = xp.zeros(
+                        vldims + derv_c_ext_dims, dtype=vv.dtype, device=vv.device
+                    )
+                    # jax only
+                    if array_api_compat.is_jax_array(virial):
+                        from deepmd.jax.common import (
+                            scatter_sum,
+                        )
+
+                        virial = scatter_sum(
+                            virial,
+                            1,
+                            mapping,
+                            model_ret[kk_derv_c],
+                        )
+                    else:
+                        raise NotImplementedError("Only JAX arrays are supported.")
+                    new_ret[kk_derv_c] = virial
+                    new_ret[kk_derv_c + "_redu"] = xp.sum(new_ret[kk_derv_c], axis=1)
+                else:
+                    new_ret[kk_derv_c] = None
+                    new_ret[kk_derv_c + "_redu"] = None
                 if not do_atomic_virial:
                     # pop atomic virial, because it is not correctly calculated.
                     new_ret.pop(kk_derv_c)

deepmd/dpmodel/utils/env_mat.py

@@ -61,7 +61,9 @@ def _make_env_mat(
     # nf x nloc x nnei x 3
     diff = coord_r - coord_l
     # nf x nloc x nnei
-    length = xp.linalg.vector_norm(diff, axis=-1, keepdims=True)
+    # the grad of JAX vector_norm is NaN at x=0
+    diff_ = xp.where(xp.abs(diff) < 1e-30, xp.full_like(diff, 1e-30), diff)
+    length = xp.linalg.vector_norm(diff_, axis=-1, keepdims=True)
     # for index 0 nloc atom
     length = length + xp.astype(~xp.expand_dims(mask, axis=-1), length.dtype)
     t0 = 1 / (length + protection)

deepmd/jax/common.py

@@ -95,3 +95,13 @@ def __dlpack__(self, *args, **kwargs):
 
     def __dlpack_device__(self, *args, **kwargs):
         return self.value.__dlpack_device__(*args, **kwargs)
+
+
+def scatter_sum(input, dim, index: jnp.ndarray, src: jnp.ndarray) -> jnp.ndarray:
+    """Reduces all values from the src tensor to the indices specified in the index tensor."""
+    idx = jnp.arange(input.size, dtype=jnp.int64).reshape(input.shape)
+    new_idx = jnp.take_along_axis(idx, index, axis=dim).ravel()
+    shape = input.shape
+    input = input.ravel()
+    input = input.at[new_idx].add(src.ravel())
+    return input.reshape(shape)

deepmd/jax/env.py

@@ -10,6 +10,7 @@
 )
 
 jax.config.update("jax_enable_x64", True)
+# jax.config.update("jax_debug_nans", True)
 
 __all__ = [
     "jax",

deepmd/jax/model/base_model.py

@@ -1,6 +1,110 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
+from typing import (
+    Optional,
+)
+
 from deepmd.dpmodel.model.base_model import (
     make_base_model,
 )
+from deepmd.dpmodel.output_def import (
+    get_deriv_name,
+    get_reduce_name,
+)
+from deepmd.jax.env import (
+    jax,
+    jnp,
+)
 
 BaseModel = make_base_model()
+
+
+def forward_common_atomic(
+    self,
+    extended_coord: jnp.ndarray,
+    extended_atype: jnp.ndarray,
+    nlist: jnp.ndarray,
+    mapping: Optional[jnp.ndarray] = None,
+    fparam: Optional[jnp.ndarray] = None,
+    aparam: Optional[jnp.ndarray] = None,
+    do_atomic_virial: bool = False,
+):
+    atomic_ret = self.atomic_model.forward_common_atomic(
+        extended_coord,
+        extended_atype,
+        nlist,
+        mapping=mapping,
+        fparam=fparam,
+        aparam=aparam,
+    )
+    atomic_output_def = self.atomic_output_def()
+    model_predict = {}
+    for kk, vv in atomic_ret.items():
+        model_predict[kk] = vv
+        vdef = atomic_output_def[kk]
+        shap = vdef.shape
+        atom_axis = -(len(shap) + 1)
+        if vdef.reducible:
+            kk_redu = get_reduce_name(kk)
+            model_predict[kk_redu] = jnp.sum(vv, axis=atom_axis)
+            kk_derv_r, kk_derv_c = get_deriv_name(kk)
+            if vdef.c_differentiable:
+                size = 1
+                for ii in vdef.shape:
+                    size *= ii
+
+                split_ff = []
+                split_vv = []
+                for ss in range(size):
+
+                    def eval_output(
+                        cc_ext,
+                        extended_atype,
+                        nlist,
+                        mapping,
+                        fparam,
+                        aparam,
+                        *,
+                        _kk=kk,
+                        _ss=ss,
+                        _atom_axis=atom_axis,
+                    ):
+                        atomic_ret = self.atomic_model.forward_common_atomic(
+                            cc_ext[None, ...],
+                            extended_atype[None, ...],
+                            nlist[None, ...],
+                            mapping=mapping[None, ...] if mapping is not None else None,
+                            fparam=fparam[None, ...] if fparam is not None else None,
+                            aparam=aparam[None, ...] if aparam is not None else None,
+                        )
+                        return jnp.sum(atomic_ret[_kk][0], axis=_atom_axis)[_ss]
+
+                    ffi = -jax.vmap(jax.grad(eval_output, argnums=0))(
+                        extended_coord,
+                        extended_atype,
+                        nlist,
+                        mapping,
+                        fparam,
+                        aparam,
+                    )
+                    aviri = ffi[..., None] @ extended_coord[..., None, :]
+                    ffi = ffi[..., None, :]
+                    split_ff.append(ffi)
+                    aviri = aviri[..., None, :]
+                    split_vv.append(aviri)
+                out_lead_shape = list(extended_coord.shape[:-1]) + vdef.shape
+                extended_force = jnp.concat(split_ff, axis=-2).reshape(
+                    *out_lead_shape, 3
+                )
+
+                model_predict[kk_derv_r] = extended_force
+            if vdef.c_differentiable:
+                assert vdef.r_differentiable
+                extended_virial = jnp.concat(split_vv, axis=-2).reshape(
+                    *out_lead_shape, 9
+                )
+                # the correction sums to zero, which does not contribute to global virial
+                if do_atomic_virial:
+                    raise NotImplementedError("Atomic virial is not implemented yet.")
+                # to [...,3,3] -> [...,9]
+                model_predict[kk_derv_c] = extended_virial
+    return model_predict

deepmd/jax/model/ener_model.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 from typing import (
     Any,
+    Optional,
 )
 
 from deepmd.dpmodel.model import EnergyModel as EnergyModelDP
@@ -10,8 +11,12 @@
 from deepmd.jax.common import (
     flax_module,
 )
+from deepmd.jax.env import (
+    jnp,
+)
 from deepmd.jax.model.base_model import (
     BaseModel,
+    forward_common_atomic,
 )
 
 
@@ -22,3 +27,24 @@ def __setattr__(self, name: str, value: Any) -> None:
         if name == "atomic_model":
             value = DPAtomicModel.deserialize(value.serialize())
         return super().__setattr__(name, value)
+
+    def forward_common_atomic(
+        self,
+        extended_coord: jnp.ndarray,
+        extended_atype: jnp.ndarray,
+        nlist: jnp.ndarray,
+        mapping: Optional[jnp.ndarray] = None,
+        fparam: Optional[jnp.ndarray] = None,
+        aparam: Optional[jnp.ndarray] = None,
+        do_atomic_virial: bool = False,
+    ):
+        return forward_common_atomic(
+            self,
+            extended_coord,
+            extended_atype,
+            nlist,
+            mapping=mapping,
+            fparam=fparam,
+            aparam=aparam,
+            do_atomic_virial=do_atomic_virial,
+        )

source/tests/consistent/common.py

@@ -69,6 +69,8 @@
     "INSTALLED_ARRAY_API_STRICT",
 ]
 
+SKIP_FLAG = object()
+
 
 class CommonTest(ABC):
     data: ClassVar[dict]
@@ -362,6 +364,8 @@ def test_dp_consistent_with_ref(self):
         data2 = dp_obj.serialize()
         np.testing.assert_equal(data1, data2)
         for rr1, rr2 in zip(ret1, ret2):
+            if rr1 is SKIP_FLAG or rr2 is SKIP_FLAG:
+                continue
             np.testing.assert_allclose(rr1, rr2, rtol=self.rtol, atol=self.atol)
             assert rr1.dtype == rr2.dtype, f"{rr1.dtype} != {rr2.dtype}"
 

source/tests/consistent/model/common.py

@@ -51,7 +51,7 @@ def build_tf_model(self, obj, natoms, coords, atype, box, suffix):
             {},
             suffix=suffix,
         )
-        return [ret["energy"], ret["atom_ener"]], {
+        return [ret["energy"], ret["atom_ener"], ret["force"], ret["virial"]], {
             t_coord: coords,
             t_type: atype,
             t_natoms: natoms,