add: support for falcon-10B architecture.

server/text_generation_server/models/custom_modeling/flash_rw_modeling.py

@@ -481,6 +481,44 @@ class FlashRWLayer(nn.Module):
 
             return mlp_output, residual
 
+class FlashRWLayerNorm(nn.Module):
+    def __init__(self, config, prefix, weights):
+        super().__init__()
+        self.num_ln = config.num_ln_in_parallel_attn
+
+        if self.num_ln == 1:
+            self.input_ln = FastLayerNorm.load(
+                prefix=f"{prefix}.input_layernorm",
+                weights=weights,
+                eps=config.layer_norm_epsilon,
+            )
+        elif self.num_ln == 2:
+            self.ln_attn = FastLayerNorm.load(
+                prefix=f"{prefix}.ln_attn",
+                weights=weights,
+                eps=config.layer_norm_epsilon,
+            )
+            self.ln_mlp = FastLayerNorm.load(
+                prefix=f"{prefix}.ln_mlp",
+                weights=weights,
+                eps=config.layer_norm_epsilon,
+            )
+        else:
+            raise ValueError("Number of layer norms can either be 1 or 2.")
+
+    def forward(
+        self,
+        hidden_states,
+        residual,
+    ):
+        if self.num_ln == 1:
+            ln_hidden_states, residual = self.input_ln(hidden_states, residual)
+            return ln_hidden_states, ln_hidden_states, residual
+        elif self.num_ln == 2:
+            ln_attn, residual = self.ln_attn(hidden_states, residual)
+            ln_mlp, _ = self.ln_mlp(residual)
+            return ln_attn, ln_mlp, residual
+
 
 class FlashRWLayerNorm(nn.Module):
     def __init__(self, config, prefix, weights):
@@ -564,7 +602,7 @@ class FlashRWLargeLayer(nn.Module):
     def __init__(self, layer_id, config, weights):
         super().__init__()
         prefix = f"transformer.h.{layer_id}"
-
+        
         self.ln_layer = FlashRWLayerNorm(config, prefix, weights)
 
         self.self_attention = FlashRWLargeAttention(