# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 """ Test suite for profile_sla dry-run functionality. This test ensures that the profile_sla script can successfully run in dry-run mode for vllm, sglang, and trtllm backends with their respective disagg.yaml configurations. """ import sys from pathlib import Path from unittest.mock import patch import pytest # Add the project root to sys.path to enable imports project_root = Path(__file__).parent.parent.parent sys.path.insert(0, str(project_root)) from benchmarks.profiler.profile_sla import run_profile # noqa: E402 from benchmarks.profiler.utils.search_space_autogen import ( # noqa: E402 auto_generate_search_space, ) # Override the logger fixture from conftest.py to prevent directory creation @pytest.fixture(autouse=True) def logger(request): """Override the logger fixture to prevent test directory creation. This replaces the logger fixture from tests/conftest.py that creates directories named after each test. """ # Simply do nothing - no directories created, no file handlers added yield class TestProfileSLADryRun: """Test class for profile_sla dry-run functionality.""" @pytest.fixture def vllm_args(self): """Create arguments for vllm backend dry-run test.""" class Args: def __init__(self): self.backend = "vllm" self.config = "components/backends/vllm/deploy/disagg.yaml" self.output_dir = "/tmp/test_profiling_results" self.namespace = "test-namespace" self.min_num_gpus_per_engine = 1 self.max_num_gpus_per_engine = 8 self.skip_existing_results = False self.force_rerun = False self.isl = 3000 self.osl = 500 self.ttft = 50 self.itl = 10 self.max_context_length = 16384 self.prefill_interpolation_granularity = 16 self.decode_interpolation_granularity = 6 self.service_name = "" self.is_moe_model = False self.dry_run = True self.use_ai_configurator = False self.aic_system = None self.aic_model_name = None self.aic_backend = "" self.aic_backend_version = None self.num_gpus_per_node = 8 self.deploy_after_profile = False return Args() @pytest.fixture def sglang_args(self): """Create arguments for sglang backend dry-run test.""" class Args: def __init__(self): self.backend = "sglang" self.config = "components/backends/sglang/deploy/disagg.yaml" self.output_dir = "/tmp/test_profiling_results" self.namespace = "test-namespace" self.min_num_gpus_per_engine = 1 self.max_num_gpus_per_engine = 8 self.skip_existing_results = False self.force_rerun = False self.isl = 3000 self.osl = 500 self.ttft = 50 self.itl = 10 self.max_context_length = 16384 self.prefill_interpolation_granularity = 16 self.decode_interpolation_granularity = 6 self.service_name = "" self.is_moe_model = False self.dry_run = True self.use_ai_configurator = False self.aic_system = None self.aic_model_name = None self.aic_backend = "" self.aic_backend_version = None self.num_gpus_per_node = 8 self.deploy_after_profile = False return Args() @pytest.mark.pre_merge @pytest.mark.asyncio async def test_vllm_dryrun(self, vllm_args): """Test that profile_sla dry-run works for vllm backend with disagg.yaml config.""" # Run the profile in dry-run mode - should complete without errors await run_profile(vllm_args) @pytest.mark.pre_merge @pytest.mark.asyncio async def test_sglang_dryrun(self, sglang_args): """Test that profile_sla dry-run works for sglang backend with disagg.yaml config.""" # Run the profile in dry-run mode - should complete without errors await run_profile(sglang_args) @pytest.fixture def trtllm_args(self): """Create arguments for trtllm backend dry-run test.""" class Args: def __init__(self): self.backend = "trtllm" self.config = "components/backends/trtllm/deploy/disagg.yaml" self.output_dir = "/tmp/test_profiling_results" self.namespace = "test-namespace" self.min_num_gpus_per_engine = 1 self.max_num_gpus_per_engine = 8 self.skip_existing_results = False self.force_rerun = False self.isl = 3000 self.osl = 500 self.ttft = 50 self.itl = 10 self.max_context_length = 16384 self.prefill_interpolation_granularity = 16 self.decode_interpolation_granularity = 6 self.service_name = "" self.is_moe_model = False self.dry_run = True self.use_ai_configurator = False self.aic_system = None self.aic_model_name = None self.aic_backend = "" self.aic_backend_version = None self.num_gpus_per_node = 8 self.deploy_after_profile = False return Args() @pytest.mark.pre_merge @pytest.mark.asyncio async def test_trtllm_dryrun(self, trtllm_args): """Test that profile_sla dry-run works for trtllm backend with disagg.yaml config.""" # Run the profile in dry-run mode - should complete without errors await run_profile(trtllm_args) @pytest.fixture def sglang_moe_args(self): """Create arguments for trtllm backend dry-run test.""" class Args: def __init__(self): self.backend = "sglang" self.config = "recipes/deepseek-r1/sglang/disagg-16gpu/deploy.yaml" self.output_dir = "/tmp/test_profiling_results" self.namespace = "test-namespace" self.min_num_gpus_per_engine = 8 self.max_num_gpus_per_engine = 32 self.skip_existing_results = False self.force_rerun = False self.isl = 3000 self.osl = 500 self.ttft = 50 self.itl = 10 self.max_context_length = 16384 self.prefill_interpolation_granularity = 16 self.decode_interpolation_granularity = 6 self.service_name = "" self.is_moe_model = True self.dry_run = True self.use_ai_configurator = False self.aic_system = None self.aic_model_name = None self.aic_backend = "" self.aic_backend_version = None self.num_gpus_per_node = 8 self.deploy_after_profile = False return Args() @pytest.mark.pre_merge @pytest.mark.asyncio async def test_sglang_moe_dryrun(self, sglang_moe_args): """Test that profile_sla dry-run works for sglang backend with MoE config.""" # Run the profile in dry-run mode - should complete without errors await run_profile(sglang_moe_args) # Example tests with mocked GPU inventory @pytest.fixture def mock_h100_gpu_info(self): """Mock GPU info for H100 80GB cluster.""" return { "gpus_per_node": 8, "model": "h100_sxm", "vram": 81920, # 80GB in MiB } @pytest.fixture def mock_model_info(self): """Mock model info for DeepSeek-R1-Distill-Llama-8B.""" return { "model_size": 16384, # 16GB model in MiB "is_moe": False, "max_context_length": 16384, # 16K tokens } @pytest.fixture def vllm_args_with_model_autogen(self): """Create arguments for vllm backend with model-based search space autogeneration.""" class Args: def __init__(self): self.backend = "vllm" self.config = "" self.output_dir = "/tmp/test_profiling_results" self.namespace = "test-namespace" self.model = "deepseek-ai/DeepSeek-R1-Distill-Llama-8B" # Specify model for autogen self.min_num_gpus_per_engine = 0 # Will be auto-generated self.max_num_gpus_per_engine = 0 # Will be auto-generated self.skip_existing_results = False self.force_rerun = False self.isl = 3000 self.osl = 500 self.ttft = 50 self.itl = 10 self.max_context_length = 0 self.prefill_interpolation_granularity = 16 self.decode_interpolation_granularity = 6 self.service_name = "" self.is_moe_model = False self.dry_run = True self.use_ai_configurator = False self.aic_system = None self.aic_model_name = None self.aic_backend = "" self.aic_backend_version = None self.num_gpus_per_node = None # Will be auto-generated self.deploy_after_profile = False return Args() @pytest.mark.pre_merge @pytest.mark.asyncio @patch("benchmarks.profiler.utils.search_space_autogen.get_gpu_summary") @patch("benchmarks.profiler.utils.search_space_autogen.get_model_info") async def test_profile_with_autogen_search_space_h100( self, mock_get_model_info, mock_get_gpu_summary, vllm_args_with_model_autogen, mock_h100_gpu_info, mock_model_info, ): """Test profile_sla with auto-generated search space on mocked H100 cluster. This test demonstrates how search space is auto-generated based on model size and available GPU memory. """ # Configure the mocks to return the appropriate info mock_get_model_info.return_value = mock_model_info mock_get_gpu_summary.return_value = mock_h100_gpu_info # Run the profile - the search space will be auto-generated # based on the model and mocked GPU info auto_generate_search_space(vllm_args_with_model_autogen) await run_profile(vllm_args_with_model_autogen) @pytest.fixture def sglang_args_with_model_autogen(self): """Create arguments for sglang backend with model-based search space autogeneration.""" class Args: def __init__(self): self.backend = "sglang" self.config = "" self.output_dir = "/tmp/test_profiling_results" self.namespace = "test-namespace" self.model = "deepseek-ai/DeepSeek-R1-Distill-Llama-8B" # Specify model for autogen self.min_num_gpus_per_engine = 0 # Will be auto-generated self.max_num_gpus_per_engine = 0 # Will be auto-generated self.skip_existing_results = False self.force_rerun = False self.isl = 3000 self.osl = 500 self.ttft = 50 self.itl = 10 self.max_context_length = 0 self.prefill_interpolation_granularity = 16 self.decode_interpolation_granularity = 6 self.service_name = "" self.is_moe_model = False self.dry_run = True self.use_ai_configurator = False self.aic_system = None self.aic_model_name = None self.aic_backend = "" self.aic_backend_version = None self.num_gpus_per_node = None # Will be auto-generated self.deploy_after_profile = False return Args() @pytest.mark.pre_merge @pytest.mark.asyncio @patch("benchmarks.profiler.utils.search_space_autogen.get_gpu_summary") @patch("benchmarks.profiler.utils.search_space_autogen.get_model_info") async def test_sglang_profile_with_autogen_search_space_h100( self, mock_get_model_info, mock_get_gpu_summary, sglang_args_with_model_autogen, mock_h100_gpu_info, mock_model_info, ): """Test profile_sla with auto-generated search space for sglang on mocked H100 cluster. This test demonstrates how search space is auto-generated based on model size and available GPU memory for sglang backend. """ # Configure the mocks to return the appropriate info mock_get_model_info.return_value = mock_model_info mock_get_gpu_summary.return_value = mock_h100_gpu_info # Run the profile - the search space will be auto-generated # based on the model and mocked GPU info auto_generate_search_space(sglang_args_with_model_autogen) await run_profile(sglang_args_with_model_autogen) @pytest.fixture def trtllm_args_with_model_autogen(self): """Create arguments for trtllm backend with model-based search space autogeneration.""" class Args: def __init__(self): self.backend = "trtllm" self.config = "" self.output_dir = "/tmp/test_profiling_results" self.namespace = "test-namespace" self.model = "deepseek-ai/DeepSeek-R1-Distill-Llama-8B" # Specify model for autogen self.min_num_gpus_per_engine = 0 # Will be auto-generated self.max_num_gpus_per_engine = 0 # Will be auto-generated self.skip_existing_results = False self.force_rerun = False self.isl = 3000 self.osl = 500 self.ttft = 50 self.itl = 10 self.max_context_length = 0 self.prefill_interpolation_granularity = 16 self.decode_interpolation_granularity = 6 self.service_name = "" self.is_moe_model = False self.dry_run = True self.use_ai_configurator = False self.aic_system = None self.aic_model_name = None self.aic_backend = "" self.aic_backend_version = None self.num_gpus_per_node = None # Will be auto-generated self.deploy_after_profile = False return Args() @pytest.mark.pre_merge @pytest.mark.asyncio @patch("benchmarks.profiler.utils.search_space_autogen.get_gpu_summary") @patch("benchmarks.profiler.utils.search_space_autogen.get_model_info") async def test_trtllm_profile_with_autogen_search_space_h100( self, mock_get_model_info, mock_get_gpu_summary, trtllm_args_with_model_autogen, mock_h100_gpu_info, mock_model_info, ): """Test profile_sla with auto-generated search space for trtllm on mocked H100 cluster. This test demonstrates how search space is auto-generated based on model size and available GPU memory for trtllm backend. """ # Configure the mocks to return the appropriate info mock_get_model_info.return_value = mock_model_info mock_get_gpu_summary.return_value = mock_h100_gpu_info # Run the profile - the search space will be auto-generated # based on the model and mocked GPU info auto_generate_search_space(trtllm_args_with_model_autogen) await run_profile(trtllm_args_with_model_autogen)