skip cpu autotest for graph global

python/oneflow/test/modules/test_consistent_0_dim_tensor.py

@@ -20,7 +20,7 @@
 from oneflow.test_utils.automated_test_util import *
 
 
-@autotest(n=1, check_graph=False)
+@autotest(n=1, check_graph=True)
 def _test_0_dim_tensor(test_case, placement, sbp):
     x1 = random_tensor(0).to_global(placement=placement, sbp=sbp)
     x2 = random_tensor(0).to_global(placement=placement, sbp=sbp)
@@ -29,7 +29,7 @@ def _test_0_dim_tensor(test_case, placement, sbp):
     return y1 + y2
 
 
-@autotest(n=1, check_graph=False)
+@autotest(n=1, check_graph=True)
 def _test_1dim_slice(test_case, placement, sbp):
     x = random_tensor(1, random(1, 4) * 8).to_global(placement=placement, sbp=sbp)
     return x[5]

python/oneflow/test/modules/test_consistent_abs.py

@@ -21,7 +21,7 @@
 import oneflow.unittest
 
 
-@autotest(n=1, check_graph=False)
+@autotest(n=1, check_graph=True)
 def _test_abs_with_ndim_data(test_case, ndim, placement, sbp):
     dims = [random(1, 3) * 8 for i in range(ndim)]
     x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp)

python/oneflow/test/modules/test_consistent_adaptive_pool.py

@@ -31,7 +31,7 @@
 ]
 
 
-@autotest(n=1, check_graph=False)
+@autotest(n=1, check_graph=True)
 def _test_adaptive_avgpoolnd(test_case, ndim, pool_size, placement, sbp):
     dims = [random(1, 3) * 8 for i in range(ndim)]
     x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp)
@@ -48,7 +48,7 @@ def _test_adaptive_avgpoolnd(test_case, ndim, pool_size, placement, sbp):
     return y
 
 
-@autotest(n=1, check_graph=False)
+@autotest(n=1, check_graph=True)
 def _test_adaptive_avgpoolnd_functional(test_case, ndim, pool_size, placement, sbp):
     dims = [random(1, 3) * 8 for i in range(ndim)]
     x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp)

python/oneflow/test/modules/test_consistent_chunk.py

@@ -21,7 +21,7 @@
 from oneflow.test_utils.automated_test_util import *
 
 
-@autotest(n=1, check_graph=False)
+@autotest(n=1, check_graph=True)
 def _test_chunk(test_case, ndim, placement, sbp):
     dims = [random(1, 3).to(int) * 8 for _ in range(ndim)]
     x = random_tensor(ndim, *dims).to_global(placement=placement, sbp=sbp)

python/oneflow/test/modules/test_consistent_diag.py

@@ -22,7 +22,7 @@
 from oneflow.test_utils.automated_test_util import *
 
 
-@autotest(n=1, check_graph=False)
+@autotest(n=1, check_graph=True)
 def do_test_diag_impl(test_case, ndim, placement, sbp):
     dims = [random(1, 4) * 8 for i in range(ndim)]
     x = random_tensor(ndim, *dims)

python/oneflow/test_utils/automated_test_util/torch_flow_dual_object.py

@@ -292,6 +292,15 @@ def get_fake_program_more_detail(oneflow, mode, func, args=None, kwargs=None):
     print("\n\n")
 
 
+# NOTE(lixiang): When the graph global test is executed, the func is used to get the device type.
+#   There is no oneflow_kwargs["device"] case for graph global test.
+def get_global_test_device(oneflow_args):
+    if isinstance(oneflow_args[0], flow.Tensor):
+        return oneflow_args[0].placement.type
+    else:
+        return oneflow_args[0][0].placement.type
+
+
 # NOTE(lixiang): When oneflow is of type nn.Module, build the following Graph for testing.
 #   graph_train_oneflow: is a deepcopy of oneflow.
 def get_module_graph_test(graph_train_oneflow, oneflow, verbose, oneflow_args, *args):
@@ -382,6 +391,9 @@ def build(self):
         elif oneflow.__name__ == "Parameter":
             # nn.Graph donot deal with Parameter creation.
             test_g_res = oneflow_res
+        # When doing the global op test, get_global_test_device() will be executed, and temporarily skipping the graph autotest on cpu device.
+        elif is_global() and (get_global_test_device(oneflow_args) == "cpu"):
+            test_g_res = oneflow_res
         else:
             test_g = TestGraphOfFunctional()
             test_g_res = test_g()
@@ -422,8 +434,12 @@ def build(self):
             return graph_tensor_oneflow(*tensor_graph_args, **tensor_graph_kwargs)
 
     try:
-        test_g = TestGraphOfTensorMethod()
-        test_g_res = test_g()
+        # Set test_g_res = None, check_eager_graph_tensor will return True, the purpose is to temporarily skip the Graph global test on cpu.
+        if is_global() and (get_global_test_device((oneflow,)) == "cpu"):
+            test_g_res = None
+        else:
+            test_g = TestGraphOfTensorMethod()
+            test_g_res = test_g()
     except Exception as e:
         if not verbose:
             get_fake_program_more_detail(
@@ -479,8 +495,12 @@ def oneflow_eager_run_with_graph_check(
             test_g = get_module_graph_test(
                 graph_train_oneflow, oneflow, verbose, oneflow_args, *args
             )
-            # When testing module methods, kwargs are not considered.
-            test_g_res = test_g(*graph_args)
+            # When doing the global op test, get_global_test_device() will be executed, and temporarily skipping the graph autotest on cpu device.
+            if is_global() and (get_global_test_device(oneflow_args) == "cpu"):
+                test_g_res = oneflow_res
+            else:
+                # When testing module methods, kwargs are not considered.
+                test_g_res = test_g(*graph_args)
         elif oneflow.__name__ in ignore_apis_list:
             find_check_module_func = False
         # 1. "oneflow.nn.modules" not in oneflow.__module__: For avoid run nn.Module branch graph test, like fold op call Fold Module actually.