feat: unify edge_degree + layer radial MLPs into single batched GEMM

src/fairchem/core/models/uma/escn_md.py

@@ -737,6 +737,8 @@ def forward(self, data_dict: AtomicData) -> dict[str, torch.Tensor]:
         # Initialize node embeddings
         ###############################################################
 
+        sys_node_embedding = csd_mixed_emb[data_dict["batch"]]
+
         # Init per node representations using an atomic number based embedding
         with record_function("atom embedding"):
             x_message = torch.zeros(
@@ -746,10 +748,9 @@ def forward(self, data_dict: AtomicData) -> dict[str, torch.Tensor]:
                 device=data_dict["pos"].device,
                 dtype=data_dict["pos"].dtype,
             )
-            x_message[:, 0, :] = self.sphere_embedding(data_dict["atomic_numbers"])
-
-        sys_node_embedding = csd_mixed_emb[data_dict["batch"]]
-        x_message[:, 0, :] = x_message[:, 0, :] + sys_node_embedding
+            x_message[:, 0, :] = (
+                self.sphere_embedding(data_dict["atomic_numbers"]) + sys_node_embedding
+            )
 
         ###
         # Hook to allow MOLE
@@ -782,9 +783,19 @@ def forward(self, data_dict: AtomicData) -> dict[str, torch.Tensor]:
             # Pre-fuse envelope into wigner_inv
             wigner_inv_envelope = wigner_inv * edge_envelope
 
+            # Get all radial embeddings: edge_degree + layer radials
+            # General backend: returns [x_edge] * (1 + N) - rad_func computed internally
+            # Fast backends: returns precomputed [edge_radial, layer_0_radial, ...]
+            all_radial_embeddings = self.backend.get_unified_radial_emb(x_edge, self)
+            edge_degree_input = all_radial_embeddings[0]
+            x_edge_per_layer = all_radial_embeddings[1:]
+
+            # Apply edge_degree_embedding
+            # General backend: rad_func computed internally
+            # Fast backends: rad_func=None, uses precomputed radial from edge_degree_input
             x_message = self.edge_degree_embedding(
                 x_message,
-                x_edge,
+                edge_degree_input,
                 graph_dict["edge_index"],
                 wigner_inv_envelope,
                 data_dict["gp_node_offset"],
@@ -794,12 +805,6 @@ def forward(self, data_dict: AtomicData) -> dict[str, torch.Tensor]:
         # Update spherical node embeddings
         ###############################################################
 
-        # Get edge embeddings for each layer
-        # General backend: raw x_edge (rad_func computed inside SO2_Convolution)
-        # Fast backends: precomputed radials
-        with record_function("layer_radial_emb"):
-            x_edge_per_layer = self.backend.get_layer_radial_emb(x_edge, self)
-
         for i in range(self.num_layers):
             with record_function(f"message passing {i}"):
                 x_message = self.blocks[i](

src/fairchem/core/models/uma/nn/embedding.py

@@ -82,7 +82,8 @@ def forward_chunk(
         wigner_inv_envelope,
         node_offset=0,
     ):
-        radial = self.rad_func(x_edge)
+        # rad_func is None when radials are precomputed by UnifiedRadialMLP
+        radial = self.rad_func(x_edge) if self.rad_func is not None else x_edge
 
         return self.backend.edge_degree_scatter(
             x,
@@ -150,13 +151,6 @@ def __init__(
         self.embedding_target = embedding_target
         assert embedding_size % 2 == 0, f"{embedding_size=} must be even"
 
-        if self.embedding_target == "charge":
-            # 100 is a conservative upper bound
-            self.target_dict = {str(x): x + 100 for x in range(-100, 101)}
-        elif self.embedding_target == "spin":
-            # 100 is a conservative upper bound
-            self.target_dict = {str(x): x for x in range(101)}
-
         if self.embedding_type == "pos_emb":
             # dividing by 2 because x_proj multiplies by 2
             if not grad:
@@ -173,7 +167,16 @@ def __init__(
                 for param in self.lin_emb.parameters():
                     param.requires_grad = False
         elif self.embedding_type == "rand_emb":
-            self.rand_emb = nn.Embedding(len(self.target_dict), embedding_size)
+            # Embedding table sizes and index offset for tensor-based computation.
+            # Charge: x in [-100, 100] -> idx = x + 100 in [0, 200], so 201 embeddings
+            # Spin:   x in [0, 100]    -> idx = x       in [0, 100], so 101 embeddings
+            if self.embedding_target == "charge":
+                self.idx_offset = 100
+                num_embeddings = 201
+            else:  # spin
+                self.idx_offset = 0
+                num_embeddings = 101
+            self.rand_emb = nn.Embedding(num_embeddings, embedding_size)
             if not grad:
                 for param in self.rand_emb.parameters():
                     param.requires_grad = False
@@ -199,13 +202,12 @@ def forward(self, x):
                 x[x == 0] = -100
             return self.lin_emb(x.unsqueeze(-1).float())
         elif self.embedding_type == "rand_emb":
-            return self.rand_emb(
-                torch.tensor(
-                    [self.target_dict[str(i)] for i in x.tolist()],
-                    device=x.device,
-                    dtype=torch.long,
-                )
-            )
+            # Convert charge/spin values to embedding indices via tensor arithmetic.
+            # This avoids the graph break caused by x.tolist() and dict lookup.
+            # For charge: x in [-100, 100] -> idx = x + 100 in [0, 200]
+            # For spin:   x in [0, 100]    -> idx = x       in [0, 100]
+            idx = (x + self.idx_offset).long()
+            return self.rand_emb(idx)
         raise ValueError(f"embedding type {self.embedding_type} not implemented")
 
 

src/fairchem/core/models/uma/nn/execution_backends.py

@@ -93,26 +93,29 @@ def prepare_model_for_inference(model: torch.nn.Module) -> None:
         """
 
     @staticmethod
-    def get_layer_radial_emb(
+    def get_unified_radial_emb(
         x_edge: torch.Tensor,
         model: torch.nn.Module,
     ) -> list[torch.Tensor]:
         """
-        Get edge embeddings for each layer.
+        Get all radial embeddings: edge_degree + layer radials.
 
-        Default implementation returns the same raw x_edge for all layers.
-        SO2_Convolution will compute rad_func(x_edge) internally.
+        Default implementation returns [x_edge] * (1 + N).
+        x_edge is passed to edge_degree_embedding and layers, which
+        compute rad_func(x_edge) internally.
 
-        Override in fast backends to precompute radials.
+        Override in fast backends to precompute all radials in one GEMM.
 
         Args:
             x_edge: Edge embeddings [E, edge_features]
             model: The backbone model
 
         Returns:
-            List of edge embeddings, one per layer
+            List [edge_degree_input, layer_0_input, ..., layer_N-1_input]
+            For general backend: all are x_edge (raw edge embeddings).
+            For fast backends: all are precomputed radial outputs.
         """
-        return [x_edge] * len(model.blocks)
+        return [x_edge] * (1 + len(model.blocks))
 
     @staticmethod
     def prepare_wigner(
@@ -247,7 +250,7 @@ def edge_degree_scatter(
 
         # Slice wigner to m=0 columns and rotate:
         # [E, L, m0] @ [E, m0, C] -> [E, L, C]
-        wigner_inv_m0 = wigner_inv[:, :, :m_0_num_coefficients]
+        wigner_inv_m0 = wigner_inv[:, :, :m_0_num_coefficients] / rescale_factor
         x_edge_embedding = torch.bmm(wigner_inv_m0, radial)
 
         # Type cast if needed
@@ -257,7 +260,7 @@ def edge_degree_scatter(
         return x.index_add(
             0,
             edge_index[1] - node_offset,
-            x_edge_embedding / rescale_factor,
+            x_edge_embedding,
         )
 
 
@@ -292,7 +295,8 @@ def prepare_model_for_inference(model: torch.nn.Module) -> None:
         Replaces so2_conv_1 with SO2_Conv1_WithRadialBlock and
         so2_conv_2 with SO2_Conv2_InternalBlock in each block's
         Edgewise module. Then creates a UnifiedRadialMLP from all
-        radial functions for efficient batched computation.
+        radial functions (edge_degree + layer rad_funcs) for efficient
+        batched computation.
         """
         from fairchem.core.models.uma.nn.so2_layers import (
             convert_so2_conv1,
@@ -303,24 +307,32 @@ def prepare_model_for_inference(model: torch.nn.Module) -> None:
             block.edge_wise.so2_conv_1 = convert_so2_conv1(block.edge_wise.so2_conv_1)
             block.edge_wise.so2_conv_2 = convert_so2_conv2(block.edge_wise.so2_conv_2)
 
-        # Create unified radial MLP for batched computation
-        rad_funcs = [block.edge_wise.so2_conv_1.rad_func for block in model.blocks]
-        model._unified_radial_mlp = UnifiedRadialMLP(rad_funcs)
+        # Create unified radial MLP: edge_degree + layer rad_funcs in one GEMM
+        edge_degree_rad_func = model.edge_degree_embedding.rad_func
+        layer_rad_funcs = [
+            block.edge_wise.so2_conv_1.rad_func for block in model.blocks
+        ]
+        model._unified_radial_mlp = UnifiedRadialMLP(
+            edge_degree_rad_func, layer_rad_funcs
+        )
+
+        # Null out rad_func so forward_chunk knows radials are precomputed
+        model.edge_degree_embedding.rad_func = None
 
     @staticmethod
-    def get_layer_radial_emb(
+    def get_unified_radial_emb(
         x_edge: torch.Tensor,
         model: torch.nn.Module,
     ) -> list[torch.Tensor]:
         """
-        Compute radial embeddings for all layers using batched UnifiedRadialMLP.
+        Compute all radial embeddings using batched UnifiedRadialMLP.
 
         Args:
             x_edge: Edge embeddings [E, edge_features]
             model: The backbone model with _unified_radial_mlp
 
         Returns:
-            List of radial embeddings, one per layer [E, radial_features]
+            List [edge_degree_radial, layer_0_radial, ..., layer_N-1_radial]
         """
         return model._unified_radial_mlp(x_edge)
 
@@ -410,15 +422,15 @@ def edge_degree_scatter(
         radial = radial_output.reshape(-1, m_0_num_coefficients, sphere_channels)
 
         # Select m=0 columns from L-ordered wigner_inv
-        wigner_inv_m0 = wigner_inv[:, :, _M0_COL_INDICES_L_ORDER]
+        wigner_inv_m0 = wigner_inv[:, :, _M0_COL_INDICES_L_ORDER] / rescale_factor
         x_edge_embedding = torch.bmm(wigner_inv_m0, radial)
 
         x_edge_embedding = x_edge_embedding.to(x.dtype)
 
         return x.index_add(
             0,
             edge_index[1] - node_offset,
-            x_edge_embedding / rescale_factor,
+            x_edge_embedding,
         )