| # Owner(s): ["module: functorch"] |
| import typing |
| import unittest |
| |
| from torch.testing._internal.common_utils import ( |
| TestCase, |
| run_tests, |
| instantiate_parametrized_tests, |
| parametrize, |
| subtest |
| ) |
| |
| from torch._C import ( |
| _dispatch_get_registrations_for_dispatch_key as get_registrations_for_dispatch_key, |
| ) |
| |
| xfail_functorch_batched = { |
| "aten::flatten.using_ints", |
| "aten::imag", |
| "aten::is_nonzero", |
| "aten::isfinite", |
| "aten::isreal", |
| "aten::item", |
| "aten::linalg_pinv", |
| "aten::linalg_pinv.atol_rtol_float", |
| "aten::linalg_slogdet", |
| "aten::linalg_lu_factor", |
| "aten::linear", |
| "aten::log_sigmoid", |
| "aten::log_softmax.int", |
| "aten::logdet", |
| "aten::masked_select_backward", |
| "aten::movedim.intlist", |
| "aten::one_hot", |
| "aten::real", |
| "aten::silu_backward", |
| "aten::special_xlogy", |
| "aten::special_xlogy.other_scalar", |
| "aten::special_xlogy.self_scalar", |
| "aten::tensor_split.indices", |
| "aten::tensor_split.sections", |
| "aten::to.device", |
| "aten::to.dtype", |
| "aten::to.dtype_layout", |
| "aten::to.other", |
| "aten::upsample_bicubic2d.vec", |
| "aten::upsample_bilinear2d.vec", |
| "aten::upsample_linear1d.vec", |
| "aten::upsample_nearest1d.vec", |
| "aten::upsample_nearest2d.vec", |
| "aten::upsample_nearest3d.vec", |
| "aten::upsample_trilinear3d.vec", |
| "aten::where", |
| } |
| |
| xfail_functorch_batched_decomposition = { |
| "aten::diagonal_copy", |
| "aten::is_same_size", |
| "aten::unfold_copy", |
| } |
| |
| xfail_not_implemented = { |
| "aten::absolute_", |
| "aten::affine_grid_generator_backward", |
| "aten::align_as", |
| "aten::align_tensors", |
| "aten::align_to", |
| "aten::align_to.ellipsis_idx", |
| "aten::alpha_dropout", |
| "aten::alpha_dropout_", |
| "aten::arccos_", |
| "aten::arccosh_", |
| "aten::arcsin_", |
| "aten::arcsinh_", |
| "aten::arctan2_", |
| "aten::arctan_", |
| "aten::arctanh_", |
| "aten::argwhere", |
| "aten::bilinear", |
| "aten::can_cast", |
| "aten::cat.names", |
| "aten::chain_matmul", |
| "aten::chalf", |
| "aten::choose_qparams_optimized", |
| "aten::clip_", |
| "aten::clip_.Tensor", |
| "aten::coalesce", |
| "aten::column_stack", |
| "aten::concat.names", |
| "aten::concatenate.names", |
| "aten::conj", |
| "aten::conv_tbc_backward", |
| "aten::ctc_loss.IntList", |
| "aten::ctc_loss.Tensor", |
| "aten::cudnn_is_acceptable", |
| "aten::cummaxmin_backward", |
| "aten::data", |
| "aten::diagflat", |
| "aten::divide.out_mode", |
| "aten::divide_.Scalar", |
| "aten::dropout", |
| "aten::dropout_", |
| "aten::embedding_bag", |
| "aten::embedding_bag.padding_idx", |
| "aten::feature_alpha_dropout", |
| "aten::feature_alpha_dropout_", |
| "aten::feature_dropout", |
| "aten::feature_dropout_", |
| "aten::fft_ihfft2", |
| "aten::fft_ihfftn", |
| "aten::fill_diagonal_", |
| "aten::fix_", |
| "aten::flatten.named_out_dim", |
| "aten::flatten.using_ints", |
| "aten::flatten.using_names", |
| "aten::flatten_dense_tensors", |
| "aten::float_power_.Scalar", |
| "aten::float_power_.Tensor", |
| "aten::floor_divide_.Scalar", |
| "aten::frobenius_norm", |
| "aten::fused_moving_avg_obs_fake_quant", |
| "aten::get_gradients", |
| "aten::greater_.Scalar", |
| "aten::greater_.Tensor", |
| "aten::greater_equal_.Scalar", |
| "aten::greater_equal_.Tensor", |
| "aten::gru.data", |
| "aten::gru.input", |
| "aten::gru_cell", |
| "aten::histogramdd", |
| "aten::histogramdd.TensorList_bins", |
| "aten::histogramdd.int_bins", |
| "aten::imag", |
| "aten::infinitely_differentiable_gelu_backward", |
| "aten::isclose", |
| "aten::isfinite", |
| "aten::isreal", |
| "aten::istft", |
| "aten::item", |
| "aten::kl_div", |
| "aten::ldexp_", |
| "aten::less_.Scalar", |
| "aten::less_.Tensor", |
| "aten::less_equal_.Scalar", |
| "aten::less_equal_.Tensor", |
| "aten::linalg_cond.p_str", |
| "aten::linalg_eigh", |
| "aten::linalg_eigh.eigvals", |
| "aten::linalg_lu_factor", |
| "aten::linalg_matrix_rank", |
| "aten::linalg_matrix_rank.out_tol_tensor", |
| "aten::linalg_matrix_rank.tol_tensor", |
| "aten::linalg_pinv", |
| "aten::linalg_pinv.atol_rtol_float", |
| "aten::linalg_pinv.out_rcond_tensor", |
| "aten::linalg_pinv.rcond_tensor", |
| "aten::linalg_slogdet", |
| "aten::linalg_svd.U", |
| "aten::linalg_tensorsolve", |
| "aten::linear", |
| "aten::log_sigmoid", |
| "aten::log_softmax.int", |
| "aten::logdet", |
| "aten::logsumexp.names", |
| "aten::lstm.data", |
| "aten::lstm.input", |
| "aten::lstm_cell", |
| "aten::lu_solve", |
| "aten::margin_ranking_loss", |
| "aten::masked_select_backward", |
| "aten::matrix_exp", |
| "aten::matrix_exp_backward", |
| "aten::max.names_dim", |
| "aten::max.names_dim_max", |
| "aten::mean.names_dim", |
| "aten::median.names_dim", |
| "aten::median.names_dim_values", |
| "aten::min.names_dim", |
| "aten::min.names_dim_min", |
| "aten::mish_backward", |
| "aten::moveaxis.int", |
| "aten::movedim.intlist", |
| "aten::multilabel_margin_loss", |
| "aten::nanmedian.names_dim", |
| "aten::nanmedian.names_dim_values", |
| "aten::nanquantile", |
| "aten::nanquantile.scalar", |
| "aten::narrow.Tensor", |
| "aten::native_channel_shuffle", |
| "aten::negative_", |
| "aten::nested_to_padded_tensor", |
| "aten::nonzero_numpy", |
| "aten::norm.names_ScalarOpt_dim", |
| "aten::norm.names_ScalarOpt_dim_dtype", |
| "aten::norm_except_dim", |
| "aten::not_equal_.Scalar", |
| "aten::not_equal_.Tensor", |
| "aten::one_hot", |
| "aten::output_nr", |
| "aten::pad_sequence", |
| "aten::pdist", |
| "aten::pin_memory", |
| "aten::promote_types", |
| "aten::qr.Q", |
| "aten::quantile", |
| "aten::quantile.scalar", |
| "aten::real", |
| "aten::refine_names", |
| "aten::rename", |
| "aten::rename_", |
| "aten::requires_grad_", |
| "aten::retain_grad", |
| "aten::retains_grad", |
| "aten::rnn_relu.data", |
| "aten::rnn_relu.input", |
| "aten::rnn_relu_cell", |
| "aten::rnn_tanh.data", |
| "aten::rnn_tanh.input", |
| "aten::rnn_tanh_cell", |
| "aten::set_.source_Tensor_storage_offset", |
| "aten::set_data", |
| "aten::silu_backward", |
| "aten::slow_conv3d", |
| "aten::smm", |
| "aten::special_chebyshev_polynomial_t.n_scalar", |
| "aten::special_chebyshev_polynomial_t.x_scalar", |
| "aten::special_chebyshev_polynomial_u.n_scalar", |
| "aten::special_chebyshev_polynomial_u.x_scalar", |
| "aten::special_chebyshev_polynomial_v.n_scalar", |
| "aten::special_chebyshev_polynomial_v.x_scalar", |
| "aten::special_chebyshev_polynomial_w.n_scalar", |
| "aten::special_chebyshev_polynomial_w.x_scalar", |
| "aten::special_hermite_polynomial_h.n_scalar", |
| "aten::special_hermite_polynomial_h.x_scalar", |
| "aten::special_hermite_polynomial_he.n_scalar", |
| "aten::special_hermite_polynomial_he.x_scalar", |
| "aten::special_laguerre_polynomial_l.n_scalar", |
| "aten::special_laguerre_polynomial_l.x_scalar", |
| "aten::special_legendre_polynomial_p.n_scalar", |
| "aten::special_legendre_polynomial_p.x_scalar", |
| "aten::special_shifted_chebyshev_polynomial_t.n_scalar", |
| "aten::special_shifted_chebyshev_polynomial_t.x_scalar", |
| "aten::special_shifted_chebyshev_polynomial_u.n_scalar", |
| "aten::special_shifted_chebyshev_polynomial_u.x_scalar", |
| "aten::special_shifted_chebyshev_polynomial_v.n_scalar", |
| "aten::special_shifted_chebyshev_polynomial_v.x_scalar", |
| "aten::special_shifted_chebyshev_polynomial_w.n_scalar", |
| "aten::special_shifted_chebyshev_polynomial_w.x_scalar", |
| "aten::special_xlogy", |
| "aten::special_xlogy.other_scalar", |
| "aten::special_xlogy.self_scalar", |
| "aten::square_", |
| "aten::sspaddmm", |
| "aten::std.correction_names", |
| "aten::std.names_dim", |
| "aten::std_mean.correction_names", |
| "aten::std_mean.names_dim", |
| "aten::stft", |
| "aten::stft.center", |
| "aten::stride.int", |
| "aten::subtract.Scalar", |
| "aten::subtract_.Scalar", |
| "aten::subtract_.Tensor", |
| "aten::svd.U", |
| "aten::tensor_split.indices", |
| "aten::tensor_split.sections", |
| "aten::tensor_split.tensor_indices_or_sections", |
| "aten::thnn_conv2d", |
| "aten::to.device", |
| "aten::to.dtype", |
| "aten::to.dtype_layout", |
| "aten::to.other", |
| "aten::to_dense", |
| "aten::to_dense_backward", |
| "aten::to_mkldnn_backward", |
| "aten::trace_backward", |
| "aten::triplet_margin_loss", |
| "aten::unflatten_dense_tensors", |
| "aten::unsafe_chunk", |
| "aten::upsample_bicubic2d.vec", |
| "aten::upsample_bilinear2d.vec", |
| "aten::upsample_linear1d.vec", |
| "aten::upsample_nearest1d.vec", |
| "aten::upsample_nearest2d.vec", |
| "aten::upsample_nearest3d.vec", |
| "aten::upsample_trilinear3d.vec", |
| "aten::vander", |
| "aten::var.correction_names", |
| "aten::var.names_dim", |
| "aten::var_mean.correction_names", |
| "aten::var_mean.names_dim", |
| "aten::where", |
| |
| } |
| |
| |
| def dispatch_registrations( |
| dispatch_key: str, xfails: set, filter_func: typing.Callable = lambda reg: True): |
| registrations = sorted(get_registrations_for_dispatch_key(dispatch_key)) |
| subtests = [ |
| subtest(reg, name=f"[{reg}]", |
| decorators=([unittest.expectedFailure] if reg in xfails else [])) |
| for reg in registrations if filter_func(reg) |
| ] |
| return parametrize("registration", subtests) |
| |
| |
| CompositeImplicitAutogradRegistrations = set( |
| get_registrations_for_dispatch_key("CompositeImplicitAutograd") |
| ) |
| FuncTorchBatchedRegistrations = set( |
| get_registrations_for_dispatch_key("FuncTorchBatched") |
| ) |
| FuncTorchBatchedDecompositionRegistrations = set( |
| get_registrations_for_dispatch_key("FuncTorchBatchedDecomposition") |
| ) |
| |
| |
| def filter_vmap_implementable(reg): |
| reg = reg.lower() |
| if not reg.startswith("aten::"): |
| return False |
| if reg.startswith("aten::_"): |
| return False |
| if reg.endswith(".out"): |
| return False |
| if reg.endswith("_out"): |
| return False |
| if '.dimname' in reg: |
| return False |
| if "_dimname" in reg: |
| return False |
| if 'fbgemm' in reg: |
| return False |
| if 'quantize' in reg: |
| return False |
| if 'sparse' in reg: |
| return False |
| if '::is_' in reg: |
| return False |
| return True |
| |
| |
| class TestFunctorchDispatcher(TestCase): |
| @dispatch_registrations("CompositeImplicitAutograd", xfail_functorch_batched) |
| def test_register_a_batching_rule_for_composite_implicit_autograd( |
| self, registration |
| ): |
| assert registration not in FuncTorchBatchedRegistrations, ( |
| f"You've added a batching rule for a CompositeImplicitAutograd operator {registration}. " |
| "The correct way to add vmap support for it is to put it into BatchRulesDecomposition to " |
| "reuse the CompositeImplicitAutograd decomposition" |
| ) |
| |
| @dispatch_registrations( |
| "FuncTorchBatchedDecomposition", xfail_functorch_batched_decomposition |
| ) |
| def test_register_functorch_batched_decomposition(self, registration): |
| assert registration in CompositeImplicitAutogradRegistrations, ( |
| f"The registrations in BatchedDecompositions.cpp must be for CompositeImplicitAutograd " |
| f"operations. If your operation {registration} is not CompositeImplicitAutograd, " |
| "then please register it to the FuncTorchBatched key in another file." |
| ) |
| |
| @dispatch_registrations( |
| "CompositeImplicitAutograd", xfail_not_implemented, filter_vmap_implementable |
| ) |
| def test_unimplemented_batched_registrations(self, registration): |
| assert registration in FuncTorchBatchedDecompositionRegistrations, ( |
| f"Please check that there is an OpInfo that covers the operator {registration} " |
| "and add a registration in BatchedDecompositions.cpp. " |
| "If your operator isn't user facing, please add it to the xfail list" |
| ) |
| |
| |
| instantiate_parametrized_tests(TestFunctorchDispatcher) |
| |
| if __name__ == "__main__": |
| run_tests() |
