disable large tensor in autotest

.devcontainer/devcontainer.json

@@ -47,7 +47,8 @@
 	// Add the IDs of extensions you want installed when the container is created.
 	"extensions": [
 		"llvm-vs-code-extensions.vscode-clangd",
-		"ms-vscode.cmake-tools"
+		"ms-vscode.cmake-tools",
+		"ms-python.python"
 	],
 	// Comment out connect as root instead. More info: https://aka.ms/vscode-remote/containers/non-root.
 	"remoteUser": "root",

.github/workflows/test.yml

@@ -1097,6 +1097,7 @@ jobs:
         if: ${{ !fromJson(matrix.cache-hit) && (matrix.test-type == 'speed-test' || (matrix.test-type == 'misc' && matrix.device == 'cpu')) && !fromJson(matrix.is-distributed) }}
         run: |
           docker exec \
+            -e ONEFLOW_TEST_TENSOR_SIZE_LIMIT_MB=1024 \
             -e ONEFLOW_TEST_DIR=$PWD/python/oneflow/test/expensive \
             ${{ env.TEST_CONTAINER_NAME }} bash ci/test/expensive_generic_test_multi_client.sh
       - name: Exception API test

python/oneflow/test/expensive/test_permute.py

@@ -118,10 +118,10 @@ def test_permute4d_tensor_with_random_data(test_case):
         shuffle(permute_list)
         x = random_tensor(
             ndim=ndim,
-            dim0=random(1, 32).to(int),
-            dim1=random(1, 59).to(int),
-            dim2=random(1, 65).to(int),
-            dim3=random(1, 127).to(int),
+            dim0=random(1, 7).to(int),
+            dim1=random(1, 15).to(int),
+            dim2=random(1, 9).to(int),
+            dim3=random(1, 19).to(int),
         ).to(device)
         y = x.permute(permute_list)
         return y
@@ -134,10 +134,10 @@ def test_permute4d_tensor_with_stride(test_case):
         shuffle(permute_list1)
         x = random_tensor(
             ndim=ndim,
-            dim0=random(1, 32).to(int),
-            dim1=random(1, 59).to(int),
-            dim2=random(1, 65).to(int),
-            dim3=random(1, 127).to(int),
+            dim0=random(1, 7).to(int),
+            dim1=random(1, 15).to(int),
+            dim2=random(1, 9).to(int),
+            dim3=random(1, 19).to(int),
         ).to(device)
         y = x.permute(permute_list1)
         permute_list2 = [0, 1, 2, 3]
@@ -168,10 +168,10 @@ def test_permute4d_tensor_bool_with_random_data(test_case):
         shuffle(permute_list)
         x = random_tensor(
             ndim=ndim,
-            dim0=random(1, 32).to(int),
-            dim1=random(1, 59).to(int),
-            dim2=random(1, 65).to(int),
-            dim3=random(1, 127).to(int),
+            dim0=random(1, 7).to(int),
+            dim1=random(1, 15).to(int),
+            dim2=random(1, 9).to(int),
+            dim3=random(1, 19).to(int),
         ).to(device=device, dtype=torch.bool)
         y = x.permute(permute_list)
         return y

python/oneflow/test/modules/test_consistent_addcmul.py

@@ -22,7 +22,7 @@
 
 @autotest(n=1, check_graph=False)
 def _test_addcmul(test_case, ndim, placement, sbp):
-    shape = [random(low=2, high=4) * 8 for i in range(ndim)]
+    shape = [random(low=2, high=3) * 8 for i in range(ndim)]
 
     input = random_tensor(ndim, *shape).to_global(placement=placement, sbp=sbp)
     tensor1 = random_tensor(len(shape), *shape).to_global(placement=placement, sbp=sbp)
@@ -35,7 +35,7 @@ def _test_addcmul(test_case, ndim, placement, sbp):
 class TestModule(flow.unittest.TestCase):
     @globaltest
     def test_addcmul(test_case):
-        ndim = random(low=2).to(int).value()
+        ndim = random(low=2, high=5).to(int).value()
         for placement in all_placement():
             for sbp in all_sbp(placement, max_dim=ndim):
                 _test_addcmul(test_case, ndim, placement, sbp)

python/oneflow/test/modules/test_consistent_maxpool.py

@@ -74,15 +74,15 @@ def _test_maxpool2d_functional(test_case, placement, sbp):
 @autotest(n=1, check_graph=False)
 def _test_maxpool3d_functional(test_case, placement, sbp):
     return_indices = random().to(bool).value()
-    dim0 = random().to(int).value() * 8
-    dim1 = random().to(int).value() * 8
+    dim0 = random(high=4).to(int).value() * 8
+    dim1 = random(high=4).to(int).value() * 8
     x = random_tensor(
         ndim=5,
         dim0=dim0,
         dim1=dim1,
-        dim2=random(20, 22),
-        dim3=random(20, 22),
-        dim4=random(20, 22),
+        dim2=random(10, 12),
+        dim3=random(10, 12),
+        dim4=random(10, 12),
     ).to_global(placement, sbp)
     y = torch.nn.functional.max_pool3d(
         x,
@@ -151,8 +151,8 @@ def _test_maxpool2d(test_case, placement, sbp):
 @autotest(n=1, check_graph=False)
 def _test_maxpool3d(test_case, placement, sbp):
     return_indices = random().to(bool).value()
-    dim0 = random().to(int).value() * 8
-    dim1 = random().to(int).value() * 8
+    dim0 = random(high=4).to(int).value() * 8
+    dim1 = random(high=4).to(int).value() * 8
     m = torch.nn.MaxPool3d(
         kernel_size=random(4, 6).to(_size_3_t),
         stride=random(1, 3).to(_size_3_t),
@@ -166,9 +166,9 @@ def _test_maxpool3d(test_case, placement, sbp):
         ndim=5,
         dim0=dim0,
         dim1=dim1,
-        dim2=random(20, 22),
-        dim3=random(20, 22),
-        dim4=random(20, 22),
+        dim2=random(10, 12),
+        dim3=random(10, 12),
+        dim4=random(10, 12),
     ).to_global(placement, sbp)
     y = m(x)
 

python/oneflow/test/modules/test_consistent_normalize.py

@@ -23,8 +23,8 @@
 
 @autotest(n=1, check_graph=False)
 def _test_functional_normalize(test_case, placement, sbp):
-    ndim = random(low=2).to(int).value()
-    shape = [random(low=2, high=4) * 8 for i in range(ndim)]
+    ndim = random(low=2, high=5).to(int).value()
+    shape = [random(low=2, high=3) * 8 for i in range(ndim)]
     x = random_tensor(len(shape), *shape).to_global(placement=placement, sbp=sbp)
     dim = random(low=0, high=ndim).to(int).value()
     y = torch.nn.functional.normalize(x, oneof(2, 3, 4), dim, 1e-12)

python/oneflow/test/modules/test_contiguous.py

@@ -57,9 +57,9 @@ def test_permute3d_tensor_with_random_data(test_case):
         shuffle(permute_list)
         x = random_tensor(
             ndim=ndim,
-            dim0=random(1, 32).to(int),
-            dim1=random(1, 59).to(int),
-            dim2=random(1, 65).to(int),
+            dim0=random(1, 7).to(int),
+            dim1=random(1, 15).to(int),
+            dim2=random(1, 9).to(int),
         ).to(device)
         y = x.permute(permute_list)
         z = y.contiguous()
@@ -73,10 +73,10 @@ def test_permute4d_tensor_with_random_data(test_case):
         shuffle(permute_list)
         x = random_tensor(
             ndim=ndim,
-            dim0=random(1, 32).to(int),
-            dim1=random(1, 59).to(int),
-            dim2=random(1, 65).to(int),
-            dim3=random(1, 127).to(int),
+            dim0=random(1, 7).to(int),
+            dim1=random(1, 15).to(int),
+            dim2=random(1, 9).to(int),
+            dim3=random(1, 19).to(int),
         ).to(device)
         y = x.permute(permute_list)
         z = y.contiguous()

python/oneflow/test_utils/automated_test_util/torch_flow_dual_object.py

@@ -785,15 +785,7 @@ def clear_note_fake_program():
     flow.set_printoptions(profile="full")
 
 
-gc_interval = int(os.getenv("ONEFLOW_TEST_GC_INTERVAL", 10))
-gc_counter = 0
-
-
-def manual_gc_collect():
-    global gc_counter
-    gc_counter += 1
-    if gc_counter % gc_interval == 0:
-        gc.collect()
+tensor_size_limit_mb = int(os.getenv("ONEFLOW_TEST_TENSOR_SIZE_LIMIT_MB", 32))
 
 
 class DualObject:
@@ -828,6 +820,10 @@ def __init__(self, name, pytorch, oneflow):
             if testing:
                 dual_modules_to_test.append(self)
         if isinstance(pytorch, torch_original.Tensor):
+            tensor_size_mb = pytorch.nelement() * pytorch.element_size() / 1024 / 1024
+            assert (
+                tensor_size_mb < tensor_size_limit_mb
+            ), f"Tensor memory in autotest cannot be larger than {tensor_size_limit_mb}MB, but got {tensor_size_mb}MB"
             if testing:
                 dual_objects_to_test.append(self)
         self.pytorch = pytorch
@@ -879,14 +875,6 @@ def __eq__(self, other):
         else:
             return self.pytorch == other
 
-    def __del__(self):
-        # force running gc to avoid the periodic gc related to metaclass
-        # 'gc' will be None if Python is shutting down
-        try:
-            manual_gc_collect()
-        except Exception:
-            pass
-
 
 dual_modules_to_test = []
 dual_objects_to_test = []