optimize ci speed in expensive eager test (#8504)

Co-authored-by: mergify[bot] <37929162+mergify[bot]@users.noreply.github.com> Co-authored-by: Shenghang Tsai <jackalcooper@gmail.com>
Oneflow-Inc · Jun 29, 2022 · 3654513 · 3654513
1 parent 99b1eff
commit 3654513
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Showing 4 changed files with 68 additions and 63 deletions.
diff --git a/python/oneflow/test/expensive/test_convtranspose.py b/python/oneflow/test/expensive/test_convtranspose.py
@@ -278,7 +278,7 @@ def test_ConvTranspose1d(test_case):
         for arg in GenArgList(arg_dict):
             arg[0](test_case, *arg[1:])
 
-    @autotest()
+    @autotest(n=5)
     def test_ConvTranspose1d_(test_case):
         channels = random(1, 6)
         m = torch.nn.ConvTranspose1d(
@@ -299,7 +299,7 @@ def test_ConvTranspose1d_(test_case):
         return y
 
     @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases")
-    @autotest(n=30)
+    @autotest(n=5)
     def test_deconv1d_group_with_random_data(test_case):
         channels = 720  # lcm(1, 2, 3, 4, 5, 6)
         m = torch.nn.ConvTranspose1d(
@@ -322,7 +322,7 @@ def test_deconv1d_group_with_random_data(test_case):
         y = m(x)
         return y
 
-    @autotest()
+    @autotest(n=5)
     def test_ConvTranspose3d_(test_case):
         channels = random(1, 2)
         m = torch.nn.ConvTranspose3d(
@@ -343,17 +343,17 @@ def test_ConvTranspose3d_(test_case):
         return y
 
     @unittest.skipIf(os.getenv("ONEFLOW_TEST_CPU_ONLY"), "only test cpu cases")
-    @autotest(n=15)
+    @autotest(n=5)
     def test_deconv3d_group_with_random_data(test_case):
-        channels = 720  # lcm(1, 2, 3, 4, 5, 6)
+        channels = 120  # lcm(1, 2, 3, 4, 5)
         m = torch.nn.ConvTranspose3d(
             in_channels=channels,
             out_channels=channels,
             kernel_size=random(1, 4),
             stride=random() | nothing(),
             padding=random(1, 3).to(int) | nothing(),
             dilation=random(1, 5) | nothing(),
-            groups=random(1, 7),
+            groups=random(1, 6),
             padding_mode=constant("zeros") | nothing(),
         )
         m.train(random())