Hotfixing after refactor.

server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py

@@ -355,7 +355,7 @@ class Block(nn.Module):
         self.ln_2 = FastLayerNorm.load(
             prefix=f"{prefix}.ln_2", weights=weights, eps=config.layer_norm_epsilon
         )
-        self.attn = FlashMQAttention(
+        self.self_attn = FlashMQAttention(
             prefix=f"{prefix}.attn",
             config=config,
             weights=weights,
@@ -378,7 +378,7 @@ class Block(nn.Module):
         max_s,
     ):
         hidden_states, residual = self.ln_1(hidden_states, residual)
-        hidden_states = self.attn(
+        hidden_states = self.self_attn(
             hidden_states,
             cu_seqlen_prefill,
             kv_cache,
@@ -412,7 +412,7 @@ class FlashSantacoderModel(nn.Module):
             reduce=False,
         )
 
-        self.h = nn.ModuleList(
+        self.layers = nn.ModuleList(
             [
                 Block(
                     layer_id,
@@ -426,8 +426,8 @@ class FlashSantacoderModel(nn.Module):
             prefix="transformer.ln_f", weights=weights, eps=config.layer_norm_epsilon
         )
 
-        self.head_size = self.h[0].attn.head_size
-        self.num_heads = self.h[0].attn.num_heads
+        self.head_size = self.layers[0].self_attn.head_size
+        self.num_heads = self.layers[0].self_attn.num_heads
 
     def forward(
         self,
@@ -446,7 +446,7 @@ class FlashSantacoderModel(nn.Module):
             torch.distributed.all_reduce(hidden_states, group=self.process_group)
 
         residual = None
-        for i, layer in enumerate(self.h):
+        for i, layer in enumerate(self.layers):
             hidden_states, residual = layer(
                 hidden_states,
                 residual,
@@ -467,7 +467,7 @@ class FlashSantacoderForCausalLM(nn.Module):
     def __init__(self, prefix, config, weights):
         super().__init__()
         config.transpose = config.architectures[0].startswith("GPT2")
-        self.transformer = FlashSantacoderModel(config, weights)
+        self.model = FlashSantacoderModel(config, weights)
         self.lm_head = SpeculativeHead.load(
             config, prefix="transformer.wte", weights=weights
         )
@@ -486,7 +486,7 @@ class FlashSantacoderForCausalLM(nn.Module):
         lm_head_indices: Optional[torch.Tensor] = None,
         adapter_data: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-        hidden_states = self.transformer(
+        hidden_states = self.model(
             input_ids,
             position_ids,
             cu_seqlen_prefill,

server/text_generation_server/models/model.py

@@ -60,7 +60,7 @@ class Model(ABC):
         self.layer_to_adapter_weights: Dict[str, LayerAdapterWeights] = defaultdict(
             LayerAdapterWeights
         )
-        self.target_to_layer = self.adapter_target_to_layer()
+        self.target_to_layer = None
         self.loaded_adapters = set()
         self.static_adapter_id = adapter_id
 
@@ -187,6 +187,8 @@ class Model(ABC):
         into model. Otherwise, the adapter weights are applied during the forward
         pass and stored separately from the base model parameters.
         """
+        if self.target_to_layer is None:
+            self.target_to_layer = self.adapter_target_to_layer()
         if adapter_index in self.loaded_adapters:
             # Adapter already loaded
             return