Add other api graph autotest

python/oneflow/test/modules/test_abs.py

@@ -23,7 +23,7 @@
 
 @flow.unittest.skip_unless_1n1d()
 class TestAbsModule(flow.unittest.TestCase):
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_abs_with_0shape_data(test_case):
         device = random_device()
         x = random_pytorch_tensor().to(device)

python/oneflow/test/modules/test_activation.py

@@ -29,7 +29,7 @@
 
 @flow.unittest.skip_unless_1n1d()
 class TestReLUModule(flow.unittest.TestCase):
-    @autotest()
+    @autotest(check_graph=True)
     def test_relu_module_with_random_data(test_case):
         m = torch.nn.ReLU()
         m.train(random())
@@ -52,7 +52,7 @@ def test_relu_module_with_0shape_data(test_case):
 
 @flow.unittest.skip_unless_1n1d()
 class TestReLU6Module(flow.unittest.TestCase):
-    @autotest()
+    @autotest(check_graph=True)
     def test_relu6_module_with_random_data(test_case):
         m = torch.nn.ReLU6()
         m.train(random())

python/oneflow/test/modules/test_add.py

@@ -194,7 +194,7 @@ def test_0dim_two_inplace_add(test_case):
         x += y.mean()
         return x
 
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_add_with_alpha(test_case):
         device = random_device()
         x1 = random_pytorch_tensor(2, 2, 3).to(device).mean()

python/oneflow/test/modules/test_addmm.py

@@ -67,7 +67,7 @@ def test_addmm(test_case):
         for arg in GenArgList(arg_dict):
             arg[0](test_case, *arg[1:])
 
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_addmm_flow_with_random_data(test_case):
         device = random_device()
         input = random_pytorch_tensor(ndim=2, dim0=2, dim1=3).to(device)
@@ -82,7 +82,7 @@ def test_addmm_flow_with_random_data(test_case):
         )
         return y
 
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_addmm_broadcast_flow_with_random_data(test_case):
         device = random_device()
         input = random_pytorch_tensor(ndim=2, dim0=1, dim1=1).to(device)

python/oneflow/test/modules/test_affine_grid.py

@@ -89,7 +89,7 @@ def test_affine_grid_3d(test_case):
             np.allclose(output.numpy(), groundtruth, rtol=1e-3, atol=1e-4)
         )
 
-    @autotest(rtol=1e-03, atol=1e-04, check_graph=False)
+    @autotest(rtol=1e-03, atol=1e-04, check_allclose=False, check_graph=True)
     def test_flow_affine_grid_2d_with_random_data(test_case):
         N = randint(1, 8)
         C = randint(1, 8)
@@ -103,7 +103,7 @@ def test_flow_affine_grid_2d_with_random_data(test_case):
         ).to(device)
         return output
 
-    @autotest(rtol=1e-03, atol=1e-03, check_graph=False)
+    @autotest(rtol=1e-03, atol=1e-03, check_allclose=False, check_graph=True)
     def test_flow_affine_grid_3d_with_random_data(test_case):
         N = randint(1, 8)
         C = randint(1, 8)

python/oneflow/test/modules/test_arange.py

@@ -67,7 +67,7 @@ def test_arange(test_case):
         for arg in GenArgList(arg_dict):
             arg[0](test_case, *arg[1:])
 
-    @autotest(n=30, auto_backward=False, rtol=1e-5, atol=1e-5, check_graph=False)
+    @autotest(n=30, auto_backward=False, rtol=1e-5, atol=1e-5, check_graph=True)
     def test_arange_with_random_data(test_case):
         start = random().to(int)
         end = start + random().to(int)
@@ -77,7 +77,7 @@ def test_arange_with_random_data(test_case):
         x.to(device)
         return x
 
-    @autotest(n=5, auto_backward=False, rtol=1e-5, atol=1e-5, check_graph=False)
+    @autotest(n=5, auto_backward=False, rtol=1e-5, atol=1e-5, check_graph=True)
     def test_arange_with_float_delta(test_case):
         start = random().to(int)
         end = start + random().to(int)

python/oneflow/test/modules/test_argmax.py

@@ -93,7 +93,7 @@ def test_argmax(test_case):
         for arg in GenArgList(arg_dict):
             arg[0](test_case, *arg[1:])
 
-    @autotest(n=5, auto_backward=False, rtol=1e-5, atol=1e-5, check_graph=False)
+    @autotest(n=5, auto_backward=False, rtol=1e-5, atol=1e-5, check_graph=True)
     def test_argmax_with_random_data(test_case):
         device = random_device()
         ndim = random(1, 6).to(int)

python/oneflow/test/modules/test_autograd.py

@@ -87,7 +87,7 @@ def test_autograd_interface(test_case):
         for arg in GenArgList(arg_dict):
             arg[0](test_case, *arg[1:])
 
-    @autotest(n=10, auto_backward=True, rtol=1e-3, atol=1e-3, check_graph=False)
+    @autotest(n=10, auto_backward=True, rtol=1e-3, atol=1e-3, check_graph=True)
     def test_accumulate_grad(test_case):
         device = random_device()
         ndim = random(1, 4).to(int)

python/oneflow/test/modules/test_constant.py

@@ -68,21 +68,21 @@ def test_flow_ones_list_with_random_data(test_case):
         ).to(device)
         return y1, y2, y3, y4
 
-    @autotest(auto_backward=False, check_graph=False)
+    @autotest(auto_backward=False, check_graph=True)
     def test_flow_zeros_like_list_with_random_data(test_case):
         device = random_device()
         x = random_pytorch_tensor().to(device)
         y = torch.zeros_like(x)
         return y
 
-    @autotest(auto_backward=False, check_graph=False)
+    @autotest(auto_backward=False, check_graph=True)
     def test_flow_ones_like_list_with_random_data(test_case):
         device = random_device()
         x = random_pytorch_tensor().to(device)
         y = torch.ones_like(x)
         return y
 
-    @autotest(auto_backward=True, check_graph=False)
+    @autotest(auto_backward=True, check_graph=True)
     def test_flow_new_ones_list_with_random_data(test_case):
         device = random_device()
         x = random_pytorch_tensor().to(device)

python/oneflow/test/modules/test_constantpad.py

@@ -83,7 +83,7 @@ def test_constantpad3d_with_random_data(test_case):
 
 @flow.unittest.skip_unless_1n1d()
 class TestFunctionalConstantPad2d(flow.unittest.TestCase):
-    @autotest(n=20, rtol=0.001, atol=0.001, check_graph=False)
+    @autotest(n=20, rtol=0.001, atol=0.001, check_graph=True)
     def test_functional_constantpad2d(test_case):
         device = random_device()
         padding = random(-1, 6).to(_size_4_t)

python/oneflow/test/modules/test_diag.py

@@ -28,13 +28,13 @@
 
 @flow.unittest.skip_unless_1n1d()
 class Test_Diag_module(flow.unittest.TestCase):
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_diag_one_dim(test_case):
         device = random_device()
         x = random_pytorch_tensor(ndim=1, dim0=random()).to(device)
         return torch.diag(x)
 
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_diag_other_dim(test_case):
         device = random_device()
         x = random_pytorch_tensor(ndim=2, dim0=random(), dim1=random()).to(device)

python/oneflow/test/modules/test_dot.py

@@ -22,7 +22,7 @@
 
 @flow.unittest.skip_unless_1n1d()
 class TestDot(flow.unittest.TestCase):
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_dot(test_case):
         device = random_device()
         k = random(1000, 10000)

python/oneflow/test/modules/test_erf.py

@@ -29,7 +29,7 @@
 
 @flow.unittest.skip_unless_1n1d()
 class TestErfModule(flow.unittest.TestCase):
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_flow_erf_with_random_data(test_case):
         device = random_device()
         x = random_pytorch_tensor().to(device)

python/oneflow/test/modules/test_erfc.py

@@ -29,7 +29,7 @@
 
 @flow.unittest.skip_unless_1n1d()
 class TestErfcModule(flow.unittest.TestCase):
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_flow_erfc_with_random_data(test_case):
         device = random_device()
         x = random_pytorch_tensor().to(device)

python/oneflow/test/modules/test_expand.py

@@ -181,7 +181,7 @@ def random_expand(x, ndim, expand_size):
 
 @flow.unittest.skip_unless_1n1d()
 class TestExpand(flow.unittest.TestCase):
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_flow_tensor_expand_with_random_data(test_case):
         random_expand_size = random(1, 6).to(int).value()
         x = random_pytorch_tensor(ndim=5, dim0=1, dim1=1, dim2=1, dim3=1, dim4=1)

python/oneflow/test/modules/test_flatten.py

@@ -67,7 +67,7 @@ def test_cast(test_case):
         for arg in GenArgList(arg_dict):
             arg[0](test_case, *arg[1:])
 
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_flatten_module_with_random_data(test_case):
         m = torch.nn.Flatten(
             start_dim=random(1, 6) | nothing(), end_dim=random(1, 6) | nothing()

python/oneflow/test/modules/test_flip.py

@@ -27,7 +27,7 @@
 
 
 class TestFlip(flow.unittest.TestCase):
-    @autotest(check_graph=False)
+    @autotest(check_graph=False, check_allclose=False)
     def test_flow_flip_list_with_random_data(test_case):
         device = random_device()
         x = random_pytorch_tensor(

python/oneflow/test/modules/test_gather.py

@@ -117,7 +117,7 @@ def test_gather(test_case):
         for arg in GenArgList(arg_dict):
             arg[0](test_case, *arg[1:])
 
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_flow_gather_with_random_data(test_case):
         device = random_device()
         input = random_pytorch_tensor(ndim=4, dim1=3, dim2=4, dim3=5).to(device)

python/oneflow/test/modules/test_glu.py

@@ -33,8 +33,8 @@ def test_glu_module_with_random_data(test_case):
         y = m(x, dim)
         return y
 
-    @autotest(n=5)
-    def test_GLU_module_with_random_data(test_case):
+    @autotest(n=5, check_graph=True)
+    def test_glu_module_with_random_data(test_case):
         device = random_device()
         m = torch.nn.GLU()
         m.train(random())

python/oneflow/test/modules/test_groupnorm.py

@@ -341,7 +341,7 @@ def test_groupnorm(test_case):
         for arg in GenArgList(arg_dict):
             arg[0](test_case, *arg[1:])
 
-    @autotest(rtol=1e-03, atol=1e-03, check_graph=False)
+    @autotest(rtol=1e-03, atol=1e-03, check_graph=True)
     def test_group_norm_with_random_data(test_case):
         channels = random(5, 20)
         m = torch.nn.GroupNorm(

python/oneflow/test/modules/test_linear.py

@@ -193,7 +193,7 @@ def test_linear_with_random_data(test_case):
         y = m(x)
         return y
 
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_nn_functional_linear_with_random_data(test_case):
         input_size = random()
         device = random_device()

python/oneflow/test/modules/test_linspace.py

@@ -27,7 +27,7 @@
 
 @flow.unittest.skip_unless_1n1d()
 class TestLinspace(flow.unittest.TestCase):
-    @autotest(n=30, auto_backward=False, rtol=1e-5, atol=1e-5, check_graph=False)
+    @autotest(n=30, auto_backward=False, rtol=1e-5, atol=1e-5, check_graph=True)
     def test_linspace_int_with_random_data(test_case):
         start = random().to(int)
         end = start + random().to(int)
@@ -37,7 +37,7 @@ def test_linspace_int_with_random_data(test_case):
         x.to(device)
         return x
 
-    @autotest(n=30, auto_backward=False, rtol=1e-5, atol=1e-5, check_graph=False)
+    @autotest(n=30, auto_backward=False, rtol=1e-5, atol=1e-5, check_graph=True)
     def test_linspace_float_with_random_data(test_case):
         start = random()
         end = start + random()

python/oneflow/test/modules/test_log1p.py

@@ -27,7 +27,7 @@
 
 @flow.unittest.skip_unless_1n1d()
 class TestLog1pModule(flow.unittest.TestCase):
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_log1p_with_random_data(test_case):
         device = random_device()
         x = random_pytorch_tensor().to(device)

python/oneflow/test/modules/test_logical_and.py

@@ -77,7 +77,7 @@ def test_scalar_logical_and(test_case):
         for arg in GenArgList(arg_dict):
             arg[0](test_case, *arg[1:])
 
-    @autotest(n=10, auto_backward=False, check_graph=False)
+    @autotest(n=10, auto_backward=False, check_graph=True)
     def test_logical_and_with_random_data(test_case):
         device = random_device()
         shape = random_tensor().value().shape

python/oneflow/test/modules/test_logical_not.py

@@ -51,7 +51,7 @@ def test_logical_not(test_case):
         for arg in GenArgList(arg_dict):
             arg[0](test_case, *arg[1:])
 
-    @autotest(n=10, auto_backward=False, check_graph=False)
+    @autotest(n=10, auto_backward=False, check_graph=True)
     def test_logical_not_with_random_data(test_case):
         device = random_device()
         shape = random_tensor().value().shape

python/oneflow/test/modules/test_logical_or.py

@@ -76,7 +76,7 @@ def test_scalar_logical_or(test_case):
         for arg in GenArgList(arg_dict):
             arg[0](test_case, *arg[1:])
 
-    @autotest(n=10, auto_backward=False, check_graph=False)
+    @autotest(n=10, auto_backward=False, check_graph=True)
     def test_logical_or_with_random_data(test_case):
         device = random_device()
         shape = random_tensor().value().shape

python/oneflow/test/modules/test_logical_xor.py

@@ -97,7 +97,7 @@ def test_scalar_logical_xor(test_case):
         for arg in GenArgList(arg_dict):
             arg[0](test_case, *arg[1:])
 
-    @autotest(n=10, auto_backward=False, check_graph=False)
+    @autotest(n=10, auto_backward=False, check_graph=True)
     def test_logical_xor_with_random_data(test_case):
         device = random_device()
         shape = random_tensor().value().shape

python/oneflow/test/modules/test_masked_fill.py

@@ -26,7 +26,7 @@
 
 @flow.unittest.skip_unless_1n1d()
 class TestMaskedFill(flow.unittest.TestCase):
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_flow_masked_fill_with_random_data(test_case):
         k1 = random(2, 6)
         k2 = random(2, 6)
@@ -36,7 +36,7 @@ def test_flow_masked_fill_with_random_data(test_case):
         value = random().to(float)
         return input.masked_fill(mask > 0, value)
 
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_flow_masked_fill_broadcast_with_random_data(test_case):
         k1 = random(2, 6)
         k2 = random(2, 6)
@@ -46,7 +46,7 @@ def test_flow_masked_fill_broadcast_with_random_data(test_case):
         value = random().to(float)
         return input.masked_fill(mask > 0, value)
 
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_flow_masked_fill_int_with_random_data(test_case):
         k1 = random(2, 6)
         k2 = random(2, 6)

python/oneflow/test/modules/test_matmul.py

@@ -24,7 +24,7 @@
 
 @flow.unittest.skip_unless_1n1d()
 class TestModule(flow.unittest.TestCase):
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_flow_matmul_with_random_data(test_case):
         device = random_device()
         k = random(1, 6)
@@ -33,15 +33,15 @@ def test_flow_matmul_with_random_data(test_case):
         z = torch.matmul(x, y)
         return z
 
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_flow_tensor_matmul_with_random_data(test_case):
         device = random_device()
         k = random(1, 6)
         x = random_pytorch_tensor(ndim=2, dim1=k).to(device)
         y = random_pytorch_tensor(ndim=2, dim0=k).to(device)
         return x.matmul(y)
 
-    @autotest(check_graph=False)
+    @autotest(check_graph=True)
     def test_flow_tensor_broadcast_matmul_with_random_data(test_case):
         device = random_device()
         k = random(1, 6)

python/oneflow/test/modules/test_maxpool.py

@@ -28,7 +28,7 @@ def unpack_indices(dual_object):
 
 @flow.unittest.skip_unless_1n1d()
 class TestMaxPooling(flow.unittest.TestCase):
-    @autotest(auto_backward=False)
+    @autotest(auto_backward=False, check_graph=False)
     def test_maxpool1d_with_random_data(test_case):
         return_indices = random().to(bool).value()
         m = torch.nn.MaxPool1d(
@@ -49,7 +49,7 @@ def test_maxpool1d_with_random_data(test_case):
         else:
             return y, y.sum().backward()
 
-    @autotest(auto_backward=False)
+    @autotest(auto_backward=False, check_graph=False)
     def test_maxpool2d_with_random_data(test_case):
         return_indices = random().to(bool).value()
         m = torch.nn.MaxPool2d(
@@ -73,7 +73,7 @@ def test_maxpool2d_with_random_data(test_case):
         else:
             return y, y.sum().backward()
 
-    @autotest(auto_backward=False)
+    @autotest(auto_backward=False, check_graph=False)
     def test_maxpool3d_with_random_data(test_case):
         return_indices = random().to(bool).value()
         m = torch.nn.MaxPool3d(