deepmodeling
diff --git a/‎deepmd/dpmodel/descriptor/dpa1.py‎
Lines changed: 10 additions & 3 deletions b/‎deepmd/dpmodel/descriptor/dpa1.py‎
Lines changed: 10 additions & 3 deletions
diff --git a/‎deepmd/dpmodel/utils/neighbor_graph/derivatives.py‎
Lines changed: 35 additions & 6 deletions b/‎deepmd/dpmodel/utils/neighbor_graph/derivatives.py‎
Lines changed: 35 additions & 6 deletions
diff --git a/‎deepmd/dpmodel/utils/neighbor_graph/graph.py‎
Lines changed: 7 additions & 2 deletions b/‎deepmd/dpmodel/utils/neighbor_graph/graph.py‎
Lines changed: 7 additions & 2 deletions
diff --git a/‎deepmd/main.py‎
Lines changed: 10 additions & 0 deletions b/‎deepmd/main.py‎
Lines changed: 10 additions & 0 deletions
diff --git a/‎deepmd/pt_expt/entrypoints/main.py‎
Lines changed: 43 additions & 3 deletions b/‎deepmd/pt_expt/entrypoints/main.py‎
Lines changed: 43 additions & 3 deletions
diff --git a/‎deepmd/pt_expt/infer/deep_eval.py‎
Lines changed: 113 additions & 0 deletions b/‎deepmd/pt_expt/infer/deep_eval.py‎
Lines changed: 113 additions & 0 deletions
@@ -764,9 +764,13 @@ def call_graph(
         )
         # FLAT node axis (N, ...): no (nf, nloc) reshape -- ragged-native, spec.
         if self.concat_output_tebd:
-            tebd = xp.asarray(type_embedding, device=dev)
+            # Use type_embedding directly (mirrors the dense path's
+            # ``xp.take(type_embedding, ...)``): ``xp.asarray(..., device=dev)``
+            # DETACHES under torch, silently severing the type-embedding weight
+            # gradient so the tebd net never trains; type_embedding already lives
+            # on the model device, so the device cast was redundant anyway.
             atype_local = xp.asarray(atype, device=dev)
-            atype_embd = xp.take(tebd, atype_local, axis=0)  # (N, tebd_dim)
+            atype_embd = xp.take(type_embedding, atype_local, axis=0)  # (N, tebd_dim)
             grrg = xp.concat([grrg, atype_embd], axis=-1)
         return grrg, rot_mat
 
@@ -1748,7 +1752,10 @@ def call_graph(
         ss = rr[:, 0:1]  # (E, 1)
         # neighbor / center type embeddings (concat mode); ghost type == owner type
         # so gathering by the LOCAL owner (src) reproduces the dense neighbor tebd.
-        tebd = xp.asarray(type_embedding, device=dev)
+        # NB: do NOT wrap in ``xp.asarray(..., device=dev)`` -- that DETACHES under
+        # torch and severs the type-embedding weight gradient (the tebd net would
+        # never train); type_embedding already lives on the model device.
+        tebd = type_embedding
         atype_embd_nlist = xp.take(tebd, nei_type, axis=0)  # (E, tebd_dim)
         if not self.type_one_side:
             atype_embd_nnei = xp.take(tebd, center_type, axis=0)  # (E, tebd_dim)
 
@@ -80,15 +80,42 @@ def edge_force_virial(
         frame via the frame of their ``dst`` node.
     """
     xp = array_api_compat.array_namespace(g_e)
-    # node-axis size; when a static ``node_capacity`` is supplied (the jax/export
-    # path) short-circuit so we never call int() on the traced ``sum(n_node)``.
-    n_out = int(node_capacity) if node_capacity is not None else int(xp.sum(n_node))
+    # node-axis size; when a ``node_capacity`` is supplied (the jax/export path)
+    # use it AS-IS so we never call int() on the traced ``sum(n_node)`` -- and,
+    # crucially, never on ``node_capacity`` itself: under symbolic make_fx /
+    # torch.export it is a SymInt (``atype.shape[0]``); ``int(SymInt)`` would
+    # SPECIALIZE the node axis to the trace-time sample size, baking a constant
+    # ``N`` into the scatter and breaking dynamic-``N`` inference.
+    n_out = node_capacity if node_capacity is not None else int(xp.sum(n_node))
     nf = n_node.shape[0]
     # zero padding/guard contributions; cast mask to g's dtype (array-API pure,
     # CLAUDE.md mask-multiply guideline — avoids bool*float under array_api_strict)
     g = g_e * xp.astype(edge_mask[:, None], g_e.dtype)
-    src = edge_index[0]
-    dst = edge_index[1]
+    # Wrap node indices into ``[0, n_out)`` so every scatter address is provably
+    # in-bounds. For a well-formed graph every real edge already has
+    # ``index < n_out`` (== ``atype.shape[0]``), so this modulo is the IDENTITY on
+    # real edges (pinned by test_modulo_clamp_leaves_real_edges_unchanged) -- a
+    # correctness-preserving guard, not a value fixup.
+    #
+    # Why it is needed (root cause, GPU-confirmed): under the dynamic-edge graph
+    # ``torch.export`` path the node count is traced as several equal-but-distinct
+    # symbols (``atype.shape[0]``, ``fit_ret.shape[0]``, ...), tied only by
+    # ``aten._assert_scalar(Eq(...))`` nodes. ``_strip_shape_assertions``
+    # (pt_expt/utils/serialization.py) neutralises ALL such asserts so export can
+    # trace -- which also drops those node-count equalities, so inductor can no
+    # longer prove the scatter index and its bound ``ks0 == n_out`` share a symbol
+    # and emits ``tl.device_assert(idx < ks0)`` (fatal on CUDA; unchecked on CPU,
+    # which is why all CPU dev/CI was green). ``% n_out`` discharges that guard
+    # unconditionally. This is the PERMANENT fix: the upstream alternative --
+    # making the SHARED, spin-export-critical ``_strip_shape_assertions``
+    # selective -- risks re-triggering the torch.export bugs it exists to bypass
+    # and the spin ``.pt2`` path, so it is deliberately NOT taken.
+    #
+    # Pure arithmetic => torch.export-safe, unlike ``xp.clip`` (SymInt bound
+    # breaks array_api_compat's clip) and unlike a mask-multiply (which misses the
+    # ``edge_mask == 1`` indices the stripped guard mis-bounds).
+    src = edge_index[0] % n_out
+    dst = edge_index[1] % n_out
     # force (output sized to the node axis, incl. any padding tail)
     force = segment_sum(g, dst, n_out) - segment_sum(g, src, n_out)
     # per-edge virial w_e[k, j] = -g_e[k] * edge_vec[j]  (broadcast, no einsum)
@@ -101,6 +128,8 @@ def edge_force_virial(
     boundaries = xp.cumulative_sum(n_node)  # (nf,) per-frame node upper bounds
     edge_frame = xp.astype(
         xp.searchsorted(boundaries, dst, side="right"), xp.int64
-    )  # (E,) in [0, nf)
+    )  # (E,) in [0, nf]
+    # wrap into [0, nf) for the same CUDA-bounds reason (export-safe modulo)
+    edge_frame = edge_frame % nf
     virial = segment_sum(w_edge, edge_frame, nf)  # (nf, 3, 3)
     return force, atom_virial, virial
@@ -153,13 +153,18 @@ def frame_id_from_n_node(n_node: Array, n_total: int | None = None) -> Array:
     dev = array_api_compat.device(n_node)
     if n_total is None:
         n_total = int(xp.sum(n_node))
-    nf = n_node.shape[0]
     idx = xp.arange(n_total, dtype=n_node.dtype, device=dev)
     boundaries = xp.cumulative_sum(n_node)  # (nf,) upper bounds, exclusive
     frame_id = xp.astype(xp.searchsorted(boundaries, idx, side="right"), xp.int64)
     # padding nodes (idx >= sum(n_node)) land at frame ``nf`` (OOB); clamp them to
     # the last real frame so the per-frame scatter never indexes out of range.
-    return xp.minimum(frame_id, xp.asarray(nf - 1, dtype=xp.int64, device=dev))
+    # Derive ``nf - 1`` as a RUNTIME 0-d tensor (sum of ones over the frame axis)
+    # rather than ``xp.asarray(n_node.shape[0] - 1)``: under symbolic make_fx /
+    # torch.export, ``shape[0]`` is a SymInt and materializing it into a constant
+    # tensor SPECIALIZES the frame axis -- baking the trace-time frame count into
+    # every downstream per-frame reduction and breaking dynamic-``nf`` inference.
+    last_frame = xp.sum(xp.ones_like(n_node)) - 1  # 0-d int == nf - 1
+    return xp.minimum(frame_id, xp.astype(last_frame, xp.int64))
 
 
 def node_validity_mask(n_node: Array, n_total: int) -> Array:
 
@@ -350,6 +350,16 @@ def main_parser() -> argparse.ArgumentParser:
         type=str,
         help="(Supported backend: PyTorch) Task head (alias: model branch) to freeze if in multi-task mode.",
     )
+    parser_frz.add_argument(
+        "--lower-kind",
+        default="nlist",
+        type=str,
+        choices=["nlist", "graph"],
+        help="(Supported backend: PyTorch Exportable) Lower-level export form of the "
+        "frozen .pt2: 'nlist' (default, dense neighbor-list lower) or 'graph' "
+        "(NeighborGraph edge-list lower; only for graph-eligible models, currently "
+        "dpa1 with attn_layer=0). 'graph' selects the C++ graph inference path.",
+    )
 
     # * test script ********************************************************************
     parser_tst = subparsers.add_parser(
 
@@ -387,6 +387,7 @@ def freeze(
     model: str,
     output: str = "frozen_model.pte",
     head: str | None = None,
+    lower_kind: str = "nlist",
 ) -> None:
     """Freeze a pt_expt checkpoint into a .pte exported model.
 
@@ -398,6 +399,13 @@ def freeze(
         Path for the output .pte file.
     head : str or None
         Head to freeze in multi-task mode.
+    lower_kind : str
+        Lower-level export form: ``"nlist"`` (default, dense neighbor-list lower)
+        or ``"graph"`` (NeighborGraph edge-list lower). ``"graph"`` is only valid
+        for graph-eligible models (``mixed_types`` and ``uses_graph_lower``,
+        currently dpa1 with ``attn_layer == 0``) and selects the C++ graph
+        inference path; the per-atom virial is enabled for it (near-free in the
+        graph path: one extra scatter off the shared single backward).
     """
     import torch
 
@@ -458,12 +466,34 @@ def freeze(
         single_model_params = model_params
 
     m.eval()
+
+    # The graph lower is opt-in and only valid for graph-eligible models (dpa1
+    # attn_layer==0 today). Fail fast with a clear message rather than emitting a
+    # broken .pt2. Enable the per-atom virial for the graph form -- it is
+    # near-free there (one extra scatter off the single shared backward).
+    do_atomic_virial = False
+    if lower_kind == "graph":
+        from deepmd.pt_expt.train.training import (
+            _model_uses_graph_lower,
+        )
+
+        if not _model_uses_graph_lower(m):
+            raise ValueError(
+                "lower_kind='graph' requires a graph-eligible model "
+                "(mixed_types and a descriptor exposing uses_graph_lower()==True, "
+                "currently dpa1 with attn_layer==0). Use lower_kind='nlist' for "
+                "this model."
+            )
+        do_atomic_virial = True
+
     model_dict_serialized = m.serialize()
     deserialize_to_file(
         output,
         {"model": model_dict_serialized, "model_def_script": single_model_params},
+        do_atomic_virial=do_atomic_virial,
+        lower_kind=lower_kind,
     )
-    log.info("Saved frozen model to %s", output)
+    log.info("Saved frozen model to %s (lower_kind=%s)", output, lower_kind)
 
 
 def change_bias(
@@ -701,9 +731,19 @@ def main(args: list[str] | argparse.Namespace | None = None) -> None:
                     f"Checkpoint path '{model_path}' does not exist."
                 )
             FLAGS.model = str(model_path)
+        _lower_kind = getattr(FLAGS, "lower_kind", "nlist")
         if not FLAGS.output.endswith((".pte", ".pt2")):
-            FLAGS.output = str(Path(FLAGS.output).with_suffix(".pte"))
-        freeze(model=FLAGS.model, output=FLAGS.output, head=FLAGS.head)
+            # Default suffix: .pt2 for the graph export (an AOTI .pt2 archive is
+            # what the C++ graph path consumes), .pte otherwise. Explicit user
+            # .pte / .pt2 suffixes are preserved for both.
+            _default_suffix = ".pt2" if _lower_kind == "graph" else ".pte"
+            FLAGS.output = str(Path(FLAGS.output).with_suffix(_default_suffix))
+        freeze(
+            model=FLAGS.model,
+            output=FLAGS.output,
+            head=FLAGS.head,
+            lower_kind=_lower_kind,
+        )
     elif FLAGS.command == "change-bias":
         change_bias(
             input_file=FLAGS.INPUT,
 
@@ -66,6 +66,20 @@
     import ase.neighborlist
 
 
+# Public output keys emitted by the graph-form AOTI forward
+# (``forward_lower_graph_exportable``) keyed by the output-variable category that
+# ``request_defs`` carries.  The graph path is LOCAL-only (``N == sum(n_node)``
+# nodes, no ghosts), so its outputs are already at local-atom resolution -- no
+# ``communicate_extended_output`` fold-back is needed.
+_GRAPH_CATEGORY_TO_KEY = {
+    OutputVariableCategory.OUT: "atom_energy",
+    OutputVariableCategory.REDU: "energy",
+    OutputVariableCategory.DERV_R: "force",
+    OutputVariableCategory.DERV_C_REDU: "virial",
+    OutputVariableCategory.DERV_C: "atom_virial",
+}
+
+
 def _reshape_charge_spin(
     charge_spin: np.ndarray, nframes: int, dim_chg_spin: int
 ) -> np.ndarray:
@@ -1423,6 +1437,10 @@ def _eval_model(
         request_defs: list[OutputVariableDef],
         charge_spin: np.ndarray | None = None,
     ) -> tuple[np.ndarray, ...]:
+        if self.metadata.get("lower_input_kind") == "graph":
+            return self._eval_model_graph(
+                coords, cells, atom_types, fparam, aparam, request_defs, charge_spin
+            )
         model_inputs, mapping_t, nframes, natoms = self._prepare_inputs(
             coords, cells, atom_types, fparam, aparam, charge_spin
         )
@@ -1621,6 +1639,101 @@ def _eval_model_spin(
                 )
         return tuple(results)
 
+    def _eval_model_graph(
+        self,
+        coords: np.ndarray,
+        cells: np.ndarray | None,
+        atom_types: np.ndarray,
+        fparam: np.ndarray | None,
+        aparam: np.ndarray | None,
+        request_defs: list[OutputVariableDef],
+        charge_spin: np.ndarray | None = None,
+    ) -> tuple[np.ndarray, ...]:
+        """Evaluate a graph-form ``.pt2`` (``lower_input_kind == "graph"``).
+
+        Builds a carry-all :class:`~deepmd.dpmodel.utils.neighbor_graph.NeighborGraph`
+        from the eval system at its exact (tight) edge count and feeds the
+        positional schema
+        ``(atype, n_node, edge_index, edge_vec, edge_mask, fparam, aparam,
+        charge_spin)`` to the exported forward.  The AOTI artifact's edge axis
+        is DYNAMIC (B2.0), so no ``edge_capacity`` padding is needed.  The
+        forward returns the LOCAL public keys directly, so results are reshaped
+        without ``communicate_extended_output``.
+        """
+        from deepmd.dpmodel.utils.neighbor_graph import (
+            build_neighbor_graph,
+        )
+        from deepmd.pt_expt.utils.env import (
+            DEVICE,
+        )
+
+        nframes = coords.shape[0]
+        if len(atom_types.shape) == 1:
+            natoms = len(atom_types)
+            atom_types = np.tile(atom_types, nframes).reshape(nframes, -1)
+        else:
+            natoms = len(atom_types[0])
+
+        coord_input = coords.reshape(nframes, natoms, 3)
+        box_input = cells.reshape(nframes, 9) if cells is not None else None
+        # Dynamic edge axis (B2.0): build the carry-all graph at its exact edge
+        # count (no static padding); the AOTI artifact accepts any E.
+        graph = build_neighbor_graph(
+            coord_input,
+            atom_types,
+            box_input,
+            self._rcut,
+        )
+
+        atype_t = torch.tensor(
+            np.asarray(atom_types).reshape(-1), dtype=torch.int64, device=DEVICE
+        )
+        n_node_t = torch.tensor(
+            np.asarray(graph.n_node), dtype=torch.int64, device=DEVICE
+        )
+        edge_index_t = torch.tensor(
+            np.asarray(graph.edge_index), dtype=torch.int64, device=DEVICE
+        )
+        edge_vec_t = torch.tensor(
+            np.asarray(graph.edge_vec), dtype=torch.float64, device=DEVICE
+        )
+        edge_mask_t = torch.tensor(
+            np.asarray(graph.edge_mask), dtype=torch.bool, device=DEVICE
+        )
+
+        fparam_t, aparam_t = self._prepare_optional_lower_inputs(
+            fparam, aparam, nframes, natoms, DEVICE
+        )
+        charge_spin_t = self._make_charge_spin_input(nframes, charge_spin)
+
+        model_inputs = (
+            atype_t,
+            n_node_t,
+            edge_index_t,
+            edge_vec_t,
+            edge_mask_t,
+            fparam_t,
+            aparam_t,
+            charge_spin_t,
+        )
+        if self._is_pt2:
+            model_ret = self._pt2_runner(*model_inputs)
+        else:
+            model_ret = self.exported_module(*model_inputs)
+
+        results = []
+        for odef in request_defs:
+            shape = self._get_output_shape(odef, nframes, natoms)
+            gkey = _GRAPH_CATEGORY_TO_KEY.get(odef.category)
+            val = model_ret.get(gkey) if gkey is not None else None
+            if val is not None:
+                results.append(val.detach().cpu().numpy().reshape(shape))
+            else:
+                results.append(
+                    np.full(np.abs(shape), np.nan, dtype=GLOBAL_NP_FLOAT_PRECISION)
+                )
+        return tuple(results)
+
     def _get_output_shape(
         self, odef: OutputVariableDef, nframes: int, natoms: int
     ) -> list[int]: