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

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Signed-off-by: Sanger Steel <sangersteel@gmail.com>
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Sanger Steel 2025-07-10 15:07:06 -04:00 committed by GitHub
parent c66e38ea4c
commit 5e53c89a74
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3 changed files with 14 additions and 19 deletions
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11
tests/lora/test_llama_tp.py
View File

@ -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

4
vllm/lora/peft_helper.py
View File

@ -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:

18
vllm/model_executor/model_loader/tensorizer.py
View File

@ -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))