# Owner(s): ["module: functorch"] # Copyright (c) Facebook, Inc. and its affiliates. # All rights reserved. # # This source code is licensed under the BSD-style license found in the # LICENSE file in the root directory of this source tree. from unittest.mock import patch from torch.testing._internal.common_utils import TestCase, run_tests, IS_ARM64 import torch import torch.nn as nn import torch.utils._pytree as pytree import unittest import warnings import itertools from functools import partial from torch.testing._internal.common_device_type import instantiate_device_type_tests from torch.testing._internal.common_methods_invocations import op_db, wrapper_set_seed from functorch import ( grad, vjp, vmap, jacrev, make_fx ) from functorch._src.aot_autograd import aot_module_simplified from functorch.compile import ( nnc_jit, compiled_function, compiled_module, min_cut_rematerialization_partition, aot_function, aot_module, nop, default_partition, default_decompositions, memory_efficient_fusion, get_aot_compilation_context ) from torch._decomp import decomposition_table from torch.testing._internal.common_device_type import ops from common_utils import ( decorate, xfail, skip, skipOps, ) 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) USE_NETWORKX = False try: import networkx # noqa: F401 USE_NETWORKX = True except ImportError: warnings.warn("Some tests use networkx but it was not installed", UserWarning) try: import sympy # noqa: F401 HAS_SYMPY = True except ImportError: HAS_SYMPY = False skipIfNoSympy = unittest.skipIf(not HAS_SYMPY, "no sympy") # NB: numpy is a testing dependency! class AOTTestCase(TestCase): def setUp(self): super().setUp() class TestPythonKey(AOTTestCase): def test_make_fx(self, device): def f(x): return torch.sin(x) inp = torch.randn(3) fx_f = make_fx(f)(inp) new_inp = torch.randn(3) self.assertEqual(fx_f(new_inp), f(new_inp)) def test_make_fx_grad(self, device): def f(x): return torch.sin(x).sum() inp = torch.randn(3) f = grad(f) fx_f = make_fx(f)(inp) new_inp = torch.randn(3) self.assertEqual(fx_f(new_inp), f(new_inp)) def test_scalar_device(self, device): def f(a, b): return a + b inps = [torch.randn(3, device=device), torch.tensor(5)] fx_f = make_fx(f)(*inps) self.assertEqual(fx_f(*inps), f(*inps)) def test_make_fx_vmap(self, device): def f(x): return torch.sin(x) inp = torch.randn(5, 3) f = vmap(f) fx_f = make_fx(f)(inp) new_inp = torch.randn(5, 3) self.assertEqual(fx_f(new_inp), f(new_inp)) def test_make_fx_jacrev(self, device): def f(x): return x.sin().sum() inp = torch.randn(3) f = jacrev(jacrev(f)) fx_f = make_fx(f)(inp) new_inp = torch.randn(3) self.assertEqual(fx_f(new_inp), f(new_inp)) def test_make_fx_vjp(self, device): def f(x): return torch.sin(x).sum() primals = torch.randn(3) _, vjp_fn = vjp(f, primals) cotangent = torch.randn(()) fx_f = make_fx(vjp_fn)(cotangent, True, True) new_cotangent = torch.randn(()) self.assertEqual(fx_f(new_cotangent, True, True), vjp_fn(new_cotangent)) def test_make_fx_functionalize(self, device): from functorch.experimental import functionalize def fn(a): a = a * 2 a.relu_() return a a = torch.randn(3, device=device) symbolic_gm = torch.fx.symbolic_trace(fn) includes_method_relu_ = any( str(n.target) == "relu_" for n in symbolic_gm.graph.nodes ) self.assertTrue(includes_method_relu_) # Also verifies fix for https://github.com/pytorch/pytorch/issues/84570 gm = make_fx(functionalize(symbolic_gm))(a) includes_aten_relu = any( n.target == torch.ops.aten.relu.default for n in gm.graph.nodes ) self.assertTrue(includes_aten_relu) def test_make_fx_no_decompose(self, device): # FIXME return self.skipTest("error: maximum recursion reached") def f(x): return torch.tanh(x).sum() fx_f = make_fx(grad(f))(torch.randn(5)) ops = set([i.target for i in fx_f.graph.nodes]) self.assertEqual(torch.ops.aten.tanh_backward in ops, True) fx_f = make_fx(grad(f), decomposition_table)(torch.randn(5)) ops = set([i.target for i in fx_f.graph.nodes]) self.assertEqual(torch.ops.aten.tanh_backward in ops, False) def test_nnc_jit(self, device): def f(x): return torch.sin(x) jit_f = nnc_jit(f) inp = torch.randn(3) self.assertEqual(jit_f(inp), f(inp)) def test_nnc_scalar(self, device): def f(x): return torch.sin(x) jit_f = nnc_jit(f) inp = torch.randn(()) self.assertEqual(jit_f(inp), f(inp)) def test_nnc_pytrees(self, device): def f(x): return [torch.sin(x[0])] jit_f = nnc_jit(f) inp = [torch.randn(3)] self.assertEqual(jit_f(inp), f(inp)) def test_external_calls(self, device): def f(a, b): return torch.mv(a, b) jit_f = nnc_jit(f) inp = [torch.randn(3, 3), torch.randn(3)] self.assertEqual(jit_f(*inp), f(*inp)) def test_nnc_passthrough(self, device): def f(x, y): return x + y, y inp = (torch.randn(3), torch.randn(3)) jit_f = nnc_jit(f) self.assertEqual(jit_f(*inp), f(*inp)) def f(x): x['a'] = x['a'] * 2 return x inp = ({'a': torch.randn(3), 'b': torch.randn(3)},) jit_f = nnc_jit(f) self.assertEqual(jit_f(*inp), f(*inp)) @unittest.skipIf(not USE_TORCHVISION, "test requires torchvision") def test_resnet18_backward_trace(self, device): mod = torchvision.models.resnet18() def f(x): out = mod(x) out.sum().backward() return [a.grad for a in mod.parameters()] inp = torch.randn(3, 3, 250, 250, requires_grad=True) grads = f(inp) mod.zero_grad() mod(inp).sum().backward() grads2 = [a.grad for a in mod.parameters()] self.assertEqual(grads, grads2) def _outs_and_grads(fn, inps): outs = fn(*inps) for out in pytree.tree_flatten(outs)[0]: if isinstance(out, torch.Tensor) and out.requires_grad: out.sum().backward(retain_graph=True) grads = [inp.grad for inp in pytree.tree_flatten(inps)[0]] for inp in pytree.tree_flatten(inps)[0]: inp.grad = None return outs, grads class TestAOTAutograd(AOTTestCase): def verify_aot_autograd(self, f, inp): if isinstance(f, nn.Module): compiled_f = aot_module(f, nop) else: compiled_f = aot_function(f, nop) ref_out, ref_grad = _outs_and_grads(f, inp) test_out, test_grad = _outs_and_grads(compiled_f, inp) self.assertEqual(ref_out, test_out) self.assertEqual(ref_grad, test_grad) def test_single_output(self): def f(a, b): return a + b inp = [torch.randn(3, 3, requires_grad=True), torch.randn(3, 3)] self.verify_aot_autograd(f, inp) def test_multi_output(self): def f(a, b): return a + b, a - b inp = [torch.randn(3, 3, requires_grad=True), torch.randn(3, 3)] self.verify_aot_autograd(f, inp) def test_multi_output_list(self): def f(a, b): return [a + b, a - b] inp = [torch.randn(3, 3, requires_grad=True), torch.randn(3, 3)] self.verify_aot_autograd(f, inp) def test_no_grad_input_output(self): def f(a, b): return a.cos(), b.cos(), a * b inp_thunks = [lambda: torch.randn(5, requires_grad=True), lambda: torch.randn(5, requires_grad=False)] for inps in itertools.product(inp_thunks, repeat=2): inps = [i() for i in inps] self.verify_aot_autograd(f, inps) def test_inner_grad(self): def foo(x): y = torch.exp(x) z = torch.autograd.grad(y, x) return z inps = [torch.randn((), requires_grad=True)] self.verify_aot_autograd(foo, inps) def test_grad_context(self): def foo(x): return x * 2 inps = [torch.randn((), requires_grad=True)] graph_size = None def get_graph_size(fx_g, _): nonlocal graph_size graph_size = len(fx_g.graph.nodes) return fx_g f = aot_function(foo, nop, get_graph_size) with torch.set_grad_enabled(False): f(*inps) self.assertIsNone(graph_size) f = aot_function(foo, nop, get_graph_size) with torch.set_grad_enabled(True): out = f(*inps) self.assertIsNone(graph_size) out.sum().backward() self.assertTrue(graph_size > 2) def test_output_dict(self): def f(x): return {'a': x, 'b': x} inp = [torch.randn(3, 3, requires_grad=True)] self.verify_aot_autograd(f, inp) def f(x, y): return {'a': x, 'b': y + x} inp = [torch.randn(3, requires_grad=True), torch.randn(3)] self.verify_aot_autograd(f, inp) def f(x): new_d = {} for k in x: new_d[k] = x[k] * 2 return new_d inp = [{'a': torch.randn(3, requires_grad=True), 'b': torch.randn(3, requires_grad=True)}] self.verify_aot_autograd(f, inp) def test_module(self): mod = nn.Sequential(nn.Linear(32, 32), nn.ReLU()) compiled_mod = compiled_module(mod, nop, nop) inp = torch.randn(32, 32) ref_out = mod(inp) ref_out.sum().backward() ref_grads = sorted([(name, p.grad) for name, p in mod.named_parameters()]) out = compiled_mod(inp) out.sum().backward() grads = sorted([(name, p.grad) for name, p in mod.named_parameters()]) self.assertEqual((out, grads), (ref_out, ref_grads)) def test_batchnorm(self): mod = compiled_module(nn.BatchNorm2d(4), nop, nop) x = torch.ones(1, 4, 2, 2) mod(x).sum().backward() def test_list_codegen(self): def list_nop(f, _): def g(inps): return f(*inps) g._boxed_call = True return g def f(a, b, c): return a.sin() * b.cos() * c.sin() f = aot_function(f, list_nop) inp = [torch.randn(5, requires_grad=True) for _ in range(3)] f(*inp).sum().backward() def test_compilation_context(self): def f(x): return x.sin().sin() count = [] def compiler(fx_g, _): context = get_aot_compilation_context() count.append((context[0], len(fx_g.graph.nodes))) return fx_g f = aot_function(f, compiler) out = f(torch.randn(5, requires_grad=True)) f = aot_function(f, compiler) f(torch.randn(5)) out.sum().backward() self.assertEqual(count, [(['forward'], 4), (['inference'], 4), (['backward'], 8)]) def test_dupe_arg(self): def f(x, y): return x + y x = torch.randn(3, 3, requires_grad=True) self.verify_aot_autograd(f, [x, x]) def test_resize_input(self): def f(x, y): y.resize_(4) y.zero_() self.assertEqual(x.shape, (4,)) return y # NB: don't use verify_aot_autograd as the inputs get # mutated and I don't trust verify to do it right compiled_f = aot_function(f, nop) ref_x = torch.randn(0) ref_out = f(ref_x, ref_x) test_x = torch.randn(0) test_out = compiled_f(test_x, test_x) self.assertEqual(ref_out, test_out) @unittest.skipIf(not torch.cuda.is_available(), "CUDA is unavailable") def test_batch_norm_amp(self): device = "cuda" input_dtype = torch.float16 param_dtype = torch.float32 weight, bias = [torch.ones(64, device=device, dtype=param_dtype, requires_grad=True) for _ in range(2)] running_mean, running_var = [torch.ones(64, device=device, dtype=param_dtype) for _ in range(2)] def bn(x): return torch.ops.aten.cudnn_batch_norm( x, weight, bias, running_mean, running_var, False, 0.1, 1e-05, ) inp = torch.ones(torch.Size([16, 64, 112, 112]), dtype=input_dtype, device=device) ref = bn(inp) cudnn_batch_norm_decomp = torch._decomp.get_decompositions({torch.ops.aten.cudnn_batch_norm}) aot_fn = make_fx(bn, decomposition_table=cudnn_batch_norm_decomp)(inp) res = aot_fn(inp) for a, b in zip(ref, res): assert torch.allclose(a, b) @unittest.expectedFailure # RuntimeError: Cannot call sizes() on tensor with symbolic sizes/strides @patch("functorch.compile.config.use_dynamic_shapes", True) @patch("functorch.compile.config.use_fake_tensor", True) def test_output_op_depending_on_symint(self): """ It won't be obvious from reading this test what it's testing for. We should probably make it into a more focused unit test. An issue with the following program was the expand op would end up depending on a symint whose proxy was incorrectly associated with one of the grad tensors rather than input tensors. It broke partitioner logic and the net result was aot_function failed to produce a function and threw an exception instead. """ inp = torch.randn(5, requires_grad=True) def f(x): return x.expand(x.shape) # TODO(whc) make this work (test setup is wrong somehow) # joint_forward_backward = create_joint_forward_backward(f) # out = f(inp) # joint_inputs = ([inp], [out.detach().contiguous()]) # fx_g = make_fx(joint_forward_backward)(*joint_inputs) # TODO: assert outputs of fwd graph trace to correct symint # e2e test that fails without symint clone fix af = aot_function(f, nop, partition_fn=partial(min_cut_rematerialization_partition, compiler="inductor")) out = af(inp) self.assertEqual(out, f(inp)) def extract_graph(fx_g, _, graph_cell): graph_cell[0] = fx_g return fx_g def get_ins_outs(fx_g): ins = [] outs = [] for n in fx_g.graph.nodes: if n.op == 'placeholder': ins.append(n) elif n.op == 'output': outs = tuple(n.args[0]) return ins, outs def get_num_ins_outs(fx_g): return tuple(len(i) for i in get_ins_outs(fx_g)) def get_fw_bw_graph(f, inps, partitioner=min_cut_rematerialization_partition): fw_graph_cell = [None] bw_graph_cell = [None] aot_function(f, fw_compiler=partial(extract_graph, graph_cell=fw_graph_cell), bw_compiler=partial(extract_graph, graph_cell=bw_graph_cell), partition_fn=partitioner, decompositions=default_decompositions)(*inps).sum().backward() return (fw_graph_cell[0], bw_graph_cell[0]) class TestPartitioning(AOTTestCase): @unittest.skipIf(not USE_NETWORKX, "networkx not available") def test_recompute_partitioning(self): def fn(a, b): return torch.sin(torch.sin(a)) + b # Reference calculation ref_a = torch.rand(10, 10, requires_grad=True) ref_b = torch.rand(10, 10, requires_grad=True) ref = fn(ref_a, ref_b) ref.sum().backward() # Compiled function calculation res_a = ref_a.clone().detach().requires_grad_(True) res_b = ref_b.clone().detach().requires_grad_(True) def compile_fn(x, _): return x compiled_fn = compiled_function(fn, compile_fn, compile_fn, min_cut_rematerialization_partition) res = compiled_fn(res_a, res_b) res.sum().backward() assert torch.allclose(ref, res, atol=1e-3, rtol=1e-3) assert torch.allclose(ref_a.grad, res_a.grad, atol=1e-3, rtol=1e-3) assert torch.allclose(ref_b.grad, res_b.grad, atol=1e-3, rtol=1e-3) def test_meta_tensor_inplace_op(self): # Following module results in inplace ops while tracing. The test checks # that the meta tensor information is stored for inplace ops. class MockModule(torch.nn.Module): def __init__(self): super().__init__() self.weight = torch.nn.Parameter(torch.randn(3072, 768, requires_grad=True)) self.bias = torch.nn.Parameter(torch.randn(3072, requires_grad=True)) def forward(self, add_4): linear_4 = torch.nn.functional.linear(add_4, self.weight, bias=self.bias) gelu = torch.nn.functional.gelu(linear_4) return gelu def check_meta_tensor(fx_g, _): for node in fx_g.graph.nodes: if node.op != 'output': assert 'tensor_meta' in node.meta return fx_g inp0 = torch.randn(16, 128, 768, requires_grad=True) inputs = [inp0, ] mod = MockModule().to(device="cpu") aot_mod = aot_module(mod, fw_compiler=check_meta_tensor) aot_mod(*inputs) def test_default_partitioner_getitem(self): mod = nn.LayerNorm([10]) def f(x, mod_weight, mod_bias): return torch.nn.functional.layer_norm(x, [10], mod_weight, mod_bias, eps=1e-6) fw_graph, bw_graph = get_fw_bw_graph(f, [torch.randn(3, 10, requires_grad=True), mod.weight, mod.bias], partitioner=default_partition) self.assertEqual(get_num_ins_outs(fw_graph), (3, 6)) self.assertEqual(get_num_ins_outs(bw_graph), (6, 3)) @unittest.skipIf(not USE_NETWORKX, "networkx not available") def test_min_cut_partitioner(self): def f(x): return x.cos().cos().cos() fw_graph, bw_graph = get_fw_bw_graph(f, [torch.randn(3, requires_grad=True)]) self.assertEqual(get_num_ins_outs(fw_graph), (1, 2)) self.assertEqual(get_num_ins_outs(bw_graph), (2, 1)) def f(a, b, c, d): x = a + b + c + d return x.cos().cos() fw_graph, bw_graph = get_fw_bw_graph(f, [torch.randn(3, requires_grad=True) for _ in range(4)]) self.assertEqual(get_num_ins_outs(fw_graph), (4, 2)) self.assertEqual(get_num_ins_outs(bw_graph), (2, 4)) def f(x): return torch.mm(x, torch.ones(x.shape)).tanh().tanh() fw_graph, bw_graph = get_fw_bw_graph(f, [torch.randn(5, 5, requires_grad=True)]) self.assertEqual(get_num_ins_outs(fw_graph), (1, 3)) ins, outs = get_ins_outs(fw_graph) self.assertEqual(outs[1].target, torch.ops.aten.mm.default) def test_contiguous(self): # The test simulates the condition where transpose followed by view # happens in the backward pass. # https://discuss.pytorch.org/t/error-on-transpose-and-view/434 def f(x): return x.view(2, 3).t() inp = torch.randn(6, requires_grad=True) out = aot_function(f, nop)(inp) torch.autograd.grad(out, inp, torch.randn(3, 2)) def test_preserve_random(self): def fn(x): return torch.nn.functional.dropout(x, 0.5) + x x = torch.randn(4) torch.manual_seed(0) ref = fn(x) torch.manual_seed(0) aot_fn = aot_function(fn, nop) res = aot_fn(x) assert torch.allclose(ref, res) @unittest.skipIf(not torch.cuda.is_available(), "CUDA is unavailable") @unittest.skipIf(not USE_TORCHVISION, "test requires torchvision") def test_autocast(self): mod = torchvision.models.resnet18().cuda() mod.train() x = torch.randn(16, 3, 32, 32, device="cuda") aot_mod = memory_efficient_fusion(mod) # Ensure that AOT Autograd works with AMP with torch.cuda.amp.autocast(True): res = aot_mod(x) res.sum().backward() class TestAOTModuleSimplified(AOTTestCase): def test_aot_module_simplified(self): class MockModule(torch.nn.Module): def __init__(self): super().__init__() self.linear = torch.nn.Linear(20, 30) def forward(self, x, y): return (self.linear(x) + y, ) mod = MockModule() mod.zero_grad() x = torch.randn(128, 20, requires_grad=True) y = torch.randn(128, 30, requires_grad=True) inputs = [x, y] cloned_inputs = [x.detach().clone().requires_grad_(True) for x in inputs] ref = mod(*inputs) ref[0].sum().backward() aot_mod = aot_module_simplified(mod, nop) aot_mod.zero_grad() res = aot_mod(*cloned_inputs) res[0].sum().backward() assert torch.allclose(ref[0], res[0]) assert torch.allclose(inputs[0].grad, cloned_inputs[0].grad) assert torch.allclose(inputs[1].grad, cloned_inputs[1].grad) def test_aot_module_simplified_preserves_stack_trace(self): class MockModule(torch.nn.Module): def __init__(self): super().__init__() self.linear = torch.nn.Linear(20, 30) def forward(self, x, y): z = self.linear(x) z = z + y z = z.relu() return (z, ) tracer = torch.fx.Tracer() tracer.record_stack_traces = True graph = tracer.trace(MockModule()) mod = torch.fx.GraphModule(tracer.root, graph) for node in mod.graph.nodes: if node.op == 'output': continue self.assertTrue(node.stack_trace is not None) assert 'test_aotdispatch.py' in node.stack_trace def assert_compiler(gm: torch.fx.GraphModule, _): for node in gm.graph.nodes: if node.op == 'output' or node.op == 'placeholder': continue self.assertTrue(node.stack_trace is not None) assert 'test_aotdispatch.py' in node.stack_trace return gm.forward # return a python callable aot_mod = aot_module_simplified(mod, fw_compiler=assert_compiler, bw_compiler=assert_compiler) x = torch.randn(128, 20, requires_grad=True) y = torch.randn(128, 30, requires_grad=True) inputs = [x, y] res = aot_mod(*inputs) res[0].sum().backward() # entries in here don't work and need to be fixed. # Each one of these is a bug (or needs to be investigated) aot_autograd_failures = { # data-dependent control flow xfail('cov'), xfail('istft'), xfail('nn.functional.gaussian_nll_loss'), xfail('tensor_split'), xfail('corrcoef'), xfail('quantile'), xfail('nanquantile'), xfail('narrow'), xfail('index_reduce'), xfail('istft'), xfail('linalg.eig'), xfail('scatter_reduce', 'prod'), # non-deterministic skip('as_strided_scatter'), # Too annoying to generate random inputs xfail('cholesky'), xfail('linalg.cholesky'), # Misc xfail('to_sparse'), xfail('corrcoef'), xfail('cov'), xfail('chalf'), # RuntimeError: "sum_cpu" not implemented for 'ComplexHalf' xfail('sparse.sampled_addmm'), skip('nn.functional.binary_cross_entropy_with_logits'), # seems to fail sometimes? skip('nn.functional.margin_ranking_loss'), # seems flaky skip('linalg.lu_solve'), # flaky decorate('matmul', decorator=unittest.skipIf(IS_ARM64, 'flaky')), decorate('__rmatmul__', decorator=unittest.skipIf(IS_ARM64, 'flaky')), } symbolic_aot_autograd_failures = { xfail('__getitem__', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('__rmatmul__', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('addbmm', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('addcdiv', ''), # aten.fill_.Scalar - couldn't find symbolic meta function/decomposition xfail('addmv', ''), # aten.addmv.default - couldn't find symbolic meta function/decomposition xfail('addr', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('amax', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('amin', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('as_strided', ''), # Tensor-likes are not close! 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('block_diag', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('broadcast_tensors', ''), # 'int' and 'torch._C.SymIntNode' xfail('cartesian_prod', ''), # Cannot call numel() on tensor with symbolic sizes/strides xfail('cat', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('cdist', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('cholesky_inverse', ''), # could not find kernel xfail('cholesky_solve', ''), # could not find kernel xfail('chunk', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('column_stack', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('combinations', ''), # aten.masked_select.default xfail('complex', ''), # aten.view_as_real.default - couldn't find symbolic meta function/decomposition xfail('constant_pad_nd', ''), # aten.fill.Scalar - couldn't find symbolic meta function/decomposition 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', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('deg2rad', ''), # aten.deg2rad.default - couldn't find symbolic meta function/decomposition xfail('diag', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('diagonal', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('diagonal_scatter', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('diff', ''), # aten.zeros_like.default - couldn't find symbolic meta function/decomposition xfail('digamma', ''), # aten.polygamma.default - couldn't find symbolic meta function/decomposition xfail('dist', ''), # aten.dist.default - couldn't find symbolic meta function/decomposition xfail('dsplit', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('dstack', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('einsum', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('expand_as', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.fft2', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.fft', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.fftn', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.fftshift', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.hfft2', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.hfft', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.hfftn', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.ifft2', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.ifft', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.ifftn', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.ifftshift', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.ihfft2', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.ihfft', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.ihfftn', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.irfft2', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.irfft', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.irfftn', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.rfft2', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.rfft', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fft.rfftn', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('flatten', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('fmax', ''), # aten.logical_or_.default - couldn't find symbolic meta function/decomposition xfail('fmin', ''), # aten.logical_or_.default - couldn't find symbolic meta function/decomposition xfail('frexp', ''), # aten.frexp.Tensor - couldn't find symbolic meta function/decomposition xfail('gather', ''), # aten.gather.default - couldn't find symbolic meta function/decomposition xfail('gradient', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('hsplit', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('hstack', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('i0', ''), # aten.i0.default - couldn't find symbolic meta function/decomposition xfail('index_add', ''), # Overloaded torch operator invoked from Python failed to many any schema: xfail('index_copy', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('index_fill', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('index_put', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('index_select', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('inner', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('kron', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('kthvalue', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('lerp', ''), # aten.lerp.Scalar - couldn't find symbolic meta function/decomposition xfail('linalg.cholesky_ex', ''), # aten.linalg_cholesky_ex.default - couldn't find symbolic meta functio... xfail('linalg.cond', ''), # Cannot call numel() on tensor with symbolic sizes/strides 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.det', 'singular'), # aten._linalg_det.default - couldn't find symbolic meta function/deco... xfail('linalg.eigh', ''), # aten._linalg_eigh.default - couldn't find symbolic meta function/decomposition xfail('linalg.eigvals', ''), # aten.linalg_eig.default - couldn't find symbolic meta function/decomposition xfail('linalg.eigvalsh', ''), # aten._linalg_eigh.default - couldn't find symbolic meta function/decompo... xfail('linalg.householder_product', ''), # aten.linalg_householder_product.default - couldn't find symbo... 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/decompos... xfail('linalg.lstsq', ''), # aten.linalg_lstsq.default - couldn't find symbolic meta function/decomposition xfail('linalg.lstsq', 'grad_oriented'), # aten.linalg_lstsq.default - couldn't find symbolic meta funct... 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... xfail('linalg.lu_factor_ex', ''), # aten.linalg_lu_factor_ex.default - couldn't find symbolic meta funct... xfail('linalg.lu_solve', ''), # aten.linalg_lu_solve.default - couldn't find symbolic meta function/deco... xfail('linalg.matrix_norm', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('linalg.matrix_power', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('linalg.multi_dot', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('linalg.norm', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('linalg.norm', 'subgradients_at_zero'), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('linalg.pinv', ''), # aten.linalg_pinv.atol_rtol_tensor - couldn't find symbolic meta function/dec... xfail('linalg.pinv', 'hermitian'), # aten.linalg_pinv.atol_rtol_tensor - couldn't find symbolic meta fu... 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/decom... xfail('linalg.solve', ''), # aten._linalg_solve_ex.default - couldn't find symbolic meta function/decomp... xfail('linalg.solve_ex', ''), # aten._linalg_solve_ex.default - couldn't find symbolic meta function/dec... xfail('linalg.solve_triangular', ''), # aten.linalg_solve_triangular.default - couldn't find symbolic me... 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/decomposi... xfail('linalg.tensorinv', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('linalg.tensorsolve', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('linalg.vander', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('linalg.vector_norm', ''), # Cannot call sizes() on tensor with symbolic sizes/strides 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', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('logsumexp', ''), # Cannot call sizes() on tensor with symbolic sizes/strides 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.amax', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('masked.amin', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('masked.cumprod', ''), # aten.cumprod.default - couldn't find symbolic meta function/decomposition xfail('masked.cumsum', ''), # aten.cumsum.default - couldn't find symbolic meta function/decomposition xfail('masked_fill', ''), # could not find kernel xfail('masked.log_softmax', ''), # argument 'size' (position 2) must be tuple of ints, not ... xfail('masked.logaddexp', ''), # aten.logaddexp.default - couldn't find symbolic meta function/decomposi... xfail('masked.logsumexp', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('masked.mean', ''), # ones() received an invalid combination of arguments - got (torch.Size, device=t... xfail('masked.median', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('masked.norm', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('masked.prod', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('masked_scatter', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('masked_select', ''), # aten.masked_select.default - couldn't find symbolic meta function/decompos... xfail('masked.softmax', ''), # argument 'size' (position 2) must be tuple of ints, not torc... xfail('masked.softmin', ''), # argument 'size' (position 2) must be tuple of ints, not torc... xfail('masked.std', ''), # ones() received an invalid combination of arguments - got (torch.Size, device=to... xfail('masked.sum', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('masked.var', ''), # ones() received an invalid combination of arguments - got (torch.Size, device=to... xfail('matmul', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('matrix_exp', ''), # aten.linalg_matrix_exp.default - couldn't find symbolic meta function/decompo... xfail('max', 'reduction_no_dim'), # aten.logical_or_.default - couldn't find symbolic meta function/dec... xfail('max', 'reduction_with_dim'), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('mean', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('median', ''), # could not find kernel xfail('meshgrid', 'list_of_tensors'), # Cannot call numel() on tensor with symbolic sizes/strides xfail('meshgrid', 'variadic_tensors'), # Cannot call numel() on tensor with symbolic sizes/strides xfail('min', 'reduction_no_dim'), # aten.logical_or_.default - couldn't find symbolic meta function/dec... xfail('min', 'reduction_with_dim'), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('mode', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('msort', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('mv', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('mvlgamma', 'mvlgamma_p_1'), # aten.digamma_.default - couldn't find symbolic meta function/decom... xfail('mvlgamma', 'mvlgamma_p_3'), # aten.digamma_.default - couldn't find symbolic meta function/decom... xfail('mvlgamma', 'mvlgamma_p_5'), # aten.digamma_.default - couldn't find symbolic meta function/decom... xfail('nanmedian', ''), # aten.logical_or_.default - couldn't find symbolic meta function/decomposition xfail('native_layer_norm', ''), # could not find kernel xfail('nn.functional._scaled_dot_product_attention', ''), # Cannot call sizes() on tensor with symbolic ... xfail('nn.functional.adaptive_avg_pool1d', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.adaptive_avg_pool2d', ''), # aten._adaptive_avg_pool2d_backward.default - couldn't ... xfail('nn.functional.adaptive_avg_pool3d', ''), # aten._adaptive_avg_pool3d_backward.default - couldn't ... xfail('nn.functional.adaptive_max_pool1d', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.adaptive_max_pool2d', ''), # aten.adaptive_max_pool2d.default - couldn't find symbo... xfail('nn.functional.adaptive_max_pool3d', ''), # argument 'output_size' (position 2... xfail('nn.functional.avg_pool1d', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.avg_pool2d', ''), # aten.avg_pool2d.default - couldn't find symbolic meta function/... xfail('nn.functional.avg_pool3d', ''), # aten.avg_pool3d.default - couldn't find symbolic meta function/... xfail('nn.functional.bilinear', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.binary_cross_entropy', ''), # aten.fill_.Scalar - couldn't find symbolic meta funct... xfail('nn.functional.conv1d', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.conv2d', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.conv_transpose1d', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.conv_transpose2d', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.conv_transpose3d', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.cosine_embedding_loss', ''), # Cannot call sizes() on tensor with symbolic sizes/st... xfail('nn.functional.cosine_similarity', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.cross_entropy', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.ctc_loss', ''), # aten._ctc_loss.Tensor - couldn't find symbolic meta function/deco... xfail('nn.functional.dropout2d', ''), # Cannot call numel() on tensor with symbolic sizes/strides xfail('nn.functional.dropout3d', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.dropout', ''), # Cannot call numel() on tensor with symbolic sizes/strides xfail('nn.functional.embedding_bag', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.feature_alpha_dropout', 'with_train'), # Cannot call numel() on tensor with symbol... xfail('nn.functional.fractional_max_pool2d', ''), # rand() received an invalid combination of arguments - g... xfail('nn.functional.fractional_max_pool3d', ''), # rand() received an invalid combination of arguments - g... xfail('nn.functional.glu', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.grid_sample', ''), # prims::arange() Expected a value of type 'number' for argument... xfail('nn.functional.group_norm', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.hinge_embedding_loss', ''), # aten.zeros_like.default - couldn't find symbolic meta... xfail('nn.functional.huber_loss', ''), # Unable to cast Python instance to C++ type (#define PYBIND11_DE... xfail('nn.functional.instance_norm', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.interpolate', 'area'), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.interpolate', 'bicubic'), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.interpolate', 'bilinear'), # Cannot call sizes() on tensor with symbolic sizes/str... xfail('nn.functional.interpolate', 'linear'), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.interpolate', 'nearest'), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.interpolate', 'trilinear'), # Cannot call sizes() on tensor with symbolic sizes/st... xfail('nn.functional.kl_div', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.l1_loss', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.layer_norm', ''), # could not find kernel xfail('nn.functional.linear', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.local_response_norm', ''), # aten.fill.Scalar - couldn't find symbolic meta functio... xfail('nn.functional.max_pool1d', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.max_pool2d', ''), # aten.max_pool2d_with_indices_backward.default - couldn't find s... xfail('nn.functional.max_pool3d', ''), # aten.max_pool3d_with_indices.default - couldn't find symbolic m... xfail('nn.functional.max_unpool1d', ''), # aten.max_unpool2d.default - couldn't find symbolic meta funct... xfail('nn.functional.max_unpool1d', 'grad'), # aten.max_unpool2d.default - couldn't find symbolic meta ... xfail('nn.functional.max_unpool2d', ''), # aten.max_unpool2d.default - couldn't find symbolic meta funct... xfail('nn.functional.max_unpool2d', 'grad'), # aten.max_unpool2d.default - couldn't find symbolic meta ... xfail('nn.functional.max_unpool3d', ''), # aten.max_unpool3d.default - couldn't find symbolic meta funct... xfail('nn.functional.max_unpool3d', 'grad'), # aten.max_unpool3d.default - couldn't find symbolic meta ... xfail('nn.functional.mse_loss', ''), # Unable to cast Python instance to C++ type (#define PYBIND11_DETA... xfail('nn.functional.multi_margin_loss', ''), # could not find kernel xfail('nn.functional.multilabel_margin_loss', ''), # could not find kernel xfail('nn.functional.multilabel_soft_margin_loss', ''), # Cannot call sizes() on tensor with symbolic si... xfail('nn.functional.nll_loss', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.normalize', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.pad', 'circular'), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.pad', 'constant'), # aten.fill.Scalar - couldn't find symbolic meta function/decom... xfail('nn.functional.pad', 'reflect'), # aten.reflection_pad1d.default - couldn't find symbolic meta fu... xfail('nn.functional.pad', 'replicate'), # aten.replication_pad1d.default - couldn't find symbolic meta... xfail('nn.functional.pairwise_distance', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.pdist', ''), # could not find kernel xfail('nn.functional.pixel_shuffle', ''), # aten.pixel_shuffle.default - couldn't find symbolic meta fun... xfail('nn.functional.pixel_unshuffle', ''), # aten.pixel_unshuffle.default - couldn't find symbolic meta... xfail('nn.functional.poisson_nll_loss', ''), # aten.add_.Tensor - couldn't find symbolic meta function/d... xfail('nn.functional.prelu', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.rrelu', ''), # aten.rrelu_with_noise.default - couldn't find symbolic meta function... xfail('nn.functional.smooth_l1_loss', ''), # could not find kernel xfail('nn.functional.triplet_margin_loss', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.triplet_margin_with_distance_loss', ''), # Cannot call sizes() on tensor with symbo... xfail('nn.functional.unfold', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.upsample_bilinear', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('nn.functional.upsample_nearest', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('norm', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('norm', 'nuc'), # aten._linalg_svd.default - couldn't find symbolic meta function/decomposition xfail('normal', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('normal', 'number_mean'), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('outer', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('pca_lowrank', ''), # could not find kernel xfail('pinverse', ''), # aten.linalg_pinv.atol_rtol_tensor - couldn't find symbolic meta function/decomp... xfail('polar', ''), # could not find kernel xfail('polygamma', 'polygamma_n_0'), # aten.polygamma.default - couldn't find symbolic meta function/de... xfail('polygamma', 'polygamma_n_1'), # aten.polygamma.default - couldn't find symbolic meta function/de... xfail('polygamma', 'polygamma_n_2'), # aten.polygamma.default - couldn't find symbolic meta function/de... xfail('polygamma', 'polygamma_n_3'), # aten.polygamma.default - couldn't find symbolic meta function/de... xfail('polygamma', 'polygamma_n_4'), # aten.polygamma.default - couldn't find symbolic meta function/de... xfail('prod', ''), # Cannot call numel() on tensor with symbolic sizes/strides xfail('put', ''), # Cannot call sizes() on tensor with symbolic sizes/strides 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('renorm', ''), # aten.renorm.default - couldn't find symbolic meta function/decomposition xfail('repeat_interleave', ''), # aten.repeat_interleave.Te... xfail('reshape_as', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('reshape', ''), # Cannot call numel() on tensor with symbolic sizes/strides xfail('roll', ''), # narrow() received an invalid combination of arguments - got (FakeTensor, int, torch._C... 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/decompos... 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/decom... xfail('scatter_reduce', 'amin'), # aten.scatter_reduce.two - couldn't find symbolic meta function/decom... xfail('scatter_reduce', 'mean'), # aten.scatter_reduce.two - couldn't find symbolic meta function/decom... xfail('scatter_reduce', 'sum'), # aten.scatter_reduce.two - couldn't find symbolic meta function/decomp... xfail('segment_reduce', 'lengths'), # aten.segment_reduce.default - couldn't find symbolic meta functio... xfail('segment_reduce', 'offsets'), # aten.segment_reduce.default - couldn't find symbolic meta functio... xfail('select', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('select_scatter', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('sgn', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('slice', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('slice_scatter', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('sort', ''), # Cannot call sizes() on tensor with symbolic sizes/strides 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/decompos... xfail('special.i1', ''), # aten.i0.default - couldn't find symbolic meta function/decomposition xfail('special.log_ndtr', ''), # aten.special_log_ndtr.default - couldn't find symbolic meta function/de... xfail('special.ndtri', ''), # aten.special_ndtri.default - couldn't find symbolic meta function/decompos... xfail('special.polygamma', 'special_polygamma_n_0'), # aten.polygamma.default - couldn't find symbolic ... xfail('special.xlog1py', ''), # aten.special_xlog1py.default - couldn't find symbolic meta function/deco... xfail('split', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('split', 'list_args'), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('split_with_sizes', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('squeeze', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('stack', ''), # aten.select.int - couldn't find symbolic meta function/decomposition xfail('std', ''), # Cannot call numel() on tensor with symbolic sizes/strides xfail('std_mean', ''), # Cannot call numel() on tensor with symbolic sizes/strides xfail('stft', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('sum_to_size', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('svd', ''), # aten._linalg_svd.default - couldn't find symbolic meta function/decomposition xfail('svd_lowrank', ''), # could not find kernel xfail('symeig', ''), # aten.symeig.default - couldn't find symbolic meta function/decomposition xfail('take_along_dim', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('take', ''), # aten.take.default - couldn't find symbolic meta function/decomposition xfail('tensordot', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('topk', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('trace', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('trapezoid', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('trapz', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('triangular_solve', ''), # aten.triangular_solve.default - couldn't find symbolic meta function/de... xfail('unbind', ''), # tensor_split() received an invalid combination of arguments - got (FakeTensor, torch... xfail('unflatten', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('unfold', ''), # could not find kernel xfail('var', ''), # Cannot call numel() on tensor with symbolic sizes/strides xfail('var_mean', ''), # Cannot call numel() on tensor with symbolic sizes/strides xfail('view_as_complex', ''), # aten.view_as_complex.default - couldn't find symbolic meta function/deco... xfail('view_as', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('vsplit', ''), # Cannot call sizes() on tensor with symbolic sizes/strides xfail('vstack', ''), # Cannot call sizes() on tensor with symbolic sizes/strides } def _test_aot_autograd_helper(self, device, dtype, op): if not op.supports_autograd: self.skipTest("Op does not support autograd") sample_inputs_itr = op.sample_inputs(device, dtype, requires_grad=True) for sample_input in sample_inputs_itr: t_args = [sample_input.input] + list(sample_input.args) t_kwargs = sample_input.kwargs flat_args, args_spec = pytree.tree_flatten((t_args, t_kwargs)) sentinel_val = -42 is_tensor_spec = [sentinel_val if isinstance(arg, torch.Tensor) else arg for arg in flat_args] args = [arg for arg in flat_args if isinstance(arg, torch.Tensor)] def f(args): cur_flat_args = list(is_tensor_spec) args = iter(args) for idx, v in enumerate(cur_flat_args): if v == sentinel_val: cur_flat_args[idx] = next(args) c_args, c_kwargs = pytree.tree_unflatten(cur_flat_args, args_spec) return op.op(*c_args, **c_kwargs) def call_forwards_backwards(f): out = wrapper_set_seed(f, args) if isinstance(out, tuple): sm = 0 for i in out: sm += i.sum() sm.backward() else: out.sum().backward() def reset_grads(): def f(x): x.grad = None pytree.tree_map(f, args) def get_grads(args): return pytree.tree_map(lambda x: x.grad, args) compiled_f = compiled_function(f, nop, nop) reset_grads() call_forwards_backwards(compiled_f) compiled_grad = get_grads(args) reset_grads() call_forwards_backwards(f) orig_grad = get_grads(args) self.assertEqual(orig_grad, compiled_grad) def create_new_arg(x): if isinstance(x, torch.Tensor) and x.dtype == torch.float32: return x.detach().uniform_(0, 1).requires_grad_(x.requires_grad) return x args = pytree.tree_map(create_new_arg, args) reset_grads() call_forwards_backwards(compiled_f) compiled_grad = get_grads(args) reset_grads() call_forwards_backwards(f) orig_grad = get_grads(args) self.assertEqual(orig_grad, compiled_grad) class TestEagerFusionOpInfo(AOTTestCase): @ops(op_db, allowed_dtypes=(torch.float,)) @skipOps('TestEagerFusionOpInfo', 'test_aot_autograd_exhaustive', aot_autograd_failures) def test_aot_autograd_exhaustive(self, device, dtype, op): _test_aot_autograd_helper(self, device, dtype, op) @ops(op_db, allowed_dtypes=(torch.float,)) @skipIfNoSympy @patch("functorch.compile.config.use_dynamic_shapes", True) @patch("functorch.compile.config.use_fake_tensor", True) @patch("functorch.compile.config.use_functionalize", False) @skipOps('TestEagerFusionOpInfo', 'test_aot_autograd_symbolic_exhaustive', aot_autograd_failures | symbolic_aot_autograd_failures) def test_aot_autograd_symbolic_exhaustive(self, device, dtype, op): _test_aot_autograd_helper(self, device, dtype, op) only_for = ("cpu") instantiate_device_type_tests( TestPythonKey, globals(), only_for=only_for, ) instantiate_device_type_tests(TestEagerFusionOpInfo, globals(), only_for=only_for) if __name__ == '__main__': run_tests()