[FEATURE] Add OpenAI-Compatible LoRA Adapter Selection (#11570)
This commit is contained in:
Neelabh Sinha
327
test/srt/lora/test_lora_openai_api.py
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327
test/srt/lora/test_lora_openai_api.py
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@@ -0,0 +1,327 @@
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"""
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Unit tests for OpenAI-compatible LoRA API support.
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Tests the model parameter parsing and LoRA adapter resolution logic
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that enables OpenAI-compatible LoRA adapter selection.
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"""
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import unittest
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from unittest.mock import MagicMock, Mock
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from sglang.srt.entrypoints.openai.serving_base import OpenAIServingBase
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from sglang.srt.server_args import ServerArgs
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class MockTokenizerManager:
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"""Mock TokenizerManager for testing."""
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def __init__(self, enable_lora=False):
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self.server_args = MagicMock(spec=ServerArgs)
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self.server_args.enable_lora = enable_lora
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self.server_args.tokenizer_metrics_allowed_custom_labels = None
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class ConcreteServingBase(OpenAIServingBase):
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"""Concrete implementation for testing abstract base class."""
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def _request_id_prefix(self) -> str:
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return "test-"
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def _convert_to_internal_request(self, request, raw_request=None):
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pass
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def _validate_request(self, request):
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pass
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class TestParseModelParameter(unittest.TestCase):
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"""Test _parse_model_parameter method."""
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def setUp(self):
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self.tokenizer_manager = MockTokenizerManager(enable_lora=True)
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self.serving = ConcreteServingBase(self.tokenizer_manager)
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def test_model_without_adapter(self):
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"""Test parsing model without adapter returns None for adapter."""
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base_model, adapter = self.serving._parse_model_parameter("llama-3.1-8B")
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self.assertEqual(base_model, "llama-3.1-8B")
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self.assertIsNone(adapter)
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def test_model_with_adapter(self):
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"""Test parsing model with adapter extracts both parts."""
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base_model, adapter = self.serving._parse_model_parameter(
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"llama-3.1-8B:sql-expert"
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)
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self.assertEqual(base_model, "llama-3.1-8B")
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self.assertEqual(adapter, "sql-expert")
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def test_model_with_path_and_adapter(self):
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"""Test parsing model path with slashes and adapter."""
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base_model, adapter = self.serving._parse_model_parameter(
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"meta-llama/Llama-3.1-8B-Instruct:adapter-name"
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)
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self.assertEqual(base_model, "meta-llama/Llama-3.1-8B-Instruct")
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self.assertEqual(adapter, "adapter-name")
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def test_model_with_multiple_colons(self):
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"""Test that only first colon is used for splitting."""
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base_model, adapter = self.serving._parse_model_parameter("model:adapter:extra")
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self.assertEqual(base_model, "model")
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self.assertEqual(adapter, "adapter:extra")
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def test_model_with_whitespace(self):
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"""Test that whitespace is stripped from both parts."""
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base_model, adapter = self.serving._parse_model_parameter(
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" model-name : adapter-name "
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)
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self.assertEqual(base_model, "model-name")
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self.assertEqual(adapter, "adapter-name")
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def test_model_with_empty_adapter(self):
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"""Test model ending with colon returns None for adapter."""
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base_model, adapter = self.serving._parse_model_parameter("model-name:")
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self.assertEqual(base_model, "model-name")
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self.assertIsNone(adapter)
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def test_model_with_only_spaces_after_colon(self):
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"""Test model with only whitespace after colon returns None for adapter."""
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base_model, adapter = self.serving._parse_model_parameter("model-name: ")
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self.assertEqual(base_model, "model-name")
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self.assertIsNone(adapter)
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class TestResolveLoraPath(unittest.TestCase):
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"""Test _resolve_lora_path method."""
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def setUp(self):
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self.tokenizer_manager = MockTokenizerManager(enable_lora=True)
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self.serving = ConcreteServingBase(self.tokenizer_manager)
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def test_no_adapter_specified(self):
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"""Test when neither model nor explicit lora_path has adapter."""
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result = self.serving._resolve_lora_path("model-name", None)
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self.assertIsNone(result)
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def test_adapter_in_model_only(self):
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"""Test adapter from model parameter when no explicit path."""
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result = self.serving._resolve_lora_path("model:sql-expert", None)
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self.assertEqual(result, "sql-expert")
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def test_adapter_in_explicit_only(self):
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"""Test adapter from explicit lora_path when not in model."""
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result = self.serving._resolve_lora_path("model-name", "python-expert")
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self.assertEqual(result, "python-expert")
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def test_model_parameter_takes_precedence(self):
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"""Test model parameter adapter takes precedence over explicit."""
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result = self.serving._resolve_lora_path("model:sql-expert", "python-expert")
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self.assertEqual(result, "sql-expert")
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def test_with_list_explicit_lora_path(self):
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"""Test that explicit list is returned when no model adapter."""
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explicit = ["adapter1", "adapter2", None]
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result = self.serving._resolve_lora_path("model-name", explicit)
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self.assertEqual(result, explicit)
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def test_model_adapter_overrides_list(self):
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"""Test model adapter overrides even when explicit is a list."""
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result = self.serving._resolve_lora_path(
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"model:sql-expert", ["adapter1", "adapter2"]
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)
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self.assertEqual(result, "sql-expert")
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def test_complex_model_name_with_adapter(self):
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"""Test resolution with complex model name."""
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result = self.serving._resolve_lora_path(
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"org/model-v2.1:adapter-name", "other-adapter"
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)
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self.assertEqual(result, "adapter-name")
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class TestValidateLoraEnabled(unittest.TestCase):
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"""Test _validate_lora_enabled method."""
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def test_validation_passes_when_lora_enabled(self):
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"""Test validation passes when LoRA is enabled."""
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tokenizer_manager = MockTokenizerManager(enable_lora=True)
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serving = ConcreteServingBase(tokenizer_manager)
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# Should not raise
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try:
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serving._validate_lora_enabled("sql-expert")
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except ValueError:
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self.fail("_validate_lora_enabled raised ValueError unexpectedly")
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def test_validation_fails_when_lora_disabled(self):
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"""Test validation fails with helpful message when LoRA is disabled."""
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tokenizer_manager = MockTokenizerManager(enable_lora=False)
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serving = ConcreteServingBase(tokenizer_manager)
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with self.assertRaises(ValueError) as context:
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serving._validate_lora_enabled("sql-expert")
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error_message = str(context.exception)
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self.assertIn("sql-expert", error_message)
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self.assertIn("--enable-lora", error_message)
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self.assertIn("not enabled", error_message)
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def test_validation_error_mentions_adapter_name(self):
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"""Test that error message includes the requested adapter name."""
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tokenizer_manager = MockTokenizerManager(enable_lora=False)
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serving = ConcreteServingBase(tokenizer_manager)
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with self.assertRaises(ValueError) as context:
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serving._validate_lora_enabled("my-custom-adapter")
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self.assertIn("my-custom-adapter", str(context.exception))
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class TestIntegrationScenarios(unittest.TestCase):
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"""Integration tests for common usage scenarios."""
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def setUp(self):
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self.tokenizer_manager = MockTokenizerManager(enable_lora=True)
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self.serving = ConcreteServingBase(self.tokenizer_manager)
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def test_openai_compatible_usage(self):
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"""Test typical OpenAI-compatible usage pattern."""
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# User specifies adapter in model parameter
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model = "meta-llama/Llama-3.1-8B:sql-expert"
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explicit_lora = None
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lora_path = self.serving._resolve_lora_path(model, explicit_lora)
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self.assertEqual(lora_path, "sql-expert")
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# Validation should pass
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self.serving._validate_lora_enabled(lora_path)
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def test_backward_compatible_usage(self):
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"""Test backward-compatible usage with explicit lora_path."""
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model = "meta-llama/Llama-3.1-8B"
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explicit_lora = "sql-expert"
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lora_path = self.serving._resolve_lora_path(model, explicit_lora)
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self.assertEqual(lora_path, "sql-expert")
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# Validation should pass
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self.serving._validate_lora_enabled(lora_path)
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def test_base_model_usage(self):
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"""Test using base model without any adapter."""
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model = "meta-llama/Llama-3.1-8B"
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explicit_lora = None
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lora_path = self.serving._resolve_lora_path(model, explicit_lora)
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self.assertIsNone(lora_path)
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# No validation needed when no adapter
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def test_batch_request_scenario(self):
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"""Test batch request with list of adapters."""
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model = "meta-llama/Llama-3.1-8B" # No adapter in model
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explicit_lora = ["sql-expert", "python-expert", None]
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lora_path = self.serving._resolve_lora_path(model, explicit_lora)
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self.assertEqual(lora_path, explicit_lora)
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# Validate first adapter in list
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if isinstance(lora_path, list) and lora_path[0]:
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self.serving._validate_lora_enabled(lora_path[0])
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def test_adapter_in_model_overrides_batch_list(self):
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"""Test that adapter in model parameter overrides batch list."""
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model = "meta-llama/Llama-3.1-8B:preferred-adapter"
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explicit_lora = ["adapter1", "adapter2"]
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lora_path = self.serving._resolve_lora_path(model, explicit_lora)
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self.assertEqual(lora_path, "preferred-adapter")
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def test_error_when_lora_not_enabled(self):
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"""Test comprehensive error flow when LoRA is not enabled."""
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# Setup server without LoRA enabled
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tokenizer_manager = MockTokenizerManager(enable_lora=False)
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serving = ConcreteServingBase(tokenizer_manager)
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# User tries to use adapter
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model = "meta-llama/Llama-3.1-8B:sql-expert"
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lora_path = serving._resolve_lora_path(model, None)
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# Should get helpful error
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with self.assertRaises(ValueError) as context:
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serving._validate_lora_enabled(lora_path)
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error = str(context.exception)
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self.assertIn("--enable-lora", error)
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self.assertIn("sql-expert", error)
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class TestEdgeCases(unittest.TestCase):
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"""Test edge cases and error conditions."""
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def setUp(self):
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self.tokenizer_manager = MockTokenizerManager(enable_lora=True)
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self.serving = ConcreteServingBase(self.tokenizer_manager)
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def test_empty_string_model(self):
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"""Test handling of empty string model."""
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base, adapter = self.serving._parse_model_parameter("")
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self.assertEqual(base, "")
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self.assertIsNone(adapter)
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def test_only_colon(self):
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"""Test model parameter that is just a colon."""
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base, adapter = self.serving._parse_model_parameter(":")
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self.assertEqual(base, "")
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self.assertIsNone(adapter)
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def test_empty_list_lora_path(self):
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"""Test validation with empty list doesn't crash."""
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lora_path = self.serving._resolve_lora_path("model-name", [])
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# Empty list is falsy, so validation won't be called
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self.assertEqual(lora_path, [])
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def test_list_with_none_first(self):
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"""Test validation finds first non-None adapter in list."""
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lora_path = self.serving._resolve_lora_path("model-name", [None, "adapter2"])
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self.assertEqual(lora_path, [None, "adapter2"])
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# In actual usage, validation would find "adapter2"
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def test_list_all_none(self):
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"""Test validation with list of all None values."""
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lora_path = self.serving._resolve_lora_path("model-name", [None, None])
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self.assertEqual(lora_path, [None, None])
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# In actual usage, no validation would occur (no non-None adapters)
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def test_unicode_in_adapter_name(self):
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"""Test Unicode characters in adapter name."""
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base, adapter = self.serving._parse_model_parameter("model:adapter-名前")
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self.assertEqual(base, "model")
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self.assertEqual(adapter, "adapter-名前")
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def test_special_characters_in_adapter(self):
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"""Test special characters in adapter name."""
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base, adapter = self.serving._parse_model_parameter("model:adapter_v2.1-final")
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self.assertEqual(base, "model")
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self.assertEqual(adapter, "adapter_v2.1-final")
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def test_none_as_explicit_lora_path(self):
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"""Test None as explicit lora_path is handled correctly."""
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result = self.serving._resolve_lora_path("model:adapter", None)
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self.assertEqual(result, "adapter")
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def test_empty_string_as_explicit_lora_path(self):
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"""Test empty string as explicit lora_path."""
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result = self.serving._resolve_lora_path("model-name", "")
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self.assertEqual(result, "")
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def test_validation_with_empty_adapter_name(self):
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"""Test validation with empty adapter name still raises error."""
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tokenizer_manager = MockTokenizerManager(enable_lora=False)
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serving = ConcreteServingBase(tokenizer_manager)
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with self.assertRaises(ValueError):
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serving._validate_lora_enabled("")
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if __name__ == "__main__":
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unittest.main()
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278
test/srt/openai_server/features/test_lora_openai_compatible.py
Normal file
278
test/srt/openai_server/features/test_lora_openai_compatible.py
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@@ -0,0 +1,278 @@
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"""
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End-to-end tests for OpenAI-compatible LoRA adapter usage.
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Tests the model:adapter syntax and backward compatibility with explicit lora_path.
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Usage:
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python3 -m unittest openai_server.features.test_lora_openai_compatible.TestLoRAOpenAICompatible.test_model_adapter_syntax
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python3 -m unittest openai_server.features.test_lora_openai_compatible.TestLoRAOpenAICompatible.test_explicit_lora_path
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python3 -m unittest openai_server.features.test_lora_openai_compatible.TestLoRAOpenAICompatible.test_priority_model_over_explicit
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python3 -m unittest openai_server.features.test_lora_openai_compatible.TestLoRAOpenAICompatible.test_base_model_no_adapter
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python3 -m unittest openai_server.features.test_lora_openai_compatible.TestLoRAOpenAICompatible.test_completions_api_with_adapter
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python3 -m unittest openai_server.features.test_lora_openai_compatible.TestLoRAOpenAICompatible.test_streaming_with_adapter
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python3 -m unittest openai_server.features.test_lora_openai_compatible.TestLoRADisabledError.test_lora_disabled_error
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"""
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import unittest
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import openai
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from sglang.srt.utils import kill_process_tree
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from sglang.test.test_utils import (
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DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
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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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popen_launch_server,
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)
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def get_real_lora_adapter() -> str:
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"""Use a real LoRA adapter from Hugging Face."""
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return "codelion/Llama-3.2-1B-Instruct-tool-calling-lora"
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def setup_class(cls, enable_lora=True):
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"""Setup test class with LoRA-enabled server."""
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cls.model = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
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cls.base_url = DEFAULT_URL_FOR_TEST
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# Use real LoRA adapter
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cls.lora_adapter_path = get_real_lora_adapter()
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other_args = [
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"--max-running-requests",
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"10",
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"--disable-radix-cache", # Disable cache for cleaner tests
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]
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if enable_lora:
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other_args.extend(
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[
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"--enable-lora",
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"--lora-paths",
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f"tool_calling={cls.lora_adapter_path}",
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]
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)
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cls.process = popen_launch_server(
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cls.model,
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cls.base_url,
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timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
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other_args=other_args,
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)
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cls.client = openai.Client(api_key="EMPTY", base_url=f"{cls.base_url}/v1")
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class TestLoRAOpenAICompatible(CustomTestCase):
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"""Test OpenAI-compatible LoRA adapter usage."""
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@classmethod
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def setUpClass(cls):
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setup_class(cls, enable_lora=True)
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@classmethod
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def tearDownClass(cls):
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kill_process_tree(cls.process.pid)
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def test_model_adapter_syntax(self):
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"""Test the new model:adapter syntax works correctly."""
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response = self.client.chat.completions.create(
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# ← New OpenAI-compatible syntax
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model=f"{self.model}:tool_calling",
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messages=[{"role": "user", "content": "What tools do you have available?"}],
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max_tokens=50,
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temperature=0,
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)
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self.assertIsNotNone(response.choices[0].message.content)
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self.assertGreater(len(response.choices[0].message.content), 0)
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print(f"Model adapter syntax response: {response.choices[0].message.content}")
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def test_explicit_lora_path(self):
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"""Test backward compatibility with explicit lora_path via extra_body."""
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[{"role": "user", "content": "What tools do you have available?"}],
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# ← Legacy explicit method
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extra_body={"lora_path": "tool_calling"},
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max_tokens=50,
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temperature=0,
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)
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self.assertIsNotNone(response.choices[0].message.content)
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self.assertGreater(len(response.choices[0].message.content), 0)
|
||||
print(f"Explicit lora_path response: {response.choices[0].message.content}")
|
||||
|
||||
def test_priority_model_over_explicit(self):
|
||||
"""Test that model:adapter syntax takes precedence over explicit lora_path."""
|
||||
# This test verifies the priority logic in _resolve_lora_path
|
||||
response = self.client.chat.completions.create(
|
||||
# ← Model specifies tool_calling adapter
|
||||
model=f"{self.model}:tool_calling",
|
||||
messages=[{"role": "user", "content": "What tools do you have available?"}],
|
||||
# ← Both specify same adapter
|
||||
extra_body={"lora_path": "tool_calling"},
|
||||
max_tokens=50,
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
# Should use tool_calling adapter (model parameter takes precedence)
|
||||
self.assertIsNotNone(response.choices[0].message.content)
|
||||
self.assertGreater(len(response.choices[0].message.content), 0)
|
||||
print(f"Priority test response: {response.choices[0].message.content}")
|
||||
|
||||
def test_base_model_no_adapter(self):
|
||||
"""Test using base model without any adapter."""
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model, # ← No adapter specified
|
||||
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
||||
max_tokens=30,
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
self.assertIsNotNone(response.choices[0].message.content)
|
||||
self.assertGreater(len(response.choices[0].message.content), 0)
|
||||
print(f"Base model response: {response.choices[0].message.content}")
|
||||
|
||||
def test_completions_api_with_adapter(self):
|
||||
"""Test completions API with LoRA adapter."""
|
||||
response = self.client.completions.create(
|
||||
model=f"{self.model}:tool_calling", # ← Using model:adapter syntax
|
||||
prompt="What tools do you have available?",
|
||||
max_tokens=50,
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
self.assertIsNotNone(response.choices[0].text)
|
||||
self.assertGreater(len(response.choices[0].text), 0)
|
||||
print(f"Completions API response: {response.choices[0].text}")
|
||||
|
||||
def test_streaming_with_adapter(self):
|
||||
"""Test streaming with LoRA adapter."""
|
||||
stream = self.client.chat.completions.create(
|
||||
model=f"{self.model}:tool_calling",
|
||||
messages=[{"role": "user", "content": "What tools do you have available?"}],
|
||||
max_tokens=50,
|
||||
temperature=0,
|
||||
stream=True,
|
||||
)
|
||||
|
||||
collected_content = ""
|
||||
for chunk in stream:
|
||||
if chunk.choices[0].delta.content:
|
||||
collected_content += chunk.choices[0].delta.content
|
||||
|
||||
self.assertGreater(len(collected_content), 0)
|
||||
print(f"Streaming response: {collected_content}")
|
||||
|
||||
def test_multiple_adapters(self):
|
||||
"""Test using different adapters in sequence."""
|
||||
# Test tool_calling adapter
|
||||
tool_response = self.client.chat.completions.create(
|
||||
model=f"{self.model}:tool_calling",
|
||||
messages=[{"role": "user", "content": "What tools do you have available?"}],
|
||||
max_tokens=30,
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
# Test base model without adapter
|
||||
base_response = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=[{"role": "user", "content": "Hello, how are you?"}],
|
||||
max_tokens=30,
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
self.assertIsNotNone(tool_response.choices[0].message.content)
|
||||
self.assertIsNotNone(base_response.choices[0].message.content)
|
||||
print(
|
||||
f"Tool calling adapter response: {tool_response.choices[0].message.content}"
|
||||
)
|
||||
print(f"Base model response: {base_response.choices[0].message.content}")
|
||||
|
||||
|
||||
class TestLoRADisabledError(CustomTestCase):
|
||||
"""Test error handling when LoRA is disabled."""
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
setup_class(cls, enable_lora=False) # ← LoRA disabled
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
kill_process_tree(cls.process.pid)
|
||||
|
||||
def test_lora_disabled_error(self):
|
||||
"""Test that using LoRA adapter when LoRA is disabled raises appropriate error."""
|
||||
with self.assertRaises(openai.APIError) as context:
|
||||
self.client.chat.completions.create(
|
||||
model=f"{self.model}:tool_calling", # ← Trying to use adapter
|
||||
messages=[
|
||||
{"role": "user", "content": "What tools do you have available?"}
|
||||
],
|
||||
max_tokens=50,
|
||||
)
|
||||
|
||||
# Verify the error message contains helpful guidance
|
||||
error_message = str(context.exception)
|
||||
self.assertIn("LoRA", error_message)
|
||||
self.assertIn("not enabled", error_message)
|
||||
print(f"Expected error message: {error_message}")
|
||||
|
||||
|
||||
class TestLoRAEdgeCases(CustomTestCase):
|
||||
"""Test edge cases for LoRA adapter usage."""
|
||||
|
||||
@classmethod
|
||||
def setUpClass(cls):
|
||||
setup_class(cls, enable_lora=True)
|
||||
|
||||
@classmethod
|
||||
def tearDownClass(cls):
|
||||
kill_process_tree(cls.process.pid)
|
||||
|
||||
def test_model_with_colon_no_adapter(self):
|
||||
"""Test model parameter ending with colon (empty adapter)."""
|
||||
response = self.client.chat.completions.create(
|
||||
model=f"{self.model}:", # ← Model ends with colon
|
||||
messages=[{"role": "user", "content": "Hello!"}],
|
||||
max_tokens=30,
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
# Should work as base model (no adapter)
|
||||
self.assertIsNotNone(response.choices[0].message.content)
|
||||
print(f"Model with colon response: {response.choices[0].message.content}")
|
||||
|
||||
def test_explicit_lora_path_none(self):
|
||||
"""Test explicit lora_path set to None."""
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=[{"role": "user", "content": "Hello!"}],
|
||||
extra_body={"lora_path": None}, # ← Explicitly None
|
||||
max_tokens=30,
|
||||
temperature=0,
|
||||
)
|
||||
|
||||
# Should work as base model
|
||||
self.assertIsNotNone(response.choices[0].message.content)
|
||||
print(
|
||||
f"Explicit None lora_path response: {response.choices[0].message.content}"
|
||||
)
|
||||
|
||||
def test_invalid_adapter_name(self):
|
||||
"""Test using non-existent adapter name."""
|
||||
with self.assertRaises(openai.APIError) as context:
|
||||
self.client.chat.completions.create(
|
||||
model=f"{self.model}:nonexistent", # ← Non-existent adapter
|
||||
messages=[{"role": "user", "content": "Hello!"}],
|
||||
max_tokens=30,
|
||||
)
|
||||
|
||||
error_message = str(context.exception)
|
||||
print(f"Invalid adapter error: {error_message}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
@@ -26,6 +26,7 @@ suites = {
|
||||
TestFile("lora/test_lora_eviction.py", 200),
|
||||
TestFile("lora/test_lora_qwen3.py", 97),
|
||||
TestFile("lora/test_lora_radix_cache.py", 100),
|
||||
TestFile("lora/test_lora_openai_api.py", 30),
|
||||
TestFile("lora/test_lora_update.py", 400),
|
||||
TestFile("lora/test_multi_lora_backend.py", 60),
|
||||
TestFile("models/test_compressed_tensors_models.py", 42),
|
||||
@@ -51,6 +52,7 @@ suites = {
|
||||
TestFile("openai_server/features/test_openai_server_ebnf.py", 95),
|
||||
TestFile("openai_server/features/test_openai_server_hidden_states.py", 240),
|
||||
TestFile("openai_server/features/test_reasoning_content.py", 89),
|
||||
TestFile("openai_server/features/test_lora_openai_compatible.py", 120),
|
||||
TestFile("openai_server/function_call/test_openai_function_calling.py", 60),
|
||||
TestFile("openai_server/function_call/test_tool_choice.py", 226),
|
||||
TestFile("openai_server/validation/test_large_max_new_tokens.py", 41),
|
||||
|
||||
Reference in New Issue
Block a user