[ci] update ci

.github/workflows/build_on_pr.yml

@@ -87,10 +87,10 @@ jobs:
     name: Build and Test Colossal-AI
     needs: detect
     if: needs.detect.outputs.anyLibraryFileChanged == 'true'
-    runs-on: [self-hosted, gpu]
+    runs-on: ubuntu-latest
     container:
-      image: hpcaitech/pytorch-cuda:2.2.2-12.1.0
-      options: --gpus all --rm -v /dev/shm -v /data/scratch:/data/scratch
+      image: image-cloud.luchentech.com/hpcaitech/pytorch-cuda:2.2.2-12.1.0
+      options: --gpus all --shm-size=2g --rm -v /dev/shm -v /data/scratch:/data/scratch
     timeout-minutes: 90
     defaults:
       run:

tests/test_fp8/test_all_to_all_single.py

@@ -6,9 +6,10 @@
 from colossalai import launch
 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import all_to_all_single_fp8
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 
 
+@clear_cache_before_run()
 @parameterize("shape", [(4,), (1, 8, 16), (4, 8, 16)])
 @parameterize("dtype", [torch.bfloat16, torch.float16])
 @parameterize("async_op", [True, False])
@@ -24,6 +25,7 @@ def check_all2all(shape, dtype, async_op):
     assert_close(output, output_fp8, rtol=0.1, atol=0.1)
 
 
+@clear_cache_before_run()
 @parameterize("shape", [(8, 8, 16)])
 @parameterize("dtype", [torch.bfloat16, torch.float16])
 @parameterize("async_op", [True, False])

tests/test_fp8/test_fp8_all_to_all.py

@@ -6,9 +6,10 @@
 from colossalai import launch
 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import _all_to_all_fp8
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 
 
+@clear_cache_before_run()
 @parameterize("shape", [(16, 8, 4)])
 @parameterize("scatter_dim", [0, 1, 2])
 @parameterize("dtype", [torch.bfloat16, torch.float16])

tests/test_fp8/test_fp8_all_to_all_single.py

@@ -6,11 +6,12 @@
 from colossalai import launch
 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import all_to_all_single_fp8
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 
 dist.all_to_all_single
 
 
+@clear_cache_before_run()
 @parameterize("shape", [(4), (8, 7), (4, 8, 16)])
 @parameterize("dtype", [torch.bfloat16, torch.float16])
 @parameterize("fp8_format", ["e4m3", "e5m2"])

tests/test_fp8/test_fp8_allgather.py

@@ -6,9 +6,10 @@
 from colossalai import launch
 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import _all_gather_fp8
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 
 
+@clear_cache_before_run()
 @parameterize(
     "shape",
     [(3, 7, 16)],

tests/test_fp8/test_fp8_allreduce.py

@@ -5,7 +5,7 @@
 from colossalai import launch
 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import all_reduce_fp8
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 
 
 @parameterize(
@@ -20,6 +20,7 @@
         (8,),
     ],
 )
+@clear_cache_before_run()
 @parameterize("dtype", [torch.float16, torch.bfloat16])
 @parameterize("fp8_format", ["e4m3", "e5m2"])
 @parameterize("async_op", [True, False])

tests/test_fp8/test_fp8_cast.py

@@ -3,9 +3,10 @@
 
 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import cast_from_fp8, cast_from_fp8_pipeline, cast_to_fp8, cast_to_fp8_pipeline
-from colossalai.testing import parameterize
+from colossalai.testing import clear_cache_before_run, parameterize
 
 
+@clear_cache_before_run()
 @parameterize("shape", [(100, 10), (10, 100), (3, 7), (2, 1), (1, 2), (2, 2), (4, 2), (5,), (4,), (2,)])
 @parameterize("dtype", [torch.bfloat16, torch.float16, torch.float32])
 @parameterize("fp8_format", ["e4m3", "e5m2"])

tests/test_fp8/test_fp8_fsdp_comm_hook.py

@@ -8,7 +8,7 @@
 from torch.testing import assert_close
 
 from colossalai import launch
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 
 # example modified from https://pytorch.org/tutorials/intermediate/ddp_tutorial.html
 
@@ -28,6 +28,7 @@ def forward(self, x):
         return self.net2(self.relu(self.net1(x)))
 
 
+@clear_cache_before_run()
 @parameterize("mode", ["grad", "params"])
 def run_model(mode):
     rank = dist.get_rank()

tests/test_fp8/test_fp8_reduce_scatter.py

@@ -6,9 +6,10 @@
 from colossalai import launch
 from colossalai.accelerator import get_accelerator
 from colossalai.quantization.fp8 import reduce_scatter_fp8
-from colossalai.testing import parameterize, rerun_if_address_is_in_use, spawn
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
 
 
+@clear_cache_before_run()
 @parameterize("shape", [(16, 8, 4)])
 @parameterize("scatter_dim", [0, 1, 2])
 @parameterize("dtype", [torch.bfloat16, torch.float16])