fix: fix errors for zero atom inputs

deepmd/dpmodel/atomic_model/base_atomic_model.py

@@ -200,8 +200,9 @@
 
         for kk in ret_dict.keys():
             out_shape = ret_dict[kk].shape
+            out_shape2 = np.prod(out_shape[2:])
             ret_dict[kk] = (
-                ret_dict[kk].reshape([out_shape[0], out_shape[1], -1])
+                ret_dict[kk].reshape([out_shape[0], out_shape[1], out_shape2])
                 * atom_mask[:, :, None]
             ).reshape(out_shape)
         ret_dict["mask"] = atom_mask

deepmd/dpmodel/descriptor/dpa1.py

@@ -487,7 +487,9 @@
         )
         # nf x nloc x (ng x ng1 + tebd_dim)
         if self.concat_output_tebd:
-            grrg = np.concatenate([grrg, atype_embd.reshape(nf, nloc, -1)], axis=-1)
+            grrg = np.concatenate(
+                [grrg, atype_embd.reshape(nf, nloc, self.tebd_dim)], axis=-1
+            )
         return grrg, rot_mat, None, None, sw
 
     def serialize(self) -> dict:
@@ -834,7 +836,8 @@
         embedding_idx,
     ):
         nfnl, nnei = ss.shape[0:2]
-        ss = ss.reshape(nfnl, nnei, -1)
+        shape2 = np.prod(ss.shape[2:])
+        ss = ss.reshape(nfnl, nnei, shape2)
         # nfnl x nnei x ng
         gg = self.embeddings[embedding_idx].call(ss)
         return gg
@@ -846,7 +849,8 @@
     ):
         assert self.embeddings_strip is not None
         nfnl, nnei = ss.shape[0:2]
-        ss = ss.reshape(nfnl, nnei, -1)
+        shape2 = np.prod(ss.shape[2:])
+        ss = ss.reshape(nfnl, nnei, shape2)
         # nfnl x nnei x ng
         gg = self.embeddings_strip[embedding_idx].call(ss)
         return gg
@@ -875,7 +879,7 @@
         # nfnl x nnei x 1
         sw = sw.reshape(nf * nloc, nnei, 1)
         # nfnl x tebd_dim
-        atype_embd = atype_embd_ext[:, :nloc, :].reshape(nf * nloc, -1)
+        atype_embd = atype_embd_ext[:, :nloc, :].reshape(nf * nloc, self.tebd_dim)
         # nfnl x nnei x tebd_dim
         atype_embd_nnei = np.tile(atype_embd[:, np.newaxis, :], (1, nnei, 1))
         # nfnl x nnei
@@ -941,10 +945,10 @@
             GLOBAL_NP_FLOAT_PRECISION
         )
         return (
-            grrg.reshape(-1, nloc, self.filter_neuron[-1] * self.axis_neuron),
-            gg.reshape(-1, nloc, self.nnei, self.filter_neuron[-1]),
-            dmatrix.reshape(-1, nloc, self.nnei, 4)[..., 1:],
-            gr[..., 1:].reshape(-1, nloc, self.filter_neuron[-1], 3),
+            grrg.reshape(nf, nloc, self.filter_neuron[-1] * self.axis_neuron),
+            gg.reshape(nf, nloc, self.nnei, self.filter_neuron[-1]),
+            dmatrix.reshape(nf, nloc, self.nnei, 4)[..., 1:],
+            gr[..., 1:].reshape(nf, nloc, self.filter_neuron[-1], 3),
             sw,
         )
 

deepmd/dpmodel/descriptor/repformers.py

@@ -395,7 +395,7 @@
         h2g2 = _cal_hg(g2, h2, nlist_mask, sw, smooth=self.smooth, epsilon=self.epsilon)
         # (nf x nloc) x ng2 x 3
         rot_mat = np.transpose(h2g2, (0, 1, 3, 2))
-        return g1, g2, h2, rot_mat.reshape(-1, nloc, self.dim_emb, 3), sw
+        return g1, g2, h2, rot_mat.reshape(nf, nloc, self.dim_emb, 3), sw
 
     def has_message_passing(self) -> bool:
         """Returns whether the descriptor block has message passing."""

deepmd/dpmodel/descriptor/se_r.py

@@ -341,7 +341,7 @@
 
         res_rescale = 1.0 / 5.0
         res = xyz_scatter * res_rescale
-        res = res.reshape(nf, nloc, -1).astype(GLOBAL_NP_FLOAT_PRECISION)
+        res = res.reshape(nf, nloc, ng).astype(GLOBAL_NP_FLOAT_PRECISION)
         return res, None, None, None, ww
 
     def serialize(self) -> dict:

deepmd/dpmodel/utils/nlist.py

@@ -95,12 +95,15 @@
     nall = coord.shape[1] // 3
     # fill virtual atoms with large coords so they are not neighbors of any
     # real atom.
-    xmax = np.max(coord) + 2.0 * rcut
+    if coord.size > 0:
+        xmax = np.max(coord) + 2.0 * rcut
+    else:
+        xmax = 2.0 * rcut
     # nf x nall
     is_vir = atype < 0
-    coord1 = np.where(is_vir[:, :, None], xmax, coord.reshape(-1, nall, 3)).reshape(
-        -1, nall * 3
-    )
+    coord1 = np.where(
+        is_vir[:, :, None], xmax, coord.reshape(batch_size, nall, 3)
+    ).reshape(batch_size, nall * 3)
     if isinstance(sel, int):
         sel = [sel]
     nsel = sum(sel)

deepmd/pt/model/atomic_model/base_atomic_model.py

@@ -256,8 +256,11 @@
 
         for kk in ret_dict.keys():
             out_shape = ret_dict[kk].shape
+            out_shape2 = 1
+            for ss in out_shape[2:]:
+                out_shape2 *= ss
             ret_dict[kk] = (
-                ret_dict[kk].reshape([out_shape[0], out_shape[1], -1])
+                ret_dict[kk].reshape([out_shape[0], out_shape[1], out_shape2])
                 * atom_mask[:, :, None]
             ).view(out_shape)
         ret_dict["mask"] = atom_mask

deepmd/pt/model/descriptor/repformers.py

@@ -505,7 +505,7 @@
         # (nb x nloc) x ng2 x 3
         rot_mat = torch.permute(h2g2, (0, 1, 3, 2))
 
-        return g1, g2, h2, rot_mat.view(-1, nloc, self.dim_emb, 3), sw
+        return g1, g2, h2, rot_mat.view(nframes, nloc, self.dim_emb, 3), sw
 
     def compute_input_stats(
         self,

deepmd/pt/model/descriptor/se_a.py

@@ -617,6 +617,7 @@
         - `torch.Tensor`: descriptor matrix with shape [nframes, natoms[0]*self.filter_neuron[-1]*self.axis_neuron].
         """
         del extended_atype_embd, mapping
+        nf = nlist.shape[0]
         nloc = nlist.shape[1]
         atype = extended_atype[:, :nloc]
         dmatrix, diff, sw = prod_env_mat(
@@ -657,7 +658,7 @@
                 device=extended_coord.device,
             )
             # nfnl x nnei
-            exclude_mask = self.emask(nlist, extended_atype).view(nfnl, -1)
+            exclude_mask = self.emask(nlist, extended_atype).view(nfnl, self.nnei)
             for embedding_idx, ll in enumerate(self.filter_layers.networks):
                 if self.type_one_side:
                     ii = embedding_idx
@@ -698,8 +699,8 @@
         result = torch.matmul(
             xyz_scatter_1, xyz_scatter_2
         )  # shape is [nframes*nall, self.filter_neuron[-1], self.axis_neuron]
-        result = result.view(-1, nloc, self.filter_neuron[-1] * self.axis_neuron)
-        rot_mat = rot_mat.view([-1, nloc] + list(rot_mat.shape[1:]))  # noqa:RUF005
+        result = result.view(nf, nloc, self.filter_neuron[-1] * self.axis_neuron)
+        rot_mat = rot_mat.view([nf, nloc] + list(rot_mat.shape[1:]))  # noqa:RUF005
         return (
             result.to(dtype=env.GLOBAL_PT_FLOAT_PRECISION),
             rot_mat.to(dtype=env.GLOBAL_PT_FLOAT_PRECISION),

deepmd/pt/model/descriptor/se_atten.py

@@ -580,10 +580,10 @@ def forward(
             xyz_scatter_1, xyz_scatter_2
         )  # shape is [nframes*nloc, self.filter_neuron[-1], self.axis_neuron]
         return (
-            result.view(-1, nloc, self.filter_neuron[-1] * self.axis_neuron),
-            gg.view(-1, nloc, self.nnei, self.filter_neuron[-1]),
-            dmatrix.view(-1, nloc, self.nnei, 4)[..., 1:],
-            rot_mat.view(-1, nloc, self.filter_neuron[-1], 3),
+            result.view(nframes, nloc, self.filter_neuron[-1] * self.axis_neuron),
+            gg.view(nframes, nloc, self.nnei, self.filter_neuron[-1]),
+            dmatrix.view(nframes, nloc, self.nnei, 4)[..., 1:],
+            rot_mat.view(nframes, nloc, self.filter_neuron[-1], 3),
             sw,
         )
 

deepmd/pt/model/descriptor/se_r.py

@@ -343,6 +343,7 @@
 
         """
         del mapping
+        nf = nlist.shape[0]
         nloc = nlist.shape[1]
         atype = atype_ext[:, :nloc]
         dmatrix, diff, sw = prod_env_mat(
@@ -367,7 +368,7 @@
         )
 
         # nfnl x nnei
-        exclude_mask = self.emask(nlist, atype_ext).view(nfnl, -1)
+        exclude_mask = self.emask(nlist, atype_ext).view(nfnl, self.nnei)
         for ii, ll in enumerate(self.filter_layers.networks):
             # nfnl x nt
             mm = exclude_mask[:, self.sec[ii] : self.sec[ii + 1]]
@@ -381,7 +382,7 @@
 
         res_rescale = 1.0 / 5.0
         result = xyz_scatter * res_rescale
-        result = result.view(-1, nloc, self.filter_neuron[-1])
+        result = result.view(nf, nloc, self.filter_neuron[-1])
         return (
             result.to(dtype=env.GLOBAL_PT_FLOAT_PRECISION),
             None,

deepmd/pt/model/descriptor/se_t.py

@@ -665,6 +665,7 @@
 
         """
         del extended_atype_embd, mapping
+        nf = nlist.shape[0]
         nloc = nlist.shape[1]
         atype = extended_atype[:, :nloc]
         dmatrix, diff, sw = prod_env_mat(
@@ -687,7 +688,7 @@
             device=extended_coord.device,
         )
         # nfnl x nnei
-        exclude_mask = self.emask(nlist, extended_atype).view(nfnl, -1)
+        exclude_mask = self.emask(nlist, extended_atype).view(nfnl, self.nnei)
         for embedding_idx, ll in enumerate(self.filter_layers.networks):
             ti = embedding_idx % self.ntypes
             nei_type_j = self.sel[ti]
@@ -714,7 +715,7 @@
                 res_ij = res_ij * (1.0 / float(nei_type_i) / float(nei_type_j))
                 result += res_ij
         # xyz_scatter /= (self.nnei * self.nnei)
-        result = result.view(-1, nloc, self.filter_neuron[-1])
+        result = result.view(nf, nloc, self.filter_neuron[-1])
         return (
             result.to(dtype=env.GLOBAL_PT_FLOAT_PRECISION),
             None,

deepmd/pt/model/model/spin_model.py

@@ -121,7 +121,12 @@
         out_real, out_mag = torch.split(out_tensor, [nloc, nloc], dim=1)
         if add_mag:
             out_real = out_real + out_mag
-        out_mag = (out_mag.view([nframes, nloc, -1]) * atomic_mask).view(out_mag.shape)
+        shape2 = 1
+        for ss in out_real.shape[2:]:
+            shape2 *= ss
+        out_mag = (out_mag.view([nframes, nloc, shape2]) * atomic_mask).view(
+            out_mag.shape
+        )
         return out_real, out_mag, atomic_mask > 0.0
 
     def process_spin_output_lower(
@@ -162,8 +167,11 @@
         )
         if add_mag:
             extended_out_real = extended_out_real + extended_out_mag
+        shape2 = 1
+        for ss in extended_out_tensor.shape[2:]:
+            shape2 *= ss
         extended_out_mag = (
-            extended_out_mag.view([nframes, nall, -1]) * atomic_mask
+            extended_out_mag.view([nframes, nall, shape2]) * atomic_mask
         ).view(extended_out_mag.shape)
         return extended_out_real, extended_out_mag, atomic_mask > 0.0
 

deepmd/pt/model/task/dipole.py

@@ -191,7 +191,7 @@
         # (nframes * nloc, 1, m1)
         out = out.view(-1, 1, self.embedding_width)
         # (nframes * nloc, m1, 3)
-        gr = gr.view(nframes * nloc, -1, 3)
+        gr = gr.view(nframes * nloc, self.embedding_width, 3)
         # (nframes, nloc, 3)
         out = torch.bmm(out, gr).squeeze(-2).view(nframes, nloc, 3)
         return {self.var_name: out.to(env.GLOBAL_PT_FLOAT_PRECISION)}

deepmd/pt/model/task/polarizability.py

@@ -242,7 +242,7 @@
             self.var_name
         ]
         out = out * (self.scale.to(atype.device))[atype]
-        gr = gr.view(nframes * nloc, -1, 3)  # (nframes * nloc, m1, 3)
+        gr = gr.view(nframes * nloc, self.embedding_width, 3)  # (nframes * nloc, m1, 3)
 
         if self.fit_diag:
             out = out.reshape(-1, self.embedding_width)

deepmd/pt/utils/nlist.py

@@ -99,12 +99,15 @@
     nall = coord.shape[1] // 3
     # fill virtual atoms with large coords so they are not neighbors of any
     # real atom.
-    xmax = torch.max(coord) + 2.0 * rcut
+    if coord.numel() > 0:
+        xmax = torch.max(coord) + 2.0 * rcut
+    else:
+        xmax = torch.zeros(1, dtype=coord.dtype, device=coord.device) + 2.0 * rcut
     # nf x nall
     is_vir = atype < 0
-    coord1 = torch.where(is_vir[:, :, None], xmax, coord.view(-1, nall, 3)).view(
-        -1, nall * 3
-    )
+    coord1 = torch.where(
+        is_vir[:, :, None], xmax, coord.view(batch_size, nall, 3)
+    ).view(batch_size, nall * 3)
     if isinstance(sel, int):
         sel = [sel]
     nsel = sum(sel)

source/tests/universal/common/cases/model/utils.py

@@ -221,6 +221,109 @@ def test_forward(self):
                 continue
             np.testing.assert_allclose(rr1, rr2, atol=aprec)
 
+    def test_zero_forward(self):
+        test_spin = getattr(self, "test_spin", False)
+        nf = 1
+        natoms = 0
+        aprec = (
+            0
+            if self.aprec_dict.get("test_forward", None) is None
+            else self.aprec_dict["test_forward"]
+        )
+        rng = np.random.default_rng(GLOBAL_SEED)
+        coord = np.zeros((nf, 0, 3), dtype=np.float64)
+        atype = np.zeros([nf, 0], dtype=int)
+        spin = np.zeros([nf, 0], dtype=np.float64)
+        cell = 6.0 * np.eye(3, dtype=np.float64).reshape([nf, 9])
+        coord_ext, atype_ext, mapping, nlist = extend_input_and_build_neighbor_list(
+            coord,
+            atype,
+            self.expected_rcut + 1.0 if test_spin else self.expected_rcut,
+            self.expected_sel,
+            mixed_types=self.module.mixed_types(),
+            box=cell,
+        )
+        spin_ext = np.take_along_axis(
+            spin.reshape(nf, -1, 3),
+            np.repeat(np.expand_dims(mapping, axis=-1), 3, axis=-1),
+            axis=1,
+        )
+        aparam = None
+        fparam = None
+        if self.module.get_dim_aparam() > 0:
+            aparam = rng.random([nf, natoms, self.module.get_dim_aparam()])
+        if self.module.get_dim_fparam() > 0:
+            fparam = rng.random([nf, self.module.get_dim_fparam()])
+        ret = []
+        ret_lower = []
+        for module in self.modules_to_test:
+            module = self.forward_wrapper(module)
+            input_dict = {
+                "coord": coord,
+                "atype": atype,
+                "box": cell,
+                "aparam": aparam,
+                "fparam": fparam,
+            }
+            if test_spin:
+                input_dict["spin"] = spin
+            ret.append(module(**input_dict))
+
+            input_dict_lower = {
+                "extended_coord": coord_ext,
+                "extended_atype": atype_ext,
+                "nlist": nlist,
+                "mapping": mapping,
+                "aparam": aparam,
+                "fparam": fparam,
+            }
+            if test_spin:
+                input_dict_lower["extended_spin"] = spin_ext
+
+            ret_lower.append(module.forward_lower(**input_dict_lower))
+        for kk in ret[0]:
+            subret = []
+            for rr in ret:
+                if rr is not None:
+                    subret.append(rr[kk])
+            if len(subret):
+                for ii, rr in enumerate(subret[1:]):
+                    if subret[0] is None:
+                        assert rr is None
+                    else:
+                        np.testing.assert_allclose(
+                            subret[0], rr, err_msg=f"compare {kk} between 0 and {ii}"
+                        )
+        for kk in ret_lower[0]:
+            subret = []
+            for rr in ret_lower:
+                if rr is not None:
+                    subret.append(rr[kk])
+            if len(subret):
+                for ii, rr in enumerate(subret[1:]):
+                    if subret[0] is None:
+                        assert rr is None
+                    else:
+                        np.testing.assert_allclose(
+                            subret[0], rr, err_msg=f"compare {kk} between 0 and {ii}"
+                        )
+        same_keys = set(ret[0].keys()) & set(ret_lower[0].keys())
+        self.assertTrue(same_keys)
+        for key in same_keys:
+            for rr in ret:
+                if rr[key] is not None:
+                    rr1 = rr[key]
+                    break
+            else:
+                continue
+            for rr in ret_lower:
+                if rr[key] is not None:
+                    rr2 = rr[key]
+                    break
+            else:
+                continue
+            np.testing.assert_allclose(rr1, rr2, atol=aprec)
+
     @unittest.skipIf(TEST_DEVICE != "cpu", "Only test on CPU.")
     def test_permutation(self):
         """Test permutation."""