[test] refactor tests with spawn (#3452)

* [test] added spawn decorator

* polish code

* polish code

* polish code

* polish code

* polish code

* polish code

.github/workflows/build_on_pr.yml

@@ -63,7 +63,6 @@ jobs:
             echo "$file was changed"
           done
 
-
   build:
     name: Build and Test Colossal-AI
     needs: detect
@@ -124,7 +123,7 @@ jobs:
       - name: Execute Unit Testing
         if: needs.detect.outputs.anyLibraryFileChanged == 'true'
         run: |
-          PYTHONPATH=$PWD pytest --cov=. --cov-report xml tests/
+          CURL_CA_BUNDLE="" PYTHONPATH=$PWD pytest --cov=. --cov-report xml tests/
         env:
           DATA: /data/scratch/cifar-10
           NCCL_SHM_DISABLE: 1

applications/Chat/tests/test_checkpoint.py

@@ -1,19 +1,16 @@
 import os
 import tempfile
 from contextlib import nullcontext
-from functools import partial
 
 import pytest
 import torch
 import torch.distributed as dist
-import torch.multiprocessing as mp
 from coati.models.gpt import GPTActor
 from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy
 from transformers.models.gpt2.configuration_gpt2 import GPT2Config
 
 from colossalai.nn.optimizer import HybridAdam
-from colossalai.testing import rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 
 GPT_CONFIG = GPT2Config(n_embd=128, n_layer=4, n_head=4)
 
@@ -90,8 +87,7 @@ def run_dist(rank, world_size, port, strategy):
 @pytest.mark.parametrize('strategy', ['ddp', 'colossalai_zero2', 'colossalai_gemini'])
 @rerun_if_address_is_in_use()
 def test_checkpoint(world_size, strategy):
-    run_func = partial(run_dist, world_size=world_size, port=free_port(), strategy=strategy)
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(run_dist, world_size, strategy=strategy)
 
 
 if __name__ == '__main__':

applications/Chat/tests/test_data.py

@@ -1,11 +1,9 @@
 import os
 from copy import deepcopy
-from functools import partial
 
 import pytest
 import torch
 import torch.distributed as dist
-import torch.multiprocessing as mp
 from coati.experience_maker import NaiveExperienceMaker
 from coati.models.base import RewardModel
 from coati.models.gpt import GPTActor, GPTCritic
@@ -13,8 +11,7 @@ from coati.replay_buffer import NaiveReplayBuffer
 from coati.trainer.strategies import ColossalAIStrategy, DDPStrategy
 from transformers.models.gpt2.configuration_gpt2 import GPT2Config
 
-from colossalai.testing import rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 
 GPT_CONFIG = GPT2Config(n_embd=128, n_layer=4, n_head=4)
 
@@ -114,8 +111,7 @@ def run_dist(rank, world_size, port, strategy):
 @pytest.mark.parametrize('strategy', ['ddp', 'colossalai'])
 @rerun_if_address_is_in_use()
 def test_data(world_size, strategy):
-    run_func = partial(run_dist, world_size=world_size, port=free_port(), strategy=strategy)
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(run_dist, world_size, strategy=strategy)
 
 
 if __name__ == '__main__':

colossalai/cli/benchmark/benchmark.py

@@ -10,7 +10,8 @@ from colossalai.context import Config
 from colossalai.context.random import reset_seeds
 from colossalai.core import global_context as gpc
 from colossalai.logging import disable_existing_loggers, get_dist_logger
-from colossalai.utils import MultiTimer, free_port
+from colossalai.testing import free_port
+from colossalai.utils import MultiTimer
 
 from .models import MLP
 

colossalai/testing/__init__.py

@@ -1,7 +1,17 @@
-from .comparison import assert_equal, assert_not_equal, assert_close, assert_close_loose, assert_equal_in_group
-from .utils import parameterize, rerun_on_exception, rerun_if_address_is_in_use, skip_if_not_enough_gpus
+from .comparison import assert_close, assert_close_loose, assert_equal, assert_equal_in_group, assert_not_equal
+from .pytest_wrapper import run_on_environment_flag
+from .utils import (
+    clear_cache_before_run,
+    free_port,
+    parameterize,
+    rerun_if_address_is_in_use,
+    rerun_on_exception,
+    skip_if_not_enough_gpus,
+    spawn,
+)
 
 __all__ = [
     'assert_equal', 'assert_not_equal', 'assert_close', 'assert_close_loose', 'assert_equal_in_group', 'parameterize',
-    'rerun_on_exception', 'rerun_if_address_is_in_use', 'skip_if_not_enough_gpus'
+    'rerun_on_exception', 'rerun_if_address_is_in_use', 'skip_if_not_enough_gpus', 'free_port', 'spawn',
+    'clear_cache_before_run', 'run_on_environment_flag'
 ]

colossalai/testing/utils.py

@@ -1,8 +1,13 @@
+import gc
+import random
 import re
-import torch
-from typing import Callable, List, Any
+import socket
 from functools import partial
 from inspect import signature
+from typing import Any, Callable, List
+
+import torch
+import torch.multiprocessing as mp
 from packaging import version
 
 
@@ -202,3 +207,72 @@ def skip_if_not_enough_gpus(min_gpus: int):
         return _execute_by_gpu_num
 
     return _wrap_func
+
+
+def free_port() -> int:
+    """Get a free port on localhost.
+
+    Returns:
+        int: A free port on localhost.
+    """
+    while True:
+        port = random.randint(20000, 65000)
+        try:
+            with socket.socket() as sock:
+                sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
+                sock.bind(("localhost", port))
+                return port
+        except OSError:
+            continue
+
+
+def spawn(func, nprocs=1, **kwargs):
+    """
+    This function is used to spawn processes for testing.
+
+    Usage:
+        # must contians arguments rank, world_size, port
+        def do_something(rank, world_size, port):
+            ...
+
+        spawn(do_something, nprocs=8)
+
+        # can also pass other arguments
+        def do_something(rank, world_size, port, arg1, arg2):
+            ...
+
+        spawn(do_something, nprocs=8, arg1=1, arg2=2)
+
+    Args:
+        func (Callable): The function to be spawned.
+        nprocs (int, optional): The number of processes to spawn. Defaults to 1.
+    """
+    port = free_port()
+    wrapped_func = partial(func, world_size=nprocs, port=port, **kwargs)
+    mp.spawn(wrapped_func, nprocs=nprocs)
+
+
+def clear_cache_before_run():
+    """
+    This function is a wrapper to clear CUDA and python cache before executing the function.
+
+    Usage:
+        @clear_cache_before_run()
+        def test_something():
+            ...
+    """
+
+    def _wrap_func(f):
+
+        def _clear_cache(*args, **kwargs):
+            torch.cuda.empty_cache()
+            torch.cuda.reset_peak_memory_stats()
+            torch.cuda.reset_max_memory_allocated()
+            torch.cuda.reset_max_memory_cached()
+            torch.cuda.synchronize()
+            gc.collect()
+            f(*args, **kwargs)
+
+        return _clear_cache
+
+    return _wrap_func

colossalai/utils/__init__.py

@@ -7,7 +7,6 @@ from .common import (
     count_zeros_fp32,
     disposable,
     ensure_path_exists,
-    free_port,
     is_ddp_ignored,
     is_dp_rank_0,
     is_model_parallel_parameter,
@@ -37,7 +36,6 @@ from .timer import MultiTimer, Timer
 
 __all__ = [
     'checkpoint',
-    'free_port',
     'print_rank_0',
     'sync_model_param',
     'is_ddp_ignored',

colossalai/utils/common.py

@@ -50,23 +50,6 @@ def ensure_path_exists(filename: str):
         Path(dirpath).mkdir(parents=True, exist_ok=True)
 
 
-def free_port() -> int:
-    """Get a free port on localhost.
-
-    Returns:
-        int: A free port on localhost.
-    """
-    while True:
-        port = random.randint(20000, 65000)
-        try:
-            with socket.socket() as sock:
-                sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)
-                sock.bind(("localhost", port))
-                return port
-        except OSError:
-            continue
-
-
 def sync_model_param(model, parallel_mode):
     r"""Make sure data parameters are consistent during Data Parallel Mode.
 

docs/requirements-doc-test.txt

@@ -4,3 +4,4 @@ packaging
 tensornvme
 psutil
 transformers
+pytest

docs/source/en/basics/colotensor_concept.md

@@ -56,12 +56,12 @@ Let's see an example. A ColoTensor is initialized and sharded on 8 GPUs using tp
 ```python
 import torch
 import torch.multiprocessing as mp
-from colossalai.utils import free_port, print_rank_0
+from colossalai.utils import print_rank_0
 from functools import partial
 
 import colossalai
 from colossalai.tensor import ProcessGroup, ColoTensor, ColoTensorSpec, ShardSpec, ComputeSpec, ComputePattern
-from colossalai.utils import free_port
+from colossalai.testing import spawn
 
 import torch
 
@@ -83,8 +83,7 @@ def run_dist_tests(rank, world_size, port):
     print_rank_0(f"shape {t1.shape}, {t1.data}")
 
 def test_dist_cases(world_size):
-    run_func = partial(run_dist_tests, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(run_dist_tests, world_size)
 
 if __name__ == '__main__':
     test_dist_cases(4)

docs/source/zh-Hans/basics/colotensor_concept.md

@@ -57,12 +57,12 @@ ColoTensor 包含额外的属性[ColoTensorSpec](https://colossalai.readthedocs.
 ```python
 import torch
 import torch.multiprocessing as mp
-from colossalai.utils import free_port, print_rank_0
+from colossalai.utils import print_rank_0
 from functools import partial
 
 import colossalai
 from colossalai.tensor import ProcessGroup, ColoTensor, ColoTensorSpec, ShardSpec, ComputeSpec, ComputePattern
-from colossalai.utils import free_port
+from colossalai.testing import spawn
 
 import torch
 
@@ -84,8 +84,7 @@ def run_dist_tests(rank, world_size, port):
     print_rank_0(f"shape {t1.shape}, {t1.data}")
 
 def test_dist_cases(world_size):
-    run_func = partial(run_dist_tests, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(run_dist_tests, world_size)
 
 if __name__ == '__main__':
     test_dist_cases(4)

examples/images/vit/test_vit.py

@@ -1,11 +1,9 @@
 import os
 import random
-from functools import partial
 
 import numpy as np
 import pytest
 import torch
-import torch.multiprocessing as mp
 from torch.nn.parallel import DistributedDataParallel as DDP
 from vit import get_training_components
 
@@ -15,8 +13,7 @@ from colossalai.context.parallel_mode import ParallelMode
 from colossalai.core import global_context as gpc
 from colossalai.nn.parallel.data_parallel import ColoDDP
 from colossalai.tensor import ComputePattern, ComputeSpec, DistSpecManager, ProcessGroup, ShardSpec
-from colossalai.testing import rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 from colossalai.utils.cuda import get_current_device
 from colossalai.zero import ColoInitContext
 
@@ -156,8 +153,7 @@ def run_dist(rank, world_size, port, use_ddp):
 @pytest.mark.parametrize('use_ddp', [False, True])
 @rerun_if_address_is_in_use()
 def test_vit(world_size, use_ddp):
-    run_func = partial(run_dist, world_size=world_size, port=free_port(), use_ddp=use_ddp)
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(run_dist, world_size, use_ddp=use_ddp)
 
 
 if __name__ == '__main__':

examples/language/gpt/experiments/auto_offload/train_gpt_offload.py

@@ -1,20 +1,20 @@
-import time
-import pytest
 import argparse
-from functools import partial
+import time
 
+import pytest
 import torch
+from model_zoo import GPTLMLoss, get_gpt2_components
 from torch.utils._pytree import tree_map
-import torch.multiprocessing as mp
 
 import colossalai
-from colossalai.nn.optimizer import HybridAdam
-from colossalai.fx.profiler import parameter_size
-from colossalai.utils import free_port, get_current_device
 from colossalai.auto_parallel.offload.amp_optimizer import AMPOptimizer
 from colossalai.auto_parallel.offload.mem_optimize import memory_optimize
 from colossalai.auto_parallel.offload.solver import NOT_NVML
-from model_zoo import get_gpt2_components, GPTLMLoss
+from colossalai.fx.profiler import parameter_size
+from colossalai.nn.optimizer import HybridAdam
+from colossalai.testing import spawn
+from colossalai.utils import get_current_device
+
 
 def parse_args():
     parser = argparse.ArgumentParser()
@@ -24,6 +24,7 @@ def parse_args():
     parser.add_argument('--memory_budget', type=float, default=16)
     return parser.parse_args()
 
+
 @pytest.mark.skipif(NOT_NVML, reason='pynvml is not installed')
 def train_gpt(args):
     memory_budget = args.memory_budget * 1024 * 1024 * 1024
@@ -33,7 +34,10 @@ def train_gpt(args):
 
     # build model
     model_builder, data_gen = get_gpt2_components(model_type=model_type, batch_size=batch_size)
-    label = torch.randint(low=0, high=128, size=(64, 8,), device=get_current_device())
+    label = torch.randint(low=0, high=128, size=(
+        64,
+        8,
+    ), device=get_current_device())
     criterion = GPTLMLoss()
 
     start_time = time.time()
@@ -77,18 +81,17 @@ def train_gpt(args):
     runtime_peak_mem_alc = torch.cuda.max_memory_allocated() / 1024**2
     runtime_peak_mem_res = torch.cuda.max_memory_reserved() / 1024**2
     print(f'solver_type: {solver_type} | model_type: {model_type}')
-    print(
-        f'| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB '
-        f'| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|'
-    )
+    print(f'| exec_time={exec_time:.3f} s | param_size={param_size:.3f} MB '
+          f'| runtime_peak_mem_alc={runtime_peak_mem_alc:.3f} MB| runtime_peak_mem_res={runtime_peak_mem_res:.3f} MB|')
     print(time_list)
 
+
 def run(rank, world_size, port, args):
     config = {}
     colossalai.launch(config=config, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     train_gpt(args)
 
+
 if __name__ == '__main__':
     args = parse_args()
-    run_func = partial(run, world_size=1, port=free_port(), args=args)
-    mp.spawn(run_func, nprocs=1)
+    spawn(run, 1, args=args)

examples/language/gpt/experiments/auto_parallel/auto_parallel_with_gpt.py

@@ -1,18 +1,13 @@
 from functools import partial
 from time import time
-from typing import Dict, Optional, Tuple, Union
 
 import psutil
 import torch
-import torch.multiprocessing as mp
-import torch.nn as nn
 import transformers
 from gpt_modules import GPT2LMHeadModel, GPTLMLoss
-from torch.fx import GraphModule
 
-from colossalai.auto_parallel.tensor_shard.initialize import autoparallelize, initialize_model
+from colossalai.auto_parallel.tensor_shard.initialize import autoparallelize
 from colossalai.core import global_context as gpc
-from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch_from_torch
 from colossalai.logging import disable_existing_loggers, get_dist_logger
 

examples/tutorial/auto_parallel/auto_ckpt_batchsize_test.py

@@ -1,19 +1,14 @@
-import time
-from argparse import ArgumentParser
 from copy import deepcopy
 from functools import partial
 
-import matplotlib.pyplot as plt
-import numpy as np
 import torch
-import torch.multiprocessing as mp
 import torchvision.models as tm
 from bench_utils import bench, data_gen_resnet
 
 import colossalai
 from colossalai.auto_parallel.checkpoint import CheckpointSolverRotor
 from colossalai.fx import metainfo_trace, symbolic_trace
-from colossalai.utils import free_port
+from colossalai.testing import spawn
 
 
 def _benchmark(rank, world_size, port):
@@ -50,9 +45,7 @@ def _benchmark(rank, world_size, port):
 
 
 def auto_activation_checkpoint_batchsize_benchmark():
-    world_size = 1
-    run_func_module = partial(_benchmark, world_size=world_size, port=free_port())
-    mp.spawn(run_func_module, nprocs=world_size)
+    spawn(_benchmark, 1)
 
 
 if __name__ == "__main__":

examples/tutorial/auto_parallel/auto_ckpt_solver_test.py

@@ -4,14 +4,13 @@ from functools import partial
 
 import matplotlib.pyplot as plt
 import torch
-import torch.multiprocessing as mp
 import torchvision.models as tm
 from bench_utils import GPTLMLoss, bench_rotor, data_gen_gpt2, data_gen_resnet, gpt2_medium
 
 import colossalai
 from colossalai.auto_parallel.checkpoint import CheckpointSolverRotor
 from colossalai.fx import metainfo_trace, symbolic_trace
-from colossalai.utils import free_port
+from colossalai.testing import spawn
 
 
 def _benchmark(rank, world_size, port, args):
@@ -77,8 +76,7 @@ def _benchmark(rank, world_size, port, args):
 
 def auto_activation_checkpoint_benchmark(args):
     world_size = 1
-    run_func_module = partial(_benchmark, world_size=world_size, port=free_port(), args=args)
-    mp.spawn(run_func_module, nprocs=world_size)
+    spawn(_benchmark, world_size, args=args)
 
 
 if __name__ == "__main__":

requirements/requirements-test.txt

@@ -12,3 +12,4 @@ contexttimer
 einops
 triton==2.0.0.dev20221202
 git+https://github.com/HazyResearch/flash-attention.git@c422fee3776eb3ea24e011ef641fd5fbeb212623#egg=flash_attn
+requests==2.27.1 # downgrade to avoid huggingface error https://github.com/huggingface/transformers/issues/17611

tests/test_amp/test_naive_fp16.py

@@ -1,14 +1,11 @@
 import copy
-from functools import partial
 
 import pytest
 import torch
-import torch.multiprocessing as mp
 
 import colossalai
 from colossalai.amp import convert_to_apex_amp, convert_to_naive_amp
-from colossalai.testing import assert_close_loose, rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.testing import assert_close_loose, clear_cache_before_run, rerun_if_address_is_in_use, spawn
 from tests.components_to_test.registry import non_distributed_component_funcs
 
 
@@ -87,10 +84,9 @@ def run_dist(rank, world_size, port):
 
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
+@clear_cache_before_run()
 def test_naive_amp():
-    world_size = 1
-    run_func = partial(run_dist, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(run_dist, 1)
 
 
 if __name__ == '__main__':

tests/test_amp/test_torch_fp16.py

@@ -1,14 +1,11 @@
 import copy
-from functools import partial
 
 import pytest
 import torch
-import torch.multiprocessing as mp
 
 import colossalai
 from colossalai.amp import convert_to_apex_amp, convert_to_torch_amp
-from colossalai.testing import assert_close_loose, rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.testing import assert_close_loose, clear_cache_before_run, rerun_if_address_is_in_use, spawn
 from tests.components_to_test.registry import non_distributed_component_funcs
 
 
@@ -87,10 +84,9 @@ def run_dist(rank, world_size, port):
 
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
+@clear_cache_before_run()
 def test_torch_amp():
-    world_size = 1
-    run_func = partial(run_dist, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(run_dist, 1)
 
 
 if __name__ == '__main__':

tests/test_analyzer/test_fx/test_bias_addition.py

@@ -3,7 +3,7 @@ import torch
 from packaging import version
 from torch.utils.checkpoint import checkpoint
 
-from colossalai.testing.utils import parameterize
+from colossalai.testing.utils import clear_cache_before_run, parameterize
 
 try:
     from colossalai._analyzer.fx import symbolic_trace
@@ -81,6 +81,7 @@ class AddmmModel(torch.nn.Module):
 
 
 @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12')
+@clear_cache_before_run()
 @parameterize("bias", [True, False])
 @parameterize("bias_addition_split", [True, False])
 @parameterize("shape", [(3, 3, 3), (3, 3, 3, 3)])

tests/test_analyzer/test_fx/test_mod_dir.py

@@ -1,6 +1,8 @@
 import pytest
 import torch
 
+from colossalai.testing import clear_cache_before_run, parameterize
+
 try:
     from colossalai._analyzer.fx import symbolic_trace
 except:
@@ -62,9 +64,10 @@ class AModel(torch.nn.Module):
 
 
 @pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12')
-@pytest.mark.parametrize("bias", [True, False])
-@pytest.mark.parametrize("bias_addition_split", [True, False])
-@pytest.mark.parametrize("shape", [(3, 3, 3), (3, 3, 3, 3)])
+@clear_cache_before_run()
+@parameterize("bias", [True, False])
+@parameterize("bias_addition_split", [True, False])
+@parameterize("shape", [(3, 3, 3), (3, 3, 3, 3)])
 def test_mod_dir(bias, bias_addition_split, shape):
     model = AModel(bias=bias)
     x = torch.rand(shape)
@@ -75,4 +78,4 @@ def test_mod_dir(bias, bias_addition_split, shape):
 
 
 if __name__ == '__main__':
-    test_mod_dir(True, True, (3, 3, 3))
+    test_mod_dir(bias=True, bias_addition_split=True, shape=(3, 3, 3))

tests/test_analyzer/test_fx/test_nested_ckpt.py

@@ -1,7 +1,9 @@
+import pytest
 import torch
 import torch.nn as nn
 from torch.utils.checkpoint import checkpoint
-import pytest
+
+from colossalai.testing import clear_cache_before_run
 
 try:
     from colossalai._analyzer.fx import symbolic_trace
@@ -42,6 +44,7 @@ class MyModule(nn.Module):
 
 
 @pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12')
+@clear_cache_before_run()
 def test_nested_ckpt():
     model = MyModule()
     x = torch.rand(10, 10)

tests/test_analyzer/test_fx/test_shape_prop.py

@@ -3,7 +3,7 @@ import torch
 import torchvision.models as tm
 from packaging import version
 
-from colossalai.testing.utils import parameterize
+from colossalai.testing.utils import clear_cache_before_run, parameterize
 from tests.test_analyzer.test_fx.zoo import tm_models, tmm_models
 
 try:
@@ -32,6 +32,7 @@ def _check_gm_validity(gm: torch.fx.GraphModule):
 
 
 @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12')
+@clear_cache_before_run()
 @parameterize('m', tm_models)
 def test_torchvision_shape_prop(m):
     with MetaTensorMode():
@@ -46,6 +47,7 @@ def test_torchvision_shape_prop(m):
 
 
 @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12')
+@clear_cache_before_run()
 @parameterize('m', tmm_models)
 def test_timm_shape_prop(m):
     with MetaTensorMode():

tests/test_analyzer/test_fx/test_symbolic_profile.py

@@ -3,7 +3,7 @@ import torch
 import torchvision.models as tm
 from packaging import version
 
-from colossalai.testing.utils import parameterize
+from colossalai.testing.utils import clear_cache_before_run, parameterize
 from tests.test_analyzer.test_fx.zoo import tm_models, tmm_models
 
 try:
@@ -19,6 +19,7 @@ def _check_gm_validity(gm: torch.fx.GraphModule):
 
 
 @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12')
+@clear_cache_before_run()
 @parameterize('m', tm_models)
 def test_torchvision_profile(m, verbose=False, bias_addition_split=False):
     with MetaTensorMode():
@@ -33,6 +34,7 @@ def test_torchvision_profile(m, verbose=False, bias_addition_split=False):
 
 
 @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12')
+@clear_cache_before_run()
 @parameterize('m', tmm_models)
 def test_timm_profile(m, verbose=False, bias_addition_split=False):
     with MetaTensorMode():

tests/test_analyzer/test_subclasses/test_aten.py

@@ -1,9 +1,11 @@
 from typing import Any, Callable, Union
-import pytest
 
+import pytest
 import torch
 import torch.nn as nn
 
+from colossalai.testing import clear_cache_before_run
+
 try:
     from colossalai._analyzer._subclasses import MetaTensor
 except:
@@ -72,6 +74,7 @@ def run_and_compare(f: Union[nn.Module, Callable], x: torch.Tensor, requires_bac
 
 
 @pytest.mark.skipif(torch.__version__ < '1.12.0', reason='torch version < 12')
+@clear_cache_before_run()
 def test_meta_aten():
     for (aten_op, requires_backward), v in registered_meta.items():
         for f, x in v:

tests/test_analyzer/test_subclasses/test_flop_tensor.py

@@ -4,6 +4,7 @@ import torch.nn.functional as F
 import torchvision.models as tm
 from packaging import version
 
+from colossalai.testing import clear_cache_before_run, parameterize
 from tests.test_analyzer.test_fx.zoo import tm_models, tmm_models
 
 try:
@@ -39,7 +40,8 @@ odd_cases = [
 
 
 @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12')
-@pytest.mark.parametrize('func, args, kwargs', odd_cases)
+@clear_cache_before_run()
+@parameterize('func, args, kwargs', odd_cases)
 def test_flop_count_function(func, args, kwargs):
     rs_fwd, rs_bwd = flop_count(func, *args, **kwargs, verbose=True)
     assert rs_fwd > 0, f'fwd flop count of {func.__name__} is {rs_fwd}'

tests/test_analyzer/test_subclasses/test_meta_mode.py

@@ -3,6 +3,8 @@ import torch
 import torchvision.models as tm
 from packaging import version
 
+from colossalai.testing import clear_cache_before_run, parameterize
+
 try:
     from colossalai._analyzer._subclasses import MetaTensor, MetaTensorMode
 except:
@@ -30,7 +32,8 @@ def run_and_compare(model):
 
 
 @pytest.mark.skipif(version.parse(torch.__version__) < version.parse('1.12.0'), reason='torch version < 12')
-@pytest.mark.parametrize('m', tm_models + tmm_models)
+@clear_cache_before_run()
+@parameterize('m', tm_models + tmm_models)
 def test_meta_mode_shape(m):
     run_and_compare(m())
 

tests/test_auto_parallel/test_ckpt_solvers/test_C_solver_consistency.py

@@ -3,7 +3,6 @@ import copy
 import pytest
 import torch
 import torch.fx
-import torch.multiprocessing as mp
 import torchvision.models as tm
 
 import colossalai
@@ -13,7 +12,7 @@ from colossalai.fx._compatibility import is_compatible_with_meta
 # from colossalai.fx.passes.algorithms import solver_rotor
 # from colossalai.fx.passes.algorithms.operation import Sequence
 from colossalai.fx.passes.meta_info_prop import MetaInfoProp
-from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 
 if is_compatible_with_meta():
     from colossalai.fx.profiler.tensor import MetaTensor
@@ -26,8 +25,8 @@ except:
     withcodegen = False
 
 
-def _run_C_solver_consistency_test(rank=0):
-    colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl')
+def _run_C_solver_consistency_test(rank, world_size, port):
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
 
     for M, mem_budget in [(tm.resnet50, 4000), (tm.densenet121, 8080)]:
         model = M()
@@ -70,8 +69,9 @@ def _run_C_solver_consistency_test(rank=0):
 
 @pytest.mark.skip("TODO(lyl): refactor all tests.")
 @pytest.mark.skipif(not withcodegen, reason="torch version is less than 1.12.0")
+@rerun_if_address_is_in_use()
 def test_C_solver_consistency():
-    mp.spawn(_run_C_solver_consistency_test, nprocs=1)
+    spawn(_run_C_solver_consistency_test, 1)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_ckpt_solvers/test_ckpt_torchvision.py

@@ -4,7 +4,6 @@ from typing import Callable
 
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torchvision.models as tm
 from torch.fx import GraphModule
 
@@ -15,7 +14,7 @@ from colossalai.fx._compatibility import is_compatible_with_meta
 from colossalai.fx.graph_module import ColoGraphModule
 # from colossalai.fx.passes.algorithms import chen_greedy, solver_rotor
 from colossalai.fx.passes.meta_info_prop import MetaInfoProp
-from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 
 if is_compatible_with_meta():
     from colossalai.fx.profiler.tensor import MetaTensor
@@ -68,8 +67,8 @@ def check_backward_consistency(m: torch.nn.Module, gm: GraphModule, solver: Call
     assert _is_all_gradient_close(m, gm), f'Solver {solver} did not work correctly in backward pass on {model_cls}'
 
 
-def _run_ckpt_solver(rank):
-    colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl')
+def _run_ckpt_solver(rank, world_size, port):
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     MODEL_LIST = [tm.densenet121]
 
     torch.backends.cudnn.deterministic = True
@@ -98,12 +97,13 @@ def _run_ckpt_solver(rank):
 
 @pytest.mark.skip("TODO(super-dainiu): refactor all tests.")
 @pytest.mark.skipif(not with_codegen, reason='torch version is lower than 1.12.0')
+@rerun_if_address_is_in_use()
 def test_ckpt_solver():
-    mp.spawn(_run_ckpt_solver, nprocs=1)
+    spawn(_run_ckpt_solver, 1)
 
 
-def _run_ckpt_solver_torch11(rank):
-    colossalai.launch(config={}, rank=rank, world_size=1, host='localhost', port=free_port(), backend='nccl')
+def _run_ckpt_solver_torch11(rank, world_size, port):
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     MODEL_LIST = [tm.densenet121]
 
     torch.backends.cudnn.deterministic = True
@@ -131,8 +131,9 @@ def _run_ckpt_solver_torch11(rank):
 
 @pytest.mark.skipif(with_codegen, reason='torch version is equal to or higher than 1.12.0')
 @pytest.mark.skip(reason="currently torch11 ColoGraphModule is not done")
+@rerun_if_address_is_in_use()
 def test_ckpt_solver_torch11():
-    mp.spawn(_run_ckpt_solver_torch11, nprocs=1)
+    spawn(_run_ckpt_solver_torch11, 1)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_ckpt_solvers/test_linearize.py

@@ -8,6 +8,7 @@ from colossalai.fx.graph_module import ColoGraphModule
 # from colossalai.fx.passes.algorithms import linearize, solver_rotor
 # from colossalai.fx.passes.algorithms.operation import (ForwardCheck, ForwardEnable, ForwardNograd, Loss)
 from colossalai.fx.passes.meta_info_prop import MetaInfoProp
+from colossalai.testing import clear_cache_before_run
 
 if is_compatible_with_meta():
     from colossalai.fx.profiler.tensor import MetaTensor
@@ -24,6 +25,7 @@ except:
 @pytest.mark.skip(reason='TODO: modify the logger')
 @pytest.mark.skip("TODO(lyl): refactor all tests.")
 @pytest.mark.skipif(not with_codegen, reason="torch version is lower than 1.12.0")
+@clear_cache_before_run()
 def test_linearize():
     MODEL_DICT = {tm.resnet18: [2100, 3000], tm.densenet121: [8100, 17000]}
     tracer = ColoTracer()
@@ -84,6 +86,7 @@ def test_linearize():
 @pytest.mark.skip("TODO(lyl): refactor all tests.")
 @pytest.mark.skip(reason="torch11 meta tensor not implemented")
 @pytest.mark.skipif(with_codegen, reason="torch version is equal to or higher than 1.12.0")
+@clear_cache_before_run()
 def test_linearize_torch11():
     MODEL_DICT = {tm.resnet18: [2100, 3000], tm.densenet121: [8100, 17000]}
     tracer = ColoTracer()

tests/test_auto_parallel/test_offload/test_perf.py

@@ -1,9 +1,7 @@
 import time
-from functools import partial
 
 import pytest
 import torch
-import torch.multiprocessing as mp
 from torch.utils._pytree import tree_map
 
 import colossalai
@@ -12,8 +10,8 @@ from colossalai.auto_parallel.offload.mem_optimize import memory_optimize
 from colossalai.auto_parallel.offload.solver import NOT_NVML
 from colossalai.fx.profiler import parameter_size
 from colossalai.nn.optimizer import HybridAdam
-from colossalai.testing import parameterize
-from colossalai.utils import free_port, get_current_device
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.utils import get_current_device
 from colossalai.zero import ColoInitContext, zero_model_wrapper, zero_optim_wrapper
 from tests.test_auto_parallel.test_offload.model_utils import *
 from tests.test_tensor.common_utils import set_seed
@@ -140,9 +138,9 @@ def run_dist(rank, world_size, port):
 
 @pytest.mark.skip("this test failed")
 @pytest.mark.skipif(NOT_NVML, reason='pynvml is not installed')
+@rerun_if_address_is_in_use()
 def test_perf():
-    run_func = partial(run_dist, world_size=1, port=free_port())
-    mp.spawn(run_func, nprocs=1)
+    spawn(run_dist, 1)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_offload/test_solver.py

@@ -3,20 +3,20 @@ import torch.fx
 from torch.fx import GraphModule
 from torch.utils._pytree import tree_map
 
+from colossalai.auto_parallel.offload.region_manager import RegionManager
+from colossalai.auto_parallel.offload.solver import NOT_NVML, SolverFactory
 from colossalai.fx import ColoTracer, is_compatible_with_meta
 from colossalai.fx.passes.meta_info_prop import MetaInfoProp
-from colossalai.auto_parallel.offload.region_manager import RegionManager
-from colossalai.auto_parallel.offload.solver import SolverFactory, NOT_NVML
-from colossalai.testing import parameterize
+from colossalai.testing import clear_cache_before_run, parameterize
 from tests.test_auto_parallel.test_offload.model_utils import *
 
+
 @pytest.mark.skipif(NOT_NVML, reason='pynvml is not installed')
+@clear_cache_before_run()
 @parameterize('model_name', ['gpt2_', 'bert_'])
 @parameterize('memory_budget', [4000])
 @parameterize('solver_name', ['syn', 'asyn'])
-def solver_test(model_name: str,
-                memory_budget: float,
-                solver_name: str):
+def solver_test(model_name: str, memory_budget: float, solver_name: str):
 
     get_components_func = non_distributed_component_funcs.get_callable(model_name)
     model_builder, data_gen = get_components_func()
@@ -52,11 +52,16 @@ def solver_test(model_name: str,
     for region in region_list:
         need_offload = region.need_offload
         to_prefetch = region.fwd_prefetch_region.r_id if region.fwd_prefetch_region is not None else None
-        print(f'| {model_name} forward | region id: {region.r_id} | need_offload: {need_offload} | to_prefetch: {to_prefetch}')
+        print(
+            f'| {model_name} forward | region id: {region.r_id} | need_offload: {need_offload} | to_prefetch: {to_prefetch}'
+        )
     for region in region_list.__reversed__():
         need_offload = region.need_offload
         to_prefetch = region.bwd_prefetch_region.r_id if region.bwd_prefetch_region is not None else None
-        print(f'| {model_name} backward | region id: {region.r_id} | need_offload: {need_offload} | to_prefetch: {to_prefetch}')
+        print(
+            f'| {model_name} backward | region id: {region.r_id} | need_offload: {need_offload} | to_prefetch: {to_prefetch}'
+        )
+
 
 if __name__ == '__main__':
     solver_test()

tests/test_auto_parallel/test_pass/test_node_converting_pass.py

@@ -6,6 +6,7 @@ from colossalai.device.device_mesh import DeviceMesh
 from colossalai.fx.graph_module import ColoGraphModule
 from colossalai.fx.tracer import ColoTracer
 from colossalai.tensor.sharding_spec import ShardingSpec
+from colossalai.testing import clear_cache_before_run
 
 
 class TestModule(torch.nn.Module):
@@ -26,6 +27,7 @@ def insert_narrow(gm, x_node):
     return gm
 
 
+@clear_cache_before_run()
 def test_node_args_converting_pass():
     model = TestModule()
     physical_mesh_id = torch.arange(0, 4)

tests/test_auto_parallel/test_pass/test_size_value_converting_pass.py

@@ -8,6 +8,7 @@ from colossalai._analyzer.fx.tracer.tracer import ColoTracer
 from colossalai.auto_parallel.passes.runtime_preparation_pass import size_value_converting_pass
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.tensor.sharding_spec import ShardingSpec
+from colossalai.testing import clear_cache_before_run
 
 
 class TestModule(torch.nn.Module):
@@ -36,6 +37,7 @@ def recover_narrow(gm, narrow_node):
 
 
 @pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0')
+@clear_cache_before_run()
 def test_size_value_converting_pass():
     model = TestModule()
     physical_mesh_id = torch.arange(0, 4)

tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py

@@ -2,7 +2,6 @@ from functools import partial
 
 import pytest
 import torch
-import torch.multiprocessing as mp
 
 try:
     from colossalai.auto_parallel.tensor_shard.initialize import initialize_model
@@ -13,9 +12,7 @@ except:
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import assert_close, rerun_if_address_is_in_use, run_on_environment_flag, spawn
 
 
 class LinearModel(torch.nn.Module):
@@ -86,11 +83,8 @@ def check_conv_module(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_bias_addition_module():
-    world_size = 4
-    run_func_linear = partial(check_linear_module, world_size=world_size, port=free_port())
-    mp.spawn(run_func_linear, nprocs=world_size)
-    run_func_conv = partial(check_conv_module, world_size=world_size, port=free_port())
-    mp.spawn(run_func_conv, nprocs=world_size)
+    spawn(check_linear_module, 4)
+    spawn(check_conv_module, 4)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_checkpoint.py

@@ -1,9 +1,7 @@
-from functools import partial
-from typing import Optional, Tuple, Union
+from typing import Optional, Tuple
 
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 from torch.utils.checkpoint import checkpoint
 from transformers.pytorch_utils import Conv1D
@@ -17,9 +15,7 @@ except:
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use, run_on_environment_flag, spawn
 
 HIDDEN_SIZE = 16
 
@@ -65,9 +61,7 @@ def check_act_ckpt(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_mlp_layer():
-    world_size = 4
-    run_func = partial(check_act_ckpt, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_act_ckpt, 4)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_ddp.py

@@ -1,9 +1,7 @@
 import copy
-from functools import partial
 
 import pytest
 import torch
-import torch.multiprocessing as mp
 from torch.nn.parallel import DistributedDataParallel as DDP
 
 try:
@@ -15,9 +13,7 @@ except:
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import assert_close, rerun_if_address_is_in_use, run_on_environment_flag, spawn
 
 
 class MLP(torch.nn.Module):
@@ -102,9 +98,7 @@ def check_compatibility_with_ddp(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_compatibility_with_ddp():
-    world_size = 4
-    run_func = partial(check_compatibility_with_ddp, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_compatibility_with_ddp, 4)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_compatibility_with_gemini.py

@@ -1,10 +1,7 @@
 import copy
-from functools import partial
 
 import pytest
 import torch
-import torch.multiprocessing as mp
-from torch.nn.parallel import DistributedDataParallel as DDP
 
 try:
     from colossalai.auto_parallel.tensor_shard.initialize import initialize_model
@@ -17,10 +14,9 @@ from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
 from colossalai.nn.optimizer import HybridAdam
 from colossalai.tensor.process_group import ProcessGroup
-from colossalai.testing import assert_close, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port, get_current_device
-from colossalai.zero import ColoInitContext, post_process_colo_init_ctx, zero_model_wrapper, zero_optim_wrapper
+from colossalai.testing import assert_close, rerun_if_address_is_in_use, run_on_environment_flag, spawn
+from colossalai.utils import get_current_device
+from colossalai.zero import post_process_colo_init_ctx, zero_model_wrapper, zero_optim_wrapper
 
 
 class MLP(torch.nn.Module):
@@ -110,9 +106,7 @@ def check_auto_parallel_with_gemini(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_auto_parallel_with_gemini():
-    world_size = 4
-    run_func = partial(check_auto_parallel_with_gemini, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_auto_parallel_with_gemini, 4)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_find_repeat_block.py

@@ -10,8 +10,7 @@ from colossalai._analyzer.fx.passes import shape_prop_pass
 # from colossalai.fx.tracer.tracer import ColoTracer
 from colossalai._analyzer.fx.tracer.tracer import ColoTracer
 from colossalai.auto_parallel.tensor_shard.utils.factory import find_repeat_blocks
-from colossalai.testing import parameterize
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.testing import clear_cache_before_run, parameterize, run_on_environment_flag
 
 NUM_REPEAT_BLOCKS = 4
 BATCH_SIZE = 1
@@ -81,6 +80,7 @@ class NonRepeatModel(nn.Module):
 
 
 @run_on_environment_flag(name='AUTO_PARALLEL')
+@clear_cache_before_run()
 @parameterize('model_cls', [RepeatModel, NonRepeatModel])
 def test_repeat_blocks(model_cls):
 

tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py

@@ -1,12 +1,10 @@
 import copy
 import random
-from functools import partial
 from typing import Dict
 
 import numpy as np
 import pytest
 import torch
-import torch.multiprocessing as mp
 import transformers
 from torch.fx import GraphModule
 
@@ -30,9 +28,8 @@ from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
 from colossalai.tensor.shape_consistency import to_global
-from colossalai.testing import assert_close, assert_close_loose, parameterize, rerun_if_address_is_in_use
+from colossalai.testing import assert_close, assert_close_loose, parameterize, rerun_if_address_is_in_use, spawn
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
 from tests.test_auto_parallel.test_tensor_shard.test_gpt.gpt_modules import GPT2MLP, GPT2Attention, GPT2Block, GPT2Model
 
 BATCH_SIZE = 1
@@ -190,9 +187,7 @@ def check_attention_layer(rank, model_cls, world_size, port):
 @parameterize('model_cls', [GPT2MLP, GPT2Block, GPT2Attention, GPT2Model])
 @rerun_if_address_is_in_use()
 def test_mlp_layer(model_cls):
-    world_size = 4
-    run_func = partial(check_attention_layer, model_cls=model_cls, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_attention_layer, 4, model_cls=model_cls)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_gpt/test_solver_with_gpt_module.py

@@ -1,5 +1,4 @@
 import torch
-import torch.nn as nn
 import transformers
 from torch.fx import GraphModule
 
@@ -7,10 +6,10 @@ from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass
 from colossalai._analyzer.fx.tracer.tracer import ColoTracer
 from colossalai.auto_parallel.tensor_shard.constants import BATCHNORM_MODULE_OP
 from colossalai.auto_parallel.tensor_shard.options import SolverOptions
-from colossalai.auto_parallel.tensor_shard.solver import CostGraph, GraphAnalyser, Solver, StrategiesConstructor
+from colossalai.auto_parallel.tensor_shard.solver import CostGraph, Solver, StrategiesConstructor
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.tensor.shape_consistency import ShapeConsistencyManager
-from colossalai.testing import parameterize
+from colossalai.testing import clear_cache_before_run, parameterize
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
 from tests.test_auto_parallel.test_tensor_shard.test_gpt.gpt_modules import GPT2MLP, GPT2Attention, GPT2Block, GPT2Model
 
@@ -20,6 +19,7 @@ HIDDEN_DIM = 384
 
 
 @run_on_environment_flag(name='AUTO_PARALLEL')
+@clear_cache_before_run()
 @parameterize('model_cls', [GPT2Block, GPT2Attention, GPT2MLP, GPT2Model])
 def test_self_attention_block(model_cls):
     config = transformers.GPT2Config(n_position=64, n_layer=2, n_head=16, n_embd=HIDDEN_DIM)

tests/test_auto_parallel/test_tensor_shard/test_liveness_analysis.py

@@ -6,6 +6,8 @@ from colossalai._analyzer.fx.graph_module import ColoGraphModule
 from colossalai._analyzer.fx.passes import shape_prop_pass
 from colossalai._analyzer.fx.tracer.tracer import ColoTracer
 from colossalai.auto_parallel.tensor_shard.solver import GraphAnalyser
+from colossalai.fx import ColoGraphModule, ColoTracer
+from colossalai.testing import clear_cache_before_run
 
 
 class LinearModel(nn.Module):
@@ -26,6 +28,7 @@ class LinearModel(nn.Module):
 
 
 @pytest.mark.skip('meta tensor has some bugs in 1.11')
+@clear_cache_before_run()
 def test_liveness_analysis():
     model = LinearModel()
     tracer = ColoTracer(bias_addition_split=True)

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_activation_metainfo.py

@@ -1,23 +1,14 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
-import torch.nn as nn
 
 from colossalai.auto_parallel.meta_profiler import meta_register
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType
-from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx import ColoGraphModule, ColoTracer
-from colossalai.initialize import launch
-from colossalai.logging import disable_existing_loggers
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use
-from colossalai.utils import free_port
-from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy, print_results
+from colossalai.testing.utils import clear_cache_before_run, parameterize
+from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results
 
 
 @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations")
+@clear_cache_before_run()
 @parameterize('func', [
     torch.nn.functional.softmax,
     torch.nn.functional.relu,

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_binary_elementwise_metainfo.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai.device.device_mesh import DeviceMesh
@@ -10,8 +7,7 @@ from colossalai.fx import ColoGraphModule, ColoTracer
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.testing.utils import rerun_if_address_is_in_use, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy
 
 
@@ -62,9 +58,7 @@ def _binary_elementwise_mem_test(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_binary_elementwise_meta_concrete_info_match():
-    world_size = 4
-    run_func_module = partial(_binary_elementwise_mem_test, world_size=world_size, port=free_port())
-    mp.spawn(run_func_module, nprocs=world_size)
+    spawn(_binary_elementwise_mem_test, 4)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_conv_metainfo.py

@@ -1,17 +1,12 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx import ColoGraphModule, ColoTracer
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.testing.utils import rerun_if_address_is_in_use, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy
 
 
@@ -25,7 +20,7 @@ class ConvFunctionModule(nn.Module):
         return nn.functional.conv2d(input, self.conv_weight)
 
 
-def _conv_module_mem_test(rank, bias, world_size, port):
+def _conv_module_mem_test(rank, world_size, port, bias):
     """This function is for conv memory test
     Test and print real memory cost and estimated, this test will not be executed except with the tag AUTO_PARALLEL
 
@@ -62,9 +57,7 @@ def _conv_module_mem_test(rank, bias, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_conv_meta_concrete_info_match(bias=False):
-    world_size = 4
-    run_func_module = partial(_conv_module_mem_test, bias=bias, world_size=world_size, port=free_port())
-    mp.spawn(run_func_module, nprocs=world_size)
+    spawn(_conv_module_mem_test, 4, bias=bias)
 
 
 def _conv_function_mem_test(rank, world_size, port):
@@ -103,9 +96,7 @@ def _conv_function_mem_test(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_conv_function_concrete_info_match():
-    world_size = 4
-    run_func_module = partial(_conv_function_mem_test, world_size=world_size, port=free_port())
-    mp.spawn(run_func_module, nprocs=world_size)
+    spawn(_conv_function_mem_test, 4)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_embedding_metainfo.py

@@ -1,33 +1,16 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
-import torch.nn as nn
-
-from colossalai.auto_parallel.tensor_shard.node_handler import LinearModuleHandler
-from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
-    MemoryCost,
-    OperationData,
-    OperationDataType,
-    ShardingStrategy,
-    StrategiesVector,
-    TrainCycleItem,
-)
-from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx import ColoGraphModule, ColoTracer
-from colossalai.initialize import launch
-from colossalai.logging import disable_existing_loggers
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use
-from colossalai.utils import free_port
+
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType
+from colossalai.testing.utils import clear_cache_before_run
 from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results
 
 if torch.__version__ >= '1.12.0':
-    from colossalai.auto_parallel.meta_profiler import ShardMetaInfo, meta_register
+    from colossalai.auto_parallel.meta_profiler import meta_register
 
 
 @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations")
+@clear_cache_before_run()
 def test_embedding_meta_info():
     meta_func = meta_register.get(torch.nn.Embedding)
 

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_linear_metainfo.py

@@ -1,24 +1,14 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
-from colossalai.auto_parallel.tensor_shard.node_handler import LinearModuleHandler
-from colossalai.auto_parallel.tensor_shard.sharding_strategy import ShardingStrategy, StrategiesVector
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx import ColoGraphModule, ColoTracer
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.testing.utils import rerun_if_address_is_in_use, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy
 
-if torch.__version__ >= '1.12.0':
-    from colossalai.auto_parallel.meta_profiler import ShardMetaInfo, meta_register
-
 
 class MyModule(nn.Module):
 
@@ -63,9 +53,7 @@ def _linear_module_mem_test(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_linear_module_meta_concrete_info_match():
-    world_size = 4
-    run_func_module = partial(_linear_module_mem_test, world_size=world_size, port=free_port())
-    mp.spawn(run_func_module, nprocs=world_size)
+    spawn(_linear_module_mem_test, 4)
 
 
 def _linear_function_mem_test(rank, world_size, port):
@@ -101,9 +89,7 @@ def _linear_function_mem_test(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_linear_function_meta_concrete_info_match():
-    world_size = 4
-    run_func_module = partial(_linear_function_mem_test, world_size=world_size, port=free_port())
-    mp.spawn(run_func_module, nprocs=world_size)
+    spawn(_linear_function_mem_test, 4)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_matmul_metainfo.py

@@ -1,26 +1,8 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
-import torch.nn as nn
-
-from colossalai.auto_parallel.tensor_shard.node_handler import LinearModuleHandler
-from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
-    MemoryCost,
-    OperationData,
-    OperationDataType,
-    ShardingStrategy,
-    StrategiesVector,
-    TrainCycleItem,
-)
-from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx import ColoGraphModule, ColoTracer
-from colossalai.initialize import launch
-from colossalai.logging import disable_existing_loggers
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use
-from colossalai.utils import free_port
+
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, TrainCycleItem
+from colossalai.testing.utils import clear_cache_before_run, parameterize
 from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results
 
 if torch.__version__ >= '1.12.0':
@@ -28,6 +10,7 @@ if torch.__version__ >= '1.12.0':
 
 
 @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations")
+@clear_cache_before_run()
 @parameterize(
     'tensor_shapes',
     [

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_norm_metainfo.py

@@ -1,29 +1,17 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
-from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
-    MemoryCost,
-    OperationData,
-    OperationDataType,
-    ShardingStrategy,
-    StrategiesVector,
-    TrainCycleItem,
-)
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, TrainCycleItem
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx import ColoGraphModule, ColoTracer
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy, print_results
 
 if torch.__version__ >= '1.12.0':
-    from colossalai.auto_parallel.meta_profiler import ShardMetaInfo, meta_register
+    from colossalai.auto_parallel.meta_profiler import meta_register
 
 
 def _batchnorm_module_mem_test(rank, world_size, port):
@@ -62,9 +50,7 @@ def _batchnorm_module_mem_test(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_batchnorm_meta_concrete_info_match():
-    world_size = 4
-    run_func_module = partial(_batchnorm_module_mem_test, world_size=world_size, port=free_port())
-    mp.spawn(run_func_module, nprocs=world_size)
+    spawn(_batchnorm_module_mem_test, 4)
 
 
 @pytest.mark.skipif(torch.__version__ < '1.12.0', reason='need pytorch 1.12.0 or higher for aten level operations')

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_pooling_metainfo.py

@@ -1,17 +1,12 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx import ColoGraphModule, ColoTracer
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.testing.utils import rerun_if_address_is_in_use, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import mem_test_for_node_strategy
 
 
@@ -51,9 +46,7 @@ def _adaptiveavgpool_module_mem_test(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_adaptiveavgpool_meta_concrete_info_match():
-    world_size = 4
-    run_func_module = partial(_adaptiveavgpool_module_mem_test, world_size=world_size, port=free_port())
-    mp.spawn(run_func_module, nprocs=world_size)
+    spawn(_adaptiveavgpool_module_mem_test, 4)
 
 
 def _maxpool_module_mem_test(rank, world_size, port):
@@ -92,9 +85,7 @@ def _maxpool_module_mem_test(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_maxpool_meta_concrete_info_match():
-    world_size = 4
-    run_func_module = partial(_maxpool_module_mem_test, world_size=world_size, port=free_port())
-    mp.spawn(run_func_module, nprocs=world_size)
+    spawn(_maxpool_module_mem_test, 4)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_tensor_metainfo.py

@@ -1,26 +1,9 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
-from colossalai.auto_parallel.tensor_shard.node_handler import LinearModuleHandler
-from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
-    MemoryCost,
-    OperationData,
-    OperationDataType,
-    ShardingStrategy,
-    StrategiesVector,
-    TrainCycleItem,
-)
-from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx import ColoGraphModule, ColoTracer
-from colossalai.initialize import launch
-from colossalai.logging import disable_existing_loggers
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType
+from colossalai.testing.utils import clear_cache_before_run
 from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results
 
 if torch.__version__ >= '1.12.0':
@@ -37,6 +20,7 @@ class SplitModule(nn.Module):
 
 
 @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations")
+@clear_cache_before_run()
 def test_tensor_meta_info():
     """test tensor related meta information
     We will just use torch.Tensor.split for the test

tests/test_auto_parallel/test_tensor_shard/test_metainfo/test_where_metainfo.py

@@ -1,24 +1,8 @@
 import pytest
 import torch
-import torch.multiprocessing as mp
-import torch.nn as nn
-
-from colossalai.auto_parallel.tensor_shard.node_handler import LinearModuleHandler
-from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
-    MemoryCost,
-    OperationData,
-    OperationDataType,
-    ShardingStrategy,
-    StrategiesVector,
-    TrainCycleItem,
-)
-from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx import ColoGraphModule, ColoTracer
-from colossalai.initialize import launch
-from colossalai.logging import disable_existing_loggers
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.testing.utils import parameterize, rerun_if_address_is_in_use
-from colossalai.utils import free_port
+
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, TrainCycleItem
+from colossalai.testing.utils import clear_cache_before_run
 from tests.test_auto_parallel.test_tensor_shard.test_metainfo.utils import print_results
 
 if torch.__version__ >= '1.12.0':
@@ -26,6 +10,7 @@ if torch.__version__ >= '1.12.0':
 
 
 @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations")
+@clear_cache_before_run()
 def test_where_meta_info():
     meta_func = meta_register.get(torch.where)
 

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addbmm_handler.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai.auto_parallel.tensor_shard.node_handler import BMMFunctionHandler
@@ -11,9 +8,7 @@ from colossalai.device.device_mesh import DeviceMesh
 from colossalai.fx import ColoGraphModule, ColoTracer
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
@@ -45,7 +40,7 @@ class AddBMMTorchFunctionModule(nn.Module):
         return output
 
 
-def check_2d_device_mesh(rank, module, bias_shape, using_kwargs, world_size, port):
+def check_2d_device_mesh(rank, world_size, port, module, bias_shape, using_kwargs):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     model = module(using_kwargs).cuda()
@@ -249,14 +244,13 @@ def check_1d_device_mesh(rank, module, bias_shape, using_kwargs, world_size, por
 @parameterize('using_kwargs', [True, False])
 @rerun_if_address_is_in_use()
 def test_2d_device_mesh(module, bias_shape, using_kwargs):
-    world_size = 4
-    run_func = partial(check_2d_device_mesh,
+    spawn(
+        check_2d_device_mesh,
+        4,
         module=module,
         bias_shape=bias_shape,
-                       world_size=world_size,
         using_kwargs=using_kwargs,
-                       port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    )
 
 
 @pytest.mark.skip("skip due to bias cases not ready")
@@ -267,14 +261,13 @@ def test_2d_device_mesh(module, bias_shape, using_kwargs):
 @parameterize('using_kwargs', [True, False])
 @rerun_if_address_is_in_use()
 def test_1d_device_mesh(module, bias_shape, using_kwargs):
-    world_size = 4
-    run_func = partial(check_1d_device_mesh,
+    spawn(
+        check_1d_device_mesh,
+        4,
         module=module,
         bias_shape=bias_shape,
         using_kwargs=using_kwargs,
-                       world_size=world_size,
-                       port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    )
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_addmm_handler.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -17,9 +14,7 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import parameterize, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
@@ -45,7 +40,7 @@ class AddmmModel_with_param(nn.Module):
         return x
 
 
-def check_addmm_function_handler(rank, input_shape, model_cls, world_size, port):
+def check_addmm_function_handler(rank, world_size, port, input_shape, model_cls):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     if model_cls == AddmmModel:
@@ -189,13 +184,7 @@ def check_addmm_function_handler(rank, input_shape, model_cls, world_size, port)
 @parameterize('model_cls', [AddmmModel, AddmmModel_with_param])
 @rerun_if_address_is_in_use()
 def test_addmm_handler(input_shape, model_cls):
-    world_size = 4
-    run_func_function = partial(check_addmm_function_handler,
-                                input_shape=input_shape,
-                                model_cls=model_cls,
-                                world_size=world_size,
-                                port=free_port())
-    mp.spawn(run_func_function, nprocs=world_size)
+    spawn(check_addmm_function_handler, 4, input_shape=input_shape, model_cls=model_cls)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_batch_norm_handler.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -13,9 +10,7 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDat
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use, run_on_environment_flag, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
@@ -114,9 +109,7 @@ def check_bn_module_handler(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_bn_module_handler():
-    world_size = 4
-    run_func = partial(check_bn_module_handler, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_bn_module_handler, 4)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_function_node.py

@@ -1,9 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
-import torch.nn as nn
 import torch.nn.functional as F
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -19,9 +15,7 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use, run_on_environment_flag, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 WEIGHT_SHAPE = (32, 16)
@@ -168,9 +162,7 @@ def check_linear_module_handler(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_linear_handler():
-    world_size = 4
-    run_func_module = partial(check_linear_module_handler, world_size=world_size, port=free_port())
-    mp.spawn(run_func_module, nprocs=world_size)
+    spawn(check_linear_module_handler)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_module_node.py

@@ -1,14 +1,10 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
-import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
 from colossalai._analyzer.fx.passes.shape_prop import shape_prop_pass
 from colossalai._analyzer.fx.tracer.tracer import ColoTracer
-from colossalai.auto_parallel.tensor_shard.node_handler import LinearFunctionHandler, LinearModuleHandler
+from colossalai.auto_parallel.tensor_shard.node_handler import LinearFunctionHandler
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
     OperationData,
     OperationDataType,
@@ -18,9 +14,7 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use, run_on_environment_flag, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
@@ -35,7 +29,7 @@ class LinearModule(torch.nn.Module):
         return x
 
 
-def check_linear_module_handler(rank, bias, world_size, port):
+def check_linear_module_handler(rank, world_size, port, bias):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     model = LinearModule(16, 32, bias=bias).cuda()
@@ -157,9 +151,7 @@ def check_linear_module_handler(rank, bias, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_linear_handler(bias=True):
-    world_size = 4
-    run_func_module = partial(check_linear_module_handler, bias=bias, world_size=world_size, port=free_port())
-    mp.spawn(run_func_module, nprocs=world_size)
+    spawn(check_linear_module_handler, bias=bias)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_binary_elementwise_handler.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -13,13 +10,11 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDat
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
-def check_binary_elementwise_handler_with_tensor(rank, op, other_dim, world_size, port):
+def check_binary_elementwise_handler_with_tensor(rank, world_size, port, op, other_dim):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
 
@@ -149,7 +144,7 @@ class BEOpModelWithIntConst(nn.Module):
         return out
 
 
-def check_binary_elementwise_handler_with_int(rank, op, other_dim, model_cls, world_size, port):
+def check_binary_elementwise_handler_with_int(rank, world_size, port, op, other_dim, model_cls):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
 
@@ -236,13 +231,12 @@ def check_binary_elementwise_handler_with_int(rank, op, other_dim, model_cls, wo
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_binary_elementwise_handler_with_tensor(op, other_dim):
-    world_size = 4
-    run_func_tensor = partial(check_binary_elementwise_handler_with_tensor,
+    spawn(
+        check_binary_elementwise_handler_with_tensor,
+        4,
         op=op,
         other_dim=other_dim,
-                              world_size=world_size,
-                              port=free_port())
-    mp.spawn(run_func_tensor, nprocs=world_size)
+    )
 
 
 @run_on_environment_flag(name='AUTO_PARALLEL')
@@ -252,14 +246,13 @@ def test_binary_elementwise_handler_with_tensor(op, other_dim):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_binary_elementwise_handler_with_int(op, model_cls, other_dim):
-    world_size = 4
-    run_func_int = partial(check_binary_elementwise_handler_with_int,
+    spawn(
+        check_binary_elementwise_handler_with_int,
+        4,
         op=op,
         model_cls=model_cls,
         other_dim=other_dim,
-                           world_size=world_size,
-                           port=free_port())
-    mp.spawn(run_func_int, nprocs=world_size)
+    )
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bmm_handler.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -13,9 +10,7 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDat
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
@@ -207,11 +202,8 @@ def check_1d_device_mesh(rank, module, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_bmm_handler(module):
-    world_size = 4
-    run_func_2d = partial(check_2d_device_mesh, module=module, world_size=world_size, port=free_port())
-    mp.spawn(run_func_2d, nprocs=world_size)
-    run_func_1d = partial(check_1d_device_mesh, module=module, world_size=world_size, port=free_port())
-    mp.spawn(run_func_1d, nprocs=world_size)
+    spawn(check_2d_device_mesh, 4, module=module)
+    spawn(check_1d_device_mesh, 4, module=module)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_conv_handler.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -13,13 +10,11 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDat
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use, run_on_environment_flag, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
-def check_conv_module_handler(rank, bias, world_size, port):
+def check_conv_module_handler(rank, world_size, port, bias):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     model = nn.Sequential(nn.Conv2d(4, 16, 3, padding=1, bias=bias)).cuda()
@@ -155,7 +150,7 @@ class ConvModel(nn.Module):
         return x
 
 
-def check_conv_function_handler(rank, bias, world_size, port):
+def check_conv_function_handler(rank, world_size, port, bias):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     model = ConvModel().cuda()
@@ -302,9 +297,7 @@ def check_conv_function_handler(rank, bias, world_size, port):
 # @parameterize('bias', [True, False])
 @rerun_if_address_is_in_use()
 def test_conv_module_handler(bias=False):
-    world_size = 4
-    run_func = partial(check_conv_module_handler, bias=bias, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_conv_module_handler, 4, bias=bias)
 
 
 @run_on_environment_flag(name='AUTO_PARALLEL')
@@ -314,9 +307,7 @@ def test_conv_module_handler(bias=False):
 # @parameterize('bias', [True, False])
 @rerun_if_address_is_in_use()
 def test_conv_function_handler(bias=False):
-    world_size = 4
-    run_func = partial(check_conv_function_handler, bias=bias, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_conv_function_handler, 4, bias=bias)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_default_reshape_handler.py

@@ -8,7 +8,7 @@ from colossalai.auto_parallel.tensor_shard.node_handler import DefaultReshapeHan
 from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import ConvFunctionHandler
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.testing import clear_cache_before_run, run_on_environment_flag
 
 
 class ReshapeModel(nn.Module):
@@ -23,6 +23,7 @@ class ReshapeModel(nn.Module):
 
 
 @run_on_environment_flag(name='AUTO_PARALLEL')
+@clear_cache_before_run()
 def test_reshape_handler():
     model = ReshapeModel()
     tracer = ColoTracer(bias_addition_split=True)

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_embedding_handler.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -16,9 +13,8 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDat
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 NUM_EMBEDDINGS = 16
@@ -272,18 +268,14 @@ def check_embedding_function_handler(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_embedding_module_handler():
-    world_size = 4
-    run_func = partial(check_embedding_module_handler, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_embedding_module_handler, 4)
 
 
 @run_on_environment_flag(name='AUTO_PARALLEL')
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_embedding_function_handler():
-    world_size = 4
-    run_func = partial(check_embedding_function_handler, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_embedding_function_handler, 4)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getattr_handler.py

@@ -8,6 +8,7 @@ from colossalai._analyzer.fx.tracer.tracer import ColoTracer
 from colossalai.auto_parallel.tensor_shard.node_handler.getattr_handler import GetattrHandler
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
 from colossalai.device.device_mesh import DeviceMesh
+from colossalai.testing import clear_cache_before_run
 
 
 class GetattrModel(nn.Module):
@@ -22,6 +23,7 @@ class GetattrModel(nn.Module):
 
 
 @pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0')
+@clear_cache_before_run()
 def test_getattr_handler():
     model = GetattrModel()
     tracer = ColoTracer(bias_addition_split=True)

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_getitem_handler.py

@@ -2,7 +2,6 @@ from functools import partial
 
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -14,12 +13,10 @@ from colossalai.auto_parallel.tensor_shard.node_handler.linear_handler import Li
 from colossalai.auto_parallel.tensor_shard.node_handler.placeholder_handler import PlaceholderHandler
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx.tracer.meta_patch.patched_module import linear
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
@@ -103,12 +100,7 @@ def check_getitem_from_tensor_handler(rank, getitem_index, world_size, port):
 # @parameterize('getitem_index', [slice(0, 2), (slice(None), slice(None))])
 @parameterize('getitem_index', [1, (1, 4), slice(0, 2), (slice(None), slice(None))])
 def test_getitem_from_tensor_handler(getitem_index):
-    world_size = 4
-    run_func = partial(check_getitem_from_tensor_handler,
-                       getitem_index=getitem_index,
-                       world_size=world_size,
-                       port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_getitem_from_tensor_handler, 4)
 
 
 class GetItemFromTupleModel(nn.Module):
@@ -123,6 +115,7 @@ class GetItemFromTupleModel(nn.Module):
 
 
 @run_on_environment_flag(name='AUTO_PARALLEL')
+@clear_cache_before_run()
 def test_getitem_from_tuple_handler():
     model = GetItemFromTupleModel()
     tracer = ColoTracer()

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_layer_norm_handler.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -11,12 +8,10 @@ from colossalai._analyzer.fx.tracer.tracer import ColoTracer
 from colossalai.auto_parallel.tensor_shard.node_handler.layer_norm_handler import LayerNormModuleHandler
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx.tracer.meta_patch.patched_module import linear
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
@@ -104,9 +99,7 @@ def check_ln_module_handler(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_ln_module_handler():
-    world_size = 4
-    run_func = partial(check_ln_module_handler, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_ln_module_handler, 4)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_linear_handler.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -18,14 +15,13 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
 from colossalai.testing.utils import parameterize
-from colossalai.utils import free_port
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
-def check_linear_module_handler(rank, bias, input_shape, world_size, port):
+def check_linear_module_handler(rank, world_size, port, bias, input_shape):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     model = nn.Sequential(nn.Linear(16, 32, bias=bias)).cuda()
@@ -172,7 +168,7 @@ class LinearModel(nn.Module):
         return x
 
 
-def check_linear_function_handler(rank, bias, input_shape, world_size, port):
+def check_linear_function_handler(rank, world_size, port, bias, input_shape):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     model = LinearModel().cuda()
@@ -313,19 +309,18 @@ def check_linear_function_handler(rank, bias, input_shape, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_linear_handler(input_shape, bias=False):
-    world_size = 4
-    run_func_module = partial(check_linear_module_handler,
+    spawn(
+        check_linear_module_handler,
+        4,
         bias=bias,
         input_shape=input_shape,
-                              world_size=world_size,
-                              port=free_port())
-    mp.spawn(run_func_module, nprocs=world_size)
-    run_func_function = partial(check_linear_function_handler,
+    )
+    spawn(
+        check_linear_function_handler,
+        4,
         bias=bias,
         input_shape=input_shape,
-                                world_size=world_size,
-                                port=free_port())
-    mp.spawn(run_func_function, nprocs=world_size)
+    )
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_matmul_handler.py

@@ -18,7 +18,7 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
     StrategiesVector,
 )
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.testing.utils import parameterize
+from colossalai.testing.utils import clear_cache_before_run, parameterize
 
 
 class MatMulModule(nn.Module):
@@ -28,6 +28,7 @@ class MatMulModule(nn.Module):
 
 
 @pytest.mark.skipif(torch.__version__ < '1.12.0', reason="need pytorch 1.12.0 or higher for aten level operations")
+@clear_cache_before_run()
 @parameterize(
     'tensor_shapes',
     [

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_norm_pooling_handler.py

@@ -1,4 +1,3 @@
-import pytest
 import torch
 import torch.nn as nn
 
@@ -8,11 +7,11 @@ from colossalai._analyzer.fx.tracer.tracer import ColoTracer
 from colossalai.auto_parallel.tensor_shard.node_handler.normal_pooling_handler import NormPoolingHandler
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx.tracer.meta_patch.patched_module import linear
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.testing import clear_cache_before_run, run_on_environment_flag
 
 
 @run_on_environment_flag(name='AUTO_PARALLEL')
+@clear_cache_before_run()
 def test_norm_pool_handler():
     model = nn.Sequential(nn.MaxPool2d(4, padding=1).to('meta'))
     tracer = ColoTracer(bias_addition_split=True)

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_output_handler.py

@@ -8,7 +8,7 @@ from colossalai._analyzer.fx.tracer.tracer import ColoTracer
 from colossalai.auto_parallel.tensor_shard.node_handler.output_handler import OutputHandler
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
+from colossalai.testing import clear_cache_before_run, parameterize
 
 
 class OutputModel(nn.Module):
@@ -23,7 +23,7 @@ class OutputModel(nn.Module):
 
 @pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0')
 @parameterize('output_option', ['distributed', 'replicated'])
-@rerun_if_address_is_in_use()
+@clear_cache_before_run()
 def test_output_handler(output_option):
     model = OutputModel()
     tracer = ColoTracer(bias_addition_split=True)

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_permute_and_transpose_handler.py

@@ -2,7 +2,6 @@ from functools import partial
 
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -15,9 +14,8 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDat
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
@@ -55,7 +53,7 @@ class LinearReshapeModel(nn.Module):
         return permute_node
 
 
-def check_view_handler(rank, call_function, reshape_dims, model_cls, world_size, port):
+def check_view_handler(rank, world_size, port, call_function, reshape_dims, model_cls):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     if call_function == torch.permute:
@@ -328,14 +326,13 @@ def check_view_handler(rank, call_function, reshape_dims, model_cls, world_size,
 @parameterize('reshape_dims', [((0, 2, 1, 3), (1, 2)), ((2, 0, 1, 3), (1, 3))])
 @parameterize('model_cls', [ConvReshapeModel, LinearReshapeModel])
 def test_view_handler(call_function, reshape_dims, model_cls):
-    world_size = 4
-    run_func = partial(check_view_handler,
+    spawn(
+        check_view_handler,
+        4,
         call_function=call_function,
         reshape_dims=reshape_dims,
         model_cls=model_cls,
-                       world_size=world_size,
-                       port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    )
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_placeholder_handler.py

@@ -8,7 +8,7 @@ from colossalai._analyzer.fx.tracer.tracer import ColoTracer
 from colossalai.auto_parallel.tensor_shard.node_handler.placeholder_handler import PlaceholderHandler
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
+from colossalai.testing import clear_cache_before_run, parameterize
 
 
 class PlaceholderModel(nn.Module):
@@ -22,7 +22,7 @@ class PlaceholderModel(nn.Module):
 
 @pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0')
 @parameterize('placeholder_option', ['distributed', 'replicated'])
-@rerun_if_address_is_in_use()
+@clear_cache_before_run()
 def test_placeholder_handler(placeholder_option):
     model = PlaceholderModel()
     tracer = ColoTracer(bias_addition_split=True)

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_shard_option.py

@@ -1,5 +1,4 @@
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -9,7 +8,7 @@ from colossalai.auto_parallel.tensor_shard.node_handler import LinearFunctionHan
 from colossalai.auto_parallel.tensor_shard.options import ShardOption
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import StrategiesVector
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.testing import clear_cache_before_run, run_on_environment_flag
 
 
 class LinearModel(nn.Module):
@@ -108,6 +107,7 @@ def check_shard_option(shard_option):
 
 
 @run_on_environment_flag(name='AUTO_PARALLEL')
+@clear_cache_before_run()
 def test_shard_option():
     # for shard_option in [ShardOption.STANDARD, ShardOption.SHARD, ShardOption.FULL_SHARD, ShardOption.SHARD_LAST_AXIS]:
     for shard_option in [ShardOption.SHARD_LAST_AXIS]:

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_softmax_handler.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 import torch.nn.functional as F
 
@@ -15,9 +12,7 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDat
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
@@ -33,7 +28,7 @@ class LinearSplitModel(nn.Module):
         return softmax_node
 
 
-def check_split_handler(rank, softmax_dim, model_cls, world_size, port):
+def check_split_handler(rank, world_size, port, softmax_dim, model_cls):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     model = model_cls(softmax_dim=softmax_dim).cuda()
@@ -176,13 +171,7 @@ def check_split_handler(rank, softmax_dim, model_cls, world_size, port):
 @parameterize('softmax_dim', [0, 1, 2, 3])
 @parameterize('model_cls', [LinearSplitModel])
 def test_split_handler(softmax_dim, model_cls):
-    world_size = 4
-    run_func = partial(check_split_handler,
-                       softmax_dim=softmax_dim,
-                       model_cls=model_cls,
-                       world_size=world_size,
-                       port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_split_handler, 4, softmax_dim=softmax_dim, model_cls=model_cls)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_split_handler.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -15,9 +12,7 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDat
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
@@ -47,7 +42,7 @@ class LinearSplitModel(nn.Module):
         return split_node
 
 
-def check_split_handler(rank, split_size, split_dim, model_cls, world_size, port):
+def check_split_handler(rank, world_size, port, split_size, split_dim, model_cls):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     model = model_cls(split_size=split_size, split_dim=split_dim).cuda()
@@ -258,14 +253,7 @@ def check_split_handler(rank, split_size, split_dim, model_cls, world_size, port
 @parameterize('split_dim', [0, 1, 2])
 @parameterize('model_cls', [ConvSplitModel, LinearSplitModel])
 def test_split_handler(split_size, split_dim, model_cls):
-    world_size = 4
-    run_func = partial(check_split_handler,
-                       split_size=split_size,
-                       split_dim=split_dim,
-                       model_cls=model_cls,
-                       world_size=world_size,
-                       port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_split_handler, 4, split_size=split_size, split_dim=split_dim, model_cls=model_cls)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_sum_handler.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -14,9 +11,7 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDat
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, run_on_environment_flag, spawn
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
@@ -36,7 +31,7 @@ class LinearSumModel(nn.Module):
         return sum_node
 
 
-def check_sum_handler(rank, sum_dims, keepdim, world_size, port):
+def check_sum_handler(rank, world_size, port, sum_dims, keepdim):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     model = LinearSumModel(sum_dims=sum_dims, keepdim=keepdim).cuda()
@@ -228,9 +223,7 @@ def check_sum_handler(rank, sum_dims, keepdim, world_size, port):
 @parameterize('sum_dims', [(0, 2), 1])
 @parameterize('keepdim', [False, True])
 def test_sum_handler(sum_dims, keepdim):
-    world_size = 4
-    run_func = partial(check_sum_handler, sum_dims=sum_dims, keepdim=keepdim, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_sum_handler, 4, sum_dims=sum_dims, keepdim=keepdim)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_tensor_constructor.py

@@ -7,7 +7,7 @@ from colossalai._analyzer.fx.tracer.tracer import ColoTracer
 from colossalai.auto_parallel.tensor_shard.node_handler.tensor_constructor_handler import TensorConstructorHandler
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.testing import clear_cache_before_run, run_on_environment_flag
 
 
 class TensorConstructorModel(nn.Module):
@@ -22,6 +22,7 @@ class TensorConstructorModel(nn.Module):
 
 
 @run_on_environment_flag(name='AUTO_PARALLEL')
+@clear_cache_before_run()
 def test_where_handler():
     model = TensorConstructorModel()
     tracer = ColoTracer(bias_addition_split=True)

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_unary_element_wise_handler.py

@@ -8,7 +8,7 @@ from colossalai.auto_parallel.tensor_shard.node_handler.conv_handler import Conv
 from colossalai.auto_parallel.tensor_shard.node_handler.unary_elementwise_handler import UnaryElementwiseHandler
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.testing import clear_cache_before_run, run_on_environment_flag
 
 
 class ReLuModel(nn.Module):
@@ -24,6 +24,7 @@ class ReLuModel(nn.Module):
 
 
 @run_on_environment_flag(name='AUTO_PARALLEL')
+@clear_cache_before_run()
 def test_elementwise_handler():
     model = ReLuModel()
     tracer = ColoTracer(bias_addition_split=True)

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_view_handler.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai._analyzer.fx.graph_module import ColoGraphModule
@@ -15,9 +12,8 @@ from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDat
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, parameterize, rerun_if_address_is_in_use
+from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
 from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
 
 
@@ -255,13 +251,7 @@ def check_view_handler(rank, tgt_shape, model_cls, world_size, port):
 @parameterize('tgt_shape', [(32, 4, 64, 16, 4), (8, 4, 4, 64, 16, 4)])
 @parameterize('model_cls', [ConvViewModel, LinearViewModel])
 def test_view_handler(tgt_shape, model_cls):
-    world_size = 4
-    run_func = partial(check_view_handler,
-                       tgt_shape=tgt_shape,
-                       model_cls=model_cls,
-                       world_size=world_size,
-                       port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_view_handler, 4, tgt_shape=tgt_shape, model_cls=model_cls)
 
 
 if __name__ == '__main__':

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_where_handler.py

@@ -8,6 +8,7 @@ from colossalai._analyzer.fx.tracer.tracer import ColoTracer
 from colossalai.auto_parallel.tensor_shard.node_handler.where_handler import WhereHandler
 from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationData, OperationDataType, StrategiesVector
 from colossalai.device.device_mesh import DeviceMesh
+from colossalai.testing import clear_cache_before_run
 
 
 class ConvModel(nn.Module):
@@ -21,6 +22,7 @@ class ConvModel(nn.Module):
 
 
 @pytest.mark.skip('ShapeProp is not compatible with PyTorch 1.11.0')
+@clear_cache_before_run()
 def test_where_handler():
     model = ConvModel()
     tracer = ColoTracer(bias_addition_split=True)

tests/test_auto_parallel/test_tensor_shard/test_solver_with_resnet_v2.py

@@ -10,10 +10,11 @@ from colossalai.auto_parallel.tensor_shard.options import SolverOptions
 from colossalai.auto_parallel.tensor_shard.solver import CostGraph, GraphAnalyser, Solver, StrategiesConstructor
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.tensor.shape_consistency import ShapeConsistencyManager
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.testing import clear_cache_before_run, run_on_environment_flag
 
 
 @run_on_environment_flag(name='AUTO_PARALLEL')
+@clear_cache_before_run()
 def test_cost_graph():
     physical_mesh_id = torch.arange(0, 8)
     mesh_shape = (2, 4)

tests/test_autochunk/test_autochunk_alphafold/benchmark_autochunk_alphafold.py

@@ -8,7 +8,7 @@ import colossalai
 from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE
 from colossalai.fx.graph_module import ColoGraphModule
 from colossalai.fx.passes.meta_info_prop import MetaInfoProp
-from colossalai.utils import free_port
+from colossalai.testing import free_port
 
 if AUTOCHUNK_AVAILABLE:
     from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen

tests/test_autochunk/test_autochunk_alphafold/test_autochunk_alphafold_utils.py

@@ -9,7 +9,7 @@ from colossalai.autochunk.utils import flat_list
 from colossalai.core import global_context as gpc
 from colossalai.fx.graph_module import ColoGraphModule
 from colossalai.fx.passes.meta_info_prop import MetaInfoProp
-from colossalai.utils import free_port
+from colossalai.testing import free_port
 
 if AUTOCHUNK_AVAILABLE:
     from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen

tests/test_autochunk/test_autochunk_alphafold/test_autochunk_evoformer_block.py

@@ -1,10 +1,8 @@
-from functools import partial
 from typing import Dict, List, Tuple
 
 import pytest
 import torch
 import torch.fx
-import torch.multiprocessing as mp
 
 try:
     from fastfold.model.nn.evoformer import EvoformerBlock
@@ -15,6 +13,7 @@ except:
 from test_autochunk_alphafold_utils import run_test
 
 from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE
+from colossalai.testing import clear_cache_before_run, parameterize, spawn
 
 
 def get_model():
@@ -66,18 +65,19 @@ def get_chunk_target() -> Dict:
     not (AUTOCHUNK_AVAILABLE and HAS_REPO),
     reason="torch version is lower than 1.12.0",
 )
-@pytest.mark.parametrize("max_memory", [None, 20, 24])
-@pytest.mark.parametrize("data_args", [(32, 64)])    # (msa_len, pair_len)
+@clear_cache_before_run()
+@parameterize("max_memory", [None, 20, 24])
+@parameterize("data_args", [(32, 64)])
 def test_evoformer_block(data_args, max_memory):
-    run_func = partial(
+    spawn(
         run_test,
+        1,
         data_args=data_args,
         max_memory=max_memory,
         get_model=get_model,
         get_data=get_data,
         get_chunk_target=get_chunk_target,
     )
-    mp.spawn(run_func, nprocs=1)
 
 
 if __name__ == "__main__":

tests/test_autochunk/test_autochunk_alphafold/test_autochunk_evoformer_stack.py

@@ -1,10 +1,8 @@
-from functools import partial
 from typing import List, Tuple
 
 import pytest
 import torch
 import torch.fx
-import torch.multiprocessing as mp
 
 try:
     from fastfold.model.nn.evoformer import EvoformerStack
@@ -15,6 +13,7 @@ except:
 from test_autochunk_alphafold_utils import run_test
 
 from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE
+from colossalai.testing import clear_cache_before_run, parameterize, spawn
 
 
 def get_model():
@@ -61,17 +60,18 @@ def get_data(msa_len: int, pair_len: int) -> Tuple[List, List]:
     not (AUTOCHUNK_AVAILABLE and HAS_REPO),
     reason="torch version is lower than 1.12.0",
 )
-@pytest.mark.parametrize("max_memory", [None, 20, 24])
-@pytest.mark.parametrize("data_args", [(32, 64)])    # (msa_len, pair_len)
+@clear_cache_before_run()
+@parameterize("max_memory", [None, 20, 24])
+@parameterize("data_args", [(32, 64)])    # (msa_len, pair_len)
 def test_evoformer_stack(data_args, max_memory):
-    run_func = partial(
+    spawn(
         run_test,
+        1,
         data_args=data_args,
         max_memory=max_memory,
         get_model=get_model,
         get_data=get_data,
     )
-    mp.spawn(run_func, nprocs=1)
 
 
 if __name__ == "__main__":

tests/test_autochunk/test_autochunk_alphafold/test_autochunk_extramsa_block.py

@@ -1,10 +1,8 @@
-from functools import partial
 from typing import Dict, List, Tuple
 
 import pytest
 import torch
 import torch.fx
-import torch.multiprocessing as mp
 
 try:
     from fastfold.model.nn.evoformer import ExtraMSABlock
@@ -14,6 +12,7 @@ except:
 from test_autochunk_alphafold_utils import run_test
 
 from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE
+from colossalai.testing import clear_cache_before_run, parameterize, spawn
 
 
 def get_model():
@@ -57,17 +56,18 @@ def get_data(msa_len: int, pair_len: int) -> Tuple[List, List]:
     not (AUTOCHUNK_AVAILABLE and HAS_REPO),
     reason="torch version is lower than 1.12.0",
 )
-@pytest.mark.parametrize("max_memory", [None, 20, 24])
-@pytest.mark.parametrize("data_args", [(32, 64)])    # (msa_len, pair_len)
+@clear_cache_before_run()
+@parameterize("max_memory", [None, 20, 24])
+@parameterize("data_args", [(32, 64)])    # (msa_len, pair_len)
 def test_extramsa_block(data_args, max_memory):
-    run_func = partial(
+    spawn(
         run_test,
+        1,
         data_args=data_args,
         max_memory=max_memory,
         get_model=get_model,
         get_data=get_data,
     )
-    mp.spawn(run_func, nprocs=1)
 
 
 if __name__ == "__main__":

tests/test_autochunk/test_autochunk_diffuser/test_autochunk_diffuser_utils.py

@@ -8,7 +8,7 @@ from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE
 from colossalai.core import global_context as gpc
 from colossalai.fx.graph_module import ColoGraphModule
 from colossalai.fx.passes.meta_info_prop import MetaInfoProp
-from colossalai.utils import free_port
+from colossalai.testing import free_port
 
 if AUTOCHUNK_AVAILABLE:
     from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen

tests/test_autochunk/test_autochunk_diffuser/test_autochunk_unet.py

@@ -1,9 +1,7 @@
-from functools import partial
 from typing import List, Tuple
 
 import pytest
 import torch
-import torch.multiprocessing as mp
 
 try:
     from diffusers import UNet2DModel
@@ -16,6 +14,7 @@ except:
 from test_autochunk_diffuser_utils import run_test
 
 from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE
+from colossalai.testing import clear_cache_before_run, parameterize, spawn
 
 BATCH_SIZE = 1
 HEIGHT = 448
@@ -37,17 +36,18 @@ def get_data(shape: tuple) -> Tuple[List, List]:
     not (AUTOCHUNK_AVAILABLE and HAS_REPO),
     reason="torch version is lower than 1.12.0",
 )
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("shape", [LATENTS_SHAPE])
-@pytest.mark.parametrize("max_memory", [None, 150, 300])
+@clear_cache_before_run()
+@parameterize("model", MODELS)
+@parameterize("shape", [LATENTS_SHAPE])
+@parameterize("max_memory", [None, 150, 300])
 def test_evoformer_block(model, shape, max_memory):
-    run_func = partial(
+    spawn(
         run_test,
+        1,
         max_memory=max_memory,
         model=model,
         data=get_data(shape),
     )
-    mp.spawn(run_func, nprocs=1)
 
 
 if __name__ == "__main__":

tests/test_autochunk/test_autochunk_transformer/test_autochunk_gpt.py

@@ -1,9 +1,7 @@
-from functools import partial
 from typing import List, Tuple
 
 import pytest
 import torch
-import torch.multiprocessing as mp
 
 try:
     from transformers import GPT2Config, GPT2Model
@@ -16,6 +14,7 @@ except:
 from test_autochunk_transformer_utils import run_test
 
 from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE
+from colossalai.testing import clear_cache_before_run, parameterize, spawn
 
 BATCH_SIZE = 1
 SEQ_LENGTH = 512
@@ -35,18 +34,19 @@ def get_data(shape: tuple) -> Tuple[List, List]:
     not (AUTOCHUNK_AVAILABLE and HAS_REPO),
     reason="torch version is lower than 1.12.0",
 )
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("shape", [(BATCH_SIZE, SEQ_LENGTH)])
-@pytest.mark.parametrize("max_memory", [None, 6, 8])
+@clear_cache_before_run()
+@parameterize("model", MODELS)
+@parameterize("shape", [(BATCH_SIZE, SEQ_LENGTH)])
+@parameterize("max_memory", [None, 6, 8])
 def test_autochunk_gpt(model, shape, max_memory):
-    run_func = partial(
+    spawn(
         run_test,
+        1,
         data=get_data(shape),
         max_memory=max_memory,
         model=model,
         config=GPT2Config(n_embd=96, n_positions=shape[1], n_layer=2, n_head=4),
     )
-    mp.spawn(run_func, nprocs=1)
 
 
 if __name__ == "__main__":

tests/test_autochunk/test_autochunk_transformer/test_autochunk_transformer_utils.py

@@ -8,7 +8,7 @@ from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE
 from colossalai.core import global_context as gpc
 from colossalai.fx.graph_module import ColoGraphModule
 from colossalai.fx.passes.meta_info_prop import MetaInfoProp
-from colossalai.utils import free_port
+from colossalai.testing import free_port
 
 if AUTOCHUNK_AVAILABLE:
     from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen

tests/test_autochunk/test_autochunk_vit/test_autochunk_vit.py

@@ -1,9 +1,7 @@
-from functools import partial
 from typing import List, Tuple
 
 import pytest
 import torch
-import torch.multiprocessing as mp
 
 try:
     from timm.models.vision_transformer import vit_large_patch16_384 as vit
@@ -16,6 +14,7 @@ except:
 from test_autochunk_vit_utils import run_test
 
 from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE
+from colossalai.testing import clear_cache_before_run, parameterize, spawn
 
 
 def get_data() -> Tuple[List, List]:
@@ -28,16 +27,17 @@ def get_data() -> Tuple[List, List]:
     not (AUTOCHUNK_AVAILABLE and HAS_REPO),
     reason="torch version is lower than 1.12.0",
 )
-@pytest.mark.parametrize("model", MODELS)
-@pytest.mark.parametrize("max_memory", [None, 32, 40])
+@clear_cache_before_run()
+@parameterize("model", MODELS)
+@parameterize("max_memory", [None, 32, 40])
 def test_evoformer_block(model, max_memory):
-    run_func = partial(
+    spawn(
         run_test,
+        1,
         max_memory=max_memory,
         model=model,
         data=get_data(),
     )
-    mp.spawn(run_func, nprocs=1)
 
 
 if __name__ == "__main__":

tests/test_autochunk/test_autochunk_vit/test_autochunk_vit_utils.py

@@ -8,7 +8,7 @@ from colossalai.autochunk.autochunk_codegen import AUTOCHUNK_AVAILABLE
 from colossalai.core import global_context as gpc
 from colossalai.fx.graph_module import ColoGraphModule
 from colossalai.fx.passes.meta_info_prop import MetaInfoProp
-from colossalai.utils import free_port
+from colossalai.testing import free_port
 
 if AUTOCHUNK_AVAILABLE:
     from colossalai.autochunk.autochunk_codegen import AutoChunkCodeGen

tests/test_booster/test_accelerator.py

@@ -1,27 +1,14 @@
-from functools import partial
-
-import torch.multiprocessing as mp
 import torch.nn as nn
 
 from colossalai.booster.accelerator import Accelerator
-from colossalai.testing import parameterize, rerun_if_address_is_in_use
+from colossalai.testing import clear_cache_before_run, parameterize
 
 
+@clear_cache_before_run()
 @parameterize('device', ['cpu', 'cuda'])
-def run_accelerator(device):
+def test_accelerator(device):
     acceleartor = Accelerator(device)
     model = nn.Linear(8, 8)
     model = acceleartor.configure_model(model)
     assert next(model.parameters()).device.type == device
     del model, acceleartor
-
-
-def run_dist(rank):
-    run_accelerator()
-
-
-@rerun_if_address_is_in_use()
-def test_accelerator():
-    world_size = 1
-    run_func = partial(run_dist)
-    mp.spawn(run_func, nprocs=world_size)

tests/test_booster/test_mixed_precision/test_fp16_torch.py

@@ -1,13 +1,9 @@
-from functools import partial
-
 import torch
-import torch.multiprocessing as mp
 from torch.optim import Adam
 
 import colossalai
 from colossalai.booster.mixed_precision import FP16TorchMixedPrecision
-from colossalai.testing import rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 from tests.kit.model_zoo import model_zoo
 
 
@@ -41,6 +37,4 @@ def run_torch_amp(rank, world_size, port):
 
 @rerun_if_address_is_in_use()
 def test_torch_ddp_plugin():
-    world_size = 1
-    run_func = partial(run_torch_amp, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(run_torch_amp, 1)

tests/test_booster/test_plugin/test_gemini_plugin.py

@@ -1,17 +1,12 @@
-from functools import partial
-
-import pytest
 import torch
 import torch.distributed as dist
-import torch.multiprocessing as mp
 
 import colossalai
 from colossalai.booster import Booster
 from colossalai.booster.plugin import GeminiPlugin
 from colossalai.nn.optimizer import HybridAdam
 from colossalai.tensor.colo_parameter import ColoParameter
-from colossalai.testing import rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 from tests.kit.model_zoo import model_zoo
 
 
@@ -119,9 +114,7 @@ def run_dist(rank, world_size, port, early_stop: bool = True):
 
 @rerun_if_address_is_in_use()
 def test_gemini_plugin(early_stop: bool = True):
-    world_size = 2
-    run_func = partial(run_dist, world_size=world_size, port=free_port(), early_stop=early_stop)
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(run_dist, 2, early_stop=early_stop)
 
 
 if __name__ == '__main__':

tests/test_booster/test_plugin/test_torch_ddp_plugin.py

@@ -1,8 +1,5 @@
-from functools import partial
-
 import torch
 import torch.distributed as dist
-import torch.multiprocessing as mp
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import SGD
 
@@ -10,8 +7,7 @@ import colossalai
 from colossalai.booster import Booster
 from colossalai.booster.plugin import TorchDDPPlugin
 from colossalai.interface import OptimizerWrapper
-from colossalai.testing import rerun_if_address_is_in_use
-from colossalai.utils import free_port
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 from tests.kit.model_zoo import model_zoo
 
 
@@ -103,6 +99,4 @@ def run_dist(rank, world_size, port):
 
 @rerun_if_address_is_in_use()
 def test_torch_ddp_plugin():
-    world_size = 2
-    run_func = partial(run_dist, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(run_dist, 2)

tests/test_checkpoint_io/test_general_checkpoint_io.py

@@ -6,6 +6,7 @@ from torch.optim import Adam
 from torchvision.models import resnet18
 
 from colossalai.checkpoint_io import GeneralCheckpointIO
+from colossalai.testing import clear_cache_before_run, parameterize
 
 # ========
 # Note:
@@ -15,7 +16,8 @@ from colossalai.checkpoint_io import GeneralCheckpointIO
 # ========
 
 
-@pytest.mark.parametrize('use_safetensors', [True, False])
+@clear_cache_before_run()
+@parameterize('use_safetensors', [True, False])
 def test_unsharded_checkpoint(use_safetensors: bool):
     # create a model and optimizer
     model = resnet18()

tests/test_cluster/test_device_mesh_manager.py

@@ -1,14 +1,9 @@
-from functools import partial
-
 import torch
-import torch.multiprocessing as mp
 
 from colossalai.cluster.device_mesh_manager import DeviceMeshInfo, DeviceMeshManager
-from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx.tracer import ColoTracer
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.utils import free_port
+from colossalai.testing import spawn
 
 
 def check_device_mesh_manager(rank, world_size, port):
@@ -31,9 +26,7 @@ def check_device_mesh_manager(rank, world_size, port):
 
 
 def test_device_mesh_manager():
-    world_size = 4
-    run_func = partial(check_device_mesh_manager, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_device_mesh_manager, 4)
 
 
 if __name__ == '__main__':

tests/test_comm/test_boardcast_send_recv_v2.py

@@ -1,17 +1,12 @@
-from functools import partial
-from typing import List
-
 import pytest
 import torch
-import torch.distributed as dist
-import torch.multiprocessing as mp
-from colossalai.communication.p2p_v2 import _send_object, _recv_object, init_process_group
+
+from colossalai.communication.p2p_v2 import _recv_object, _send_object
 from colossalai.context import ParallelMode
 from colossalai.core import global_context as gpc
 from colossalai.initialize import launch
-from colossalai.utils import free_port, get_current_device
-from colossalai.testing import rerun_if_address_is_in_use
 from colossalai.logging import disable_existing_loggers
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 
 disable_existing_loggers()
 world_size = 4
@@ -45,9 +40,7 @@ def check_layer(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_object_list_p2p():
-    disable_existing_loggers()
-    run_func = partial(check_layer, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_layer, world_size)
 
 
 if __name__ == '__main__':

tests/test_comm/test_comm.py

@@ -1,15 +1,13 @@
-from functools import partial
-
 import pytest
 import torch
 import torch.distributed as dist
-import torch.multiprocessing as mp
+
 from colossalai.communication import all_gather, all_reduce, reduce_scatter
 from colossalai.context import ParallelMode
 from colossalai.core import global_context as gpc
 from colossalai.initialize import launch
-from colossalai.utils import free_port, get_current_device
-from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.testing import rerun_if_address_is_in_use, spawn
+from colossalai.utils import get_current_device
 
 CONFIG = dict(parallel=dict(data=8, pipeline=1, tensor=dict(mode=None, size=1)))
 
@@ -66,9 +64,7 @@ def check_layer(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_comm():
-    world_size = 4
-    run_func = partial(check_layer, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_layer, 4)
 
 
 if __name__ == '__main__':

tests/test_comm/test_object_list_p2p.py

@@ -1,15 +1,18 @@
-from functools import partial
-
 import pytest
 import torch
-import torch.distributed as dist
-import torch.multiprocessing as mp
-from colossalai.communication.p2p import send_forward, recv_forward, send_backward, recv_backward, send_forward_recv_backward, send_backward_recv_forward
+
+from colossalai.communication.p2p import (
+    recv_backward,
+    recv_forward,
+    send_backward,
+    send_backward_recv_forward,
+    send_forward,
+    send_forward_recv_backward,
+)
 from colossalai.context import ParallelMode
 from colossalai.core import global_context as gpc
 from colossalai.initialize import launch
-from colossalai.utils import free_port, get_current_device
-from colossalai.testing import rerun_if_address_is_in_use
+from colossalai.testing import rerun_if_address_is_in_use, spawn
 
 CONFIG = dict(parallel=dict(pipeline=2))
 torch.manual_seed(123)
@@ -96,9 +99,7 @@ def check_layer(rank, world_size, port):
 @pytest.mark.dist
 @rerun_if_address_is_in_use()
 def test_object_list_p2p():
-    world_size = 2
-    run_func = partial(check_layer, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
+    spawn(check_layer, 2)
 
 
 if __name__ == '__main__':