Enable some PyTorch core tests with inductor (#87490)

Summary:
1) Graph break on torch.random.set_rng_state since it blocks running
inductor core tests;
2) Add several inductor-specific skips;
3) Enable several core tests for inductor CI;

cc @jansel @mlazos @soumith @voznesenskym @yanboliang @penguinwu @anijain2305
Pull Request resolved: https://github.com/pytorch/pytorch/pull/87490
Approved by: https://github.com/eellison

.jenkins/pytorch/test.sh

@@ -251,13 +251,10 @@ test_dynamo_shard() {
 
 
 test_inductor() {
-  echo "TODO: enable inductor unit tests"
-  # time python test/run_test.py --core --exclude test_autograd --continue-through-error --verbose
-
-  # PYTORCH_TEST_WITH_DYNAMO and PYTORCH_TEST_WITH_INDUCTOR are only needed for PyTorch tests not written with
-  # using dynamo/inductor. For dynamo/inductor unit tests, specifiying them will trigger an error like
-  # "Detected two calls to `torchdynamo.optimize(...)` with a different backend compiler arguments."
-  # PYTORCH_TEST_WITH_DYNAMO=0 PYTORCH_TEST_WITH_INDUCTOR=0 pytest test/inductor
+  python test/test_modules.py --verbose
+  # TODO: investigate "RuntimeError: CUDA driver API confirmed a leak"
+  # seen intest_ops_gradients.py
+  # pytest test/test_ops_gradients.py --verbose -k "not _complex and not test_inplace_grad_acos_cuda_float64"
 }
 
 test_inductor_huggingface_shard() {

test/dynamo/test_repros.py

@@ -1016,6 +1016,8 @@ class ReproTests(torch._dynamo.test_case.TestCase):
         self.assertEqual(cnt.frame_count, 1)
         self.assertEqual(cnt.op_count, 8)
 
+    # TODO: make set_rng_state work with FakeTensor/aot_autograd
+    @patch.object(torch._dynamo.config, "fake_tensor_propagation", False)
     def test_rng_state(self):
         def fn():
             state = torch.get_rng_state()

test/test_modules.py

@@ -11,7 +11,8 @@ from torch.testing._internal.common_device_type import (
     instantiate_device_type_tests, onlyCUDA, toleranceOverride, tol, skipMeta)
 from torch.testing._internal.common_modules import module_db, modules, TrainEvalMode
 from torch.testing._internal.common_utils import (
-    TestCase, run_tests, freeze_rng_state, mock_wrapper, get_tensors_from, gradcheck, gradgradcheck, skipIfMps)
+    TestCase, run_tests, freeze_rng_state, mock_wrapper, get_tensors_from, gradcheck,
+    gradgradcheck, skipIfMps, skipIfTorchInductor)
 from unittest.mock import patch, call
 
 
@@ -326,6 +327,7 @@ class TestModule(TestCase):
 
     @skipIfMps
     @modules(module_db)
+    @skipIfTorchInductor("to be fixed")
     def test_non_contiguous_tensors(self, device, dtype, module_info, training):
         # Check modules work with non-contiguous tensors
 
@@ -489,6 +491,7 @@ class TestModule(TestCase):
     @toleranceOverride({torch.float32: tol(5e-2, 0),
                         torch.float64: tol(4e-4, 0)})
     @modules(module_db)
+    @skipIfTorchInductor("to be fixed")
     def test_cpu_gpu_parity(self, device, dtype, module_info, training):
         # TODO: RNN / GRU / LSTM don't support backwards on eval mode for cuDNN; skip this in a
         # nicer way for eval mode only.
@@ -579,6 +582,7 @@ class TestModule(TestCase):
 
     @skipIfMps
     @modules(module_db)
+    @skipIfTorchInductor("to be fixed")
     def test_memory_format(self, device, dtype, module_info, training):
         is_sm86 = device.startswith("cuda") and torch.cuda.get_device_capability(0) == (8, 6)
         # TODO tighten it to a specific module

test/test_ops.py

@@ -36,6 +36,7 @@ from torch.testing._internal.common_utils import (
     first_sample,
     parametrize,
     skipIfSlowGradcheckEnv,
+    skipIfTorchInductor,
     slowTest,
 )
 from torch.testing._internal.common_methods_invocations import (
@@ -209,6 +210,7 @@ class TestCommon(TestCase):
     @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
     @onlyNativeDeviceTypes
     @ops(python_ref_db)
+    @skipIfTorchInductor("Takes too long for inductor")
     def test_python_ref_meta(self, device, dtype, op):
         with FakeTensorMode() as mode:
             pass
@@ -374,6 +376,7 @@ class TestCommon(TestCase):
     @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
     @onlyNativeDeviceTypes
     @ops(python_ref_db)
+    @skipIfTorchInductor("Takes too long for inductor")
     def test_python_ref(self, device, dtype, op):
         # In this test, primTorch refs call into the refs namespace
         # For example, a ref with torch.foo in it will calls refs.foo instead
@@ -386,6 +389,7 @@ class TestCommon(TestCase):
     @unittest.skipIf(TEST_WITH_ASAN, "Skipped under ASAN")
     @onlyNativeDeviceTypes
     @ops(python_ref_db)
+    @skipIfTorchInductor("Takes too long for inductor")
     def test_python_ref_torch_fallback(self, device, dtype, op):
         # In this test, refs call into the torch namespace (after the initial invocation)
         # For example, a ref with torch.foo in it will call torch.foo instead of refs.foo
@@ -397,6 +401,7 @@ class TestCommon(TestCase):
     @skipCUDAIfRocm
     @ops(python_ref_db)
     @parametrize('executor', ['aten', 'nvfuser'])
+    @skipIfTorchInductor("Takes too long for inductor")
     def test_python_ref_executor(self, device, dtype, op, executor):
         # TODO: Not all dtypes are supported with nvfuser
         from torch._prims_common import _torch_dtype_to_nvfuser_dtype_map
@@ -457,6 +462,7 @@ class TestCommon(TestCase):
     @skipMeta
     @onlyNativeDeviceTypes
     @ops([op for op in python_ref_db if op.error_inputs_func is not None], dtypes=OpDTypes.none)
+    @skipIfTorchInductor("Takes too long for inductor")
     def test_python_ref_errors(self, device, op):
         mode = FakeTensorMode()
         with mode:

test/test_ops_gradients.py

@@ -4,8 +4,9 @@ from functools import partial, wraps
 from itertools import chain
 import torch
 
-from torch.testing._internal.common_utils import \
-    (TestCase, is_iterable_of_tensors, run_tests, gradcheck, gradgradcheck, is_slow_gradcheck_env)
+from torch.testing._internal.common_utils import (
+    TestCase, is_iterable_of_tensors, run_tests, gradcheck, gradgradcheck, is_slow_gradcheck_env,
+    skipIfTorchInductor)
 from torch.testing._internal.common_methods_invocations import op_db
 from torch.testing._internal.common_device_type import \
     (instantiate_device_type_tests, ops, OpDTypes)
@@ -253,6 +254,7 @@ class TestGradients(TestCase):
         self._forward_grad_helper(device, dtype, op, op.get_op(), is_inplace=False)
 
     @_gradcheck_ops(op_db)
+    @skipIfTorchInductor("to be fixed")
     def test_inplace_forward_mode_AD(self, device, dtype, op):
         self._skip_helper(op, device, dtype)
 

torch/_dynamo/variables/torch.py

@@ -320,6 +320,9 @@ class TorchVariable(VariableTracker):
             assert isinstance(args[0], TensorVariable)
 
             if config.fake_tensor_propagation:
+                unimplemented(
+                    "TODO: make torch.random.set_rng_state work with FakeTensor/aot_autograd"
+                )
                 # In fake tensor case, this state doesn't matter, but
                 # it needs to be valid to not segfault. Pull a real tensor out.
                 # The value won't matter since we are running with fake tensors anyway, so rng doesn't matter.