273 lines
8.9 KiB
Python
273 lines
8.9 KiB
Python
import argparse
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import glob
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import json
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import os
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import random
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import sys
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import unittest
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from types import SimpleNamespace
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from sglang.srt.utils import kill_process_tree
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from sglang.test.ci.ci_register import register_cuda_ci
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from sglang.test.kits.mmmu_vlm_kit import _run_lmms_eval_with_retry
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from sglang.test.test_utils import (
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DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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DEFAULT_URL_FOR_TEST,
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CustomTestCase,
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is_in_ci,
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popen_launch_server,
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)
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register_cuda_ci(est_time=500, suite="nightly-4-gpu", nightly=True)
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MODELS = [
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SimpleNamespace(model="Qwen/Qwen2.5-VL-72B-Instruct", mmmu_accuracy=0.55),
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SimpleNamespace(model="Qwen/Qwen3-VL-32B-Instruct", mmmu_accuracy=0.55),
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SimpleNamespace(model="OpenGVLab/InternVL2_5-8B", mmmu_accuracy=0.52),
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SimpleNamespace(model="zai-org/GLM-4.1V-9B-Thinking", mmmu_accuracy=0.68),
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]
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# Set default mem_fraction_static to 0.8
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DEFAULT_MEM_FRACTION_STATIC = 0.8
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class TestVLMEncoderDP(CustomTestCase):
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parsed_args = None # Class variable to store args
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@classmethod
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def setUpClass(cls):
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# Removed argument parsing from here
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cls.base_url = DEFAULT_URL_FOR_TEST
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cls.api_key = "sk-123456"
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cls.time_out = DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH
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if cls.parsed_args is None:
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cls.parsed_args = SimpleNamespace(
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mem_fraction_static=DEFAULT_MEM_FRACTION_STATIC
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)
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# Set OpenAI API key and base URL environment variables. Needed for lmm-evals to work.
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os.environ["OPENAI_API_KEY"] = cls.api_key
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os.environ["OPENAI_API_BASE"] = f"{cls.base_url}/v1"
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def run_mmmu_eval(
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self,
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model_version: str,
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output_path: str,
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*,
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env: dict | None = None,
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):
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"""
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Evaluate a VLM on the MMMU validation set with lmms‑eval.
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Only `model_version` (checkpoint) and `chat_template` vary;
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We are focusing only on the validation set due to resource constraints.
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"""
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# -------- fixed settings --------
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model = "openai_compatible"
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tp = 1
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tasks = "mmmu_val"
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batch_size = 32
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log_suffix = "openai_compatible"
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os.makedirs(output_path, exist_ok=True)
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# -------- compose --model_args --------
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model_args = f'model_version="{model_version}",' f"tp={tp}"
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# -------- build command list --------
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cmd = [
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"python3",
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"-m",
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"lmms_eval",
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"--model",
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model,
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"--model_args",
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model_args,
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"--tasks",
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tasks,
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"--batch_size",
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str(batch_size),
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"--log_samples",
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"--log_samples_suffix",
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log_suffix,
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"--output_path",
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str(output_path),
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]
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_run_lmms_eval_with_retry(cmd, timeout=3600)
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def _run_vlm_mmmu_test(
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self,
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model,
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output_path,
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test_name="",
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custom_env=None,
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log_level="info",
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capture_output=False,
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):
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"""
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Common method to run VLM MMMU benchmark test.
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Args:
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model: Model to test
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output_path: Path for output logs
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test_name: Optional test name for logging
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custom_env: Optional custom environment variables
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log_level: Log level for server (default: "info")
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capture_output: Whether to capture server stdout/stderr
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"""
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print(f"\nTesting model: {model.model}{test_name}")
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process = None
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mmmu_accuracy = 0 # Initialize to handle potential exceptions
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server_output = ""
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try:
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# Prepare environment variables
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process_env = os.environ.copy()
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if custom_env:
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process_env.update(custom_env)
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# if test vlm with cuda_ipc feature, open this env_var
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process_env["SGLANG_USE_CUDA_IPC_TRANSPORT"] = "1"
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# Prepare stdout/stderr redirection if needed
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stdout_file = None
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stderr_file = None
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if capture_output:
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stdout_file = open("/tmp/server_stdout.log", "w")
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stderr_file = open("/tmp/server_stderr.log", "w")
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# Launch server for testing
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process = popen_launch_server(
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model.model,
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base_url=self.base_url,
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timeout=self.time_out,
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api_key=self.api_key,
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other_args=[
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"--trust-remote-code",
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"--cuda-graph-max-bs",
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"32",
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"--mm-enable-dp-encoder",
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"--tp=4",
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"--mem-fraction-static",
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str(self.parsed_args.mem_fraction_static), # Use class variable
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"--log-level",
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log_level,
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],
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env=process_env,
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return_stdout_stderr=(
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(stdout_file, stderr_file) if capture_output else None
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),
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)
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# Run evaluation
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self.run_mmmu_eval(model.model, output_path)
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# Get the result file
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# Search recursively for JSON result files (lmms-eval v0.4.1+ creates subdirectories)
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result_files = glob.glob(f"{output_path}/**/*.json", recursive=True)
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if not result_files:
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result_files = glob.glob(f"{output_path}/*.json")
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if not result_files:
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raise FileNotFoundError(f"No JSON result files found in {output_path}")
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result_file_path = result_files[0]
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with open(result_file_path, "r") as f:
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result = json.load(f)
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print(f"Result{test_name}\n: {result}")
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# Process the result
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mmmu_accuracy = result["results"]["mmmu_val"]["mmmu_acc,none"]
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print(
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f"Model {model.model} achieved accuracy{test_name}: {mmmu_accuracy:.4f}"
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)
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# Capture server output if requested
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if capture_output and process:
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server_output = self._read_output_from_files()
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# Assert performance meets expected threshold
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self.assertGreaterEqual(
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mmmu_accuracy,
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model.mmmu_accuracy,
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f"Model {model.model} accuracy ({mmmu_accuracy:.4f}) below expected threshold ({model.mmmu_accuracy:.4f}){test_name}",
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)
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return server_output
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except Exception as e:
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print(f"Error testing {model.model}{test_name}: {e}")
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self.fail(f"Test failed for {model.model}{test_name}: {e}")
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finally:
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# Ensure process cleanup happens regardless of success/failure
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if process is not None and process.poll() is None:
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print(f"Cleaning up process {process.pid}")
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try:
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kill_process_tree(process.pid)
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except Exception as e:
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print(f"Error killing process: {e}")
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# clean up temporary files
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if capture_output:
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if stdout_file:
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stdout_file.close()
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if stderr_file:
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stderr_file.close()
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for filename in ["/tmp/server_stdout.log", "/tmp/server_stderr.log"]:
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try:
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if os.path.exists(filename):
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os.remove(filename)
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except Exception as e:
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print(f"Error removing {filename}: {e}")
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def _read_output_from_files(self):
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output_lines = []
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log_files = [
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("/tmp/server_stdout.log", "[STDOUT]"),
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("/tmp/server_stderr.log", "[STDERR]"),
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]
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for filename, tag in log_files:
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try:
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if os.path.exists(filename):
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with open(filename, "r") as f:
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for line in f:
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output_lines.append(f"{tag} {line.rstrip()}")
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except Exception as e:
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print(f"Error reading {tag.lower()} file: {e}")
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return "\n".join(output_lines)
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def test_vlm_mmmu_benchmark(self):
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"""Test VLM models against MMMU benchmark."""
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models_to_test = MODELS
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if is_in_ci():
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models_to_test = [random.choice(MODELS)]
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for model in models_to_test:
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self._run_vlm_mmmu_test(model, "./logs")
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if __name__ == "__main__":
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# Define and parse arguments here, before unittest.main
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parser = argparse.ArgumentParser(description="Test VLM models")
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parser.add_argument(
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"--mem-fraction-static",
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type=float,
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help="Static memory fraction for the model",
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default=DEFAULT_MEM_FRACTION_STATIC,
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)
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# Parse args intended for unittest
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args = parser.parse_args()
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# Store the parsed args object on the class
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TestVLMEncoderDP.parsed_args = args
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# Pass args to unittest
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unittest.main(argv=[sys.argv[0]])
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