Support LoRA in MMMU benchmark script. (#7218)

benchmark/mmmu/bench_sglang.py

@@ -15,7 +15,7 @@ import sys
 import time
 import traceback
 from dataclasses import dataclass, field
-from typing import Any, List, Tuple
+from typing import Any, List, Optional, Tuple
 
 import aiohttp
 import openai
@@ -73,7 +73,7 @@ def _get_prefix_suffix(prompt: str) -> Tuple[str, str]:
 
 
 async def process_sample(
-    client: Any, sample: dict, sampling_params: dict
+    client: Any, sample: dict, sampling_params: dict, lora_path: Optional[str] = None
 ) -> Tuple[dict, str]:
     """Send a single sample to the LLM and return (sample, response)."""
     prompt = sample["final_input_prompt"]
@@ -81,6 +81,7 @@ async def process_sample(
     image = sample["image"]
     assert image is not None
     image_path = sample["image_path"]
+    extra_body = None if lora_path is None else {"lora_path": lora_path}
     response = await client.chat.completions.create(
         model="default",
         messages=[
@@ -96,16 +97,21 @@ async def process_sample(
         temperature=0,
         max_completion_tokens=sampling_params["max_new_tokens"],
         max_tokens=sampling_params["max_new_tokens"],
+        extra_body=extra_body,
     )
     return sample, response.choices[0].message.content
 
 
 async def process_sample_with_semaphore(
-    semaphore: asyncio.Semaphore, client: Any, sample: dict, sampling_params: dict
+    semaphore: asyncio.Semaphore,
+    client: Any,
+    sample: dict,
+    sampling_params: dict,
+    lora_path: Optional[str] = None,
 ) -> Tuple[dict, str]:
     """Wrap process_sample with a semaphore for concurrency control."""
     async with semaphore:
-        return await process_sample(client, sample, sampling_params)
+        return await process_sample(client, sample, sampling_params, lora_path)
 
 
 async def eval_mmmu(args) -> None:
@@ -113,6 +119,7 @@ async def eval_mmmu(args) -> None:
     eval_args = EvalArgs.from_cli_args(args)
     sampling_params = get_sampling_params(eval_args)
     samples = prepare_samples(eval_args)
+    lora_path = eval_args.lora_path
     answer_dict = {}
     out_samples = {}
     client = openai.AsyncOpenAI(
@@ -133,7 +140,9 @@ async def eval_mmmu(args) -> None:
         samples = samples[: args.profile_number]
 
     tasks = [
-        process_sample_with_semaphore(semaphore, client, sample, sampling_params)
+        process_sample_with_semaphore(
+            semaphore, client, sample, sampling_params, lora_path
+        )
         for sample in samples
     ]