[Fix] Fix bugs and refactor codes in lora for better scalability. (#3652)

Co-authored-by: ShenAo1111 <1377693092@qq.com>
Co-authored-by: zhaochenyang20 <zhaochen20@outlook.com>

test/srt/models/lora/utils.py

@@ -0,0 +1,49 @@
+# Copyright 2023-2024 SGLang Team
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+import dataclasses
+from typing import List
+
+import torch
+
+
+@dataclasses.dataclass
+class LoRAAdaptor:
+    name: str
+    prefill_tolerance: float = None
+    decode_tolerance: float = None
+    rouge_l_tolerance: float = None
+
+
+@dataclasses.dataclass
+class LoRAModelCase:
+    base: str
+    adaptors: List[LoRAAdaptor]
+    tp_size: int = 1
+    prefill_tolerance: float = 5e-2
+    decode_tolerance: float = 5e-2
+    rouge_l_tolerance: float = 1.0
+    max_loras_per_batch: int = 1
+    skip_long_prompt: bool = False
+
+    def __post_init__(self):
+        if len(self.adaptors) > self.max_loras_per_batch:
+            raise ValueError(
+                f"For base '{self.base}', number of adaptors ({len(self.adaptors)}) "
+                f"must be <= max_loras_per_batch ({self.max_loras_per_batch})"
+            )
+
+
+TORCH_DTYPES = [torch.float16]
+BACKENDS = ["triton"]