speedup global test

python/oneflow/test/modules/test_consistent_adaptive_pool.py

@@ -65,12 +65,12 @@ class TestAdaptiveAvgPool(flow.unittest.TestCase):
     def test_adaptive_avgpool(test_case):
         for placement in all_placement():
             ndim = 3
-            for sbp in all_sbp(placement, max_dim=ndim):
+            for sbp in all_sbp(placement, max_dim=2):
                 _test_adaptive_avgpoolnd(test_case, ndim, 1, placement, sbp)
                 _test_adaptive_avgpoolnd_functional(test_case, ndim, 1, placement, sbp)
 
             ndim = 4
-            for sbp in all_sbp(placement, max_dim=ndim):
+            for sbp in all_sbp(placement, max_dim=2):
                 _test_adaptive_avgpoolnd(test_case, ndim, 2, placement, sbp)
                 _test_adaptive_avgpoolnd_functional(test_case, ndim, 2, placement, sbp)
 
@@ -81,7 +81,7 @@ def test_adaptive_avgpool(test_case):
             ):
                 continue
             ndim = 5
-            for sbp in all_sbp(placement, max_dim=ndim):
+            for sbp in all_sbp(placement, max_dim=2):
                 _test_adaptive_avgpoolnd(test_case, ndim, 3, placement, sbp)
                 _test_adaptive_avgpoolnd_functional(test_case, ndim, 3, placement, sbp)
 

python/oneflow/test/modules/test_consistent_rnn_cell.py

@@ -22,7 +22,7 @@
 from oneflow.test_utils.automated_test_util import *
 
 
-@autotest(n=2, check_graph=False)
+@autotest(n=1, check_graph=False)
 def _test_lstm_cell(test_case, placement, sbp):
     batch_size = random(2, 3) * 8
     time_steps = random(2, 3) * 8
@@ -68,7 +68,7 @@ def _test_lstm_cell(test_case, placement, sbp):
     return res[0]
 
 
-@autotest(n=2, check_graph=False)
+@autotest(n=1, check_graph=False)
 def _test_rnn_relu_cell(test_case, placement, sbp):
     batch_size = random(2, 3) * 8
     time_steps = random(2, 3) * 8
@@ -112,7 +112,7 @@ def _test_rnn_relu_cell(test_case, placement, sbp):
     return hx
 
 
-@autotest(n=2, check_graph=False)
+@autotest(n=1, check_graph=False)
 def _test_rnn_tanh_cell(test_case, placement, sbp):
     batch_size = random(2, 3) * 8
     time_steps = random(2, 3) * 8
@@ -156,7 +156,7 @@ def _test_rnn_tanh_cell(test_case, placement, sbp):
     return hx
 
 
-@autotest(n=2, check_graph=False)
+@autotest(n=1, check_graph=False)
 def _test_gru_cell(test_case, placement, sbp):
     batch_size = random(2, 3) * 8
     time_steps = random(2, 3) * 8

python/oneflow/test/modules/test_consistent_slice.py

@@ -99,7 +99,11 @@ def _test_slice_with_bool(test_case, placement, sbp):
     test_case.assertTrue(np.array_equal(y.numpy(), x_numpy[0:1:1]))
 
 
-def _test_slice_with_grad(test_case, placement, sbp):
+@autotest(
+    n=2, auto_backward=False, check_graph=False,
+)
+def _test_slice_with_grad(test_case, placement):
+    sbp = random_sbp(placement, max_dim=2).value()
     x = random_tensor(2, 8, 16, requires_grad=True).oneflow
     x_numpy = x.detach().cpu().numpy()
 
@@ -157,7 +161,11 @@ def test_slice(test_case):
                 _test_negative_index(test_case, placement, sbp)
                 _test_slice_ellipsis_type(test_case, placement, sbp)
                 _test_slice_with_bool(test_case, placement, sbp)
-                _test_slice_with_grad(test_case, placement, sbp)
+
+    @globaltest
+    def test_graph_slice(test_case):
+        for placement in all_placement():
+            _test_slice_with_grad(test_case, placement)
 
 
 if __name__ == "__main__":

python/oneflow/test/modules/test_consistent_slice_update.py

@@ -119,12 +119,13 @@ class TestGlobalSliceUpdate(flow.unittest.TestCase):
     @globaltest
     def test_slice_update(test_case):
         for placement in all_placement():
-            for sbp in all_sbp(placement, max_dim=2):
-                # TODO(wyg): It will be infer all broadcast sbp when 1n1d,
-                #            slice_update will get error when doing inplace operator.
-                #            Remove this judgement after refactor sbp infer method in Operator class.
-                if placement.ranks.size == 1:
-                    continue
+            # TODO(wyg): It will be infer all broadcast sbp when 1n1d,
+            #            slice_update will get error when doing inplace operator.
+            #            Remove this judgement after refactor sbp infer method in Operator class.
+            if placement.ranks.size == 1:
+                continue
+            for _ in range(2):
+                sbp = random_sbp(placement, max_dim=2).value()
                 _test_slice_update(test_case, placement, sbp)
                 _test_graph_slice_update(test_case, placement, sbp)
 

python/oneflow/test/modules/test_consistent_var.py

@@ -25,28 +25,20 @@
 @autotest(n=1, check_graph=False)
 def _test_flow_global_var_all_dim_with_random_data(test_case, placement, sbp):
     x = random_tensor(
-        ndim=4,
-        dim0=random(1, 3).to(int) * 8,
-        dim1=random(1, 3).to(int) * 8,
-        dim2=random(1, 3).to(int) * 8,
-        dim3=random(1, 3).to(int) * 8,
+        ndim=2, dim0=random(1, 3).to(int) * 8, dim1=random(1, 3).to(int) * 8,
     ).to_global(placement, sbp)
     y = torch.var(x)
     return y
 
 
-@autotest(n=2, check_graph=False)
+@autotest(n=1, check_graph=False)
 def _test_flow_global_var_one_dim_with_random_data(test_case, placement, sbp):
     x = random_tensor(
-        ndim=4,
-        dim0=random(1, 3).to(int) * 8,
-        dim1=random(1, 3).to(int) * 8,
-        dim2=random(1, 3).to(int) * 8,
-        dim3=random(1, 3).to(int) * 8,
+        ndim=2, dim0=random(1, 3).to(int) * 8, dim1=random(1, 3).to(int) * 8,
     ).to_global(placement, sbp)
     y = torch.var(
         x,
-        dim=random(low=0, high=4).to(int),
+        dim=random(low=0, high=2).to(int),
         unbiased=random().to(bool),
         keepdim=random().to(bool),
     )
@@ -55,10 +47,10 @@ def _test_flow_global_var_one_dim_with_random_data(test_case, placement, sbp):
 
 @autotest(n=1, auto_backward=True, check_graph=False)
 def _test_flow_var_0_size_data_with_random_data(test_case, placement, sbp):
-    x = random_tensor(4, 8, 16, 0, 8).to_global(placement, sbp)
+    x = random_tensor(3, 8, 0, 8).to_global(placement, sbp)
     y = torch.var(
         x,
-        dim=random(low=0, high=4).to(int),
+        dim=random(low=0, high=3).to(int),
         unbiased=random().to(bool),
         keepdim=random().to(bool),
     )
@@ -69,23 +61,23 @@ class TestVar(flow.unittest.TestCase):
     @globaltest
     def test_flow_global_var_all_dim_with_random_data(test_case):
         for placement in all_placement():
-            for sbp in all_sbp(placement, max_dim=4):
+            for sbp in all_sbp(placement, max_dim=2):
                 _test_flow_global_var_all_dim_with_random_data(
                     test_case, placement, sbp
                 )
 
     @globaltest
     def test_flow_global_var_one_dim_with_random_data(test_case):
         for placement in all_placement():
-            for sbp in all_sbp(placement, max_dim=4):
+            for sbp in all_sbp(placement, max_dim=2):
                 _test_flow_global_var_one_dim_with_random_data(
                     test_case, placement, sbp
                 )
 
     @globaltest
     def test_flow_var_0_size_data_with_random_data(test_case):
         for placement in all_placement():
-            for sbp in all_sbp(placement, max_dim=4, valid_split_axis=[0, 1, 3]):
+            for sbp in all_sbp(placement, max_dim=2, valid_split_axis=[0]):
                 _test_flow_var_0_size_data_with_random_data(test_case, placement, sbp)