hotfix: mixtral

server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py

@@ -464,9 +464,9 @@ class DenseMoE(nn.Module):
 
 
 class MixtralLayer(nn.Module):
-    def __init__(self, layer_id, config, weights):
+    def __init__(self, prefix, layer_id, config, weights):
         super().__init__()
-        prefix = f"model.layers.{layer_id}"
+        prefix = f"{prefix}.layers.{layer_id}"
 
         self.self_attn = MixtralAttention(
             prefix=f"{prefix}.self_attn", config=config, weights=weights
@@ -525,16 +525,20 @@ class MixtralLayer(nn.Module):
 
 
 class MixtralModel(torch.nn.Module):
-    def __init__(self, config, weights):
+    def __init__(self, prefix, config, weights):
         super().__init__()
 
         self.embed_tokens = TensorParallelEmbedding(
-            prefix="model.embed_tokens", weights=weights
+            prefix=(
+                "model.embed_tokens" if not prefix else f"{prefix}.model.embed_tokens"
+            ),
+            weights=weights,
         )
 
         self.layers = nn.ModuleList(
             [
                 MixtralLayer(
+                    "model" if not prefix else f"{prefix}.model",
                     layer_id,
                     config,
                     weights,
@@ -543,7 +547,9 @@ class MixtralModel(torch.nn.Module):
             ]
         )
         self.norm = FastRMSNorm.load(
-            prefix="model.norm", weights=weights, eps=config.rms_norm_eps
+            prefix="model.norm" if not prefix else f"{prefix}.model.norm",
+            weights=weights,
+            eps=config.rms_norm_eps,
         )
 
         self.head_size = self.layers[0].self_attn.head_size
@@ -593,13 +599,13 @@ class MixtralModel(torch.nn.Module):
 
 
 class FlashMixtralForCausalLM(torch.nn.Module):
-    def __init__(self, config, weights):
+    def __init__(self, prefix, config, weights):
         super().__init__()
 
-        self.model = MixtralModel(config, weights)
+        self.model = MixtralModel(prefix, config, weights)
         self.lm_head = SpeculativeHead.load(
             config,
-            prefix="lm_head",
+            prefix="lm_head" if not prefix else f"{prefix}.lm_head",
             weights=weights,
         )
         self.max_past = config.sliding_window