Handling bloom prefix. (#1090)

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Fixes # (issue)


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server/text_generation_server/models/bloom.py

@@ -74,7 +74,7 @@ class BLOOMSharded(CausalLM):
         torch.distributed.barrier(group=self.process_group)
         filenames = weight_files(model_id, revision=revision, extension=".safetensors")
         weights = Weights(
-            filenames, device=device, dtype=dtype, process_group=self.process_group
+            filenames, device=device, dtype=dtype, process_group=self.process_group, prefix="transformer",
         )
         if config.quantize == "gptq":
             weights._set_gptq_params(model_id)

server/text_generation_server/utils/weights.py

@@ -16,6 +16,7 @@ class Weights:
         dtype,
         process_group,
         aliases: Optional[Dict[str, List[str]]] = None,
+        prefix: Optional[str] = None
     ):
         routing = {}
         for filename in filenames:
@@ -33,6 +34,7 @@ class Weights:
         self.device = device
         self.dtype = dtype
         self.process_group = process_group
+        self.prefix = prefix
         self._handles = {}
 
     def _get_handle(self, filename):
@@ -43,15 +45,22 @@ class Weights:
         return self._handles[filename]
 
     def get_filename(self, tensor_name: str) -> (str, str):
-        filename = self.routing.get(tensor_name, None)
+
-        if filename is None:
+        names = [tensor_name]
-            aliases = self.aliases.get(tensor_name, [])
+        if self.prefix is not None:
+            prefixed = f"{self.prefix}.{tensor_name}"
+            names.append(prefixed)
+        for name in names:
+            filename = self.routing.get(name, None)
+            if filename is not None:
+                return str(filename), name
+
+            aliases = self.aliases.get(name, [])
             for alias in aliases:
                 filename = self.routing.get(alias, None)
                 if filename is not None:
                     return str(filename), alias
-            raise RuntimeError(f"weight {tensor_name} does not exist")
+        raise RuntimeError(f"weight {tensor_name} does not exist")
-        return str(filename), tensor_name
 
     def _get_slice(self, tensor_name: str):
         filename, tensor_name = self.get_filename(tensor_name)