[test] fixed rerun_on_exception and adapted test cases (#487)

colossalai/testing/utils.py

 import re
 from typing import Callable, List, Any
 from functools import partial
+from inspect import signature
 
 
 def parameterize(argument: str, values: List[Any]) -> Callable:
@@ -105,6 +106,12 @@ def rerun_on_exception(exception_type: Exception = Exception, pattern: str = Non
             If max_try is None, it will rerun foreven if exception keeps occurings
     """
 
+    def _match_lines(lines, pattern):
+        for line in lines:
+            if re.match(pattern, line):
+                return True
+        return False
+
     def _wrapper(func):
 
         def _run_until_success(*args, **kwargs):
@@ -115,15 +122,25 @@ def rerun_on_exception(exception_type: Exception = Exception, pattern: str = Non
             while max_try is None or try_count < max_try:
                 try:
                     try_count += 1
-                    func(*args, **kwargs)
+                    ret = func(*args, **kwargs)
+                    return ret
                 except exception_type as e:
-                    if pattern is None or re.match(pattern, str(e)):
+                    error_lines = str(e).split('\n')
+                    if try_count < max_try and (pattern is None or _match_lines(error_lines, pattern)):
+                        print('Exception is caught, retrying...')
                         # when pattern is not specified, we always skip the exception
                         # when pattern is specified, we only skip when pattern is matched
                         continue
                     else:
+                        print('Maximum number of attempts is reached or pattern is not matched, no more retrying...')
                         raise e
 
+        # Override signature
+        # otherwise pytest.mark.parameterize will raise the following error:
+        # function does not use argumetn xxx
+        sig = signature(func)
+        _run_until_success.__signature__ = sig
+
         return _run_until_success
 
     return _wrapper

tests/test_amp/test_naive_fp16.py

 import torch
-import torch.multiprocessing as mp
-
 import colossalai
-from colossalai.testing import assert_close_loose
+import torch.multiprocessing as mp
+from colossalai.amp import convert_to_naive_amp, convert_to_apex_amp
+from tests.components_to_test.registry import non_distributed_component_funcs
+from colossalai.testing import assert_close_loose, rerun_on_exception
 from colossalai.utils import free_port
 from colossalai.amp import convert_to_naive_amp, convert_to_apex_amp
 
@@ -83,6 +84,7 @@ def run_dist(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_naive_amp():
     world_size = 1
     run_func = partial(run_dist, world_size=world_size, port=free_port())

tests/test_comm/test_comm.py

@@ -9,6 +9,7 @@ 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_on_exception
 
 CONFIG = dict(parallel=dict(data=8, pipeline=1, tensor=dict(mode=None, size=1)))
 
@@ -63,6 +64,7 @@ def check_layer(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_comm():
     world_size = 4
     run_func = partial(check_layer, world_size=world_size, port=free_port())

tests/test_context/test_hybrid_parallel.py

@@ -13,6 +13,7 @@ from colossalai.core import global_context as gpc
 from colossalai.utils import free_port
 from colossalai.context import reset_seeds
 from colossalai.global_variables import tensor_parallel_env as tp_env
+from colossalai.testing import rerun_on_exception
 
 CONFIG_PATH_LIST = list(Path(__file__).parent.glob('configs/*.py'))
 
@@ -140,6 +141,7 @@ def run_dist(rank, world_size, backend, port_list, host):
 
 
 @pytest.mark.cpu
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_context():
     """
     As no computation or communication is done, we can run this test on CPU.

tests/test_data/test_data_parallel_sampler.py

@@ -12,29 +12,26 @@ import torch.multiprocessing as mp
 from torch.utils.data import DataLoader
 
 import colossalai
-from colossalai.builder import build_dataset, build_data_sampler, build_transform
+from colossalai.builder import build_dataset, build_transform
 from torchvision import transforms
 from colossalai.context import ParallelMode, Config
 from colossalai.core import global_context as gpc
-from colossalai.utils import get_dataloader
+from colossalai.utils import get_dataloader, free_port
+from colossalai.testing import rerun_on_exception
 
 CONFIG = Config(
     dict(
-        train_data=dict(
-            dataset=dict(
-                type='CIFAR10',
-                root=Path(os.environ['DATA']),
-                train=True,
-                download=True,
-            ),
-            dataloader=dict(
-                batch_size=8,
-            ),
-            transform_pipeline=[
-                dict(type='ToTensor'),
-                dict(type='Normalize', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
-            ]
+        train_data=dict(dataset=dict(
+            type='CIFAR10',
+            root=Path(os.environ['DATA']),
+            train=True,
+            download=True,
         ),
+                        dataloader=dict(batch_size=8,),
+                        transform_pipeline=[
+                            dict(type='ToTensor'),
+                            dict(type='Normalize', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
+                        ]),
         parallel=dict(
             pipeline=dict(size=1),
             tensor=dict(size=1, mode=None),
@@ -43,15 +40,8 @@ CONFIG = Config(
     ))
 
 
-def run_data_sampler(rank, world_size):
-    dist_args = dict(
-        config=CONFIG,
-        rank=rank,
-        world_size=world_size,
-        backend='gloo',
-        port='29903',
-        host='localhost'
-    )
+def run_data_sampler(rank, world_size, port):
+    dist_args = dict(config=CONFIG, rank=rank, world_size=world_size, backend='gloo', port=port, host='localhost')
     colossalai.launch(**dist_args)
     print('finished initialization')
 
@@ -71,15 +61,16 @@ def run_data_sampler(rank, world_size):
     dist.broadcast(img_to_compare, src=0, group=gpc.get_group(ParallelMode.DATA))
 
     if gpc.get_local_rank(ParallelMode.DATA) != 0:
-        assert not torch.equal(img,
-                               img_to_compare), 'Same image was distributed across ranks but expected it to be different'
+        assert not torch.equal(
+            img, img_to_compare), 'Same image was distributed across ranks but expected it to be different'
     torch.cuda.empty_cache()
 
 
 @pytest.mark.cpu
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_data_sampler():
     world_size = 4
-    test_func = partial(run_data_sampler, world_size=world_size)
+    test_func = partial(run_data_sampler, world_size=world_size, port=free_port())
     mp.spawn(test_func, nprocs=world_size)
 
 

tests/test_data/test_deterministic_dataloader.py

@@ -16,45 +16,33 @@ import colossalai
 from colossalai.builder import build_dataset, build_transform
 from colossalai.context import ParallelMode, Config
 from colossalai.core import global_context as gpc
+from colossalai.utils import free_port
+from colossalai.testing import rerun_on_exception
 
 CONFIG = Config(
     dict(
-        train_data=dict(
-            dataset=dict(
-                type='CIFAR10',
-                root=Path(os.environ['DATA']),
-                train=True,
-                download=True,
-            ),
-            dataloader=dict(
-                num_workers=2,
-                batch_size=2,
-                shuffle=True
-            ),
-            transform_pipeline=[
-                dict(type='ToTensor'),
-                dict(type='RandomCrop', size=32),
-                dict(type='Normalize', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
-            ]
+        train_data=dict(dataset=dict(
+            type='CIFAR10',
+            root=Path(os.environ['DATA']),
+            train=True,
+            download=True,
         ),
+                        dataloader=dict(num_workers=2, batch_size=2, shuffle=True),
+                        transform_pipeline=[
+                            dict(type='ToTensor'),
+                            dict(type='RandomCrop', size=32),
+                            dict(type='Normalize', mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
+                        ]),
         parallel=dict(
             pipeline=dict(size=1),
             tensor=dict(size=1, mode=None),
         ),
         seed=1024,
-    )
-)
-
-
-def run_data_sampler(rank, world_size):
-    dist_args = dict(
-        config=CONFIG,
-        rank=rank,
-        world_size=world_size,
-        backend='gloo',
-        port='29904',
-        host='localhost'
-    )
+    ))
+
+
+def run_data_sampler(rank, world_size, port):
+    dist_args = dict(config=CONFIG, rank=rank, world_size=world_size, backend='gloo', port=port, host='localhost')
     colossalai.launch(**dist_args)
 
     dataset_cfg = gpc.config.train_data.dataset
@@ -91,9 +79,10 @@ def run_data_sampler(rank, world_size):
 
 
 @pytest.mark.cpu
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_data_sampler():
     world_size = 4
-    test_func = partial(run_data_sampler, world_size=world_size)
+    test_func = partial(run_data_sampler, world_size=world_size, port=free_port())
     mp.spawn(test_func, nprocs=world_size)
 
 

tests/test_data_pipeline_tensor_parallel/test_cifar_with_data_pipeline_tensor.py

@@ -13,8 +13,9 @@ from colossalai.logging import get_dist_logger
 from colossalai.nn import LinearWarmupLR
 from colossalai.nn.loss import CrossEntropyLoss
 from colossalai.trainer import Trainer, hooks
-from colossalai.utils import MultiTimer, free_port, get_dataloader
+from colossalai.utils import free_port, get_dataloader
 from colossalai.utils.gradient_accumulation import GradAccumLrSchedulerByStep
+from colossalai.testing import rerun_on_exception
 from model_zoo.vit import vit_tiny_patch4_32
 from torchvision import transforms
 from torchvision.datasets import CIFAR10
@@ -79,6 +80,7 @@ def run_trainer(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_hybrid_parallel():
     world_size = 8
     run_func = partial(run_trainer, world_size=world_size, port=free_port())

tests/test_engine/test_engine.py

@@ -4,11 +4,10 @@ import colossalai
 import pytest
 import torch.multiprocessing as mp
 from colossalai.amp import AMP_TYPE
-from colossalai.context import Config
 from colossalai.core import global_context as gpc
 from colossalai.utils import free_port
 from tests.components_to_test.registry import non_distributed_component_funcs
-from colossalai.testing import parameterize
+from colossalai.testing import parameterize, rerun_on_exception
 
 CONFIG = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=1, mode=None)),
               fp16=dict(mode=None),
@@ -57,6 +56,7 @@ def run_engine(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_engine():
     world_size = 2
     run_func = partial(run_engine, world_size=world_size, port=free_port())

tests/test_layers/test_1d/test_1d.py

@@ -10,28 +10,15 @@ from colossalai.core import global_context as gpc
 from colossalai.logging import disable_existing_loggers
 from colossalai.initialize import launch
 from colossalai.utils import free_port
-
+from colossalai.testing import rerun_on_exception
 from checks_1d.check_layer_1d import *
 
-CONFIG = dict(
-    parallel=dict(
-        pipeline=dict(size=1),
-        tensor=dict(
-            size=4,
-            mode='1d'
-        )
-    ),
-)
+CONFIG = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=4, mode='1d')),)
 
 
 def check_layer(rank, world_size, port):
     disable_existing_loggers()
-    launch(config=CONFIG,
-           rank=rank,
-           world_size=world_size,
-           host='localhost',
-           port=port,
-           backend='nccl')
+    launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
 
     check_linear_col()
     check_linear_row()
@@ -48,6 +35,7 @@ def check_layer(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_1d():
     world_size = 4
     run_func = partial(check_layer, world_size=world_size, port=free_port())

tests/test_layers/test_2d/test_2d.py

@@ -10,7 +10,7 @@ from colossalai.core import global_context as gpc
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
 from colossalai.utils import free_port
-
+from colossalai.testing import rerun_on_exception
 from checks_2d.check_layer_2d import (check_classifier_given_embed_weight, check_classifier_no_given_weight,
                                       check_embed, check_layernorm, check_linear, check_loss, check_patch_embed,
                                       check_vocab_parallel_classifier_given_embed_weight,
@@ -18,7 +18,7 @@ from checks_2d.check_layer_2d import (check_classifier_given_embed_weight, check
                                       check_vocab_parallel_loss)
 from checks_2d.check_operation_2d import check_AB, check_ABT, check_ATB
 
-CONFIG = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=4, mode='2d')), )
+CONFIG = dict(parallel=dict(pipeline=dict(size=1), tensor=dict(size=4, mode='2d')),)
 
 
 def check_operations():
@@ -55,6 +55,7 @@ def check_layer_and_operation(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_2d():
     world_size = 4
     run_func = partial(check_layer_and_operation, world_size=world_size, port=free_port())

tests/test_layers/test_2p5d/test_2p5d.py

@@ -7,16 +7,14 @@ from colossalai.core import global_context as gpc
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
 from colossalai.utils import free_port
-
+from colossalai.testing import rerun_on_exception
 from checks_2p5d.check_layer_2p5d import *
 from checks_2p5d.check_operation_2p5d import check_AB, check_ABT, check_ATB
 
-CONFIG = dict(
-    parallel=dict(
-        pipeline=dict(size=1),
-        tensor=dict(size=4, mode='2.5d', depth=1),
-    ),
-)
+CONFIG = dict(parallel=dict(
+    pipeline=dict(size=1),
+    tensor=dict(size=4, mode='2.5d', depth=1),
+),)
 
 
 def check_operations():
@@ -41,12 +39,7 @@ def check_layer():
 
 def check_layer_and_operation(rank, world_size, port):
     disable_existing_loggers()
-    launch(config=CONFIG,
-           rank=rank,
-           world_size=world_size,
-           host='localhost',
-           port=port,
-           backend='nccl')
+    launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
 
     torch.backends.cuda.matmul.allow_tf32 = False
     torch.backends.cudnn.allow_tf32 = False
@@ -58,6 +51,7 @@ def check_layer_and_operation(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_2p5d():
     world_size = 4
     run_func = partial(check_layer_and_operation, world_size=world_size, port=free_port())

tests/test_layers/test_3d/test_3d.py

@@ -9,7 +9,7 @@ from colossalai.core import global_context as gpc
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
 from colossalai.utils import free_port
-
+from colossalai.testing import rerun_on_exception
 from checks_3d.check_layer_3d import (check_classifier_given_embed_weight, check_classifier_no_given_weight,
                                       check_embed, check_layernorm, check_linear, check_loss, check_patch_embed,
                                       check_vocab_parallel_classifier_given_embed_weight,
@@ -51,6 +51,7 @@ def check_layer_and_operation(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_3d():
     world_size = 8
     run_func = partial(check_layer_and_operation, world_size=world_size, port=free_port())

tests/test_layers/test_sequence/test_sequence.py

@@ -7,14 +7,10 @@ import pytest
 
 from colossalai.core import global_context as gpc
 from colossalai.context import ParallelMode
+from colossalai.testing import rerun_on_exception
 from functools import partial
 
-
-CONFIG = dict(
-    parallel=dict(
-        tensor=dict(size=4, mode='sequence')
-    )
-)
+CONFIG = dict(parallel=dict(tensor=dict(size=4, mode='sequence')))
 
 
 def check_ring_qk(rank, world_size):
@@ -26,14 +22,14 @@ def check_ring_qk(rank, world_size):
     sub_seq_length = seq_length // world_size
 
     # create master tensors
-    q = torch.rand(batch_size*num_heads, seq_length, attention_head_size).cuda()
-    k = torch.rand(batch_size*num_heads, seq_length, attention_head_size).cuda()
+    q = torch.rand(batch_size * num_heads, seq_length, attention_head_size).cuda()
+    k = torch.rand(batch_size * num_heads, seq_length, attention_head_size).cuda()
     dist.broadcast(q, src=0, group=gpc.get_group(ParallelMode.SEQUENCE))
     dist.broadcast(k, src=0, group=gpc.get_group(ParallelMode.SEQUENCE))
 
     # create distributed tensors
-    sub_q = q.clone()[:, rank*sub_seq_length:(rank+1)*sub_seq_length].contiguous()
-    sub_k = k.clone()[:, rank*sub_seq_length:(rank+1)*sub_seq_length].contiguous()
+    sub_q = q.clone()[:, rank * sub_seq_length:(rank + 1) * sub_seq_length].contiguous()
+    sub_k = k.clone()[:, rank * sub_seq_length:(rank + 1) * sub_seq_length].contiguous()
 
     # set autograd attributes
     q.requires_grad = True
@@ -53,7 +49,7 @@ def check_ring_qk(rank, world_size):
     sub_a = ring_qk(sub_q, sub_k, batch_size, num_heads, sub_seq_length)
 
     # check master and distributed attetion scores
-    sub_master_a = a[:, rank*sub_seq_length:(rank+1)*sub_seq_length]
+    sub_master_a = a[:, rank * sub_seq_length:(rank + 1) * sub_seq_length]
     assert torch.allclose(sub_a, sub_master_a, rtol=1e-5, atol=1e-2)
 
     # run master backward
@@ -61,11 +57,11 @@ def check_ring_qk(rank, world_size):
     a.mean().backward()
 
     # run distributed backward
-    partial_master_a_grad = a.grad[:, rank*sub_seq_length:(rank+1)*sub_seq_length]
+    partial_master_a_grad = a.grad[:, rank * sub_seq_length:(rank + 1) * sub_seq_length]
     torch.autograd.backward(sub_a, partial_master_a_grad)
 
     # check master and distributed grads
-    partial_master_q_grad = q.grad[:, rank*sub_seq_length:(rank+1)*sub_seq_length]
+    partial_master_q_grad = q.grad[:, rank * sub_seq_length:(rank + 1) * sub_seq_length]
     assert torch.allclose(sub_q.grad, partial_master_q_grad, rtol=1e-5, atol=1e-2), \
         'attention score cannot match'
 
@@ -79,14 +75,14 @@ def check_ring_av(rank, world_size):
     sub_seq_length = seq_length // world_size
 
     # create master tensors
-    a = torch.rand(batch_size*num_heads, seq_length, seq_length).cuda()
-    v = torch.rand(batch_size*num_heads, seq_length, attention_head_size).cuda()
+    a = torch.rand(batch_size * num_heads, seq_length, seq_length).cuda()
+    v = torch.rand(batch_size * num_heads, seq_length, attention_head_size).cuda()
     dist.broadcast(a, src=0, group=gpc.get_group(ParallelMode.SEQUENCE))
     dist.broadcast(v, src=0, group=gpc.get_group(ParallelMode.SEQUENCE))
 
     # create distributed tensors
-    sub_a = a.clone()[:, rank*sub_seq_length:(rank+1)*sub_seq_length].contiguous()
-    sub_v = v.clone()[:, rank*sub_seq_length:(rank+1)*sub_seq_length].contiguous()
+    sub_a = a.clone()[:, rank * sub_seq_length:(rank + 1) * sub_seq_length].contiguous()
+    sub_v = v.clone()[:, rank * sub_seq_length:(rank + 1) * sub_seq_length].contiguous()
 
     # set autograd attributes
     a.requires_grad = True
@@ -108,7 +104,7 @@ def check_ring_av(rank, world_size):
     # print(f'master output shape: {out.shape}, partial output shape: {sub_out.shape}')
 
     # check master and distributed output
-    sub_master_out = out[:, rank*sub_seq_length:(rank+1)*sub_seq_length]
+    sub_master_out = out[:, rank * sub_seq_length:(rank + 1) * sub_seq_length]
     assert torch.allclose(sub_out, sub_master_out, rtol=1e-5, atol=1e-2)
 
     # # run master backward
@@ -116,23 +112,17 @@ def check_ring_av(rank, world_size):
     out.mean().backward()
 
     # # run distributed backward
-    partial_master_out_grad = out.grad[:, rank*sub_seq_length:(rank+1)*sub_seq_length]
+    partial_master_out_grad = out.grad[:, rank * sub_seq_length:(rank + 1) * sub_seq_length]
     torch.autograd.backward(sub_out, partial_master_out_grad)
 
     # # check master and distributed grads
-    partial_master_a_grad = a.grad[:, rank*sub_seq_length:(rank+1)*sub_seq_length]
+    partial_master_a_grad = a.grad[:, rank * sub_seq_length:(rank + 1) * sub_seq_length]
     assert torch.allclose(sub_a.grad, partial_master_a_grad, rtol=1e-5, atol=1e-2), \
         'attention output cannot match'
 
 
 def run_test(rank, world_size):
-    colossalai.launch(
-        rank=rank,
-        world_size=world_size,
-        config=CONFIG,
-        host='localhost',
-        port=29500
-    )
+    colossalai.launch(rank=rank, world_size=world_size, config=CONFIG, host='localhost', port=29500)
 
     # check_ring_qk(rank, world_size)
     check_ring_av(rank, world_size)
@@ -142,6 +132,7 @@ def run_test(rank, world_size):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_sequence():
     world_size = 4
     run_func = partial(run_test, world_size=world_size)

tests/test_moe/test_grad_handler.py

@@ -11,6 +11,7 @@ from colossalai.context.moe_context import MOE_CONTEXT
 from colossalai.utils.moe import sync_moe_model_param
 from colossalai.engine.gradient_handler import MoeGradientHandler
 from colossalai.testing import assert_equal_in_group
+from colossalai.testing import rerun_on_exception
 
 BATCH_SIZE = 4
 DIM = 16
@@ -62,6 +63,7 @@ def run_test(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_grad_handler():
     world_size = 4
     run_func = partial(run_test, world_size=world_size, port=free_port())

tests/test_moe/test_kernel.py

@@ -9,6 +9,7 @@ from colossalai.core import global_context as gpc
 from colossalai.utils import free_port, get_current_device
 from colossalai.nn.layer.moe import Top1Router, Top2Router, MoeLayer, Experts
 from colossalai.context.moe_context import MOE_CONTEXT
+from colossalai.testing import rerun_on_exception
 
 BATCH_SIZE = 16
 NUM_EXPERTS = 4
@@ -86,6 +87,7 @@ def run_routing(rank, world_size, port, rs=2, hidden_size=128, data_type=torch.f
 @pytest.mark.parametrize("hidden_size", [32, 144])
 @pytest.mark.parametrize("data_type", [torch.float32, torch.float16])
 @pytest.mark.parametrize("router", [Top1Router, Top2Router])
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_moe_kernel(rs, hidden_size, data_type, router):
     world_size = 4
     run_func = partial(run_routing,

tests/test_moe/test_moe_group.py

@@ -8,7 +8,7 @@ from colossalai.utils import free_port, get_current_device
 from colossalai.nn.layer.moe import Experts
 from colossalai.context.moe_context import MOE_CONTEXT
 from colossalai.utils.moe import sync_moe_model_param
-from colossalai.testing import assert_equal_in_group
+from colossalai.testing import assert_equal_in_group, rerun_on_exception
 
 D_MODEL = 4
 D_FF = 8
@@ -60,6 +60,7 @@ def run_test(rank, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_moe_initialization():
     world_size = 4
     run_func = partial(run_test, port=free_port())

tests/test_trainer/test_pipeline/test_p2p.py

@@ -15,6 +15,7 @@ from colossalai.core import global_context as gpc
 from colossalai.initialize import launch
 from colossalai.logging import get_dist_logger
 from colossalai.utils import free_port, get_current_device
+from colossalai.testing import rerun_on_exception
 
 BATCH_SIZE = 4
 SEQ_LENGTH = 2
@@ -92,6 +93,7 @@ def run_check(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_p2p():
     world_size = 4
     run_func = partial(run_check, world_size=world_size, port=free_port())

tests/test_trainer/test_pipeline/test_partition.py

@@ -10,19 +10,14 @@ from colossalai.initialize import launch
 from colossalai.logging import get_dist_logger
 from functools import partial
 from colossalai.utils import free_port
+from colossalai.testing import rerun_on_exception
 
 DIR_PATH = osp.dirname(osp.realpath(__file__))
 CONFIG_PATH = osp.join(DIR_PATH, 'resnet_config.py')
 
 
 def run_partition(rank, world_size, port):
-    launch(config=CONFIG_PATH,
-           rank=rank,
-           world_size=world_size,
-           host='localhost',
-           port=port,
-           backend='nccl'
-           )
+    launch(config=CONFIG_PATH, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     logger = get_dist_logger()
     logger.info('finished initialization')
 
@@ -37,6 +32,7 @@ def run_partition(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_partition():
     world_size = 4
     run_func = partial(run_partition, world_size=world_size, port=free_port())

tests/test_trainer/test_pipeline/test_pipeline_schedule.py

@@ -14,6 +14,7 @@ from colossalai.core import global_context as gpc
 from colossalai.engine.schedule import PipelineSchedule
 from colossalai.initialize import launch
 from colossalai.utils import free_port, get_dataloader, print_rank_0
+from colossalai.testing import rerun_on_exception
 from torchvision import transforms
 from torchvision.datasets import CIFAR10
 
@@ -67,6 +68,7 @@ def run_schedule(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_pipeline_schedule():
     world_size = 4
     run_func = partial(run_schedule, world_size=world_size, port=free_port())

tests/test_trainer/test_trainer_with_non_pipe_schedule.py

@@ -9,7 +9,7 @@ from colossalai.logging import get_dist_logger
 from colossalai.trainer import Trainer
 from colossalai.utils import MultiTimer, free_port
 from tests.components_to_test.registry import non_distributed_component_funcs
-from colossalai.testing import parameterize
+from colossalai.testing import parameterize, rerun_on_exception
 
 BATCH_SIZE = 4
 IMG_SIZE = 32
@@ -51,6 +51,7 @@ def run_dist(rank, world_size, port):
 
 
 @pytest.mark.dist
+@rerun_on_exception(exception_type=mp.ProcessRaisedException, pattern=".*Address already in use.*")
 def test_trainer_no_pipeline():
     world_size = 4
     run_func = partial(run_dist, world_size=world_size, port=free_port())