Add MMMU benchmark results (#4491)

Co-authored-by: Ravi Theja Desetty <ravitheja@Ravis-MacBook-Pro.local>

benchmark/mmmu/README.md

@@ -21,10 +21,3 @@ It's recommended to reduce the memory usage by appending something ike `--mem-fr
 ```
 python benchmark/mmmu/bench_hf.py --model-path Qwen/Qwen2-VL-7B-Instruct
 ```
-
-Some popular model results:
-
-1. Qwen/Qwen2-VL-2B-Instruct: 0.241
-2. Qwen/Qwen2-VL-7B-Instruct: 0.255
-3. Qwen/Qwen2.5-VL-3B-Instruct: 0.245
-4. Qwen/Qwen2.5-VL-7B-Instruct: 0.242

scripts/ci_install_dependency.sh

@@ -27,3 +27,7 @@ pip install transformers==4.51.0 sentence_transformers accelerate peft pandas da
 
 # For compling xgrammar kernels
 pip install cuda-python nvidia-cuda-nvrtc-cu12
+
+# For lmms_evals evaluating MMMU
+git clone --branch v0.3.3 --depth 1 https://github.com/EvolvingLMMs-Lab/lmms-eval.git
+pip install -e lmms-eval/

test/srt/models/test_vlm_models.py

+import argparse
+import glob
+import json
+import os
+import random
+import subprocess
+import sys
+import unittest
+from types import SimpleNamespace
+
+from sglang.srt.utils import kill_process_tree
+from sglang.test.test_utils import (
+    DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
+    DEFAULT_URL_FOR_TEST,
+    CustomTestCase,
+    is_in_ci,
+    popen_launch_server,
+)
+
+# VLM models for testing
+MODELS = [
+    SimpleNamespace(
+        model="google/gemma-3-27b-it", chat_template="gemma-it", mmmu_accuracy=0.45
+    ),
+    SimpleNamespace(
+        model="Qwen/Qwen2.5-VL-3B-Instruct",
+        chat_template="qwen2-vl",
+        mmmu_accuracy=0.4,
+    ),
+    SimpleNamespace(
+        model="openbmb/MiniCPM-V-2_6", chat_template="minicpmv", mmmu_accuracy=0.4
+    ),
+]
+
+
+class TestVLMModels(CustomTestCase):
+    parsed_args = None  # Class variable to store args
+
+    @classmethod
+    def setUpClass(cls):
+        # Removed argument parsing from here
+        cls.base_url = DEFAULT_URL_FOR_TEST
+        cls.api_key = "sk-123456"
+        cls.time_out = DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH
+
+        # Set OpenAI API key and base URL environment variables. Needed for lmm-evals to work.
+        os.environ["OPENAI_API_KEY"] = cls.api_key
+        os.environ["OPENAI_API_BASE"] = f"{cls.base_url}/v1"
+
+    def run_mmmu_eval(
+        self,
+        model_version: str,
+        chat_template: str,
+        output_path: str,
+        *,
+        env: dict | None = None,
+    ):
+        """
+        Evaluate a VLM on the MMMU validation set with lmms‑eval.
+        Only `model_version` (checkpoint) and `chat_template` vary;
+        We are focusing only on the validation set due to resource constraints.
+        """
+        # -------- fixed settings --------
+        model = "openai_compatible"
+        tp = 1
+        tasks = "mmmu_val"
+        batch_size = 1
+        log_suffix = "openai_compatible"
+        os.makedirs(output_path, exist_ok=True)
+
+        # -------- compose --model_args --------
+        model_args = (
+            f'model_version="{model_version}",'
+            f'chat_template="{chat_template}",'
+            f"tp={tp}"
+        )
+
+        # -------- build command list --------
+        cmd = [
+            "python3",
+            "-m",
+            "lmms_eval",
+            "--model",
+            model,
+            "--model_args",
+            model_args,
+            "--tasks",
+            tasks,
+            "--batch_size",
+            str(batch_size),
+            "--log_samples",
+            "--log_samples_suffix",
+            log_suffix,
+            "--output_path",
+            str(output_path),
+        ]
+
+        subprocess.run(
+            cmd,
+            check=True,
+            timeout=3600,
+        )
+
+    def test_vlm_mmmu_benchmark(self):
+        """Test VLM models against MMMU benchmark."""
+        models_to_test = MODELS
+
+        if is_in_ci():
+            models_to_test = [random.choice(MODELS)]
+
+        for model in models_to_test:
+            print(f"\nTesting model: {model.model}")
+
+            process = None
+            mmmu_accuracy = 0  # Initialize to handle potential exceptions
+
+            try:
+                # Launch server for testing
+                process = popen_launch_server(
+                    model.model,
+                    base_url=self.base_url,
+                    timeout=self.time_out,
+                    api_key=self.api_key,
+                    other_args=[
+                        "--chat-template",
+                        model.chat_template,
+                        "--trust-remote-code",
+                        "--mem-fraction-static",
+                        str(self.parsed_args.mem_fraction_static),  # Use class variable
+                    ],
+                )
+
+                # Run evaluation
+                self.run_mmmu_eval(model.model, model.chat_template, "./logs")
+
+                # Get the result file
+                result_file_path = glob.glob("./logs/*.json")[0]
+
+                with open(result_file_path, "r") as f:
+                    result = json.load(f)
+                    print(f"Result \n: {result}")
+                # Process the result
+                mmmu_accuracy = result["results"]["mmmu_val"]["mmmu_acc,none"]
+                print(f"Model {model.model} achieved accuracy: {mmmu_accuracy:.4f}")
+
+                # Assert performance meets expected threshold
+                self.assertGreaterEqual(
+                    mmmu_accuracy,
+                    model.mmmu_accuracy,
+                    f"Model {model.model} accuracy ({mmmu_accuracy:.4f}) below expected threshold ({model.mmmu_accuracy:.4f})",
+                )
+
+            except Exception as e:
+                print(f"Error testing {model.model}: {e}")
+                self.fail(f"Test failed for {model.model}: {e}")
+
+            finally:
+                # Ensure process cleanup happens regardless of success/failure
+                if process is not None and process.poll() is None:
+                    print(f"Cleaning up process {process.pid}")
+                    try:
+                        kill_process_tree(process.pid)
+                    except Exception as e:
+                        print(f"Error killing process: {e}")
+
+
+if __name__ == "__main__":
+    # Define and parse arguments here, before unittest.main
+    parser = argparse.ArgumentParser(description="Test VLM models")
+    parser.add_argument(
+        "--mem-fraction-static",
+        type=float,
+        help="Static memory fraction for the model",
+        default=0.6,
+    )
+
+    # Parse args intended for unittest
+    args = parser.parse_args()
+
+    # Store the parsed args object on the class
+    TestVLMModels.parsed_args = args
+
+    # Pass args to unittest
+    unittest.main(argv=[sys.argv[0]])

test/srt/run_suite.py

@@ -24,6 +24,7 @@ suites = {
         TestFile("models/test_reward_models.py", 83),
         TestFile("models/test_gme_qwen_models.py", 45),
         TestFile("models/test_clip_models.py", 100),
+        TestFile("models/test_vlm_models.py", 100),
         TestFile("test_abort.py", 51),
         TestFile("test_block_int8.py", 22),
         TestFile("test_chunked_prefill.py", 336),