# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project """ WARNING: This test runs in both single-node (4 GPUs) and multi-node (2 node with 2 GPUs each) modes. If the test only uses 2 GPUs, it is important to set the distributed backend to "mp" to avoid Ray scheduling all workers in a node other than the head node, which can cause the test to fail. """ import json import os from dataclasses import dataclass from typing import Literal, NamedTuple, Optional import pytest from vllm.config import TaskOption from vllm.logger import init_logger from ..models.registry import HF_EXAMPLE_MODELS from ..utils import compare_two_settings, create_new_process_for_each_test logger = init_logger("test_sequence_parallel") VLLM_MULTI_NODE = os.getenv("VLLM_MULTI_NODE", "0") == "1" class ParallelSetup(NamedTuple): tp_size: int pp_size: int enable_fusion: bool eager_mode: bool chunked_prefill: bool class SPTestOptions(NamedTuple): multi_node_only: bool load_format: Optional[str] = None @dataclass class SPTestSettings: parallel_setups: list[ParallelSetup] # NOTE: the length of distributed_backends and # vllm_major_versions should be the same, and they # are first zipped together to iterate over all # test settings. distributed_backends: list[str] # vllm major version: "0" for V0, "1" for V1 vllm_major_versions: list[str] task: TaskOption test_options: SPTestOptions def __post_init__(self): if len(self.distributed_backends) != len(self.vllm_major_versions): raise ValueError( f"Length mismatch: distributed_backends " f"({len(self.distributed_backends)}) != " f"vllm_major_versions ({len(self.vllm_major_versions)})") @staticmethod def detailed( *, tp_base: int = 2, pp_base: int = 1, multi_node_only: bool = False, task: TaskOption = "auto", load_format: Optional[str] = None, ): parallel_setups = [] for eager_mode_val in [False, True]: for pp_multiplier in [1, 2]: for chunked_prefill_val in [False, True]: parallel_setups.append( ParallelSetup(tp_size=tp_base, pp_size=pp_multiplier * pp_base, enable_fusion=False, eager_mode=eager_mode_val, chunked_prefill=chunked_prefill_val)) return SPTestSettings( parallel_setups=parallel_setups, distributed_backends=["mp", "ray"], vllm_major_versions=["1", "1"], task=task, test_options=SPTestOptions(multi_node_only=multi_node_only, load_format=load_format), ) @staticmethod def fast( *, tp_base: int = 2, pp_base: int = 1, task: TaskOption = "auto", multi_node_only: bool = False, load_format: Optional[str] = None, ): parallel_setups = [] for eager_mode_val in [False, True]: for pp_multiplier in [1, 2]: for chunked_prefill_val in [False, True]: parallel_setups.append( ParallelSetup(tp_size=tp_base, pp_size=pp_multiplier * pp_base, enable_fusion=False, eager_mode=eager_mode_val, chunked_prefill=chunked_prefill_val)) return SPTestSettings( parallel_setups=parallel_setups, distributed_backends=["mp", "ray"], vllm_major_versions=["1", "1"], task=task, test_options=SPTestOptions(multi_node_only=multi_node_only, load_format=load_format), ) @staticmethod def fp8_quant( *, tp_base: int = 2, pp_base: int = 1, task: TaskOption = "auto", multi_node_only: bool = False, load_format: Optional[str] = None, ): parallel_setups = [] for fusion_val in [False, True]: parallel_setups.append( ParallelSetup(tp_size=tp_base, pp_size=pp_base, enable_fusion=fusion_val, eager_mode=True, chunked_prefill=False)) return SPTestSettings( parallel_setups=parallel_setups, distributed_backends=["mp", "ray"], vllm_major_versions=["1", "1"], task=task, test_options=SPTestOptions(multi_node_only=multi_node_only, load_format=load_format), ) def iter_params(self, model_id: str): opts = self.test_options for parallel_setup in self.parallel_setups: for backend, vllm_major_version in zip(self.distributed_backends, self.vllm_major_versions): yield (model_id, parallel_setup, backend, vllm_major_version, self.task, opts) def _compare_sp( model_id: str, parallel_setup: ParallelSetup, distributed_backend: str, vllm_major_version: str, task: TaskOption, test_options: SPTestOptions, num_gpus_available: int, *, method: Literal["generate", "encode"], is_multimodal: bool, ): ( tp_size, pp_size, enable_fusion, eager_mode, chunked_prefill, ) = parallel_setup multi_node_only, load_format = test_options model_info = HF_EXAMPLE_MODELS.find_hf_info(model_id) model_info.check_transformers_version(on_fail="skip") trust_remote_code = model_info.trust_remote_code tokenizer_mode = model_info.tokenizer_mode hf_overrides = model_info.hf_overrides if load_format == "dummy": # Avoid OOM text_overrides = { "num_hidden_layers": 4, "hidden_size": 512, "intermediate_size": 800, "num_attention_heads": 4, "num_key_value_heads": 1, } if is_multimodal: hf_overrides.update({"text_config": text_overrides}) else: hf_overrides.update(text_overrides) else: model_info.check_available_online(on_fail="skip") if num_gpus_available < tp_size * pp_size: pytest.skip(f"Need at least {tp_size} x {pp_size} GPUs") if VLLM_MULTI_NODE and distributed_backend == "mp": pytest.skip("Skipping multi-node pipeline parallel test for " "multiprocessing distributed backend") if multi_node_only and not VLLM_MULTI_NODE: pytest.skip("Not in multi-node setting") common_args = [ # use half precision for speed and memory savings in CI environment "--dtype", "float16", "--max-model-len", "2048", "--max-num-seqs", "8", ] if chunked_prefill: common_args.append("--enable-chunked-prefill") if eager_mode: common_args.append("--enforce-eager") if task != "auto": common_args.extend(["--task", task]) if trust_remote_code: common_args.append("--trust-remote-code") if tokenizer_mode: common_args.extend(["--tokenizer-mode", tokenizer_mode]) if load_format: common_args.extend(["--load-format", load_format]) if hf_overrides: common_args.extend(["--hf-overrides", json.dumps(hf_overrides)]) compilation_config = { 'level': 3, 'custom_ops': ["+rms_norm"], 'compile_sizes': [4, 8], 'splitting_ops': [], 'pass_config': { 'enable_sequence_parallelism': True, 'enable_fusion': enable_fusion, 'enable_noop': True, }, } tp_sp_env = tp_env = { "VLLM_USE_V1": vllm_major_version, } tp_sp_args = [ *common_args, "--tensor-parallel-size", str(tp_size), "--distributed-executor-backend", distributed_backend, "--compilation_config", json.dumps(compilation_config), ] tp_env = { "VLLM_USE_V1": vllm_major_version, } tp_args = [ *common_args, "--tensor-parallel-size", str(tp_size), "--distributed-executor-backend", "mp", ] try: compare_two_settings(model_id, tp_sp_args, tp_args, tp_sp_env, tp_env, method=method) except Exception: testing_ray_compiled_graph = tp_sp_env is not None if testing_ray_compiled_graph and vllm_major_version == "0": # Ray Compiled Graph tests are flaky for V0, # so we don't want to fail the test logger.exception("Ray Compiled Graph tests failed") else: raise SP_TEXT_GENERATION_MODELS = { # [Decoder-only] "meta-llama/Llama-3.2-1B-Instruct": SPTestSettings.fast(), "RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8": SPTestSettings.fp8_quant(), } SP_TEST_MODELS = [ # TODO support other models # [LANGUAGE GENERATION] "meta-llama/Llama-3.2-1B-Instruct", "RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8" ] @pytest.mark.parametrize( ("model_id", "parallel_setup", "distributed_backend", "vllm_major_version", "task", "test_options"), [ params for model_id, settings in SP_TEXT_GENERATION_MODELS.items() for params in settings.iter_params(model_id) if model_id in SP_TEST_MODELS ], ) @create_new_process_for_each_test() def test_tp_sp_generation( model_id: str, parallel_setup: ParallelSetup, distributed_backend: str, vllm_major_version: str, task: TaskOption, test_options: SPTestOptions, num_gpus_available, ): _compare_sp(model_id, parallel_setup, distributed_backend, vllm_major_version, task, test_options, num_gpus_available, method="generate", is_multimodal=False)