pt: support dpa2 model parallel inference (deepmodeling#3657)

Signed-off-by: Jinzhe Zeng <[email protected]> Signed-off-by: Lysithea <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Jinzhe Zeng <[email protected]> Co-authored-by: Han Wang <[email protected]>
mtaillefumier · Sep 18, 2024 · 926b050 · 926b050
1 parent 11bd81b
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Showing 26 changed files with 1,553 additions and 35 deletions.
diff --git a/.github/workflows/test_cuda.yml b/.github/workflows/test_cuda.yml
@@ -76,7 +76,7 @@ jobs:
     - run: |
         export LD_LIBRARY_PATH=$GITHUB_WORKSPACE/dp_test/lib:$GITHUB_WORKSPACE/libtorch/lib:$CUDA_PATH/lib64:$LD_LIBRARY_PATH
         export PATH=$GITHUB_WORKSPACE/dp_test/bin:$PATH
-        python -m pytest source/lmp/tests
+        python -m pytest -s source/lmp/tests || (cat log.lammps && exit 1)
         python -m pytest source/ipi/tests
       env:
         OMP_NUM_THREADS: 1

diff --git a/deepmd/pt/entrypoints/main.py b/deepmd/pt/entrypoints/main.py
@@ -286,10 +286,14 @@ def train(FLAGS):
 
 def freeze(FLAGS):
     model = torch.jit.script(inference.Tester(FLAGS.model, head=FLAGS.head).model)
+    if '"type": "dpa2"' in model.model_def_script:
+        extra_files = {"type": "dpa2"}
+    else:
+        extra_files = {"type": "else"}
     torch.jit.save(
         model,
         FLAGS.output,
-        {},
+        extra_files,
     )
 
 

diff --git a/deepmd/pt/model/atomic_model/base_atomic_model.py b/deepmd/pt/model/atomic_model/base_atomic_model.py
@@ -186,6 +186,7 @@ def forward_common_atomic(
         mapping: Optional[torch.Tensor] = None,
         fparam: Optional[torch.Tensor] = None,
         aparam: Optional[torch.Tensor] = None,
+        comm_dict: Optional[Dict[str, torch.Tensor]] = None,
     ) -> Dict[str, torch.Tensor]:
         """Common interface for atomic inference.
 
@@ -207,6 +208,8 @@ def forward_common_atomic(
             frame parameters, shape: nf x dim_fparam
         aparam
             atomic parameter, shape: nf x nloc x dim_aparam
+        comm_dict
+            The data needed for communication for parallel inference.
 
         Returns
         -------
@@ -234,6 +237,7 @@ def forward_common_atomic(
             mapping=mapping,
             fparam=fparam,
             aparam=aparam,
+            comm_dict=comm_dict,
         )
         ret_dict = self.apply_out_stat(ret_dict, atype)
 

diff --git a/deepmd/pt/model/atomic_model/dp_atomic_model.py b/deepmd/pt/model/atomic_model/dp_atomic_model.py
@@ -130,6 +130,7 @@ def forward_atomic(
         mapping: Optional[torch.Tensor] = None,
         fparam: Optional[torch.Tensor] = None,
         aparam: Optional[torch.Tensor] = None,
+        comm_dict: Optional[Dict[str, torch.Tensor]] = None,
     ) -> Dict[str, torch.Tensor]:
         """Return atomic prediction.
 
@@ -163,6 +164,7 @@ def forward_atomic(
             extended_atype,
             nlist,
             mapping=mapping,
+            comm_dict=comm_dict,
         )
         assert descriptor is not None
         # energy, force

diff --git a/deepmd/pt/model/atomic_model/linear_atomic_model.py b/deepmd/pt/model/atomic_model/linear_atomic_model.py
@@ -144,6 +144,7 @@ def forward_atomic(
         mapping: Optional[torch.Tensor] = None,
         fparam: Optional[torch.Tensor] = None,
         aparam: Optional[torch.Tensor] = None,
+        comm_dict: Optional[Dict[str, torch.Tensor]] = None,
     ) -> Dict[str, torch.Tensor]:
         """Return atomic prediction.
 

diff --git a/deepmd/pt/model/atomic_model/pairtab_atomic_model.py b/deepmd/pt/model/atomic_model/pairtab_atomic_model.py
@@ -228,6 +228,7 @@ def forward_atomic(
         fparam: Optional[torch.Tensor] = None,
         aparam: Optional[torch.Tensor] = None,
         do_atomic_virial: bool = False,
+        comm_dict: Optional[Dict[str, torch.Tensor]] = None,
     ) -> Dict[str, torch.Tensor]:
         nframes, nloc, nnei = nlist.shape
         extended_coord = extended_coord.view(nframes, -1, 3)

diff --git a/deepmd/pt/model/descriptor/dpa1.py b/deepmd/pt/model/descriptor/dpa1.py
@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 from typing import (
     Callable,
+    Dict,
     List,
     Optional,
     Tuple,
@@ -453,6 +454,7 @@ def forward(
         extended_atype: torch.Tensor,
         nlist: torch.Tensor,
         mapping: Optional[torch.Tensor] = None,
+        comm_dict: Optional[Dict[str, torch.Tensor]] = None,
     ):
         """Compute the descriptor.
 
@@ -466,6 +468,8 @@ def forward(
             The neighbor list. shape: nf x nloc x nnei
         mapping
             The index mapping, not required by this descriptor.
+        comm_dict
+            The data needed for communication for parallel inference.
 
         Returns
         -------

diff --git a/deepmd/pt/model/descriptor/dpa2.py b/deepmd/pt/model/descriptor/dpa2.py
@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 from typing import (
     Callable,
+    Dict,
     List,
     Optional,
     Tuple,
@@ -395,6 +396,7 @@ def forward(
         extended_atype: torch.Tensor,
         nlist: torch.Tensor,
         mapping: Optional[torch.Tensor] = None,
+        comm_dict: Optional[Dict[str, torch.Tensor]] = None,
     ):
         """Compute the descriptor.
 
@@ -408,6 +410,8 @@ def forward(
             The neighbor list. shape: nf x nloc x nnei
         mapping
             The index mapping, mapps extended region index to local region.
+        comm_dict
+            The data needed for communication for parallel inference.
 
         Returns
         -------
@@ -450,11 +454,13 @@ def forward(
         # linear to change shape
         g1 = self.g1_shape_tranform(g1)
         # mapping g1
-        assert mapping is not None
-        mapping_ext = (
-            mapping.view(nframes, nall).unsqueeze(-1).expand(-1, -1, g1.shape[-1])
-        )
-        g1_ext = torch.gather(g1, 1, mapping_ext)
+        if comm_dict is None:
+            assert mapping is not None
+            mapping_ext = (
+                mapping.view(nframes, nall).unsqueeze(-1).expand(-1, -1, g1.shape[-1])
+            )
+            g1_ext = torch.gather(g1, 1, mapping_ext)
+            g1 = g1_ext
         # repformer
         g1, g2, h2, rot_mat, sw = self.repformers(
             nlist_dict[
@@ -464,8 +470,9 @@ def forward(
             ],
             extended_coord,
             extended_atype,
-            g1_ext,
+            g1,
             mapping,
+            comm_dict,
         )
         if self.concat_output_tebd:
             g1 = torch.cat([g1, g1_inp], dim=-1)

diff --git a/deepmd/pt/model/descriptor/hybrid.py b/deepmd/pt/model/descriptor/hybrid.py
@@ -168,6 +168,7 @@ def forward(
         atype_ext: torch.Tensor,
         nlist: torch.Tensor,
         mapping: Optional[torch.Tensor] = None,
+        comm_dict: Optional[Dict[str, torch.Tensor]] = None,
     ):
         """Compute the descriptor.
 
@@ -181,6 +182,8 @@ def forward(
             The neighbor list. shape: nf x nloc x nnei
         mapping
             The index mapping, not required by this descriptor.
+        comm_dict
+            The data needed for communication for parallel inference.
 
         Returns
         -------
@@ -443,6 +446,7 @@ def forward(
         extended_atype: torch.Tensor,
         extended_atype_embd: Optional[torch.Tensor] = None,
         mapping: Optional[torch.Tensor] = None,
+        comm_dict: Optional[Dict[str, torch.Tensor]] = None,
     ):
         """Calculate decoded embedding for each atom.
 

diff --git a/deepmd/pt/model/descriptor/repformers.py b/deepmd/pt/model/descriptor/repformers.py
@@ -54,6 +54,27 @@ def torch_linear(*args, **kwargs):
 mylinear = simple_linear
 
 
+if not hasattr(torch.ops.deepmd, "border_op"):
+
+    def border_op(
+        argument0,
+        argument1,
+        argument2,
+        argument3,
+        argument4,
+        argument5,
+        argument6,
+        argument7,
+        argument8,
+    ) -> torch.Tensor:
+        raise NotImplementedError(
+            "border_op is not available since customized PyTorch OP library is not built when freezing the model."
+        )
+
+    # Note: this hack cannot actually save a model that can be runned using LAMMPS.
+    torch.ops.deepmd.border_op = border_op
+
+
 @DescriptorBlock.register("se_repformer")
 @DescriptorBlock.register("se_uni")
 class DescrptBlockRepformers(DescriptorBlock):
@@ -234,9 +255,11 @@ def forward(
         extended_atype: torch.Tensor,
         extended_atype_embd: Optional[torch.Tensor] = None,
         mapping: Optional[torch.Tensor] = None,
+        comm_dict: Optional[Dict[str, torch.Tensor]] = None,
     ):
-        assert mapping is not None
-        assert extended_atype_embd is not None
+        if comm_dict is None:
+            assert mapping is not None
+            assert extended_atype_embd is not None
         nframes, nloc, nnei = nlist.shape
         nall = extended_coord.view(nframes, -1).shape[1] // 3
         atype = extended_atype[:, :nloc]
@@ -257,9 +280,13 @@ def forward(
         sw = sw.masked_fill(~nlist_mask, 0.0)
 
         # [nframes, nloc, tebd_dim]
-        atype_embd = extended_atype_embd[:, :nloc, :]
-        assert list(atype_embd.shape) == [nframes, nloc, self.g1_dim]
-
+        if comm_dict is None:
+            assert isinstance(extended_atype_embd, torch.Tensor)  # for jit
+            atype_embd = extended_atype_embd[:, :nloc, :]
+            assert list(atype_embd.shape) == [nframes, nloc, self.g1_dim]
+        else:
+            atype_embd = extended_atype_embd
+        assert isinstance(atype_embd, torch.Tensor)  # for jit
         g1 = self.act(atype_embd)
         # nb x nloc x nnei x 1,  nb x nloc x nnei x 3
         if not self.direct_dist:
@@ -275,11 +302,40 @@ def forward(
         # if the a neighbor is real or not is indicated by nlist_mask
         nlist[nlist == -1] = 0
         # nb x nall x ng1
-        mapping = mapping.view(nframes, nall).unsqueeze(-1).expand(-1, -1, self.g1_dim)
+        if comm_dict is None:
+            assert mapping is not None
+            mapping = (
+                mapping.view(nframes, nall).unsqueeze(-1).expand(-1, -1, self.g1_dim)
+            )
         for idx, ll in enumerate(self.layers):
             # g1:     nb x nloc x ng1
             # g1_ext: nb x nall x ng1
-            g1_ext = torch.gather(g1, 1, mapping)
+            if comm_dict is None:
+                assert mapping is not None
+                g1_ext = torch.gather(g1, 1, mapping)
+            else:
+                n_padding = nall - nloc
+                g1 = torch.nn.functional.pad(
+                    g1.squeeze(0), (0, 0, 0, n_padding), value=0.0
+                )
+                assert "send_list" in comm_dict
+                assert "send_proc" in comm_dict
+                assert "recv_proc" in comm_dict
+                assert "send_num" in comm_dict
+                assert "recv_num" in comm_dict
+                assert "communicator" in comm_dict
+                ret = torch.ops.deepmd.border_op(
+                    comm_dict["send_list"],
+                    comm_dict["send_proc"],
+                    comm_dict["recv_proc"],
+                    comm_dict["send_num"],
+                    comm_dict["recv_num"],
+                    g1,
+                    comm_dict["communicator"],
+                    torch.tensor(nloc),
+                    torch.tensor(nall - nloc),
+                )
+                g1_ext = ret[0].unsqueeze(0)
             g1, g2, h2 = ll.forward(
                 g1_ext,
                 g2,

diff --git a/deepmd/pt/model/descriptor/se_a.py b/deepmd/pt/model/descriptor/se_a.py
@@ -191,6 +191,7 @@ def forward(
         atype_ext: torch.Tensor,
         nlist: torch.Tensor,
         mapping: Optional[torch.Tensor] = None,
+        comm_dict: Optional[Dict[str, torch.Tensor]] = None,
     ):
         """Compute the descriptor.
 
@@ -204,6 +205,8 @@ def forward(
             The neighbor list. shape: nf x nloc x nnei
         mapping
             The index mapping, not required by this descriptor.
+        comm_dict
+            The data needed for communication for parallel inference.
 
         Returns
         -------

diff --git a/deepmd/pt/model/model/ener_model.py b/deepmd/pt/model/model/ener_model.py
@@ -83,6 +83,7 @@ def forward_lower(
         fparam: Optional[torch.Tensor] = None,
         aparam: Optional[torch.Tensor] = None,
         do_atomic_virial: bool = False,
+        comm_dict: Optional[Dict[str, torch.Tensor]] = None,
     ):
         model_ret = self.forward_common_lower(
             extended_coord,
@@ -92,6 +93,7 @@ def forward_lower(
             fparam=fparam,
             aparam=aparam,
             do_atomic_virial=do_atomic_virial,
+            comm_dict=comm_dict,
         )
         if self.get_fitting_net() is not None:
             model_predict = {}

diff --git a/deepmd/pt/model/model/make_model.py b/deepmd/pt/model/model/make_model.py
@@ -211,6 +211,7 @@ def forward_common_lower(
             fparam: Optional[torch.Tensor] = None,
             aparam: Optional[torch.Tensor] = None,
             do_atomic_virial: bool = False,
+            comm_dict: Optional[Dict[str, torch.Tensor]] = None,
         ):
             """Return model prediction. Lower interface that takes
             extended atomic coordinates and types, nlist, and mapping
@@ -233,6 +234,8 @@ def forward_common_lower(
                 atomic parameter. nf x nloc x nda
             do_atomic_virial
                 whether calculate atomic virial.
+            comm_dict
+                The data needed for communication for parallel inference.
 
             Returns
             -------
@@ -254,6 +257,7 @@ def forward_common_lower(
                 mapping=mapping,
                 fparam=fp,
                 aparam=ap,
+                comm_dict=comm_dict,
             )
             model_predict = fit_output_to_model_output(
                 atomic_ret,

diff --git a/source/api_c/include/c_api.h b/source/api_c/include/c_api.h
@@ -24,12 +24,49 @@ extern DP_Nlist* DP_NewNlist(int inum_,
                              int* ilist_,
                              int* numneigh_,
                              int** firstneigh_);
+/*
+ * @brief Create a new neighbor list with communication capabilities.
+ * @details This function extends DP_NewNlist by adding support for parallel
+ * communication, allowing the neighbor list to be used in distributed
+ * environments.
+ * @param[in] inum_ Number of core region atoms.
+ * @param[in] ilist_ Array storing the core region atom's index.
+ * @param[in] numneigh_ Array storing the core region atom's neighbor atom
+ * number.
+ * @param[in] firstneigh_ Array storing the core region atom's neighbor index.
+ * @param[in] nswap Number of swaps to be performed in communication.
+ * @param[in] sendnum Array storing the number of atoms to send for each swap.
+ * @param[in] recvnum Array storing the number of atoms to receive for each
+ * swap.
+ * @param[in] firstrecv Index of the first receive operation for each swap.
+ * @param[in] sendlist List of atoms to be sent for each swap.
+ * @param[in] sendproc Array of processor IDs to send atoms to for each swap.
+ * @param[in] recvproc Array of processor IDs from which atoms are received for
+ * each swap.
+ * @param[in] world Pointer to the MPI communicator or similar communication
+ * world used for the operation.
+ * @returns A pointer to the initialized neighbor list with communication
+ * capabilities.
+ */
+extern DP_Nlist* DP_NewNlist_comm(int inum_,
+                                  int* ilist_,
+                                  int* numneigh_,
+                                  int** firstneigh_,
+                                  int nswap,
+                                  int* sendnum,
+                                  int* recvnum,
+                                  int* firstrecv,
+                                  int** sendlist,
+                                  int* sendproc,
+                                  int* recvproc,
+                                  void* world);
 
 /**
  * @brief Delete a neighbor list.
  *
  * @param nl Neighbor list to delete.
- */
+ *
+ **/
 extern void DP_DeleteNlist(DP_Nlist* nl);
 
 /**