feat(pt): Implement type embedding compression for se_e3_tebd (#5059)

The unit test for this descriptor will be committed through another pr.

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## Summary by CodeRabbit

* **Breaking Changes**
* Model compression configuration updated to include new required
parameters

* **Improvements**
* Precomputed type-embedding data improves model compression efficiency
and inference performance

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settings.</sub>

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Author: OutisLi

deepmd/pt/model/descriptor/se_t_tebd.py

@@ -553,6 +553,8 @@ def enable_compression(
         assert not self.se_ttebd.resnet_dt, (
             "Model compression error: descriptor resnet_dt must be false!"
         )
+        if self.tebd_input_mode != "strip":
+            raise RuntimeError("Cannot compress model when tebd_input_mode != 'strip'")
         for tt in self.se_ttebd.exclude_types:
             if (tt[0] not in range(self.se_ttebd.ntypes)) or (
                 tt[1] not in range(self.se_ttebd.ntypes)
@@ -573,9 +575,6 @@ def enable_compression(
                 "Empty embedding-nets are not supported in model compression!"
             )
 
-        if self.tebd_input_mode != "strip":
-            raise RuntimeError("Cannot compress model when tebd_input_mode == 'concat'")
-
         data = self.serialize()
         self.table = DPTabulate(
             self,
@@ -597,7 +596,11 @@ def enable_compression(
         )
 
         self.se_ttebd.enable_compression(
-            self.table.data, self.table_config, self.lower, self.upper
+            self.type_embedding,
+            self.table.data,
+            self.table_config,
+            self.lower,
+            self.upper,
         )
         self.compress = True
 
@@ -694,12 +697,17 @@ def __init__(
         self.stats = None
         # compression related variables
         self.compress = False
+        # For geometric compression
         self.compress_info = nn.ParameterList(
             [nn.Parameter(torch.zeros(0, dtype=self.prec, device="cpu"))]
         )
         self.compress_data = nn.ParameterList(
             [nn.Parameter(torch.zeros(0, dtype=self.prec, device=env.DEVICE))]
         )
+        # For type embedding compression
+        self.register_buffer(
+            "type_embd_data", torch.zeros(0, dtype=self.prec, device=env.DEVICE)
+        )
 
     def get_rcut(self) -> float:
         """Returns the cut-off radius."""
@@ -986,31 +994,24 @@ def forward(
             nei_type_j = nei_type.unsqueeze(1).expand([-1, nnei, -1])
             idx_i = nei_type_i * ntypes_with_padding
             idx_j = nei_type_j
-            # (nf x nl x nt_i x nt_j) x ng
-            idx = (
-                (idx_i + idx_j)
-                .view(-1, 1)
-                .expand(-1, ng)
-                .type(torch.long)
-                .to(torch.long)
-            )
-            # ntypes * (ntypes) * nt
-            type_embedding_i = torch.tile(
-                type_embedding.view(ntypes_with_padding, 1, nt),
-                [1, ntypes_with_padding, 1],
-            )
-            # (ntypes) * ntypes * nt
-            type_embedding_j = torch.tile(
-                type_embedding.view(1, ntypes_with_padding, nt),
-                [ntypes_with_padding, 1, 1],
-            )
-            # (ntypes * ntypes) * (nt+nt)
-            two_side_type_embedding = torch.cat(
-                [type_embedding_i, type_embedding_j], -1
-            ).reshape(-1, nt * 2)
-            tt_full = self.filter_layers_strip.networks[0](two_side_type_embedding)
+            idx = (idx_i + idx_j).reshape(-1).to(torch.long)
+            if self.compress:
+                tt_full = self.type_embd_data
+            else:
+                type_embedding_i = torch.tile(
+                    type_embedding.view(ntypes_with_padding, 1, nt),
+                    [1, ntypes_with_padding, 1],
+                )
+                type_embedding_j = torch.tile(
+                    type_embedding.view(1, ntypes_with_padding, nt),
+                    [ntypes_with_padding, 1, 1],
+                )
+                two_side_type_embedding = torch.cat(
+                    [type_embedding_i, type_embedding_j], -1
+                ).reshape(-1, nt * 2)
+                tt_full = self.filter_layers_strip.networks[0](two_side_type_embedding)
             # (nfnl x nt_i x nt_j) x ng
-            gg_t = torch.gather(tt_full, dim=0, index=idx)
+            gg_t = tt_full[idx]
             # (nfnl x nt_i x nt_j) x ng
             gg_t = gg_t.reshape(nfnl, nnei, nnei, ng)
             if self.smooth:
@@ -1042,6 +1043,7 @@ def forward(
 
     def enable_compression(
         self,
+        type_embedding_net: TypeEmbedNet,
         table_data: dict,
         table_config: dict,
         lower: dict,
@@ -1051,6 +1053,8 @@ def enable_compression(
 
         Parameters
         ----------
+        type_embedding_net : TypeEmbedNet
+            The type embedding network
         table_data : dict
             The tabulated data from DPTabulate
         table_config : dict
@@ -1060,6 +1064,13 @@ def enable_compression(
         upper : dict
             Upper bounds for compression
         """
+        if self.tebd_input_mode != "strip":
+            raise RuntimeError("Type embedding compression only works in strip mode")
+        if self.filter_layers_strip is None:
+            raise RuntimeError(
+                "filter_layers_strip must exist for type embedding compression"
+            )
+
         # Compress the main geometric embedding network (self.filter_layers)
         net_key = "filter_net"
         self.compress_info[0] = torch.as_tensor(
@@ -1078,6 +1089,22 @@ def enable_compression(
             device=env.DEVICE, dtype=self.prec
         )
 
+        # Compress the type embedding network (self.filter_layers_strip)
+        with torch.no_grad():
+            full_embd = type_embedding_net.get_full_embedding(env.DEVICE)
+            nt, t_dim = full_embd.shape
+            type_embedding_i = full_embd.view(nt, 1, t_dim).expand(nt, nt, t_dim)
+            type_embedding_j = full_embd.view(1, nt, t_dim).expand(nt, nt, t_dim)
+            two_side_type_embedding = torch.cat(
+                [type_embedding_i, type_embedding_j], dim=-1
+            ).reshape(-1, t_dim * 2)
+            embd_tensor = self.filter_layers_strip.networks[0](
+                two_side_type_embedding
+            ).detach()
+            if hasattr(self, "type_embd_data"):
+                del self.type_embd_data
+            self.register_buffer("type_embd_data", embd_tensor)
+
         self.compress = True
 
     def has_message_passing(self) -> bool: