[misc] Remove unit test cases that fail the admission criteria (round 2) (#30703)
This commit is contained in:
Liangsheng Yin
@@ -101,88 +101,6 @@ class TestModelOptModelLoader(CustomTestCase):
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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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@@ -486,49 +404,6 @@ class TestModelOptModelLoader(CustomTestCase):
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class TestModelOptLoaderIntegration(CustomTestCase):
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"""Integration tests for ModelOptModelLoader with Engine API."""
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@patch("sglang.srt.model_loader.loader.get_model_loader")
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@patch("sglang.srt.entrypoints.engine.Engine.__init__")
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def test_engine_with_modelopt_quant_parameter(
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self, mock_engine_init, mock_get_model_loader
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):
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"""Test that Engine properly handles modelopt_quant parameter."""
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# Mock the Engine.__init__ to avoid actual initialization
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mock_engine_init.return_value = None
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# Mock get_model_loader to return our ModelOptModelLoader
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mock_loader = MagicMock(spec=ModelOptModelLoader)
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mock_get_model_loader.return_value = mock_loader
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# Import here to avoid circular imports during test discovery
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# import sglang as sgl # Commented out since not directly used
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# Test that we can create an engine with modelopt_quant parameter
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# This would normally trigger the ModelOptModelLoader selection
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try:
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engine_args = {
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"model_path": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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"modelopt_quant": "fp8",
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"log_level": "error", # Suppress logs during testing
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}
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# This tests the parameter parsing and server args creation
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from sglang.srt.server_args import ServerArgs
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server_args = ServerArgs(**engine_args)
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# Verify that modelopt_quant is properly set
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self.assertEqual(server_args.modelopt_quant, "fp8")
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except Exception as e:
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# If there are missing dependencies or initialization issues,
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# we can still verify the parameter is accepted
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if "modelopt_quant" not in str(e):
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# The parameter was accepted, which is what we want to test
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pass
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else:
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self.fail(f"modelopt_quant parameter not properly handled: {e}")
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@patch("sglang.srt.model_loader.loader.get_model_loader")
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@patch("sglang.srt.entrypoints.engine.Engine.__init__")
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def test_engine_with_modelopt_quant_cli_argument(
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@@ -743,12 +618,6 @@ class TestModelOptMixedPrecisionConfig(CustomTestCase):
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)
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)
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def test_mixed_precision_uses_nvfp4_min_capability(self):
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self.assertEqual(
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ModelOptMixedPrecisionConfig.get_min_capability(),
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ModelOptFp4Config.get_min_capability(),
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)
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def test_mixed_precision_quant_layer_resolution_after_mapping(self):
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quant_config = ModelOptMixedPrecisionConfig.from_config(
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{
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