diff --git a/.buildkite/test-pipeline.yaml b/.buildkite/test-pipeline.yaml index c102a532..6931659d 100644 --- a/.buildkite/test-pipeline.yaml +++ b/.buildkite/test-pipeline.yaml @@ -45,9 +45,6 @@ steps: num_gpus: 2 commands: - bash ../.buildkite/download-images.sh - # FIXIT: find out which code initialize cuda before running the test - # before the fix, we need to use spawn to test it - - export VLLM_WORKER_MULTIPROC_METHOD=spawn - VLLM_TEST_SAME_HOST=1 torchrun --nproc-per-node=4 distributed/test_same_node.py - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=ray pytest -v -s distributed/test_basic_distributed_correctness.py @@ -60,8 +57,7 @@ steps: - TEST_DIST_MODEL=facebook/opt-125m DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_chunked_prefill_distributed.py - TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_chunked_prefill_distributed.py - TEST_DIST_MODEL=llava-hf/llava-1.5-7b-hf DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_multimodal_broadcast.py - # FIXIT: find out why TP is failing with mp backend on phi3-v - # - TEST_DIST_MODEL=microsoft/Phi-3-vision-128k-instruct DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_multimodal_broadcast.py + - TEST_DIST_MODEL=microsoft/Phi-3-vision-128k-instruct DISTRIBUTED_EXECUTOR_BACKEND=mp pytest -v -s distributed/test_multimodal_broadcast.py - pytest -v -s spec_decode/e2e/test_integration_dist_tp2.py - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s test_sharded_state_loader.py - CUDA_VISIBLE_DEVICES=0,1 pytest -v -s distributed/test_utils.py @@ -71,9 +67,6 @@ steps: working_dir: "/vllm-workspace/tests" num_gpus: 4 commands: - # FIXIT: find out which code initialize cuda before running the test - # before the fix, we need to use spawn to test it - - export VLLM_WORKER_MULTIPROC_METHOD=spawn - pytest -v -s distributed/test_pynccl.py # We want to test that models which use 2 GPUs work with 4 GPUs, which is why we duplicate them here. # See https://github.com/vllm-project/vllm/pull/5473#issuecomment-2166601837 for context. @@ -225,9 +218,6 @@ steps: gpu: a100 num_gpus: 4 commands: - # FIXIT: find out which code initialize cuda before running the test - # before the fix, we need to use spawn to test it - - export VLLM_WORKER_MULTIPROC_METHOD=spawn # NOTE: don't test llama model here, it seems hf implementation is buggy # see https://github.com/vllm-project/vllm/pull/5689 for details - pytest -v -s distributed/test_custom_all_reduce.py diff --git a/tests/conftest.py b/tests/conftest.py index b429d8d0..0bd24905 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,8 +5,8 @@ from collections import UserList from dataclasses import dataclass from functools import cached_property from pathlib import Path -from typing import (Any, Dict, List, Literal, Optional, Tuple, TypedDict, - TypeVar) +from typing import (TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, + TypedDict, TypeVar) import pytest import torch @@ -14,7 +14,7 @@ import torch.nn as nn import torch.nn.functional as F from PIL import Image from transformers import (AutoModelForCausalLM, AutoModelForVision2Seq, - AutoProcessor, AutoTokenizer, BatchEncoding) + AutoTokenizer, BatchEncoding) from vllm import LLM, SamplingParams from vllm.config import TokenizerPoolConfig, VisionLanguageConfig @@ -22,8 +22,12 @@ from vllm.distributed import (destroy_distributed_environment, destroy_model_parallel) from vllm.inputs import TextPrompt from vllm.logger import init_logger -from vllm.multimodal import MultiModalData -from vllm.multimodal.image import ImageFeatureData, ImagePixelData + +if TYPE_CHECKING: + from vllm.multimodal import MultiModalData +else: + # it will call torch.cuda.device_count() + MultiModalData = None from vllm.sequence import SampleLogprobs from vllm.utils import cuda_device_count_stateless, is_cpu @@ -63,6 +67,10 @@ class ImageAsset: return self.pil_image def for_vllm(self, vision_config: VisionLanguageConfig) -> MultiModalData: + # don't put this import at the top level + # it will call torch.cuda.device_count() + from vllm.multimodal.image import ImageFeatureData # noqa: F401 + from vllm.multimodal.image import ImagePixelData image_input_type = vision_config.image_input_type ImageInputType = VisionLanguageConfig.ImageInputType @@ -216,6 +224,9 @@ class HfRunner: ) try: + # don't put this import at the top level + # it will call torch.cuda.device_count() + from transformers import AutoProcessor # noqa: F401 self.processor = AutoProcessor.from_pretrained( model_name, torch_dtype=torch_dtype, diff --git a/tests/distributed/test_basic_distributed_correctness.py b/tests/distributed/test_basic_distributed_correctness.py index 1f5fff3e..7a0e5673 100644 --- a/tests/distributed/test_basic_distributed_correctness.py +++ b/tests/distributed/test_basic_distributed_correctness.py @@ -15,7 +15,8 @@ TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf \ import os import pytest -import torch + +from vllm.utils import cuda_device_count_stateless from ..models.utils import check_outputs_equal @@ -25,7 +26,7 @@ MODELS = [ DISTRIBUTED_EXECUTOR_BACKEND = "DISTRIBUTED_EXECUTOR_BACKEND" -@pytest.mark.skipif(torch.cuda.device_count() < 2, +@pytest.mark.skipif(cuda_device_count_stateless() < 2, reason="Need at least 2 GPUs to run the test.") @pytest.mark.parametrize("model", MODELS) @pytest.mark.parametrize("dtype", ["half"]) @@ -40,9 +41,10 @@ def test_models( ) -> None: distributed_executor_backend = os.getenv(DISTRIBUTED_EXECUTOR_BACKEND) - with hf_runner(model, dtype=dtype) as hf_model: - hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens) - + # NOTE: take care of the order. run vLLM first, and then run HF. + # vLLM needs a fresh new process without cuda initialization. + # if we run HF first, the cuda initialization will be done and it + # will hurt multiprocessing backend with fork method (the default method). with vllm_runner(model, dtype=dtype, tensor_parallel_size=2, @@ -50,6 +52,9 @@ def test_models( ) as vllm_model: vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens) + with hf_runner(model, dtype=dtype) as hf_model: + hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens) + check_outputs_equal( outputs_0_lst=hf_outputs, outputs_1_lst=vllm_outputs, diff --git a/tests/distributed/test_chunked_prefill_distributed.py b/tests/distributed/test_chunked_prefill_distributed.py index fd89147a..1ef085b9 100644 --- a/tests/distributed/test_chunked_prefill_distributed.py +++ b/tests/distributed/test_chunked_prefill_distributed.py @@ -14,7 +14,8 @@ TEST_DIST_MODEL=meta-llama/Llama-2-7b-hf \ import os import pytest -import torch + +from vllm.utils import cuda_device_count_stateless from ..models.utils import check_outputs_equal @@ -24,7 +25,7 @@ MODELS = [ DISTRIBUTED_EXECUTOR_BACKEND = "DISTRIBUTED_EXECUTOR_BACKEND" -@pytest.mark.skipif(torch.cuda.device_count() < 2, +@pytest.mark.skipif(cuda_device_count_stateless() < 2, reason="Need at least 2 GPUs to run the test.") @pytest.mark.parametrize("model", MODELS) @pytest.mark.parametrize("dtype", ["half"]) @@ -47,8 +48,10 @@ def test_models( enable_chunked_prefill = True max_num_batched_tokens = chunked_prefill_token_size - with hf_runner(model, dtype=dtype) as hf_model: - hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens) + # NOTE: take care of the order. run vLLM first, and then run HF. + # vLLM needs a fresh new process without cuda initialization. + # if we run HF first, the cuda initialization will be done and it + # will hurt multiprocessing backend with fork method (the default method). with vllm_runner( model, @@ -61,6 +64,9 @@ def test_models( ) as vllm_model: vllm_outputs = vllm_model.generate_greedy(example_prompts, max_tokens) + with hf_runner(model, dtype=dtype) as hf_model: + hf_outputs = hf_model.generate_greedy(example_prompts, max_tokens) + check_outputs_equal( outputs_0_lst=hf_outputs, outputs_1_lst=vllm_outputs, diff --git a/tests/models/test_llava.py b/tests/models/test_llava.py index c60b15af..b4220dc5 100644 --- a/tests/models/test_llava.py +++ b/tests/models/test_llava.py @@ -88,17 +88,11 @@ def run_test( """ model_id, vlm_config = model_and_config hf_images = [asset.for_hf() for asset in image_assets] - vllm_images = [asset.for_vllm(vlm_config) for asset in image_assets] - with hf_runner(model_id, dtype=dtype, is_vision_model=True) as hf_model: - hf_outputs = hf_model.generate_greedy(HF_IMAGE_PROMPTS, - max_tokens, - images=hf_images) - - vllm_image_prompts = [ - p.replace("", "" * vlm_config.image_feature_size) - for p in HF_IMAGE_PROMPTS - ] + # NOTE: take care of the order. run vLLM first, and then run HF. + # vLLM needs a fresh new process without cuda initialization. + # if we run HF first, the cuda initialization will be done and it + # will hurt multiprocessing backend with fork method (the default method). with vllm_runner(model_id, dtype=dtype, @@ -106,10 +100,26 @@ def run_test( distributed_executor_backend=distributed_executor_backend, enforce_eager=True, **vlm_config.as_cli_args_dict()) as vllm_model: + + # NOTE: `asset.for_vllm` will call `torch.cuda.device_count()` + # we must put it inside the vllm_runner context manager + # i.e. after creating vLLM instance. + vllm_images = [asset.for_vllm(vlm_config) for asset in image_assets] + + vllm_image_prompts = [ + p.replace("", "" * vlm_config.image_feature_size) + for p in HF_IMAGE_PROMPTS + ] + vllm_outputs = vllm_model.generate_greedy(vllm_image_prompts, max_tokens, images=vllm_images) + with hf_runner(model_id, dtype=dtype, is_vision_model=True) as hf_model: + hf_outputs = hf_model.generate_greedy(HF_IMAGE_PROMPTS, + max_tokens, + images=hf_images) + check_outputs_equal( hf_outputs, [ diff --git a/tests/models/test_phi3v.py b/tests/models/test_phi3v.py index 2e34fa8c..ba71763f 100644 --- a/tests/models/test_phi3v.py +++ b/tests/models/test_phi3v.py @@ -96,7 +96,34 @@ def run_test( """ model_id, vlm_config = model_and_config hf_images = [asset.for_hf() for asset in image_assets] - vllm_images = [asset.for_vllm(vlm_config) for asset in image_assets] + + # NOTE: take care of the order. run vLLM first, and then run HF. + # vLLM needs a fresh new process without cuda initialization. + # if we run HF first, the cuda initialization will be done and it + # will hurt multiprocessing backend with fork method (the default method). + + with vllm_runner(model_id, + max_model_len=2048, + dtype=dtype, + tensor_parallel_size=tensor_parallel_size, + enforce_eager=True, + distributed_executor_backend=distributed_executor_backend, + **vlm_config.as_cli_args_dict()) as vllm_model: + # NOTE: `asset.for_vllm` will call `torch.cuda.device_count()` + # we must put it inside the vllm_runner context manager + # i.e. after creating vLLM instance. + + vllm_images = [asset.for_vllm(vlm_config) for asset in image_assets] + + vllm_image_prompts = [ + p.replace("<|image_1|>", + "<|image|>" * vlm_config.image_feature_size + "") + for p in HF_IMAGE_PROMPTS + ] + + vllm_outputs = vllm_model.generate_greedy(vllm_image_prompts, + max_tokens, + images=vllm_images) # use eager mode for hf runner, since phi3_v didn't work with flash_attn hf_model_kwargs = {"_attn_implementation": "eager"} @@ -108,23 +135,6 @@ def run_test( images=hf_images, eos_token_id=hf_model.processor.tokenizer.eos_token_id) - vllm_image_prompts = [ - p.replace("<|image_1|>", - "<|image|>" * vlm_config.image_feature_size + "") - for p in HF_IMAGE_PROMPTS - ] - - with vllm_runner(model_id, - max_model_len=2048, - dtype=dtype, - tensor_parallel_size=tensor_parallel_size, - enforce_eager=True, - distributed_executor_backend=distributed_executor_backend, - **vlm_config.as_cli_args_dict()) as vllm_model: - vllm_outputs = vllm_model.generate_greedy(vllm_image_prompts, - max_tokens, - images=vllm_images) - check_outputs_equal( hf_outputs, [