[Bugfix] [CI] Fix Tensorizer LoRA test (#20760)

Signed-off-by: Sanger Steel <sangersteel@gmail.com>

tests/lora/test_llama_tp.py

@@ -4,8 +4,6 @@ import subprocess
 import sys
 from typing import Union
 
-import pytest
-
 import vllm
 from vllm import LLM
 from vllm.lora.request import LoRARequest
@@ -151,8 +149,6 @@ def test_llama_lora_tp4_fully_sharded_loras(sql_lora_files):
     generate_and_test(llm, sql_lora_files)
 
 
-@pytest.mark.skip(reason=("Skipping this test as tensorizer is not "
-                          "working with LoRA as of #19619"))
 @multi_gpu_test(num_gpus=2)
 @create_new_process_for_each_test()
 def test_tp2_serialize_and_deserialize_lora(tmp_path, sql_lora_files,
@@ -189,7 +185,6 @@ def test_tp2_serialize_and_deserialize_lora(tmp_path, sql_lora_files,
 
     model_uri = tmp_path / "vllm" / model_ref / suffix / model_name
     tensorizer_config = TensorizerConfig(tensorizer_uri=str(model_uri))
-    tensorizer_config.lora_dir = tensorizer_config.tensorizer_dir
 
     loaded_vllm_model = LLM(model=model_ref,
                             load_format="tensorizer",
@@ -200,16 +195,16 @@ def test_tp2_serialize_and_deserialize_lora(tmp_path, sql_lora_files,
                             tensor_parallel_size=2,
                             max_loras=2)
 
-    tensorizer_config_dict = tensorizer_config.to_serializable()
+    tc_as_dict = tensorizer_config.to_serializable()
 
     print("lora adapter created")
     assert do_sample(loaded_vllm_model,
                      sql_lora_files,
-                     tensorizer_config_dict=tensorizer_config_dict,
+                     tensorizer_config_dict=tc_as_dict,
                      lora_id=0) == EXPECTED_NO_LORA_OUTPUT
 
     print("lora 1")
     assert do_sample(loaded_vllm_model,
                      sql_lora_files,
-                     tensorizer_config_dict=tensorizer_config_dict,
+                     tensorizer_config_dict=tc_as_dict,
                      lora_id=1) == EXPECTED_LORA_OUTPUT

vllm/lora/peft_helper.py

@@ -102,7 +102,7 @@ class PEFTHelper:
             tensorizer_config = TensorizerConfig(**tensorizer_config_dict)
             tensorizer_args = tensorizer_config._construct_tensorizer_args()
             from tensorizer.stream_io import open_stream
-            lora_config_path = os.path.join(tensorizer_config.lora_dir,
+            lora_config_path = os.path.join(tensorizer_config.tensorizer_dir,
                                             "adapter_config.json")
             with open_stream(lora_config_path,
                              mode="rb",
@@ -110,7 +110,7 @@ class PEFTHelper:
                 config = json.load(f)
 
             logger.info("Successfully deserialized LoRA config from %s",
-                        tensorizer_config.lora_dir)
+                        tensorizer_config.tensorizer_dir)
 
         else:
             with open(lora_config_path) as f:

vllm/model_executor/model_loader/tensorizer.py

@@ -222,17 +222,17 @@ class TensorizerConfig(MutableMapping):
         self._is_sharded = isinstance(self.tensorizer_uri, str) \
             and re.search(r'%0\dd', self.tensorizer_uri) is not None
 
+        if self.tensorizer_dir and self.lora_dir:
+            raise ValueError(
+                "Only one of tensorizer_dir or lora_dir may be specified. "
+                "Use lora_dir exclusively when serializing LoRA adapters, "
+                "and tensorizer_dir or tensorizer_uri otherwise.")
         if self.tensorizer_dir and self.tensorizer_uri:
             logger.warning_once(
                 "Provided both tensorizer_dir and tensorizer_uri. "
                 "Inferring tensorizer_dir from tensorizer_uri as the "
                 "latter takes precedence.")
             self.tensorizer_dir = os.path.dirname(self.tensorizer_uri)
-        if self.tensorizer_dir and self.lora_dir:
-            raise ValueError(
-                "Only one of tensorizer_dir or lora_dir may be specified. "
-                "Use lora_dir exclusively when serializing LoRA adapters, "
-                "and tensorizer_dir or tensorizer_uri otherwise.")
         if not self.tensorizer_uri:
             if self.lora_dir:
                 self.tensorizer_uri = f"{self.lora_dir}/adapter_model.tensors"
@@ -695,7 +695,7 @@ def tensorize_lora_adapter(lora_path: str,
     needed to load a LoRA adapter are a safetensors-format file called
     adapter_model.safetensors and a json config file called adapter_config.json.
 
-    Serializes the files in the tensorizer_config.lora_dir
+    Serializes the files in the tensorizer_config.tensorizer_dir
     """
     import safetensors
 
@@ -725,13 +725,13 @@ def tensorize_lora_adapter(lora_path: str,
 
     tensorizer_args = tensorizer_config._construct_tensorizer_args()
 
-    with open_stream(f"{tensorizer_config.lora_dir}/adapter_config.json",
+    with open_stream(f"{tensorizer_config.tensorizer_dir}/adapter_config.json",
                      mode="wb+",
                      **tensorizer_args.stream_kwargs) as f:
 
         f.write(json.dumps(config).encode("utf-8"))
 
-    lora_uri = (f"{tensorizer_config.lora_dir}"
+    lora_uri = (f"{tensorizer_config.tensorizer_dir}"
                 f"/adapter_model.tensors")
     with open_stream(lora_uri, mode="wb+",
                      **tensorizer_args.stream_kwargs) as f:
@@ -740,4 +740,4 @@ def tensorize_lora_adapter(lora_path: str,
         serializer.close()
 
     logger.info("Successfully serialized LoRA files to %s",
-                str(tensorizer_config.lora_dir))
+                str(tensorizer_config.tensorizer_dir))