[Fix, LoRA] fix LoRA with updates in main (#1545)

examples/runtime/lora.py

@@ -1,5 +1,5 @@
 # launch server
-# python -m sglang.launch_server --model mistralai/Mistral-7B-Instruct-v0.3 --lora-paths /home/ying/test_lora /home/ying/test_lora_1 /home/ying/test_lora_2 lora3=/home/ying/test_lora_3 lora4=/home/ying/test_lora_4 --disable-radix --disable-cuda-graph --max-loras-per-batch 4
+# python -m sglang.launch_server --model mistralai/Mistral-7B-Instruct-v0.3 --lora-paths /home/ying/test_lora lora1=/home/ying/test_lora_1 lora2=/home/ying/test_lora_2 --disable-radix --disable-cuda-graph --max-loras-per-batch 4
 
 # send requests
 # lora_path[i] specifies the LoRA used for text[i], so make sure they have the same length
@@ -22,12 +22,12 @@ json_data = {
     "sampling_params": {"max_new_tokens": 32},
     "lora_path": [
         "/home/ying/test_lora",
-        "/home/ying/test_lora_1",
-        "/home/ying/test_lora_2",
-        "lora3",
-        "lora4",
-        "/home/ying/test_lora",
-        "/home/ying/test_lora_1",
+        "lora1",
+        "lora2",
+        "lora1",
+        "lora2",
+        None,
+        None,
     ],
 }
 response = requests.post(

python/sglang/srt/lora/lora.py

@@ -28,18 +28,18 @@ from typing import Any, Dict, List, Optional, Tuple
 import safetensors.torch
 import torch
 from torch import nn
-from vllm.model_executor.layers.linear import (
-    ColumnParallelLinear,
-    MergedColumnParallelLinear,
-    QKVParallelLinear,
-    RowParallelLinear,
-)
 from vllm.model_executor.layers.vocab_parallel_embedding import (
     ParallelLMHead,
     VocabParallelEmbedding,
 )
 from vllm.model_executor.model_loader.loader import DefaultModelLoader
 
+from sglang.srt.layers.linear import (
+    ColumnParallelLinear,
+    MergedColumnParallelLinear,
+    QKVParallelLinear,
+    RowParallelLinear,
+)
 from sglang.srt.model_executor.forward_batch_info import ForwardBatch, ForwardMode
 
 

python/sglang/srt/server_args.py

@@ -594,6 +594,16 @@ class ServerArgs:
             "Please use sglang<=0.3.2 or wait for later updates."
         )
 
+        if isinstance(self.lora_paths, list):
+            lora_paths = self.lora_paths
+            self.lora_paths = {}
+            for lora_path in lora_paths:
+                if "=" in lora_path:
+                    name, path = lora_path.split("=", 1)
+                    self.lora_paths[name] = path
+                else:
+                    self.lora_paths[lora_path] = lora_path
+
 
 def prepare_server_args(argv: List[str]) -> ServerArgs:
     """

test/srt/models/test_lora.py

@@ -97,9 +97,7 @@ class TestLoRA(unittest.TestCase):
             )
 
         with HFRunner(
-            base_path,
-            torch_dtype=torch_dtype,
-            is_generation=True,
+            base_path, torch_dtype=torch_dtype, model_type="generation"
         ) as hf_runner:
             hf_outputs = hf_runner.forward(
                 prompts, max_new_tokens=max_new_tokens, lora_paths=batch_lora_paths
@@ -108,7 +106,7 @@ class TestLoRA(unittest.TestCase):
         with HFRunner(
             base_path,
             torch_dtype=torch_dtype,
-            is_generation=True,
+            model_type="generation",
         ) as hf_runner:
             hf_no_lora_outputs = hf_runner.forward(
                 prompts, max_new_tokens=max_new_tokens
@@ -118,7 +116,7 @@ class TestLoRA(unittest.TestCase):
             base_path,
             tp_size=tp_size,
             torch_dtype=torch_dtype,
-            is_generation=True,
+            model_type="generation",
         ) as srt_runner:
             srt_no_lora_outputs = srt_runner.forward(
                 prompts, max_new_tokens=max_new_tokens
@@ -198,7 +196,7 @@ class TestLoRA(unittest.TestCase):
             base_path,
             tp_size=tp_size,
             torch_dtype=torch_dtype,
-            is_generation=True,
+            model_type="generation",
             lora_paths=all_lora_paths,
             max_loras_per_batch=3,
             disable_cuda_graph=True,
@@ -211,7 +209,7 @@ class TestLoRA(unittest.TestCase):
         with HFRunner(
             base_path,
             torch_dtype=torch_dtype,
-            is_generation=True,
+            model_type="generation",
             output_str_only=True,
         ) as hf_runner:
             hf_outputs = hf_runner.forward(
@@ -237,7 +235,7 @@ class TestLoRA(unittest.TestCase):
             base_path,
             tp_size=tp_size,
             torch_dtype=torch_dtype,
-            is_generation=True,
+            model_type="generation",
         ) as srt_runner:
             srt_no_lora_outputs = srt_runner.forward(
                 prompts, max_new_tokens=max_new_tokens
@@ -247,7 +245,7 @@ class TestLoRA(unittest.TestCase):
             base_path,
             tp_size=tp_size,
             torch_dtype=torch_dtype,
-            is_generation=True,
+            model_type="generation",
             lora_paths=all_lora_paths,
         ) as srt_runner:
             srt_outputs = srt_runner.forward(

test/srt/run_suite.py

@@ -7,7 +7,7 @@ suites = {
     "minimal": [
         "models/test_embedding_models.py",
         "models/test_generation_models.py",
-        # "models/test_lora.py",
+        "models/test_lora.py",
         "models/test_reward_models.py",
         "sampling/penaltylib",
         "test_chunked_prefill.py",