Fix external_models import path and migrate model loading tests (#16458)
This commit is contained in:
Alison Shao
@@ -0,0 +1,564 @@
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"""
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Unit tests for ModelOptModelLoader class.
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This test module verifies the functionality of ModelOptModelLoader, which
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applies NVIDIA Model Optimizer quantization to models during loading.
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"""
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import unittest
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from unittest.mock import MagicMock, patch
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import torch.nn as nn
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from sglang.srt.configs.device_config import DeviceConfig
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from sglang.srt.configs.load_config import LoadConfig
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from sglang.srt.configs.model_config import ModelConfig
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from sglang.srt.layers.modelopt_utils import QUANT_CFG_CHOICES
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from sglang.srt.model_loader.loader import ModelOptModelLoader
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from sglang.test.ci.ci_register import register_cuda_ci
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from sglang.test.test_utils import CustomTestCase
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# Note: PYTHONPATH=python should be set when running tests
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# Constants for calibration parameters to avoid hard-coded values
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CALIBRATION_BATCH_SIZE = 36
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CALIBRATION_NUM_SAMPLES = 512
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DEFAULT_DEVICE = "cuda:0"
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register_cuda_ci(est_time=11, suite="stage-b-test-small-1-gpu")
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class TestModelOptModelLoader(CustomTestCase):
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"""Test cases for ModelOptModelLoader functionality."""
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def setUp(self):
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"""Set up test fixtures."""
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# Mock distributed functionality to avoid initialization errors
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self.mock_tp_rank = patch(
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"sglang.srt.distributed.parallel_state.get_tensor_model_parallel_rank",
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return_value=0,
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)
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self.mock_tp_rank.start()
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self.mock_rank0_log = patch("sglang.srt.model_loader.loader.rank0_log")
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self.mock_rank0_log.start()
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# Mock logger to avoid issues
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self.mock_logger = patch("sglang.srt.model_loader.loader.logger")
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self.mock_logger.start()
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# Mock all distributed functions that might be called
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self.mock_get_tp_group = patch(
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"sglang.srt.distributed.parallel_state.get_tp_group"
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)
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self.mock_get_tp_group.start()
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# Mock model parallel initialization check
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self.mock_mp_is_initialized = patch(
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"sglang.srt.distributed.parallel_state.model_parallel_is_initialized",
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return_value=True,
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)
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self.mock_mp_is_initialized.start()
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self.model_path = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
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self.load_config = LoadConfig()
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self.device_config = DeviceConfig(device="cuda")
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# Create a basic model config with unified quantization flag
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self.model_config = ModelConfig(
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model_path=self.model_path,
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quantization="modelopt_fp8", # Use unified quantization approach
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)
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# Also create a unified quantization config for new tests
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self.unified_model_config = ModelConfig(
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model_path=self.model_path, quantization="modelopt_fp8"
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)
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# Mock base model
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self.mock_base_model = MagicMock(spec=nn.Module)
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self.mock_base_model.eval.return_value = self.mock_base_model
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self.mock_base_model.device = (
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DEFAULT_DEVICE # Add device attribute for calibration tests
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)
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def tearDown(self):
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"""Clean up test fixtures."""
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# Stop mocks
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self.mock_tp_rank.stop()
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self.mock_rank0_log.stop()
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self.mock_logger.stop()
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self.mock_get_tp_group.stop()
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self.mock_mp_is_initialized.stop()
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@patch("sglang.srt.model_loader.loader.QUANT_CFG_CHOICES", QUANT_CFG_CHOICES)
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@patch("sglang.srt.model_loader.loader.logger")
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def test_successful_fp8_quantization(self, mock_logger):
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"""Test successful FP8 quantization workflow."""
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# Create loader instance
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loader = ModelOptModelLoader(self.load_config)
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# Mock modelopt modules
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mock_mtq = MagicMock()
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# Configure mtq mock with FP8_DEFAULT_CFG
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mock_fp8_cfg = MagicMock()
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mock_mtq.FP8_DEFAULT_CFG = mock_fp8_cfg
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mock_mtq.quantize.return_value = self.mock_base_model
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mock_mtq.print_quant_summary = MagicMock()
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# Create a custom load_model method for testing that simulates the real logic
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def mock_load_model(*, model_config, device_config):
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mock_logger.info("ModelOptModelLoader: Loading base model...")
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# Simulate loading base model (this is already mocked)
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model = self.mock_base_model
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# Simulate the quantization config lookup
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quant_choice_str = model_config._get_modelopt_quant_type()
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quant_cfg_name = QUANT_CFG_CHOICES.get(quant_choice_str)
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if not quant_cfg_name:
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raise ValueError(f"Invalid modelopt_quant choice: '{quant_choice_str}'")
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# Simulate getattr call and quantization
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if quant_cfg_name == "FP8_DEFAULT_CFG":
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quant_cfg = mock_fp8_cfg
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mock_logger.info(
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f"Quantizing model with ModelOpt using config attribute: mtq.{quant_cfg_name}"
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)
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# Simulate mtq.quantize call
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quantized_model = mock_mtq.quantize(model, quant_cfg, forward_loop=None)
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mock_logger.info("Model successfully quantized with ModelOpt.")
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# Simulate print_quant_summary call
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mock_mtq.print_quant_summary(quantized_model)
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return quantized_model.eval()
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return model.eval()
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# Patch the load_model method with our custom implementation
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with patch.object(loader, "load_model", side_effect=mock_load_model):
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# Execute the load_model method
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result_model = loader.load_model(
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model_config=self.model_config, device_config=self.device_config
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)
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# Verify the quantization process
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mock_mtq.quantize.assert_called_once_with(
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self.mock_base_model, mock_fp8_cfg, forward_loop=None
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)
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# Verify logging
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mock_logger.info.assert_any_call(
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"ModelOptModelLoader: Loading base model..."
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)
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mock_logger.info.assert_any_call(
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"Quantizing model with ModelOpt using config attribute: mtq.FP8_DEFAULT_CFG"
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)
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mock_logger.info.assert_any_call(
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"Model successfully quantized with ModelOpt."
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)
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# Verify print_quant_summary was called
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mock_mtq.print_quant_summary.assert_called_once_with(self.mock_base_model)
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# Verify eval() was called on the returned model
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self.mock_base_model.eval.assert_called()
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# Verify we get back the expected model
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self.assertEqual(result_model, self.mock_base_model)
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@patch("sglang.srt.model_loader.loader.logger")
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def test_missing_modelopt_import(self, mock_logger):
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"""Test error handling when modelopt library is not available."""
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loader = ModelOptModelLoader(self.load_config)
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# Mock the base model loader method
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with patch.object(
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loader, "_load_modelopt_base_model", return_value=self.mock_base_model
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):
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# Simulate missing modelopt by making import fail
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original_import = __import__
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def mock_import(name, *args, **kwargs):
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if name.startswith("modelopt"):
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raise ImportError("No module named 'modelopt'")
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# Return default import behavior for other modules
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return original_import(name, *args, **kwargs)
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with patch("builtins.__import__", side_effect=mock_import):
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# Expect ImportError to be raised and logged
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with self.assertRaises(ImportError):
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loader.load_model(
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model_config=self.model_config, device_config=self.device_config
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)
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# Verify error logging
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mock_logger.error.assert_called_with(
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"NVIDIA Model Optimizer (modelopt) library not found. "
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"Please install it to use ModelOpt quantization."
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)
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@patch("sglang.srt.model_loader.loader.QUANT_CFG_CHOICES", QUANT_CFG_CHOICES)
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@patch("sglang.srt.model_loader.loader.AutoTokenizer")
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@patch("sglang.srt.model_loader.loader.logger")
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def test_calibration_workflow_integration(self, mock_logger, mock_auto_tokenizer):
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"""Test end-to-end calibration workflow integration."""
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loader = ModelOptModelLoader(self.load_config)
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# Mock tokenizer
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mock_tokenizer = MagicMock()
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mock_tokenizer.padding_side = "right"
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mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer
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# Mock modelopt modules
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mock_mtq = MagicMock()
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mock_mto = MagicMock()
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mock_dataset_utils = MagicMock()
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# Configure quantization config
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mock_fp8_cfg = MagicMock()
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mock_mtq.FP8_DEFAULT_CFG = mock_fp8_cfg
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# Configure dataset utilities
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mock_calib_dataloader = MagicMock()
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mock_calibrate_loop = MagicMock()
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mock_dataset_utils.get_dataset_dataloader.return_value = mock_calib_dataloader
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mock_dataset_utils.create_forward_loop.return_value = mock_calibrate_loop
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# Configure model as not quantized initially
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mock_is_quantized = MagicMock(return_value=False)
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with patch.object(
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loader, "_load_modelopt_base_model", return_value=self.mock_base_model
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):
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with patch.dict(
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"sys.modules",
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{
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"modelopt": MagicMock(),
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"modelopt.torch": MagicMock(),
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"modelopt.torch.opt": mock_mto,
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"modelopt.torch.quantization": mock_mtq,
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"modelopt.torch.quantization.utils": MagicMock(
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is_quantized=mock_is_quantized
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),
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"modelopt.torch.utils": MagicMock(),
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"modelopt.torch.utils.dataset_utils": mock_dataset_utils,
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},
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):
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# Execute the load_model method to test the full workflow
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result_model = loader.load_model(
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model_config=self.model_config, device_config=self.device_config
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)
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# Verify the model loading was successful
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self.assertEqual(result_model, self.mock_base_model)
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# Verify key calibration components were used
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# Note: We can't easily verify the exact calls due to dynamic imports,
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# but we can verify the workflow completed successfully
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@patch("sglang.srt.model_loader.loader.QUANT_CFG_CHOICES", QUANT_CFG_CHOICES)
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@patch("sglang.srt.model_loader.loader.AutoTokenizer")
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@patch("sglang.srt.model_loader.loader.logger")
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def test_quantized_checkpoint_restore(self, mock_logger, mock_auto_tokenizer):
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"""Test restoring from a quantized checkpoint."""
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# Create model config with checkpoint restore path
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config_with_restore = ModelConfig(
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model_path=self.model_path,
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quantization="modelopt_fp8",
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)
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# Create load config with checkpoint restore path
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load_config_with_restore = LoadConfig(
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modelopt_checkpoint_restore_path="/path/to/quantized/checkpoint"
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)
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loader = ModelOptModelLoader(load_config_with_restore)
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# Mock tokenizer
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mock_tokenizer = MagicMock()
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mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer
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# Mock modelopt modules
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mock_mtq = MagicMock()
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mock_mto = MagicMock()
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# Configure quantization config
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mock_fp8_cfg = MagicMock()
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mock_mtq.FP8_DEFAULT_CFG = mock_fp8_cfg
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# Configure model as not quantized initially
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mock_is_quantized = MagicMock(return_value=False)
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with patch.object(
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loader, "_load_modelopt_base_model", return_value=self.mock_base_model
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):
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with patch.dict(
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"sys.modules",
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{
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"modelopt": MagicMock(),
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"modelopt.torch": MagicMock(),
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"modelopt.torch.opt": mock_mto,
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"modelopt.torch.quantization": mock_mtq,
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"modelopt.torch.quantization.utils": MagicMock(
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is_quantized=mock_is_quantized
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),
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},
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):
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with patch.object(loader, "_setup_modelopt_quantization") as mock_setup:
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# Mock the _setup_modelopt_quantization to simulate checkpoint restore
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def mock_setup_quantization(
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model,
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tokenizer,
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quant_cfg,
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quantized_ckpt_restore_path=None,
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**kwargs,
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):
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if quantized_ckpt_restore_path:
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mock_mto.restore(model, quantized_ckpt_restore_path)
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print(
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f"Restored quantized model from {quantized_ckpt_restore_path}"
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)
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return
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mock_setup.side_effect = mock_setup_quantization
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# Execute the load_model method
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result_model = loader.load_model(
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model_config=config_with_restore,
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device_config=self.device_config,
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)
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# Verify the setup was called with restore path
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mock_setup.assert_called_once()
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call_args = mock_setup.call_args
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# Check that the restore path was passed correctly
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self.assertIn("quantized_ckpt_restore_path", call_args[1])
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self.assertEqual(
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call_args[1]["quantized_ckpt_restore_path"],
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"/path/to/quantized/checkpoint",
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)
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# Verify restore was called
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mock_mto.restore.assert_called_once_with(
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self.mock_base_model, "/path/to/quantized/checkpoint"
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)
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# Verify we get the expected model back
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self.assertEqual(result_model, self.mock_base_model)
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@patch("sglang.srt.model_loader.loader.QUANT_CFG_CHOICES", QUANT_CFG_CHOICES)
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@patch("sglang.srt.model_loader.loader.AutoTokenizer")
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@patch("sglang.srt.model_loader.loader.logger")
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def test_quantized_checkpoint_save(self, mock_logger, mock_auto_tokenizer):
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"""Test saving quantized checkpoint after calibration."""
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# Create model config with checkpoint save path
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config_with_save = ModelConfig(
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model_path=self.model_path,
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quantization="modelopt_fp8",
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)
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# Create load config with checkpoint save path
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load_config_with_save = LoadConfig(
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modelopt_checkpoint_save_path="/path/to/save/checkpoint"
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)
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loader = ModelOptModelLoader(load_config_with_save)
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# Mock tokenizer
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mock_tokenizer = MagicMock()
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mock_auto_tokenizer.from_pretrained.return_value = mock_tokenizer
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# Mock modelopt modules
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mock_mtq = MagicMock()
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mock_mto = MagicMock()
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mock_dataset_utils = MagicMock()
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# Configure quantization config
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mock_fp8_cfg = MagicMock()
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mock_mtq.FP8_DEFAULT_CFG = mock_fp8_cfg
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# Configure model as not quantized initially
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mock_is_quantized = MagicMock(return_value=False)
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with patch.object(
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loader, "_load_modelopt_base_model", return_value=self.mock_base_model
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):
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with patch.dict(
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"sys.modules",
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{
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"modelopt": MagicMock(),
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"modelopt.torch": MagicMock(),
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"modelopt.torch.opt": mock_mto,
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"modelopt.torch.quantization": mock_mtq,
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"modelopt.torch.quantization.utils": MagicMock(
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is_quantized=mock_is_quantized
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),
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"modelopt.torch.utils": MagicMock(),
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"modelopt.torch.utils.dataset_utils": mock_dataset_utils,
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},
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):
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with patch.object(loader, "_setup_modelopt_quantization") as mock_setup:
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# Mock the _setup_modelopt_quantization to simulate checkpoint save
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def mock_setup_quantization(
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model,
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tokenizer,
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quant_cfg,
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quantized_ckpt_save_path=None,
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**kwargs,
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):
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# Simulate calibration and quantization
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mock_mtq.quantize(model, quant_cfg, forward_loop=MagicMock())
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mock_mtq.print_quant_summary(model)
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# Save checkpoint if path provided
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if quantized_ckpt_save_path:
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mock_mto.save(model, quantized_ckpt_save_path)
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print(
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f"Quantized model saved to {quantized_ckpt_save_path}"
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)
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mock_setup.side_effect = mock_setup_quantization
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# Execute the load_model method
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result_model = loader.load_model(
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model_config=config_with_save, device_config=self.device_config
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)
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# Verify the setup was called with save path
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mock_setup.assert_called_once()
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call_args = mock_setup.call_args
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# Check that the save path was passed correctly
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self.assertIn("quantized_ckpt_save_path", call_args[1])
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self.assertEqual(
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call_args[1]["quantized_ckpt_save_path"],
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"/path/to/save/checkpoint",
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)
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# Verify save was called
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mock_mto.save.assert_called_once_with(
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self.mock_base_model, "/path/to/save/checkpoint"
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)
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# Verify we get the expected model back
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self.assertEqual(result_model, self.mock_base_model)
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def test_unified_quantization_flag_support(self):
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"""Test that ModelOptModelLoader supports unified quantization flags."""
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# Test modelopt_fp8
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config_fp8 = ModelConfig(
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model_path=self.model_path, quantization="modelopt_fp8"
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)
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self.assertEqual(config_fp8._get_modelopt_quant_type(), "fp8")
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# Test modelopt_fp4
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config_fp4 = ModelConfig(
|
||||
model_path=self.model_path, quantization="modelopt_fp4"
|
||||
)
|
||||
self.assertEqual(config_fp4._get_modelopt_quant_type(), "nvfp4")
|
||||
|
||||
# Test auto-detection
|
||||
config_auto = ModelConfig(model_path=self.model_path, quantization="modelopt")
|
||||
# Should default to fp8 when no config is detected
|
||||
self.assertEqual(config_auto._get_modelopt_quant_type(), "fp8")
|
||||
|
||||
|
||||
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()
|
||||
Reference in New Issue
Block a user