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https://github.com/pytorch/pytorch.git
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98b9dfa129
This makes symbolic tracing tests for logsigmoid and xlogy start working again. While I'm at it, add pin_memory and layout kwargs to empty; but they don't actually do anything and raise an error if they are non standard. Signed-off-by: Edward Z. Yang <ezyang@fb.com> Pull Request resolved: https://github.com/pytorch/pytorch/pull/82332 Approved by: https://github.com/eellison
888 lines
57 KiB
Python
888 lines
57 KiB
Python
# Owner(s): ["module: ProxyTensor"]
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from torch.testing._internal.common_utils import TestCase, run_tests
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import torch
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import unittest
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import warnings
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import torch.nn.utils._stateless as stateless
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from collections.abc import Iterable
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from torch.testing._internal.common_device_type import instantiate_device_type_tests
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from torch.testing._internal.common_methods_invocations import DecorateInfo
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from torch.testing._internal.common_methods_invocations import op_db, wrapper_set_seed
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from torch._subclasses.fake_tensor import DynamicOutputShapeException
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from torch._decomp import decomposition_table
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from torch.testing._internal.common_device_type import ops
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from torch.fx.experimental.proxy_tensor import make_fx, DecompositionInterpreter
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from torch.utils._pytree import tree_map
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import re
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aten = torch.ops.aten
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try:
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import sympy # noqa: F401
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HAS_SYMPY = True
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except ImportError:
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HAS_SYMPY = False
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skipIfNoSympy = unittest.skipIf(not HAS_SYMPY, "no sympy")
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def process_failures():
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"""
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Takes file containing failures like
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FAILED test/test_proxy_tensor.py::TestProxyTensorOpInfoCPU::test_make_fx_symbolic_exhaustive___getitem___cpu_float32 - RuntimeError: aten.size.default - couldn't find symbolic meta function/decomposition # noqa: B950
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and processes them into a list of opinfo xfails
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"""
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f = open('pytest_failures')
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failures = f.readlines()
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failures = [i.strip() for i in failures]
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def process_failure_string(s, matcher):
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out = re.search(matcher, s)
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return out.groups()
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SYMBOLIC_TRACE_MATCH = r'exhaustive_(.*)_cpu.*: (.*)'
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failures = [process_failure_string(s, SYMBOLIC_TRACE_MATCH) for s in failures]
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def create_normalized_name(op):
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if op.variant_test_name == '':
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s = op.name
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else:
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s = f"{op.name}.{op.variant_test_name}"
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return s.replace('.', '_')
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remap_opinfo = {create_normalized_name(op): (op.name, op.variant_test_name) for op in op_db}
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print("symbolic_tensor_failures = {")
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for failure, reason in failures:
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print(f" xfail{remap_opinfo[failure]}, # {reason}")
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print("}")
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# Copied from functorch
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def xfail(op_name, variant_name='', *, device_type=None, dtypes=None):
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return (op_name, variant_name, device_type, dtypes, True)
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def skip(op_name, variant_name='', *, device_type=None, dtypes=None):
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return (op_name, variant_name, device_type, dtypes, False)
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def skipOps(test_case_name, base_test_name, to_skip):
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all_opinfos = op_db
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for xfail in to_skip:
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op_name, variant_name, device_type, dtypes, expected_failure = xfail
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matching_opinfos = [o for o in all_opinfos
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if o.name == op_name and o.variant_test_name == variant_name]
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assert len(matching_opinfos) >= 1, f"Couldn't find OpInfo for {xfail}"
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for opinfo in matching_opinfos:
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decorators = list(opinfo.decorators)
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if expected_failure:
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decorator = DecorateInfo(unittest.expectedFailure,
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test_case_name, base_test_name,
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device_type=device_type, dtypes=dtypes)
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decorators.append(decorator)
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else:
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decorator = DecorateInfo(unittest.skip("Skipped!"),
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test_case_name, base_test_name,
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device_type=device_type, dtypes=dtypes)
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decorators.append(decorator)
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opinfo.decorators = tuple(decorators)
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# This decorator doesn't modify fn in any way
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def wrapped(fn):
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return fn
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return wrapped
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USE_TORCHVISION = False
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try:
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import torchvision
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USE_TORCHVISION = True
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except ImportError:
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warnings.warn("Couldn't import torchvision. Some of our tests use it, try "
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"to install it with commands from pytorch.org, post-fixed with "
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"`--no-deps` to avoid overwriting the pytorch installation",
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UserWarning)
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def _create_new_input(x):
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if not isinstance(x, torch.Tensor):
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return x
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if x.dtype != torch.float:
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return x + 1
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if x.is_leaf:
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return torch.rand_like(x, requires_grad=True)
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else:
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return torch.rand_like(x)
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class TestProxyTensor(TestCase):
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def _test(self, f, inps):
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fx_f = make_fx(f)(*inps)
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new_inps = tree_map(_create_new_input, inps)
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self.assertEqual(fx_f(*new_inps), f(*new_inps))
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def test_make_fx_simple(self, device):
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def f(x):
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return torch.sin(x)
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self._test(f, (torch.randn(3),))
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def test_scalar_device(self, device):
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def f(a, b):
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return a + b
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self._test(f, [torch.randn(3, device=device), torch.tensor(5)])
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@unittest.skipIf(not USE_TORCHVISION, "test requires torchvision")
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def test_resnet18_backward_trace(self, device):
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mod = torchvision.models.resnet18()
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def f(x):
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for a in mod.parameters():
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a.grad = None
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out = mod(x)
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out.sum().backward()
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return [a.grad for a in mod.parameters()]
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inp = torch.randn(3, 3, 250, 250, requires_grad=True)
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self._test(f, [inp])
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def test_proxy_tensor(self):
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def f_grad(x):
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val = x.cos().cos().sum()
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return torch.autograd.grad(val, x)
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def f_backward(x):
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val = x.cos().cos().sum()
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val.backward()
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return x.grad
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for f in [f_grad, f_backward]:
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self._test(f, [torch.randn(3, requires_grad=True)])
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def test_inplace_metadata(self):
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def f(x):
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x = x.clone()
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x.unsqueeze_(-1)
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assert x.shape[-1] == 1
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return x
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self._test(f, [torch.randn(5)])
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def test_mode_tracing_factory_function(self):
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def f(x):
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return x + torch.randn(x.shape)
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# default behavior should trace factory functions
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traced = make_fx(f)(torch.randn(3))
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self.assertTrue(
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any(
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node.target == aten.randn.default
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for node in traced.graph.nodes
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)
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)
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def test_mode_tracing_factory_function_no_factory_function(self):
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def f(x):
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return x + torch.randn(x.shape)
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# setting the flag to false should not trace factory functions
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traced = make_fx(f, trace_factory_functions=False)(torch.randn(3))
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self.assertFalse(
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any(
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node.target == aten.randn.default
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for node in traced.graph.nodes
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)
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)
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def test_make_fx_overloads(self):
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def f(x):
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return x.cos() + torch.randn(x.shape)
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traced = make_fx(f)(torch.randn(3))
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self.assertTrue(all([isinstance(node.target, torch._ops.OpOverload)
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for node in traced.graph.nodes if node.op == 'call_function']))
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def test_tensor_constants(self):
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def f():
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val = torch.tensor(float('inf'))
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return torch.full((100, 100), val)
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self._test(f, [])
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def test_constant_proxy_tensor(self):
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from torch.fx.experimental.proxy_tensor import make_fx
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def f():
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val = torch.tensor(float('inf'))
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return torch.full((100, 100), val)
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g = make_fx(f)()
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self.assertEqual(g(), f())
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def test_constant_proxy_tensor_mut(self):
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from torch.fx.experimental.proxy_tensor import make_fx
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def f():
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val = torch.tensor(float(1))
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val.add_(2)
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return torch.full((100, 100), val)
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g = make_fx(f)()
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self.assertEqual(g(), f())
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# In case we mutated shared state in the g graph!
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self.assertEqual(g(), f())
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g = make_fx(f, tracing_mode="fake")()
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self.assertEqual(g(), f())
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# In case we mutated shared state in the g graph!
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self.assertEqual(g(), f())
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def test_use_fake_and_tensor(self):
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def f(x, y):
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z = torch.tensor([2.0, 3.0])
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return x + y + z
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g = make_fx(f, tracing_mode="fake")(torch.randn(2), torch.randn(2))
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x, y = torch.randn(2), torch.randn(2)
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self.assertEqual(g(x, y), f(x, y))
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def test_decomposition_interpreter(self):
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def fn(x):
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return torch.nn.functional.silu(x)
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x = torch.rand((4, 4))
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fx_module = make_fx(fn, decomposition_table=None)(x)
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found_silu = False
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for n in fx_module.graph.nodes:
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if n.target == torch.ops.aten.silu or n.target == torch.ops.aten.silu.default:
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found_silu = True
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self.assertTrue(found_silu)
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new_graph = torch.fx.Graph()
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silu_decomp_table = {torch.ops.aten.silu.default: decomposition_table[torch.ops.aten.silu.default]}
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DecompositionInterpreter(
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fx_module,
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new_graph=new_graph,
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decomposition_table=silu_decomp_table,
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).run(x)
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decomposed_module = torch.fx.GraphModule(fx_module, new_graph)
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for n in decomposed_module.graph.nodes:
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self.assertTrue(n.target != torch.ops.aten.silu)
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self.assertTrue(n.target != torch.ops.aten.silu.default)
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self.assertEqual(fx_module(x), decomposed_module(x))
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def test_make_fx_model_fwd_bwd(self, device):
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class Foo(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear = torch.nn.Linear(5, 5)
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def forward(self, x):
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return self.linear(x).relu()
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model = Foo()
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def f(x, params):
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out = stateless.functional_call(model, params, x).sum()
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out.backward()
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return list(params.values())
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input = torch.randn(3, 5, requires_grad=True)
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params = dict(model.named_parameters())
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fx_f = make_fx(f)(input, params)
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# fx may change the order of parameters in list, so using set() to compare
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self.assertTrue(
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torch.allclose(fx_f(input, params)[0], f(input, params)[0])
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or
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torch.allclose(fx_f(input, params)[0], f(input, params)[1])
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)
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self.assertTrue(
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torch.allclose(fx_f(input, params)[1], f(input, params)[0])
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or
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torch.allclose(fx_f(input, params)[1], f(input, params)[1])
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)
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def test_make_fx_model_fwd_bwd_wgtupdate(self, device):
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class Foo(torch.nn.Module):
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def __init__(self):
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super().__init__()
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self.linear = torch.nn.Linear(5, 5)
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def forward(self, x):
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return self.linear(x).relu()
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model = Foo()
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def f(args, params, buffers):
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if not isinstance(args, Iterable):
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args = [args]
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params_and_buffers = {**params, **buffers}
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out = stateless.functional_call(model, params_and_buffers, args)
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out.sum().backward()
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return [p - 1e-4 * p.grad for p in params.values()]
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input = torch.randn(3, 5, requires_grad=True)
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params = dict(model.named_parameters())
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buffers = dict(model.named_buffers())
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fx_f = make_fx(f)(input, params, buffers)
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# fx may change the order of parameters in list, so using set() to compare
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# also there is a numerical difference in results so changing atol from 1e-08 to 1e-03
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self.assertTrue(
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torch.allclose(fx_f(input, params, buffers)[0], f(input, params, buffers)[0], atol=1e-03)
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or
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torch.allclose(fx_f(input, params, buffers)[0], f(input, params, buffers)[1], atol=1e-03)
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)
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self.assertTrue(
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torch.allclose(fx_f(input, params, buffers)[1], f(input, params, buffers)[0], atol=1e-03)
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or
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torch.allclose(fx_f(input, params, buffers)[1], f(input, params, buffers)[1], atol=1e-03)
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)
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# TODO: Need to test the guards themselves specifically as well
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@skipIfNoSympy
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class TestSymbolicTracing(TestCase):
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def _test_dynamic(self, fn, trace_inputs, test_inputs):
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"""
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Tests fn traced with trace_inputs against test_inputs
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Also returns shape env
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"""
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trace_inputs = [torch.randn(shape) for shape in trace_inputs]
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traced_f = make_fx(fn, tracing_mode="symbolic")(*trace_inputs)
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for input in test_inputs:
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input = [torch.randn(shape) for shape in input]
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self.assertEqual(traced_f(*input), fn(*input))
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return traced_f.shape_env
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def test_unary(self):
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def f(x):
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assert x.shape[0] < 20
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return x.cos()
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test_inputs = []
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test_inputs.append([(2, 5)])
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test_inputs.append([(6, 8)])
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shape_env = self._test_dynamic(f, [(3, 4)], test_inputs)
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self.assertTrue(shape_env.evaluate_guards_for_args(torch.randn(4, 5)))
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self.assertFalse(shape_env.evaluate_guards_for_args(torch.randn(25, 5)))
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assert len(shape_env.guards) == 1
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def test_binary_broadcast(self):
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def f(a, b):
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c = a * b
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return c
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test_inputs = []
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test_inputs.append([(1, 5), (3, 1)])
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test_inputs.append([(1, 4), (4, 1)])
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shape_env = self._test_dynamic(f, [(1, 2), (3, 1)], test_inputs)
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assert len(shape_env.guards) == 0
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def test_cat(self):
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def f(a, b):
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val = torch.mul(a, b)
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out = torch.cat([val, val])
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if out.shape[0] * out.shape[1] > 20:
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out = out.cos()
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return out
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test_inputs = []
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test_inputs.append([(1, 5), (6, 1)])
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test_inputs.append([(1, 4), (3, 1)])
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shape_env = self._test_dynamic(f, [(1, 6), (8, 1)], test_inputs)
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self.assertTrue(shape_env.evaluate_guards_for_args(torch.randn(1, 10), torch.randn(6, 1)))
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self.assertFalse(shape_env.evaluate_guards_for_args(torch.randn(1, 2), torch.randn(4, 1)))
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assert len(shape_env.guards) == 1
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make_fx_failures = {
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# unknown
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xfail('allclose'),
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xfail('equal'),
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xfail('linalg.eigvals'),
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xfail('nn.functional.max_pool1d', device_type='cpu'),
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# empty
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skip('new_empty'),
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skip('empty_like'),
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skip('empty'),
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# flaky
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skip('linalg.lstsq', 'grad_oriented'),
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skip('nn.functional.max_unpool1d', '', device_type='cpu'),
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skip('nn.functional.max_unpool2d', '', device_type='cpu'),
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skip('nn.functional.max_unpool3d', '', device_type='cpu'),
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skip('linalg.lstsq'), # flaky, probably just a precision issue
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# data-dependent control flow
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xfail('cov'),
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xfail('istft'),
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xfail('nanquantile'),
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xfail('nn.functional.gaussian_nll_loss'),
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xfail('quantile'),
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xfail('tensor_split'),
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xfail('corrcoef'),
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# Seems like it's creating a sparse tensor that isn't captured by tensor.is_sparse
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xfail('sparse.sampled_addmm'),
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# ???
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xfail('nn.functional.ctc_loss'),
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# Sparse tensors are not supported with faketensors for now
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xfail('to_sparse'),
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# segfaults
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skip('block_diag'),
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}
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fake_tensor_failures = {
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# FakeTensor fallback doesn't work
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xfail('segment_reduce', 'lengths'),
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xfail('multinomial'),
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xfail('mvlgamma', 'mvlgamma_p_1'),
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xfail('mvlgamma', 'mvlgamma_p_3'),
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xfail('mvlgamma', 'mvlgamma_p_5'),
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xfail('cholesky'),
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xfail('cholesky_inverse'),
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# ASAN failures due to divide by 0
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skip('nn.functional.nll_loss'),
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}
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symbolic_tensor_failures = {
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# Needs complex-value support
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xfail('polar'),
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xfail('complex'),
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xfail('linalg.eig'),
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xfail('__getitem__', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
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xfail('__rmatmul__', ''), # aten.new_empty.default - couldn't find symbolic meta function/decomposition
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xfail('__rpow__', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
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xfail('_masked.amax', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
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xfail('_masked.amin', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
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xfail('_masked.argmax', ''), # aten.argmax.default - couldn't find symbolic meta function/decomposition
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xfail('_masked.argmin', ''), # aten.argmin.default - couldn't find symbolic meta function/decomposition
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xfail('_masked.cumprod', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('_masked.cumsum', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('_masked.log_softmax', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('_masked.logaddexp', ''), # aten.logaddexp.default - couldn't find symbolic meta function/decomposition
|
|
xfail('_masked.logsumexp', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('_masked.mean', ''), # ones() received an invalid combination of arguments - got (torch.Size, device=torch.device, ...
|
|
xfail('_masked.median', ''), # aten.nanmedian.dim - couldn't find symbolic meta function/decomposition
|
|
xfail('_masked.norm', ''), # aten.linalg_vector_norm.default - couldn't find symbolic meta function/decomposition
|
|
xfail('_masked.normalize', ''), # aten.linalg_vector_norm.default - couldn't find symbolic meta function/decomposition
|
|
xfail('_masked.prod', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('_masked.softmax', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('_masked.softmin', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('_masked.std', ''), # ones() received an invalid combination of arguments - got (torch.Size, device=torch.device, d...
|
|
xfail('_masked.sum', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('_masked.var', ''), # ones() received an invalid combination of arguments - got (torch.Size, device=torch.device, d...
|
|
xfail('addbmm', ''), # aten.addbmm.default - couldn't find symbolic meta function/decomposition
|
|
xfail('addmm', ''), # aten.mm.default - couldn't find symbolic meta function/decomposition
|
|
xfail('addmm', 'decomposed'), # aten.mm.default - couldn't find symbolic meta function/decomposition
|
|
xfail('addmv', ''), # aten.addmv.default - couldn't find symbolic meta function/decomposition
|
|
xfail('addr', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('all', ''), # Unexpected type <class 'torch.SymbolicIntNode'> when computing elementwise type promotion!
|
|
xfail('aminmax', ''), # aten.aminmax.default - couldn't find symbolic meta function/decomposition
|
|
xfail('argmax', ''), # aten.argmax.default - couldn't find symbolic meta function/decomposition
|
|
xfail('argmin', ''), # aten.argmin.default - couldn't find symbolic meta function/decomposition
|
|
xfail('argsort', ''), # aten.sort.default - couldn't find symbolic meta function/decomposition
|
|
xfail('argwhere', ''), # aten.nonzero.default - couldn't find symbolic meta function/decomposition
|
|
xfail('as_strided', ''), # aten.as_strided.default - couldn't find symbolic meta function/decomposition
|
|
xfail('as_strided_scatter', ''), # aten.as_strided_scatter.default - couldn't find symbolic meta function/decomposition
|
|
xfail('baddbmm', ''), # aten.baddbmm.default - couldn't find symbolic meta function/decomposition
|
|
xfail('bernoulli', ''), # aten.bernoulli.default - couldn't find symbolic meta function/decomposition
|
|
xfail('bfloat16', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('bmm', ''), # aten.bmm.default - couldn't find symbolic meta function/decomposition
|
|
xfail('bool', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('broadcast_tensors', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('bucketize', ''), # aten.bucketize.Tensor - couldn't find symbolic meta function/decomposition
|
|
xfail('byte', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('cartesian_prod', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('cdist', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('chalf', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('char', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('cholesky_solve', ''), # Could not run 'aten::_cholesky_solve_helper' with arguments from the 'Meta' back...
|
|
xfail('chunk', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('clamp_max', ''), # Received type <class 'NoneType'> that is neither a tensor or a number!
|
|
xfail('clone', ''), # aten.clone.default - couldn't find symbolic meta function/decomposition
|
|
xfail('column_stack', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('constant_pad_nd', ''), # aten.fill.Scalar - couldn't find symbolic meta function/decomposition
|
|
xfail('count_nonzero', ''), # Could not run 'aten::count_nonzero.dim_IntList' with arguments from the 'Meta' ba...
|
|
xfail('cross', ''), # aten.linalg_cross.default - couldn't find symbolic meta function/decomposition
|
|
xfail('cummax', ''), # aten.cummax.default - couldn't find symbolic meta function/decomposition
|
|
xfail('cummin', ''), # aten.cummin.default - couldn't find symbolic meta function/decomposition
|
|
xfail('cumprod', ''), # aten.cumprod.default - couldn't find symbolic meta function/decomposition
|
|
xfail('cumsum', ''), # aten.cumsum.default - couldn't find symbolic meta function/decomposition
|
|
xfail('cumulative_trapezoid', ''), # aten.slice.Tensor - couldn't find symbolic meta function/decomposition
|
|
xfail('deg2rad', ''), # aten.deg2rad.default - couldn't find symbolic meta function/decomposition
|
|
xfail('diag_embed', ''), # aten.diag_embed.default - couldn't find symbolic meta function/decomposition
|
|
xfail('diagflat', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('diagonal', ''), # aten.diagonal.default - couldn't find symbolic meta function/decomposition
|
|
xfail('diagonal_scatter', ''), # aten.diagonal_scatter.default - couldn't find symbolic meta function/decomposition
|
|
xfail('diff', ''), # aten.empty_like.default - couldn't find symbolic meta function/decomposition
|
|
xfail('dist', ''), # aten.dist.default - couldn't find symbolic meta function/decomposition
|
|
xfail('double', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('dsplit', ''), # aten.slice.Tensor - couldn't find symbolic meta function/decomposition
|
|
xfail('eig', ''), # aten.eig.default - couldn't find symbolic meta function/decomposition
|
|
xfail('einsum', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('expand_as', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.fft2', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.fft', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.fftn', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.fftshift', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.hfft2', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.hfft', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.hfftn', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.ifft2', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.ifft', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.ifftn', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.ifftshift', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.ihfft2', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.ihfft', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.ihfftn', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.irfft2', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.irfft', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.irfftn', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.rfft2', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.rfft', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fft.rfftn', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('fill', ''), # The underlying op of 'aten.stride' has no overload name '_schema'
|
|
xfail('flatten', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('float', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('float_power', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('frexp', ''), # aten.frexp.Tensor - couldn't find symbolic meta function/decomposition
|
|
xfail('full_like', ''), # aten.full_like.default - couldn't find symbolic meta function/decomposition
|
|
xfail('gather', ''), # aten.gather.default - couldn't find symbolic meta function/decomposition
|
|
xfail('geqrf', ''), # aten.geqrf.default - couldn't find symbolic meta function/decomposition
|
|
xfail('gradient', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('half', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('histc', ''), # Could not run 'aten::histc' with arguments from the 'Meta' backend. This could be because...
|
|
xfail('histogram', ''), # Could not run 'aten::histogram.bin_ct' with arguments from the 'Meta' backend. This c...
|
|
xfail('histogramdd', ''), # aten._histogramdd_bin_edges.default - couldn't find symbolic meta function/decomposition
|
|
xfail('hsplit', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('hstack', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('i0', ''), # aten.i0.default - couldn't find symbolic meta function/decomposition
|
|
xfail('index_add', ''), # Float
|
|
xfail('index_copy', ''), # Expected a long tensor for index, but got Float
|
|
xfail('index_fill', ''), # aten.index_fill.int_Scalar - couldn't find symbolic meta function/decomposition
|
|
xfail('index_put', ''), # aten.index_put.default - couldn't find symbolic meta function/decomposition
|
|
xfail('index_reduce', ''), # Float
|
|
xfail('index_select', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('inner', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('int', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('inverse', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('isclose', ''), # The underlying op of 'aten.stride' has no overload name '_schema'
|
|
xfail('isin', ''), # aten.isin.Tensor_Tensor - couldn't find symbolic meta function/decomposition
|
|
xfail('isreal', ''), # aten.empty_like.default - couldn't find symbolic meta function/decomposition
|
|
xfail('kron', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('kthvalue', ''), # aten.kthvalue.default - couldn't find symbolic meta function/decomposition
|
|
xfail('lerp', ''), # aten.lerp.Scalar - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.cholesky', ''), # aten.linalg_cholesky_ex.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.cholesky_ex', ''), # aten.linalg_cholesky_ex.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.cond', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('linalg.cross', ''), # aten.linalg_cross.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.det', ''), # aten._linalg_det.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.eigh', ''), # aten._linalg_eigh.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.eigvalsh', ''), # aten._linalg_eigh.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.householder_product', ''), # aten.linalg_householder_product.default - couldn't find symbolic meta funct...
|
|
xfail('linalg.inv', ''), # aten.linalg_inv_ex.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.inv_ex', ''), # aten.linalg_inv_ex.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.ldl_factor', ''), # aten.linalg_ldl_factor_ex.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.ldl_factor_ex', ''), # aten.linalg_ldl_factor_ex.default - couldn't find symbolic meta function/decompos...
|
|
xfail('linalg.ldl_solve', ''), # aten.linalg_ldl_solve.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.lu', ''), # aten.linalg_lu.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.lu_factor', ''), # aten.linalg_lu_factor_ex.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.lu_factor_ex', ''), # aten.linalg_lu_factor_ex.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.lu_solve', ''), # aten.linalg_lu_solve.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.matrix_power'), # RuntimeError: Trying to call aten.size on a tensor with symbolic shape
|
|
xfail('linalg.matrix_norm', ''), # aten.linalg_vector_norm.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.matrix_rank', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.matrix_rank', 'hermitian'), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.multi_dot', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('norm', 'fro'), # TensorImpl do not have numel
|
|
xfail('norm', 'inf'), # TensorImpl do not have numel
|
|
xfail('linalg.norm', ''), # TensorImpl do not have numel
|
|
xfail('linalg.norm', 'subgradients_at_zero'), # TensorImpl do not have numel
|
|
xfail('linalg.pinv', ''), # aten.linalg_pinv.atol_rtol_tensor - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.pinv', 'singular'), # aten.linalg_cholesky_ex.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.pinv', 'hermitian'), # aten.linalg_pinv.atol_rtol_tensor - couldn't find symbolic meta function/decompo...
|
|
xfail('linalg.qr', ''), # aten.linalg_qr.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.slogdet', ''), # aten._linalg_slogdet.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.solve', ''), # aten._linalg_solve_ex.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.solve_ex', ''), # aten._linalg_solve_ex.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.solve_triangular', ''), # aten.linalg_solve_triangular.default - couldn't find symbolic meta function/de...
|
|
xfail('linalg.svd', ''), # aten._linalg_svd.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.svdvals', ''), # aten._linalg_svd.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.tensorinv', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.tensorsolve', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.vander', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('linalg.vecdot', ''), # Could not run 'aten::vdot' with arguments from the 'Meta' backend. This could be ...
|
|
xfail('linalg.vector_norm', ''), # TensorImpl do not have numel
|
|
xfail('log_softmax', 'dtype'), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('logaddexp2', ''), # aten.logaddexp2.default - couldn't find symbolic meta function/decomposition
|
|
xfail('logaddexp', ''), # aten.logaddexp.default - couldn't find symbolic meta function/decomposition
|
|
xfail('logcumsumexp', ''), # aten.logcumsumexp.default - couldn't find symbolic meta function/decomposition
|
|
xfail('logdet', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('logsumexp', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('long', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('lu', ''), # aten.linalg_lu_factor_ex.default - couldn't find symbolic meta function/decomposition
|
|
xfail('lu_solve', ''), # aten.linalg_lu_solve.default - couldn't find symbolic meta function/decomposition
|
|
xfail('lu_unpack', ''), # aten.lu_unpack.default - couldn't find symbolic meta function/decomposition
|
|
xfail('masked_fill', ''), # expected predicate to be bool, got torch.float32
|
|
xfail('masked_scatter', ''), # aten.masked_scatter.default - couldn't find symbolic meta function/decomposition
|
|
xfail('masked_select', ''), # aten.masked_select.default - couldn't find symbolic meta function/decomposition
|
|
xfail('matmul', ''), # aten.new_empty.default - couldn't find symbolic meta function/decomposition
|
|
xfail('matrix_exp', ''), # aten.linalg_matrix_exp.default - couldn't find symbolic meta function/decomposition
|
|
xfail('max', 'reduction_with_dim'), # aten.max.dim - couldn't find symbolic meta function/decomposition
|
|
xfail('mean', ''), # Unexpected type <class 'torch.SymbolicIntNode'> when computing elementwise type promotion!
|
|
xfail('median', ''), # Could not run 'aten::median' with arguments from the 'Meta' backend. This could be becau...
|
|
xfail('meshgrid', 'list_of_tensors'), # Tensors of type TensorImpl do not have numel
|
|
xfail('meshgrid', 'variadic_tensors'), # Tensors of type TensorImpl do not have numel
|
|
xfail('min', 'reduction_with_dim'), # aten.min.dim - couldn't find symbolic meta function/decomposition
|
|
xfail('mm', ''), # aten.mm.default - couldn't find symbolic meta function/decomposition
|
|
xfail('mode', ''), # aten.mode.default - couldn't find symbolic meta function/decomposition
|
|
xfail('msort', ''), # aten.sort.default - couldn't find symbolic meta function/decomposition
|
|
xfail('mv', ''), # aten.mv.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nanmean', ''), # The underlying op of 'aten.stride' has no overload name '_schema'
|
|
xfail('narrow', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('native_layer_norm', ''), # Unexpected type <class 'torch.SymbolicIntNode'> when computing elementwise type promot...
|
|
xfail('nn.functional.adaptive_avg_pool1d', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.adaptive_avg_pool2d', ''), # argument 'size' must be tuple of ints, but found element o...
|
|
xfail('nn.functional.adaptive_avg_pool3d', ''), # aten._adaptive_avg_pool3d.default - couldn't find symbolic meta func...
|
|
xfail('nn.functional.adaptive_max_pool1d', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.adaptive_max_pool2d', ''), # aten.adaptive_max_pool2d.default - couldn't find symbolic meta funct...
|
|
xfail('nn.functional.adaptive_max_pool3d', ''), # argument 'output_size' (position 2) must be tupl...
|
|
xfail('nn.functional.avg_pool1d', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.avg_pool2d', ''), # aten.avg_pool2d.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.avg_pool3d', ''), # aten.avg_pool3d.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.batch_norm', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.bilinear', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.binary_cross_entropy', ''), # aten.new_empty.default - couldn't find symbolic meta function/decom...
|
|
xfail('nn.functional.binary_cross_entropy_with_logits', ''), # aten.binary_cross_entropy_with_logits.default - couldn'...
|
|
xfail('nn.functional.conv1d', ''), # aten.convolution.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.conv2d', ''), # aten.convolution.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.conv_transpose1d', ''), # aten.convolution.default - couldn't find symbolic meta function/decompo...
|
|
xfail('nn.functional.conv_transpose2d', ''), # aten.convolution.default - couldn't find symbolic meta function/decompo...
|
|
xfail('nn.functional.conv_transpose3d', ''), # aten.convolution.default - couldn't find symbolic meta function/decompo...
|
|
xfail('nn.functional.cosine_embedding_loss', ''), # The underlying op of 'aten.stride' has no overload name '_schema'
|
|
xfail('nn.functional.cosine_similarity', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.cross_entropy', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.dropout2d', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('nn.functional.dropout3d', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('nn.functional.dropout', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('nn.functional.embedding_bag', ''), # aten._embedding_bag_forward_only.default - couldn't find symbolic meta fun...
|
|
xfail('nn.functional.embedding', ''), # argument 'size' must be tuple of ints, but found element of type tor...
|
|
xfail('nn.functional.feature_alpha_dropout', 'with_train'), # Tensors of type TensorImpl do not have numel
|
|
xfail('nn.functional.fractional_max_pool2d', ''), # argument 'size' must be tuple of ints, but found element of t...
|
|
xfail('nn.functional.fractional_max_pool3d', ''), # argument 'size' must be tuple of ints, but found element of t...
|
|
xfail('nn.functional.glu', ''), # aten.glu.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.grid_sample', ''), # aten.grid_sampler_2d.default - couldn't find symbolic meta function/decompos...
|
|
xfail('nn.functional.group_norm', ''), # 'torch._C.SymbolicIntNode' and 'int'
|
|
xfail('nn.functional.hardsigmoid', ''), # Received type <class 'NoneType'> that is neither a tensor or a number!
|
|
xfail('nn.functional.hardswish', ''), # Received type <class 'NoneType'> that is neither a tensor or a number!
|
|
xfail('nn.functional.hinge_embedding_loss', ''), # aten.empty_like.default - couldn't find symbolic meta function/deco...
|
|
xfail('nn.functional.huber_loss', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.instance_norm', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.interpolate', 'area'), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.interpolate', 'bicubic'), # aten.upsample_bicubic2d.vec - couldn't find symbolic meta function/d...
|
|
xfail('nn.functional.interpolate', 'bilinear'), # aten.upsample_bilinear2d.vec - couldn't find symbolic meta function...
|
|
xfail('nn.functional.interpolate', 'linear'), # aten.upsample_linear1d.vec - couldn't find symbolic meta function/dec...
|
|
xfail('nn.functional.interpolate', 'nearest'), # aten.upsample_nearest1d.vec - couldn't find symbolic meta function/d...
|
|
xfail('nn.functional.interpolate', 'trilinear'), # aten.upsample_trilinear3d.vec - couldn't find symbolic meta functi...
|
|
xfail('nn.functional.kl_div', ''), # Unexpected type <class 'torch.SymbolicIntNode'> when computing elementwise type pro...
|
|
xfail('nn.functional.l1_loss', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.layer_norm', ''), # Unexpected type <class 'torch.SymbolicIntNode'> when computing elementwise type...
|
|
xfail('nn.functional.linear', ''), # aten.mv.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.local_response_norm', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('nn.functional.margin_ranking_loss', ''), # The underlying op of 'aten.stride' has no overload name '_schema'
|
|
xfail('nn.functional.max_pool2d', ''), # aten.max_pool2d_with_indices.default - couldn't find symbolic meta function/d...
|
|
xfail('nn.functional.max_pool3d', ''), # aten.max_pool3d_with_indices.default - couldn't find symbolic meta function/d...
|
|
xfail('nn.functional.max_unpool1d', 'grad'), # aten.max_unpool2d.default - couldn't find symbolic meta function/decom...
|
|
xfail('nn.functional.max_unpool2d', 'grad'), # aten.max_unpool2d.default - couldn't find symbolic meta function/decom...
|
|
xfail('nn.functional.max_unpool3d', 'grad'), # aten.max_unpool3d.default - couldn't find symbolic meta function/decom...
|
|
xfail('nn.functional.mse_loss', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.multi_margin_loss', ''), # Could not run 'aten::multi_margin_loss' with arguments from the...
|
|
xfail('nn.functional.multilabel_margin_loss', ''), # Could not run 'aten::multilabel_margin_loss_forward' with ...
|
|
xfail('nn.functional.multilabel_soft_margin_loss', ''), # aten.new_empty.default - couldn't find symbolic meta functio...
|
|
xfail('nn.functional.normalize', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.pad', 'circular'), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.pad', 'constant'), # aten.fill.Scalar - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.pad', 'reflect'), # aten.reflection_pad1d.default - couldn't find symbolic meta function/decompo...
|
|
xfail('nn.functional.pad', 'replicate'), # aten.replication_pad1d.default - couldn't find symbolic meta function/deco...
|
|
xfail('nn.functional.pairwise_distance', ''), # TensorImpl does not have numel
|
|
xfail('nn.functional.pdist', ''), # Could not run 'aten::_pdist_forward' with arguments from the 'Meta' backend...
|
|
xfail('nn.functional.pixel_shuffle', ''), # aten.pixel_shuffle.default - couldn't find symbolic meta function/decompos...
|
|
xfail('nn.functional.pixel_unshuffle', ''), # aten.pixel_unshuffle.default - couldn't find symbolic meta function/deco...
|
|
xfail('nn.functional.poisson_nll_loss', ''), # The underlying op of 'aten.stride' has no overload name '_schema'
|
|
xfail('nn.functional.prelu', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('nn.functional.rrelu', ''), # aten.empty_like.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.smooth_l1_loss', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.soft_margin_loss', ''), # aten.soft_margin_loss.default - couldn't find symbolic meta function/de...
|
|
xfail('nn.functional.softmin', 'with_dtype'), # aten._to_copy.default - couldn't find symbolic meta function/decompos...
|
|
xfail('nn.functional.triplet_margin_loss', ''), # Unexpected type <class 'torch.SymbolicIntNode'> when computing element...
|
|
xfail('nn.functional.triplet_margin_with_distance_loss', ''), # Unexpected type <class 'torch.SymbolicIntNode'> when com...
|
|
xfail('nn.functional.unfold', ''), # aten.im2col.default - couldn't find symbolic meta function/decomposition
|
|
xfail('nn.functional.upsample_bilinear', ''), # aten.upsample_bilinear2d.vec - couldn't find symbolic meta function/de...
|
|
xfail('nn.functional.upsample_nearest', ''), # aten.upsample_nearest1d.vec - couldn't find symbolic meta function/deco...
|
|
xfail('norm', ''), # TensorImpl does not have numel
|
|
xfail('norm', 'nuc'), # aten._linalg_svd.default - couldn't find symbolic meta function/decomposition
|
|
xfail('normal', ''), # aten.normal.Tensor_Tensor - couldn't find symbolic meta function/decomposition
|
|
xfail('normal', 'number_mean'), # aten.normal.float_Tensor - couldn't find symbolic meta function/decomposition
|
|
xfail('ones_like', ''), # aten.ones_like.default - couldn't find symbolic meta function/decomposition
|
|
xfail('ormqr', ''), # aten.ormqr.default - couldn't find symbolic meta function/decomposition
|
|
xfail('outer', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('pca_lowrank', ''), # aten.mm.default - couldn't find symbolic meta function/decomposition
|
|
xfail('pinverse', ''), # aten.linalg_pinv.atol_rtol_tensor - couldn't find symbolic meta function/decomposition
|
|
xfail('polygamma', 'polygamma_n_0'), # aten.polygamma.default - couldn't find symbolic meta function/decomposition
|
|
xfail('polygamma', 'polygamma_n_1'), # aten.polygamma.default - couldn't find symbolic meta function/decomposition
|
|
xfail('polygamma', 'polygamma_n_2'), # aten.polygamma.default - couldn't find symbolic meta function/decomposition
|
|
xfail('polygamma', 'polygamma_n_3'), # aten.polygamma.default - couldn't find symbolic meta function/decomposition
|
|
xfail('polygamma', 'polygamma_n_4'), # aten.polygamma.default - couldn't find symbolic meta function/decomposition
|
|
xfail('put', ''), # aten.clone.default - couldn't find symbolic meta function/decomposition
|
|
xfail('qr', ''), # aten.linalg_qr.default - couldn't find symbolic meta function/decomposition
|
|
xfail('rad2deg', ''), # aten.rad2deg.default - couldn't find symbolic meta function/decomposition
|
|
xfail('rand_like', ''), # aten.randn_like.default - couldn't find symbolic meta function/decomposition
|
|
xfail('randint_like', ''), # aten.randint_like.default - couldn't find symbolic meta function/decomposition
|
|
xfail('randn_like', ''), # aten.randn_like.default - couldn't find symbolic meta function/decomposition
|
|
xfail('ravel', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('renorm', ''), # aten.renorm.default - couldn't find symbolic meta function/decomposition
|
|
xfail('repeat', ''), # aten.repeat.default - couldn't find symbolic meta function/decomposition
|
|
xfail('reshape_as', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('reshape', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('resize_', ''), # aten.clone.default - couldn't find symbolic meta function/decomposition
|
|
xfail('resize_as_', ''), # aten.clone.default - couldn't find symbolic meta function/decomposition
|
|
xfail('roll', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('rot90', ''), # aten.empty_like.default - couldn't find symbolic meta function/decomposition
|
|
xfail('round', ''), # aten.round.default - couldn't find symbolic meta function/decomposition
|
|
xfail('round', 'decimals_0'), # aten.round.decimals - couldn't find symbolic meta function/decomposition
|
|
xfail('round', 'decimals_3'), # aten.round.decimals - couldn't find symbolic meta function/decomposition
|
|
xfail('round', 'decimals_neg_3'), # aten.round.decimals - couldn't find symbolic meta function/decomposition
|
|
xfail('scatter_add', ''), # aten.scatter_add.default - couldn't find symbolic meta function/decomposition
|
|
xfail('scatter', ''), # aten.scatter.src - couldn't find symbolic meta function/decomposition
|
|
xfail('scatter_reduce', 'amax'), # aten.scatter_reduce.two - couldn't find symbolic meta function/decomposition
|
|
xfail('scatter_reduce', 'amin'), # aten.scatter_reduce.two - couldn't find symbolic meta function/decomposition
|
|
xfail('scatter_reduce', 'mean'), # aten.scatter_reduce.two - couldn't find symbolic meta function/decomposition
|
|
xfail('scatter_reduce', 'prod'), # aten.scatter_reduce.two - couldn't find symbolic meta function/decomposition
|
|
xfail('scatter_reduce', 'sum'), # aten.scatter_reduce.two - couldn't find symbolic meta function/decomposition
|
|
xfail('searchsorted', ''), # Could not run 'aten::searchsorted.Tensor' with arguments from the 'Meta' backend. ...
|
|
xfail('segment_reduce', 'offsets'), # aten.segment_reduce.default - couldn't find symbolic meta function/decomposition
|
|
xfail('select', ''), # aten.select.int - couldn't find symbolic meta function/decomposition
|
|
xfail('select_scatter', ''), # aten.select_scatter.default - couldn't find symbolic meta function/decomposition
|
|
xfail('sgn', ''), # aten.sgn.default - couldn't find symbolic meta function/decomposition
|
|
xfail('short', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('sinc', ''), # aten.sinc.default - couldn't find symbolic meta function/decomposition
|
|
xfail('slice_scatter', ''), # aten.slice_scatter.default - couldn't find symbolic meta function/decomposition
|
|
xfail('softmax', 'with_dtype'), # aten._to_copy.default - couldn't find symbolic meta function/decomposition
|
|
xfail('sort', ''), # aten.sort.default - couldn't find symbolic meta function/decomposition
|
|
xfail('special.airy_ai', ''), # aten.special_airy_ai.default - couldn't find symbolic meta function/decomposition
|
|
xfail('special.bessel_j0', ''), # aten.special_bessel_j0.default - couldn't find symbolic meta function/decomposition
|
|
xfail('special.bessel_j1', ''), # aten.special_bessel_j1.default - couldn't find symbolic meta function/decomposition
|
|
xfail('special.bessel_y0', ''), # aten.special_bessel_y0.default - couldn't find symbolic meta function/decomposition
|
|
xfail('special.bessel_y1', ''), # aten.special_bessel_y1.default - couldn't find symbolic meta function/decomposition
|
|
xfail('special.chebyshev_polynomial_t', ''), # aten.special_chebyshev_polynomial_t.default - couldn't find symbolic me...
|
|
xfail('special.chebyshev_polynomial_u', ''), # aten.special_chebyshev_polynomial_u.default - couldn't find symbolic me...
|
|
xfail('special.entr', ''), # aten.special_entr.default - couldn't find symbolic meta function/decomposition
|
|
xfail('special.erfcx', ''), # aten.special_erfcx.default - couldn't find symbolic meta function/decomposition
|
|
xfail('special.hermite_polynomial_h', ''), # aten.special_hermite_polynomial_h.default - couldn't find symbolic meta f...
|
|
xfail('special.hermite_polynomial_he', ''), # aten.special_hermite_polynomial_he.default - couldn't find symbolic meta...
|
|
xfail('special.laguerre_polynomial_l', ''), # aten.special_laguerre_polynomial_l.default - couldn't find symbolic meta...
|
|
xfail('special.log_ndtr', ''), # aten.special_log_ndtr.default - couldn't find symbolic meta function/decomposition
|
|
xfail('special.modified_bessel_i0', ''), # aten.special_modified_bessel_i0.default - couldn't find symbolic meta funct...
|
|
xfail('special.modified_bessel_i1', ''), # aten.special_modified_bessel_i1.default - couldn't find symbolic meta funct...
|
|
xfail('special.modified_bessel_k0', ''), # aten.special_modified_bessel_k0.default - couldn't find symbolic meta funct...
|
|
xfail('special.modified_bessel_k1', ''), # aten.special_modified_bessel_k1.default - couldn't find symbolic meta funct...
|
|
xfail('special.ndtri', ''), # aten.special_ndtri.default - couldn't find symbolic meta function/decomposition
|
|
xfail('special.polygamma', 'special_polygamma_n_0'), # aten.polygamma.default - couldn't find symbolic meta function/...
|
|
xfail('special.scaled_modified_bessel_k0', ''), # aten.special_scaled_modified_bessel_k0.default - couldn't find symbo...
|
|
xfail('special.scaled_modified_bessel_k1', ''), # aten.special_scaled_modified_bessel_k1.default - couldn't find symbo...
|
|
xfail('special.spherical_bessel_j0', ''), # aten.special_spherical_bessel_j0.default - couldn't find symbolic meta fun...
|
|
xfail('special.xlog1py', ''), # aten.special_xlog1py.default - couldn't find symbolic meta function/decomposition
|
|
xfail('split', ''), # 'torch._C.SymbolicIntNode' and 'int'
|
|
xfail('split', 'list_args'), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('split_with_sizes', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('stack', ''), # argument 'size' must be tuple of ints, but found element of type torch._C.SymbolicIntNode a...
|
|
xfail('std', ''), # Unexpected type <class 'torch.SymbolicIntNode'> when computing elementwise type promotion!
|
|
xfail('std_mean', ''), # Unexpected type <class 'torch.SymbolicIntNode'> when computing elementwise type promotion!
|
|
xfail('stft', ''), # argument 'size' must be tuple of ints, but found element of type torch._C.SymbolicIntNode at...
|
|
xfail('sum_to_size', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('svd', ''), # aten._linalg_svd.default - couldn't find symbolic meta function/decomposition
|
|
xfail('svd_lowrank', ''), # aten.mm.default - couldn't find symbolic meta function/decomposition
|
|
xfail('symeig', ''), # aten.symeig.default - couldn't find symbolic meta function/decomposition
|
|
xfail('take_along_dim', ''), # dtype of indices should be Long but got Float
|
|
xfail('take', ''), # aten.take.default - couldn't find symbolic meta function/decomposition
|
|
xfail('tensordot', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('tile', ''), # aten.repeat.default - couldn't find symbolic meta function/decomposition
|
|
xfail('topk', ''), # aten.topk.default - couldn't find symbolic meta function/decomposition
|
|
xfail('trapezoid', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('trapz', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('triangular_solve', ''), # aten.triangular_solve.default - couldn't find symbolic meta function/decomposition
|
|
xfail('tril', ''), # aten.tril.default - couldn't find symbolic meta function/decomposition
|
|
xfail('triu', ''), # aten.triu.default - couldn't find symbolic meta function/decomposition
|
|
xfail('unfold', ''), # aten.unfold.default - couldn't find symbolic meta function/decomposition
|
|
xfail('var_mean', ''), # Unexpected type <class 'torch.SymbolicIntNode'> when computing elementwise type promotion!
|
|
xfail('var', ''), # Unexpected type <class 'torch.SymbolicIntNode'> when computing elementwise type promotion!
|
|
xfail('vdot', ''), # aten.vdot.default - couldn't find symbolic meta function/decomposition
|
|
xfail('view_as_complex', ''), # aten.view_as_complex.default - couldn't find symbolic meta function/decomposition
|
|
xfail('view_as', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('view', ''), # Tensors of type TensorImpl do not have numel
|
|
xfail('vsplit', ''), # aten.size.default - couldn't find symbolic meta function/decomposition
|
|
xfail('where', ''), # expected predicate to be bool, got torch.float32
|
|
xfail('zero_', ''), # aten.clone.default - couldn't find symbolic meta function/decomposition
|
|
xfail('zeros_like', ''), # aten.zeros_like.default - couldn't find symbolic meta function/decomposition
|
|
}
|
|
|
|
def _test_make_fx_helper(self, device, dtype, op, tracing_mode):
|
|
def f(args, kwargs):
|
|
return op.op(*args, **kwargs)
|
|
sample_inputs_itr = op.sample_inputs(device, dtype, requires_grad=False)
|
|
new_f = None
|
|
for sample_input in sample_inputs_itr:
|
|
args = [sample_input.input] + list(sample_input.args)
|
|
kwargs = sample_input.kwargs
|
|
|
|
try:
|
|
new_f = make_fx(f, tracing_mode=tracing_mode)(args, kwargs)
|
|
except DynamicOutputShapeException as e:
|
|
self.skipTest("Dynamic output shape operation in trace")
|
|
|
|
for arg in args:
|
|
if isinstance(arg, torch.Tensor) and arg.dtype == torch.float:
|
|
arg.uniform_(0, 1)
|
|
try:
|
|
old_out = f(args, kwargs)
|
|
except Exception:
|
|
continue
|
|
new_out = wrapper_set_seed(new_f, args, kwargs)
|
|
self.assertEqual(new_out, old_out)
|
|
|
|
class TestProxyTensorOpInfo(TestCase):
|
|
@ops(op_db, allowed_dtypes=(torch.float,))
|
|
@skipOps('TestProxyTensorOpInfo', 'test_make_fx_exhaustive', make_fx_failures)
|
|
def test_make_fx_exhaustive(self, device, dtype, op):
|
|
_test_make_fx_helper(self, device, dtype, op, "real")
|
|
|
|
@ops(op_db, allowed_dtypes=(torch.float,))
|
|
@skipOps('TestProxyTensorOpInfo', 'test_make_fx_fake_exhaustive', make_fx_failures.union(fake_tensor_failures))
|
|
def test_make_fx_fake_exhaustive(self, device, dtype, op):
|
|
_test_make_fx_helper(self, device, dtype, op, "fake")
|
|
|
|
@skipIfNoSympy
|
|
@ops(op_db, allowed_dtypes=(torch.float,))
|
|
@skipOps('TestProxyTensorOpInfo', 'test_make_fx_symbolic_exhaustive',
|
|
make_fx_failures | fake_tensor_failures | symbolic_tensor_failures)
|
|
def test_make_fx_symbolic_exhaustive(self, device, dtype, op):
|
|
_test_make_fx_helper(self, device, dtype, op, "symbolic")
|
|
|
|
|
|
only_for = ("cpu")
|
|
instantiate_device_type_tests(
|
|
TestProxyTensor,
|
|
globals(),
|
|
only_for=only_for,
|
|
)
|
|
instantiate_device_type_tests(TestProxyTensorOpInfo, globals(), only_for=only_for)
|
|
|
|
|
|
if __name__ == '__main__':
|
|
run_tests()
|