init

tests/lora/utils.py

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+from typing import List, Optional
+
+import torch
+
+from vllm.lora.lora import LoRALayerWeights, PackedLoRALayerWeights
+
+
+class DummyLoRAManager:
+
+    def __init__(self):
+        super().__init__()
+        self._loras = {}
+
+    def set_module_lora(self, module_name: str, lora: LoRALayerWeights):
+        self._loras[module_name] = lora
+
+    def get_module_lora(self, module_name: str) -> Optional[LoRALayerWeights]:
+        return self._loras.get(module_name, None)
+
+    def init_random_lora(self,
+                         module_name: str,
+                         weight: torch.Tensor,
+                         rank: int = 8,
+                         generate_embeddings_tensor: int = 0):
+        lora = LoRALayerWeights(
+            module_name,
+            rank=rank,
+            lora_alpha=1,
+            lora_a=torch.rand([weight.shape[1], rank],
+                              dtype=weight.dtype,
+                              device="cuda"),
+            lora_b=torch.rand([rank, weight.shape[0]],
+                              dtype=weight.dtype,
+                              device="cuda"),
+        )
+        if generate_embeddings_tensor:
+            lora.embeddings_tensor = torch.rand(5,
+                                                generate_embeddings_tensor,
+                                                dtype=weight.dtype,
+                                                device="cuda")
+        self.set_module_lora(module_name, lora)
+
+        return lora
+
+    def init_lora(self,
+                  module_name: str,
+                  input_dim: int,
+                  output_dim: int,
+                  rank=8,
+                  noop=False,
+                  embeddings_tensor=None):
+        lora = LoRALayerWeights(
+            module_name,
+            rank=rank,
+            lora_alpha=1,
+            lora_a=torch.rand([input_dim, rank], device="cuda"),
+            lora_b=torch.rand([rank, output_dim], device="cuda"),
+            embeddings_tensor=embeddings_tensor,
+        )
+        self.set_module_lora(module_name, lora)
+        return lora
+
+    def reset_lora(self):
+        self._loras = {}
+
+    def init_packed_lora(
+        self,
+        module_name: str,
+        input_dim: int,
+        output_dims: List[int],
+        noop_lora_index: List[int] = None,
+        rank=8,
+    ):
+        base_loras = []
+        noop_lora_index = set(noop_lora_index or [])
+
+        for i, out_dim in enumerate(output_dims):
+            base_lora = self.init_lora(
+                module_name + "_000_" + str(i),
+                input_dim,
+                out_dim,
+                rank=rank,
+                noop=i in noop_lora_index,
+            )
+            base_loras.append(base_lora)
+        packed_lora = PackedLoRALayerWeights.pack(base_loras)
+        self.set_module_lora(module_name, packed_lora)
+        return packed_lora