# 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.model import _FLOAT16_NOT_SUPPORTED_MODELS, RunnerOption from vllm.logger import init_logger from vllm.transformers_utils.config import get_config from ..models.registry import HF_EXAMPLE_MODELS from ..utils import compare_two_settings, create_new_process_for_each_test logger = init_logger("test_pipeline_parallel") VLLM_MULTI_NODE = os.getenv("VLLM_MULTI_NODE", "0") == "1" class ParallelSetup(NamedTuple): tp_size: int pp_size: int eager_mode: bool class PPTestOptions(NamedTuple): multi_node_only: bool load_format: Optional[str] = None @dataclass class PPTestSettings: parallel_setups: list[ParallelSetup] distributed_backends: list[str] runner: RunnerOption test_options: PPTestOptions @staticmethod def detailed( *, tp_base: int = 1, pp_base: int = 2, multi_node_only: bool = False, runner: RunnerOption = "auto", load_format: Optional[str] = None, ): return PPTestSettings( parallel_setups=[ ParallelSetup(tp_size=tp_base, pp_size=pp_base, eager_mode=False), ParallelSetup(tp_size=tp_base, pp_size=2 * pp_base, eager_mode=False), ParallelSetup(tp_size=tp_base, pp_size=2 * pp_base, eager_mode=True), ParallelSetup(tp_size=2 * tp_base, pp_size=pp_base, eager_mode=False), ParallelSetup(tp_size=2 * tp_base, pp_size=pp_base, eager_mode=True), ], distributed_backends=["mp", "ray"], runner=runner, test_options=PPTestOptions(multi_node_only=multi_node_only, load_format=load_format), ) @staticmethod def fast( *, tp_base: int = 1, pp_base: int = 2, runner: RunnerOption = "auto", multi_node_only: bool = False, load_format: Optional[str] = None, ): return PPTestSettings( parallel_setups=[ ParallelSetup(tp_size=tp_base, pp_size=pp_base, eager_mode=True), ], distributed_backends=["mp"], runner=runner, test_options=PPTestOptions(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 in self.distributed_backends: yield (model_id, parallel_setup, backend, self.runner, opts) # NOTE: You can adjust tp_base and/or pp_base locally to fit the model in GPU # The values displayed here are only a rough indicator of the size of the model # yapf: disable TEXT_GENERATION_MODELS = { # [Decoder-only] # Uses Llama # "BAAI/AquilaChat-7B": PPTestSettings.fast(), "Snowflake/snowflake-arctic-instruct": PPTestSettings.fast(load_format="dummy"), # noqa: E501 "baichuan-inc/Baichuan-7B": PPTestSettings.fast(), "baichuan-inc/Baichuan2-13B-Chat": PPTestSettings.fast(), "bigscience/bloomz-1b1": PPTestSettings.fast(), "zai-org/chatglm3-6b": PPTestSettings.fast(), "CohereForAI/c4ai-command-r-v01": PPTestSettings.fast(load_format="dummy"), "databricks/dbrx-instruct": PPTestSettings.fast(load_format="dummy"), "Deci/DeciLM-7B-instruct": PPTestSettings.fast(), "deepseek-ai/deepseek-llm-7b-chat": PPTestSettings.fast(), "deepseek-ai/DeepSeek-V2-Lite-Chat": PPTestSettings.fast(tp_base=2), "LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct": PPTestSettings.fast(), "tiiuae/falcon-7b": PPTestSettings.fast(), "google/gemma-1.1-2b-it": PPTestSettings.fast(), "google/gemma-2-9b": PPTestSettings.fast(), "gpt2": PPTestSettings.fast(), "bigcode/starcoder": PPTestSettings.fast(), "EleutherAI/gpt-j-6b": PPTestSettings.fast(), "EleutherAI/pythia-1.4b": PPTestSettings.fast(), "ibm/PowerLM-3b": PPTestSettings.fast(), "ibm/PowerMoE-3b": PPTestSettings.fast(), # Uses Llama # "internlm/internlm-chat-7b": PPTestSettings.fast(), "internlm/internlm2-chat-7b": PPTestSettings.fast(), "inceptionai/jais-13b-chat": PPTestSettings.fast(), "ai21labs/Jamba-tiny-dev": PPTestSettings.fast(), "pfnet/plamo-2-1b": PPTestSettings.fast(), "meta-llama/Llama-3.2-1B-Instruct": PPTestSettings.detailed(), # Tests TransformersForCausalLM "hmellor/Ilama-3.2-1B": PPTestSettings.fast(), "openbmb/MiniCPM-2B-sft-bf16": PPTestSettings.fast(), "openbmb/MiniCPM3-4B": PPTestSettings.fast(), # Uses Llama # "mistralai/Mistral-7B-Instruct-v0.1": PPTestSettings.fast(), "state-spaces/mamba-130m-hf": PPTestSettings.fast(), "mistralai/Mixtral-8x7B-Instruct-v0.1": PPTestSettings.fast(load_format="dummy"), # noqa: E501 "mosaicml/mpt-7b": PPTestSettings.fast(), "nvidia/Minitron-8B-Base": PPTestSettings.fast(), "allenai/OLMo-1B-hf": PPTestSettings.fast(), "allenai/OLMo-2-0425-1B": PPTestSettings.fast(), "allenai/OLMoE-1B-7B-0924-Instruct": PPTestSettings.fast(), "facebook/opt-iml-max-1.3b": PPTestSettings.fast(), "OrionStarAI/Orion-14B-Chat": PPTestSettings.fast(), "adept/persimmon-8b-chat": PPTestSettings.fast(), "microsoft/phi-2": PPTestSettings.fast(), "microsoft/Phi-3-small-8k-instruct": PPTestSettings.fast(), "microsoft/Phi-3.5-MoE-instruct": PPTestSettings.detailed(multi_node_only=True, load_format="dummy"), # noqa: E501 "Qwen/Qwen-7B-Chat": PPTestSettings.fast(), "Qwen/Qwen2.5-0.5B-Instruct": PPTestSettings.fast(), "Qwen/Qwen1.5-MoE-A2.7B-Chat": PPTestSettings.fast(), "stabilityai/stablelm-3b-4e1t": PPTestSettings.fast(), "bigcode/starcoder2-3b": PPTestSettings.fast(), "upstage/solar-pro-preview-instruct": PPTestSettings.fast(load_format="dummy"), # noqa: E501 # FIXME: Cannot load tokenizer in latest transformers version. # Need to use tokenizer from `meta-llama/Llama-2-7b-chat-hf` # "xverse/XVERSE-7B-Chat": PPTestSettings.fast(), # [Encoder-only] # TODO: Implement PP # "facebook/bart-base": PPTestSettings.fast(), } EMBEDDING_MODELS = { # type: ignore[var-annotated] # [Text-only] "intfloat/e5-mistral-7b-instruct": PPTestSettings.fast(runner="pooling"), "BAAI/bge-multilingual-gemma2": PPTestSettings.fast(runner="pooling"), "Qwen/Qwen2.5-Math-RM-72B": PPTestSettings.fast( load_format="dummy", runner="pooling" ), } MULTIMODAL_MODELS = { # [Decoder-only] "Salesforce/blip2-opt-6.7b": PPTestSettings.fast(), "facebook/chameleon-7b": PPTestSettings.fast(), "adept/fuyu-8b": PPTestSettings.fast(), "zai-org/glm-4v-9b": PPTestSettings.fast(), "OpenGVLab/InternVL2-1B": PPTestSettings.fast(), "llava-hf/llava-1.5-7b-hf": PPTestSettings.fast(), "llava-hf/llava-v1.6-mistral-7b-hf": PPTestSettings.fast(), "llava-hf/LLaVA-NeXT-Video-7B-hf": PPTestSettings.fast(), "llava-hf/llava-onevision-qwen2-0.5b-ov-hf": PPTestSettings.fast(), "openbmb/MiniCPM-Llama3-V-2_5": PPTestSettings.fast(), "allenai/Molmo-7B-D-0924": PPTestSettings.fast(), "AIDC-AI/Ovis2-1B": PPTestSettings.fast(), "AIDC-AI/Ovis2.5-2B": PPTestSettings.fast(), "microsoft/Phi-3.5-vision-instruct": PPTestSettings.fast(), "mistralai/Pixtral-12B-2409": PPTestSettings.fast(load_format="dummy"), "Qwen/Qwen-VL-Chat": PPTestSettings.fast(), "Qwen/Qwen2-Audio-7B-Instruct": PPTestSettings.fast(), "Qwen/Qwen2-VL-2B-Instruct": PPTestSettings.fast(), "fixie-ai/ultravox-v0_5-llama-3_2-1b": PPTestSettings.fast(), } # yapf: enable # NOTE: You can update this on your local machine to run specific tests TEST_MODELS = [ # [LANGUAGE GENERATION] "microsoft/Phi-3.5-MoE-instruct", "meta-llama/Llama-3.2-1B-Instruct", "hmellor/Ilama-3.2-1B", "ibm/PowerLM-3b", "deepseek-ai/DeepSeek-V2-Lite-Chat", # [LANGUAGE EMBEDDING] "intfloat/e5-mistral-7b-instruct", "BAAI/bge-multilingual-gemma2", # [MULTIMODAL GENERATION] "OpenGVLab/InternVL2-1B", "microsoft/Phi-3.5-vision-instruct", "fixie-ai/ultravox-v0_5-llama-3_2-1b", # [LANGUAGE GENERATION - HYBRID ARCH] "ai21labs/Jamba-tiny-dev", ] def _compare_tp( model_id: str, parallel_setup: ParallelSetup, distributed_backend: str, runner: RunnerOption, test_options: PPTestOptions, num_gpus_available: int, *, method: Literal["generate", "encode"], is_multimodal: bool, ): ( tp_size, pp_size, eager_mode, ) = 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 hf_config = get_config(model_id, trust_remote_code) skip_tokenizer_init = model_info.skip_tokenizer_init max_num_seqs = model_info.max_num_seqs dtype = "float16" if hf_config.model_type in _FLOAT16_NOT_SUPPORTED_MODELS: dtype = "bfloat16" 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", dtype, "--max-model-len", "2048", "--max-num-seqs", "8", ] if eager_mode: common_args.append("--enforce-eager") if runner != "auto": common_args.extend(["--runner", runner]) 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)]) if skip_tokenizer_init: common_args.append("--skip-tokenizer-init") if max_num_seqs: common_args.extend(["--max-num-seqs", f"{max_num_seqs}"]) if distributed_backend == "ray": # For V1, test Ray Compiled Graph for all the tests pp_env = { "VLLM_USE_V1": "1", "VLLM_USE_RAY_COMPILED_DAG": "1", "VLLM_USE_RAY_SPMD_WORKER": "1", "VLLM_USE_RAY_COMPILED_DAG_NCCL_CHANNEL": "1", } # Temporary. Currently when zeromq + SPMD is used, it does not properly # terminate because of a Ray Compiled Graph issue. common_args.append("--disable-frontend-multiprocessing") elif distributed_backend == "mp": pp_env = { "VLLM_USE_V1": "1", } else: pp_env = None tp_env = { "VLLM_USE_V1": "1", } pp_args = [ *common_args, "--pipeline-parallel-size", str(pp_size), "--tensor-parallel-size", str(tp_size), "--distributed-executor-backend", distributed_backend, ] # compare without pipeline parallelism # NOTE: use mp backend for TP # PP tests might involve multiple nodes, and ray might # schedule all workers in a node other than the head node, # which can cause the test to fail. tp_args = [ *common_args, "--tensor-parallel-size", str(tp_size), "--distributed-executor-backend", "mp", ] compare_two_settings(model_id, pp_args, tp_args, pp_env, tp_env, method=method) @pytest.mark.parametrize( ("model_id", "parallel_setup", "distributed_backend", "runner", "test_options"), [ params for model_id, settings in TEXT_GENERATION_MODELS.items() for params in settings.iter_params(model_id) if model_id in TEST_MODELS ], ) @create_new_process_for_each_test() def test_tp_language_generation( model_id: str, parallel_setup: ParallelSetup, distributed_backend: str, runner: RunnerOption, test_options: PPTestOptions, num_gpus_available, ): _compare_tp(model_id, parallel_setup, distributed_backend, runner, test_options, num_gpus_available, method="generate", is_multimodal=False) @pytest.mark.parametrize( ("model_id", "parallel_setup", "distributed_backend", "runner", "test_options"), [ params for model_id, settings in EMBEDDING_MODELS.items() for params in settings.iter_params(model_id) if model_id in TEST_MODELS ], ) @create_new_process_for_each_test() def test_tp_language_embedding( model_id: str, parallel_setup: ParallelSetup, distributed_backend: str, runner: RunnerOption, test_options: PPTestOptions, num_gpus_available, ): _compare_tp(model_id, parallel_setup, distributed_backend, runner, test_options, num_gpus_available, method="encode", is_multimodal=False) @pytest.mark.parametrize( ("model_id", "parallel_setup", "distributed_backend", "runner", "test_options"), [ params for model_id, settings in MULTIMODAL_MODELS.items() for params in settings.iter_params(model_id) if model_id in TEST_MODELS ], ) @create_new_process_for_each_test() def test_tp_multimodal_generation( model_id: str, parallel_setup: ParallelSetup, distributed_backend: str, runner: RunnerOption, test_options: PPTestOptions, num_gpus_available, ): _compare_tp(model_id, parallel_setup, distributed_backend, runner, test_options, num_gpus_available, method="generate", is_multimodal=True)