| # Owner(s): ["module: ProxyTensor"] |
| |
| from torch.testing._internal.common_utils import TestCase, run_tests |
| import torch |
| import unittest |
| import warnings |
| from torch.testing._internal.common_device_type import instantiate_device_type_tests |
| from torch.testing._internal.common_methods_invocations import DecorateInfo |
| from torch.testing._internal.common_methods_invocations import op_db, wrapper_set_seed |
| from torch._subclasses.fake_tensor import DynamicOutputShapeException |
| |
| from torch._decomp import decomposition_table |
| from torch.testing._internal.common_device_type import ops |
| from torch.fx.experimental.proxy_tensor import make_fx, DecompositionInterpreter |
| from torch.utils._pytree import tree_map |
| import re |
| |
| try: |
| import sympy # noqa: F401 |
| HAS_SYMPY = True |
| except ImportError: |
| HAS_SYMPY = False |
| skipIfNoSympy = unittest.skipIf(not HAS_SYMPY, "no sympy") |
| |
| |
| def process_failures(): |
| """ |
| Takes file containing failures like |
| |
| 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 |
| |
| and processes them into a list of opinfo xfails |
| """ |
| f = open('pytest_failures') |
| failures = f.readlines() |
| failures = [i.strip() for i in failures] |
| |
| def process_failure_string(s, matcher): |
| out = re.search(matcher, s) |
| return out.groups() |
| |
| SYMBOLIC_TRACE_MATCH = r'exhaustive_(.*)_cpu.*: (.*)' |
| failures = [process_failure_string(s, SYMBOLIC_TRACE_MATCH) for s in failures] |
| |
| def create_normalized_name(op): |
| if op.variant_test_name == '': |
| s = op.name |
| else: |
| s = f"{op.name}.{op.variant_test_name}" |
| return s.replace('.', '_') |
| |
| remap_opinfo = {create_normalized_name(op): (op.name, op.variant_test_name) for op in op_db} |
| |
| print("symbolic_tensor_failures = {") |
| for failure, reason in failures: |
| print(f" xfail{remap_opinfo[failure]}, # {reason}") |
| print("}") |
| |
| |
| # Copied from functorch |
| def xfail(op_name, variant_name='', *, device_type=None, dtypes=None): |
| return (op_name, variant_name, device_type, dtypes, True) |
| |
| |
| def skip(op_name, variant_name='', *, device_type=None, dtypes=None): |
| return (op_name, variant_name, device_type, dtypes, False) |
| |
| |
| def skipOps(test_case_name, base_test_name, to_skip): |
| all_opinfos = op_db |
| for xfail in to_skip: |
| op_name, variant_name, device_type, dtypes, expected_failure = xfail |
| matching_opinfos = [o for o in all_opinfos |
| if o.name == op_name and o.variant_test_name == variant_name] |
| assert len(matching_opinfos) >= 1, f"Couldn't find OpInfo for {xfail}" |
| for opinfo in matching_opinfos: |
| decorators = list(opinfo.decorators) |
| if expected_failure: |
| decorator = DecorateInfo(unittest.expectedFailure, |
| test_case_name, base_test_name, |
| device_type=device_type, dtypes=dtypes) |
| decorators.append(decorator) |
| else: |
| decorator = DecorateInfo(unittest.skip("Skipped!"), |
| test_case_name, base_test_name, |
| device_type=device_type, dtypes=dtypes) |
| decorators.append(decorator) |
| opinfo.decorators = tuple(decorators) |
| |
| # This decorator doesn't modify fn in any way |
| def wrapped(fn): |
| return fn |
| return wrapped |
| |
| |
| USE_TORCHVISION = False |
| try: |
| import torchvision |
| USE_TORCHVISION = True |
| except ImportError: |
| warnings.warn("Couldn't import torchvision. Some of our tests use it, try " |
| "to install it with commands from pytorch.org, post-fixed with " |
| "`--no-deps` to avoid overwriting the pytorch installation", |
| UserWarning) |
| |
| |
| def _create_new_input(x): |
| if not isinstance(x, torch.Tensor): |
| return x |
| if x.dtype != torch.float: |
| return x + 1 |
| if x.is_leaf: |
| return torch.rand_like(x, requires_grad=True) |
| else: |
| return torch.rand_like(x) |
| |
| class TestProxyTensor(TestCase): |
| def _test(self, f, inps): |
| fx_f = make_fx(f)(*inps) |
| new_inps = tree_map(_create_new_input, inps) |
| self.assertEqual(fx_f(*new_inps), f(*new_inps)) |
| |
| def test_make_fx_simple(self, device): |
| def f(x): |
| return torch.sin(x) |
| self._test(f, (torch.randn(3),)) |
| |
| def test_scalar_device(self, device): |
| def f(a, b): |
| return a + b |
| self._test(f, [torch.randn(3, device=device), torch.tensor(5)]) |
| |
| @unittest.skipIf(not USE_TORCHVISION, "test requires torchvision") |
| def test_resnet18_backward_trace(self, device): |
| mod = torchvision.models.resnet18() |
| |
| def f(x): |
| for a in mod.parameters(): |
| a.grad = None |
| out = mod(x) |
| out.sum().backward() |
| return [a.grad for a in mod.parameters()] |
| |
| inp = torch.randn(3, 3, 250, 250, requires_grad=True) |
| self._test(f, [inp]) |
| |
| def test_proxy_tensor(self): |
| def f_grad(x): |
| val = x.cos().cos().sum() |
| return torch.autograd.grad(val, x) |
| |
| def f_backward(x): |
| val = x.cos().cos().sum() |
| val.backward() |
| return x.grad |
| |
| for f in [f_grad, f_backward]: |
| self._test(f, [torch.randn(3, requires_grad=True)]) |
| |
| def test_inplace_metadata(self): |
| def f(x): |
| x = x.clone() |
| x.unsqueeze_(-1) |
| assert x.shape[-1] == 1 |
| return x |
| |
| self._test(f, [torch.randn(5)]) |
| |
| def test_mode_tracing_factory_function(self): |
| def f(x): |
| return x + torch.randn(x.shape) |
| |
| # default behavior should trace factory functions |
| traced = make_fx(f)(torch.randn(3)) |
| self.assertTrue( |
| any( |
| node.target == torch.ops.aten.randn.default |
| for node in traced.graph.nodes |
| ) |
| ) |
| |
| def test_mode_tracing_factory_function_no_factory_function(self): |
| def f(x): |
| return x + torch.randn(x.shape) |
| # setting the flag to false should not trace factory functions |
| traced = make_fx(f, trace_factory_functions=False)(torch.randn(3)) |
| self.assertFalse( |
| any( |
| node.target == torch.ops.aten.randn.default |
| for node in traced.graph.nodes |
| ) |
| ) |
| |
| def test_make_fx_overloads(self): |
| def f(x): |
| return x.cos() + torch.randn(x.shape) |
| |
| traced = make_fx(f)(torch.randn(3)) |
| |
| self.assertTrue(all([isinstance(node.target, torch._ops.OpOverload) |
| for node in traced.graph.nodes if node.op == 'call_function'])) |
| |
| def test_tensor_constants(self): |
| def f(): |
| val = torch.tensor(float('inf')) |
| return torch.full((100, 100), val) |
| |
| self._test(f, []) |
| |
| def test_constant_proxy_tensor(self): |
| from torch.fx.experimental.proxy_tensor import make_fx |
| |
| def f(): |
| val = torch.tensor(float('inf')) |
| return torch.full((100, 100), val) |
| |
| g = make_fx(f)() |
| self.assertEqual(g(), f()) |
| |
| def test_constant_proxy_tensor_mut(self): |
| from torch.fx.experimental.proxy_tensor import make_fx |
| |
| def f(): |
| val = torch.tensor(float(1)) |
| val.add_(2) |
| return torch.full((100, 100), val) |
| |
| g = make_fx(f)() |
| self.assertEqual(g(), f()) |
| # In case we mutated shared state in the g graph! |
| self.assertEqual(g(), f()) |
| |
| g = make_fx(f, tracing_mode="fake")() |
| self.assertEqual(g(), f()) |
| # In case we mutated shared state in the g graph! |
| self.assertEqual(g(), f()) |
| |
| def test_use_fake_and_tensor(self): |
| def f(x, y): |
| z = torch.tensor([2.0, 3.0]) |
| return x + y + z |
| |
| g = make_fx(f, tracing_mode="fake")(torch.randn(2), torch.randn(2)) |
| x, y = torch.randn(2), torch.randn(2) |
| self.assertEqual(g(x, y), f(x, y)) |
| |
| def test_decomposition_interpreter(self): |
| def fn(x): |
| return torch.nn.functional.silu(x) |
| |
| x = torch.rand((4, 4)) |
| fx_module = make_fx(fn, decomposition_table=None)(x) |
| |
| found_silu = False |
| for n in fx_module.graph.nodes: |
| if n.target == torch.ops.aten.silu or n.target == torch.ops.aten.silu.default: |
| found_silu = True |
| |
| self.assertTrue(found_silu) |
| |
| new_graph = torch.fx.Graph() |
| silu_decomp_table = {torch.ops.aten.silu.default: decomposition_table[torch.ops.aten.silu.default]} |
| DecompositionInterpreter( |
| fx_module, |
| new_graph=new_graph, |
| decomposition_table=silu_decomp_table, |
| ).run(x) |
| |
| decomposed_module = torch.fx.GraphModule(fx_module, new_graph) |
| |
| for n in decomposed_module.graph.nodes: |
| self.assertTrue(n.target != torch.ops.aten.silu) |
| self.assertTrue(n.target != torch.ops.aten.silu.default) |
| |
| self.assertEqual(fx_module(x), decomposed_module(x)) |
| |
| # TODO: Need to test the guards themselves specifically as well |
| @skipIfNoSympy |
| class TestSymbolicTracing(TestCase): |
| def _test_dynamic(self, fn, trace_inputs, test_inputs): |
| """ |
| Tests fn traced with trace_inputs against test_inputs |
| Also returns shape env |
| """ |
| trace_inputs = [torch.randn(shape) for shape in trace_inputs] |
| traced_f = make_fx(fn, tracing_mode="symbolic")(*trace_inputs) |
| for input in test_inputs: |
| input = [torch.randn(shape) for shape in input] |
| self.assertEqual(traced_f(*input), fn(*input)) |
| return traced_f.shape_env |
| |
| |
| def test_unary(self): |
| def f(x): |
| assert x.shape[0] < 20 |
| return x.cos() |
| test_inputs = [] |
| test_inputs.append([(2, 5)]) |
| test_inputs.append([(6, 8)]) |
| shape_env = self._test_dynamic(f, [(3, 4)], test_inputs) |
| self.assertTrue(shape_env.evaluate_guards_for_args(torch.randn(4, 5))) |
| self.assertFalse(shape_env.evaluate_guards_for_args(torch.randn(25, 5))) |
| assert len(shape_env.guards) == 1 |
| |
| def test_binary_broadcast(self): |
| def f(a, b): |
| c = a * b |
| return c |
| |
| test_inputs = [] |
| test_inputs.append([(1, 5), (3, 1)]) |
| test_inputs.append([(1, 4), (4, 1)]) |
| shape_env = self._test_dynamic(f, [(1, 2), (3, 1)], test_inputs) |
| assert len(shape_env.guards) == 0 |
| |
| def test_cat(self): |
| def f(a, b): |
| val = torch.mul(a, b) |
| out = torch.cat([val, val]) |
| if out.shape[0] * out.shape[1] > 20: |
| out = out.cos() |
| return out |
| |
| test_inputs = [] |
| test_inputs.append([(1, 5), (6, 1)]) |
| test_inputs.append([(1, 4), (3, 1)]) |
| shape_env = self._test_dynamic(f, [(1, 6), (8, 1)], test_inputs) |
| self.assertTrue(shape_env.evaluate_guards_for_args(torch.randn(1, 10), torch.randn(6, 1))) |
| self.assertFalse(shape_env.evaluate_guards_for_args(torch.randn(1, 2), torch.randn(4, 1))) |
| assert len(shape_env.guards) == 1 |
| |
| make_fx_failures = { |
| # unknown |
| xfail('allclose'), |
| xfail('equal'), |
| xfail('linalg.eigvals'), |
| xfail('nn.functional.max_pool1d', device_type='cpu'), |
| # empty |
| skip('new_empty'), |
| skip('empty_like'), |
| skip('empty'), |
| # flaky |
| skip('linalg.lstsq', 'grad_oriented'), |
| skip('nn.functional.max_unpool1d', '', device_type='cpu'), |
| skip('nn.functional.max_unpool2d', '', device_type='cpu'), |
| skip('nn.functional.max_unpool3d', '', device_type='cpu'), |
| skip('linalg.lstsq'), # flaky, probably just a precision issue |
| |
| # data-dependent control flow |
| xfail('cov'), |
| xfail('istft'), |
| xfail('nanquantile'), |
| xfail('nn.functional.gaussian_nll_loss'), |
| xfail('quantile'), |
| xfail('tensor_split'), |
| xfail('corrcoef'), |
| |
| # Seems like it's creating a sparse tensor that isn't captured by tensor.is_sparse |
| xfail('sparse.sampled_addmm'), |
| |
| # ??? |
| xfail('nn.functional.ctc_loss'), |
| # Sparse tensors are not supported with faketensors for now |
| xfail('to_sparse'), |
| # segfaults |
| skip('block_diag'), |
| } |
| |
| fake_tensor_failures = { |
| # Needs complex-value support |
| xfail('polar'), |
| xfail('complex'), |
| xfail('linalg.eig'), |
| # FakeTensor fallback doesn't work |
| xfail('linalg.matrix_power'), |
| xfail('segment_reduce', 'lengths'), |
| xfail('multinomial'), |
| xfail('mvlgamma', 'mvlgamma_p_1'), |
| xfail('mvlgamma', 'mvlgamma_p_3'), |
| xfail('mvlgamma', 'mvlgamma_p_5'), |
| xfail('cholesky'), |
| xfail('cholesky_inverse'), |
| # ASAN failures due to divide by 0 |
| skip('nn.functional.nll_loss'), |
| } |
| |
| symbolic_tensor_failures = { |
| xfail('__getitem__', ''), # aten.size.default - couldn't find symbolic meta function/decomposition |
| xfail('__rmatmul__', ''), # aten.new_empty.default - couldn't find symbolic meta function/decomposition |
| xfail('__rpow__', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition |
| xfail('_masked.amax', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition |
| xfail('_masked.amin', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition |
| xfail('_masked.argmax', ''), # aten.argmax.default - couldn't find symbolic meta function/decomposition |
| xfail('_masked.argmin', ''), # aten.argmin.default - couldn't find symbolic meta function/decomposition |
| 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('angle', ''), # argument 'size' (position 1) must be tuple of ints, not torch.Size |
| 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.size.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', ''), # argument 'size' (position 1) must be tuple of ints, not tuple |
| 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('dot', ''), # aten.new_empty.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_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('linalg.norm', ''), # aten.linalg_vector_norm.default - couldn't find symbolic meta function/decomposition |
| xfail('linalg.norm', 'subgradients_at_zero'), # aten.linalg_vector_norm.default - couldn't find symbolic meta functio... |
| 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', ''), # aten.linalg_vector_norm.default - couldn't find symbolic meta function/decomposition |
| 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_no_dim'), # aten.new_empty.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_no_dim'), # aten.new_empty.default - couldn't find symbolic meta function/decomposition |
| 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('nanmedian', ''), # aten.nanmedian.default - couldn't find symbolic meta function/decomposition |
| xfail('nansum', ''), # aten.nansum.default - couldn't find symbolic meta function/decomposition |
| 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('new_full', ''), # aten.new_empty.default - couldn't find symbolic meta function/decomposition |
| xfail('new_ones', ''), # aten.new_empty.default - couldn't find symbolic meta function/decomposition |
| xfail('new_zeros', ''), # aten.new_empty.default - couldn't find symbolic meta function/decomposition |
| 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.logsigmoid', ''), # aten.new_empty.default - couldn't find symbolic meta function/decomposition |
| 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.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', ''), # aten._to_copy.default - couldn't find symbolic meta function/decomposition |
| 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('trace', ''), # argument 'size' (position 1) must be tuple of ints, not tuple |
| 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', ''), # Unexpected type <class 'torch.SymbolicIntNode'> when computing elementwise type promotion! |
| xfail('var_mean', ''), # aten.new_empty.default - couldn't find symbolic meta function/decomposition |
| 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('xlogy', ''), # aten.new_empty.default - couldn't find symbolic meta function/decomposition |
| 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() |
