Add runtime check

e2e_testing/torchscript/basic.py

@@ -36,10 +36,10 @@ def MmModule_basic(module, tu: TestUtils):
 # are mixed with it, it fails with a mysterious-sounding low level ctypes error
 # that exceeds my current ability to debug.
 #
-# @register_test_case(module_factory=lambda: MmModule())
-# def MmModule_chained(module, tu: TestUtils):
-#     res = module.forward(tu.rand(4, 4), tu.rand(4, 4))
-#     module.forward(res, res)
+@register_test_case(module_factory=lambda: MmModule())
+def MmModule_chained(module, tu: TestUtils):
+    res = module.forward(tu.rand(4, 4), tu.rand(4, 4))
+    module.forward(res, res)
 
 # ==============================================================================
 

e2e_testing/torchscript/xfail_sets.py

@@ -18,7 +18,11 @@ _common_npcomp_lowering_xfails = {
     'QuantizedMLP_basic',
 }
 
-XFAIL_SETS['refbackend'] = _common_npcomp_lowering_xfails
+XFAIL_SETS['refbackend'] = _common_npcomp_lowering_xfails | {
+    # The first test in the e2e test batch would fail with SystemError: null
+    # argument to internal routine. Might be some issue with refbackend.
+    'MmModule_basic',
+}
 
 XFAIL_SETS['torchscript'] = {}
 

python/test/torchscript_e2e_test/runtime_failure.py

@@ -0,0 +1,42 @@
+#  Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+#  See https://llvm.org/LICENSE.txt for license information.
+#  SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+# RUN: %PYTHON %s | FileCheck %s
+
+import torch
+
+from npcomp_torchscript.e2e_test.framework import run_tests, TestUtils
+from npcomp_torchscript.e2e_test.reporting import report_results
+from npcomp_torchscript.e2e_test.registry import register_test_case, GLOBAL_TEST_REGISTRY
+from npcomp_torchscript_e2e_test_configs import TorchScriptTestConfig
+
+
+class MmModule(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, t):
+        # Input of `torch.tensor` only allows ints, floats, or bools while empty
+        # list defaults to tensor type
+        return torch.tensor([])
+
+
+# CHECK: FAIL - "MmModule_basic"
+# CHECK:     Runtime error:
+# Assume that the diagnostic from the TorchScript runtime will at least contain
+# the offending "return torch.tensor([])".
+# CHECK:     return torch.tensor([])
+@register_test_case(module_factory=lambda: MmModule())
+def MmModule_basic(module, tu: TestUtils):
+    module.forward(torch.ones([]))
+
+
+def main():
+    config = TorchScriptTestConfig()
+    results = run_tests(GLOBAL_TEST_REGISTRY, config)
+    report_results(results, set(), verbose=True)
+
+
+if __name__ == '__main__':
+    main()

python/torch_support/npcomp_torchscript/e2e_test/framework.py

@@ -242,6 +242,10 @@ class TestResult(NamedTuple):
     # If this is not None, then the `trace` and `golden_trace` fields are None,
     # and vice-versa.
     compilation_error: Optional[str]
+    # If runtime failed, a string describing the failure.
+    # If this is not None, then the `trace` and `golden_trace` fields are None,
+    # and vice-versa.
+    runtime_error: Optional[str]
     # The trace produced by the backend.
     trace: Optional[Trace]
     # The golden trace which `trace` is expected to match.
@@ -303,14 +307,26 @@ def run_tests(tests: List[Test], config: TestConfig) -> List[TestResult]:
             results.append(
                 TestResult(unique_name=test.unique_name,
                            compilation_error=str(e),
+                           runtime_error=None,
                            trace=None,
                            golden_trace=None))
             continue
         # TODO: Run in parallel.
-        trace = config.run(compiled, golden_trace)
+        try:
+            trace = config.run(compiled, golden_trace)
+        except Exception as e:
+            results.append(
+                TestResult(unique_name=test.unique_name,
+                           compilation_error=None,
+                           runtime_error=str(e),
+                           trace=None,
+                           golden_trace=None))
+            continue
+
         results.append(
             TestResult(unique_name=test.unique_name,
                        compilation_error=None,
+                       runtime_error=None,
                        trace=trace,
                        golden_trace=golden_trace))
     return results

python/torch_support/npcomp_torchscript/e2e_test/reporting.py

@@ -222,7 +222,7 @@ class SingleTestReport:
         self.result = result
         self.context = context
         self.item_reports = None
-        if result.compilation_error is None:
+        if result.compilation_error is None and result.runtime_error is None:
             self.item_reports = []
             for i, (item, golden_item) in enumerate(
                     zip(result.trace, result.golden_trace)):
@@ -236,6 +236,8 @@ class SingleTestReport:
     def failed(self):
         if self.result.compilation_error is not None:
             return True
+        elif self.result.runtime_error is not None:
+            return True
         return any(r.failed for r in self.item_reports)
 
     def error_str(self):
@@ -244,6 +246,8 @@ class SingleTestReport:
         p = lambda *x: print(*x, file=f)
         if self.result.compilation_error is not None:
             return 'Compilation error: ' + self.result.compilation_error
+        elif self.result.runtime_error is not None:
+            return 'Runtime error: ' + self.result.runtime_error
         for report in self.item_reports:
             if report.failed:
                 p(report.error_str())