""" Unit tests for ModelOptModelLoader class. This test module verifies the functionality of ModelOptModelLoader, which applies NVIDIA Model Optimizer quantization to models during loading. """ import os import sys import unittest from unittest.mock import MagicMock, patch import torch.nn as nn # Add the sglang path for testing sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../../python")) from sglang.srt.configs.device_config import DeviceConfig from sglang.srt.configs.load_config import LoadConfig from sglang.srt.configs.model_config import ModelConfig from sglang.srt.layers.modelopt_utils import QUANT_CFG_CHOICES from sglang.srt.model_loader.loader import ModelOptModelLoader from sglang.test.test_utils import CustomTestCase class TestModelOptModelLoader(CustomTestCase): """Test cases for ModelOptModelLoader functionality.""" def setUp(self): """Set up test fixtures.""" self.model_path = "TinyLlama/TinyLlama-1.1B-Chat-v1.0" self.load_config = LoadConfig() self.device_config = DeviceConfig(device="cuda") # Create a basic model config with modelopt_quant self.model_config = ModelConfig( model_path=self.model_path, modelopt_quant="fp8" ) # Mock base model self.mock_base_model = MagicMock(spec=nn.Module) self.mock_base_model.eval.return_value = self.mock_base_model @patch("sglang.srt.model_loader.loader.QUANT_CFG_CHOICES", QUANT_CFG_CHOICES) @patch("sglang.srt.model_loader.loader.logger") def test_successful_fp8_quantization(self, mock_logger): """Test successful FP8 quantization workflow.""" # Create loader instance loader = ModelOptModelLoader(self.load_config) # Mock modelopt modules mock_mtq = MagicMock() # Configure mtq mock with FP8_DEFAULT_CFG mock_fp8_cfg = MagicMock() mock_mtq.FP8_DEFAULT_CFG = mock_fp8_cfg mock_mtq.quantize.return_value = self.mock_base_model mock_mtq.print_quant_summary = MagicMock() # Create a custom load_model method for testing that simulates the real logic def mock_load_model(*, model_config, device_config): mock_logger.info("ModelOptModelLoader: Loading base model...") # Simulate loading base model (this is already mocked) model = self.mock_base_model # Simulate the quantization config lookup quant_choice_str = model_config.modelopt_quant quant_cfg_name = QUANT_CFG_CHOICES.get(quant_choice_str) if not quant_cfg_name: raise ValueError(f"Invalid modelopt_quant choice: '{quant_choice_str}'") # Simulate getattr call and quantization if quant_cfg_name == "FP8_DEFAULT_CFG": quant_cfg = mock_fp8_cfg mock_logger.info( f"Quantizing model with ModelOpt using config attribute: mtq.{quant_cfg_name}" ) # Simulate mtq.quantize call quantized_model = mock_mtq.quantize(model, quant_cfg, forward_loop=None) mock_logger.info("Model successfully quantized with ModelOpt.") # Simulate print_quant_summary call mock_mtq.print_quant_summary(quantized_model) return quantized_model.eval() return model.eval() # Patch the load_model method with our custom implementation with patch.object(loader, "load_model", side_effect=mock_load_model): # Execute the load_model method result_model = loader.load_model( model_config=self.model_config, device_config=self.device_config ) # Verify the quantization process mock_mtq.quantize.assert_called_once_with( self.mock_base_model, mock_fp8_cfg, forward_loop=None ) # Verify logging mock_logger.info.assert_any_call( "ModelOptModelLoader: Loading base model..." ) mock_logger.info.assert_any_call( "Quantizing model with ModelOpt using config attribute: mtq.FP8_DEFAULT_CFG" ) mock_logger.info.assert_any_call( "Model successfully quantized with ModelOpt." ) # Verify print_quant_summary was called mock_mtq.print_quant_summary.assert_called_once_with(self.mock_base_model) # Verify eval() was called on the returned model self.mock_base_model.eval.assert_called() # Verify we get back the expected model self.assertEqual(result_model, self.mock_base_model) class TestModelOptLoaderIntegration(CustomTestCase): """Integration tests for ModelOptModelLoader with Engine API.""" @patch("sglang.srt.model_loader.loader.get_model_loader") @patch("sglang.srt.entrypoints.engine.Engine.__init__") def test_engine_with_modelopt_quant_parameter( self, mock_engine_init, mock_get_model_loader ): """Test that Engine properly handles modelopt_quant parameter.""" # Mock the Engine.__init__ to avoid actual initialization mock_engine_init.return_value = None # Mock get_model_loader to return our ModelOptModelLoader mock_loader = MagicMock(spec=ModelOptModelLoader) mock_get_model_loader.return_value = mock_loader # Import here to avoid circular imports during test discovery # import sglang as sgl # Commented out since not directly used # Test that we can create an engine with modelopt_quant parameter # This would normally trigger the ModelOptModelLoader selection try: engine_args = { "model_path": "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "modelopt_quant": "fp8", "log_level": "error", # Suppress logs during testing } # This tests the parameter parsing and server args creation from sglang.srt.server_args import ServerArgs server_args = ServerArgs(**engine_args) # Verify that modelopt_quant is properly set self.assertEqual(server_args.modelopt_quant, "fp8") except Exception as e: # If there are missing dependencies or initialization issues, # we can still verify the parameter is accepted if "modelopt_quant" not in str(e): # The parameter was accepted, which is what we want to test pass else: self.fail(f"modelopt_quant parameter not properly handled: {e}") @patch("sglang.srt.model_loader.loader.get_model_loader") @patch("sglang.srt.entrypoints.engine.Engine.__init__") def test_engine_with_modelopt_quant_cli_argument( self, mock_engine_init, mock_get_model_loader ): """Test that CLI argument --modelopt-quant is properly parsed.""" # Mock the Engine.__init__ to avoid actual initialization mock_engine_init.return_value = None # Mock get_model_loader to return our ModelOptModelLoader mock_loader = MagicMock(spec=ModelOptModelLoader) mock_get_model_loader.return_value = mock_loader # Test CLI argument parsing import argparse from sglang.srt.server_args import ServerArgs # Create parser and add arguments parser = argparse.ArgumentParser() ServerArgs.add_cli_args(parser) # Test parsing with modelopt_quant argument args = parser.parse_args( [ "--model-path", "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "--modelopt-quant", "fp8", ] ) # Convert to ServerArgs using the proper from_cli_args method server_args = ServerArgs.from_cli_args(args) # Verify that modelopt_quant was properly parsed self.assertEqual(server_args.modelopt_quant, "fp8") self.assertEqual(server_args.model_path, "TinyLlama/TinyLlama-1.1B-Chat-v1.0") if __name__ == "__main__": unittest.main()