def test_aliases(self):
# tests that op aliases are correctly being normalized
# does not check for other properties such as correctness because
# the common method registry gets tested for those in test_jit.py
op_registry = {}
for op in method_tests():
op_registry[op[0]] = op
for alias, mapping in op_alias_mappings.items():
assert alias in op_registry, "Test not found for {} alias".format(alias)
name, self_size, args, kwargs, output_process_fn = get_defaults(*op_registry[alias])
def fn(*inputs, **kwargs):
attr = getattr(inputs[0], name)
output = attr(*inputs[1:], **kwargs)
return output_process_fn(output)
self_variable = create_input((self_size,))[0][0]
args_variable, kwargs_variable = create_input(args, requires_grad=False, call_kwargs=kwargs)
traced_fn = create_traced_fn(self, fn)
inputs = (self_variable,) + args_variable
traced_fn(*inputs, **kwargs)
last_graph = traced_fn.last_graph
FileCheck().check(mapping).check_not(alias).run(last_graph)
script_fn = create_script_fn(self, name, 'method', output_process_fn)
script_fn(*inputs, **kwargs)
last_graph = script_fn.last_graph
FileCheck().check(mapping).check_not(alias).run(last_graph)
from torch.testing._internal.common_utils import \
(TestCase, is_iterable_of_tensors, run_tests, IS_SANDCASTLE, clone_input_helper, make_tensor)
from torch.testing._internal.common_methods_invocations import \
(op_db, method_tests)
from torch.testing._internal.common_device_type import \
(instantiate_device_type_tests, ops, onlyCPU, onlyOnCPUAndCUDA, skipCUDAIfRocm, OpDTypes)
from torch.testing._internal.common_jit import JitCommonTestCase, check_against_reference
from torch.autograd.gradcheck import gradcheck, gradgradcheck
from torch.testing._internal.jit_metaprogramming_utils import create_script_fn, create_traced_fn, \
check_alias_annotation
from torch.testing._internal.jit_utils import disable_autodiff_subgraph_inlining
# Get names of all the operators which have entry in `method_tests` (legacy testing infra)
method_tested_operators = set(
map(lambda test_details: test_details[0], method_tests()))
# Tests that apply to all operators
class TestOpInfo(TestCase):
exact_dtype = True
# Verifies that ops have their unsupported dtypes
# registered correctly by testing that each claimed unsupported dtype
# throws a runtime error
@skipCUDAIfRocm
@onlyOnCPUAndCUDA
@ops(op_db, dtypes=OpDTypes.unsupported)
def test_unsupported_dtypes(self, device, dtype, op):
# sample_inputs can have a function for generating the input that doesn't work for specified dtype
from torch.testing._internal.common_utils import \
(TestCase, is_iterable_of_tensors, run_tests, IS_SANDCASTLE, clone_input_helper, make_tensor,
gradcheck, gradgradcheck)
from torch.testing._internal.common_methods_invocations import \
(op_db, method_tests)
from torch.testing._internal.common_device_type import \
(instantiate_device_type_tests, ops, onlyCPU, onlyOnCPUAndCUDA, skipCUDAIfRocm, OpDTypes)
from torch.testing._internal.common_jit import JitCommonTestCase, check_against_reference
from torch.testing._internal.jit_metaprogramming_utils import create_script_fn, create_traced_fn, \
check_alias_annotation
from torch.testing._internal.jit_utils import disable_autodiff_subgraph_inlining
# Get names of all the operators which have entry in `method_tests` (legacy testing infra)
method_tested_operators = set(map(lambda test_details: test_details[0], method_tests()))
# Tests that apply to all operators
class TestOpInfo(TestCase):
exact_dtype = True
# Verifies that ops have their unsupported dtypes
# registered correctly by testing that each claimed unsupported dtype
# throws a runtime error
@skipCUDAIfRocm
@onlyOnCPUAndCUDA
@ops(op_db, dtypes=OpDTypes.unsupported)
def test_unsupported_dtypes(self, device, dtype, op):
# sample_inputs can have a function for generating the input that doesn't work for specified dtype
# https://github.com/pytorch/pytorch/issues/49024
