# Owner(s): ["module: unknown"] import os import re import yaml import textwrap import torch from torch.testing._internal.common_utils import TestCase, run_tests from collections import namedtuple path = os.path.dirname(os.path.realpath(__file__)) aten_native_yaml = os.path.join(path, '../aten/src/ATen/native/native_functions.yaml') all_operators_with_namedtuple_return = { 'max', 'min', 'aminmax', 'median', 'nanmedian', 'mode', 'kthvalue', 'svd', 'qr', 'geqrf', 'slogdet', 'sort', 'topk', 'linalg_inv_ex', 'triangular_solve', 'cummax', 'cummin', 'linalg_eigh', "_linalg_eigh", "_unpack_dual", 'linalg_qr', 'linalg_svd', '_linalg_svd', 'linalg_slogdet', '_linalg_slogdet', 'fake_quantize_per_tensor_affine_cachemask', 'fake_quantize_per_channel_affine_cachemask', 'linalg_lstsq', 'linalg_eig', 'linalg_cholesky_ex', 'frexp', 'lu_unpack', 'histogram', 'histogramdd', '_fake_quantize_per_tensor_affine_cachemask_tensor_qparams', '_fused_moving_avg_obs_fq_helper', 'linalg_lu_factor', 'linalg_lu_factor_ex', 'linalg_lu', '_linalg_det', '_lu_with_info', 'linalg_ldl_factor_ex', 'linalg_ldl_factor', 'linalg_solve_ex', '_linalg_solve_ex' } all_operators_with_namedtuple_return_skip_list = { '_scaled_dot_product_flash_attention', '_scaled_dot_product_fused_attention_overrideable', '_scaled_dot_product_flash_attention_for_cpu', '_scaled_dot_product_efficient_attention', '_scaled_dot_product_cudnn_attention', } class TestNamedTupleAPI(TestCase): def test_import_return_types(self): import torch.return_types # noqa: F401 exec('from torch.return_types import *') def test_native_functions_yaml(self): operators_found = set() regex = re.compile(r"^(\w*)(\(|\.)") with open(aten_native_yaml) as file: for f in yaml.safe_load(file.read()): f = f['func'] ret = f.split('->')[1].strip() name = regex.findall(f)[0][0] if name in all_operators_with_namedtuple_return : operators_found.add(name) continue if '_backward' in name or name.endswith('_forward'): continue if not ret.startswith('('): continue if ret == '()': continue if name in all_operators_with_namedtuple_return_skip_list: continue ret = ret[1:-1].split(',') for r in ret: r = r.strip() self.assertEqual(len(r.split()), 1, 'only allowlisted ' 'operators are allowed to have named ' 'return type, got ' + name) self.assertEqual(all_operators_with_namedtuple_return, operators_found, textwrap.dedent(""" Some elements in the `all_operators_with_namedtuple_return` of test_namedtuple_return_api.py could not be found. Do you forget to update test_namedtuple_return_api.py after renaming some operator? """)) def test_namedtuple_return(self): a = torch.randn(5, 5) per_channel_scale = torch.randn(5) per_channel_zp = torch.zeros(5, dtype=torch.int64) op = namedtuple('op', ['operators', 'input', 'names', 'hasout']) operators = [ op(operators=['max', 'min', 'median', 'nanmedian', 'mode', 'sort', 'topk', 'cummax', 'cummin'], input=(0,), names=('values', 'indices'), hasout=True), op(operators=['kthvalue'], input=(1, 0), names=('values', 'indices'), hasout=True), op(operators=['svd'], input=(), names=('U', 'S', 'V'), hasout=True), op(operators=['linalg_svd', '_linalg_svd'], input=(), names=('U', 'S', 'Vh'), hasout=True), op(operators=['slogdet', 'linalg_slogdet'], input=(), names=('sign', 'logabsdet'), hasout=True), op(operators=['_linalg_slogdet'], input=(), names=('sign', 'logabsdet', 'LU', 'pivots'), hasout=True), op(operators=['qr', 'linalg_qr'], input=(), names=('Q', 'R'), hasout=True), op(operators=['geqrf'], input=(), names=('a', 'tau'), hasout=True), op(operators=['triangular_solve'], input=(a,), names=('solution', 'cloned_coefficient'), hasout=True), op(operators=['linalg_eig'], input=(), names=('eigenvalues', 'eigenvectors'), hasout=True), op(operators=['linalg_eigh'], input=("L",), names=('eigenvalues', 'eigenvectors'), hasout=True), op(operators=['_linalg_eigh'], input=("L",), names=('eigenvalues', 'eigenvectors'), hasout=True), op(operators=['linalg_cholesky_ex'], input=(), names=('L', 'info'), hasout=True), op(operators=['linalg_inv_ex'], input=(), names=('inverse', 'info'), hasout=True), op(operators=['linalg_solve_ex'], input=(a,), names=('result', 'info'), hasout=True), op(operators=['_linalg_solve_ex'], input=(a,), names=('result', 'LU', 'pivots', 'info'), hasout=True), op(operators=['linalg_lu_factor'], input=(), names=('LU', 'pivots'), hasout=True), op(operators=['linalg_lu_factor_ex'], input=(), names=('LU', 'pivots', 'info'), hasout=True), op(operators=['linalg_ldl_factor'], input=(), names=('LD', 'pivots'), hasout=True), op(operators=['linalg_ldl_factor_ex'], input=(), names=('LD', 'pivots', 'info'), hasout=True), op(operators=['linalg_lu'], input=(), names=('P', 'L', 'U'), hasout=True), op(operators=['fake_quantize_per_tensor_affine_cachemask'], input=(0.1, 0, 0, 255), names=('output', 'mask',), hasout=False), op(operators=['fake_quantize_per_channel_affine_cachemask'], input=(per_channel_scale, per_channel_zp, 1, 0, 255), names=('output', 'mask',), hasout=False), op(operators=['_unpack_dual'], input=(0,), names=('primal', 'tangent'), hasout=False), op(operators=['linalg_lstsq'], input=(a,), names=('solution', 'residuals', 'rank', 'singular_values'), hasout=False), op(operators=['frexp'], input=(), names=('mantissa', 'exponent'), hasout=True), op(operators=['lu_unpack'], input=(torch.tensor([3, 2, 1, 4, 5], dtype=torch.int32), True, True), names=('P', 'L', 'U'), hasout=True), op(operators=['histogram'], input=(1,), names=('hist', 'bin_edges'), hasout=True), op(operators=['histogramdd'], input=(1,), names=('hist', 'bin_edges'), hasout=False), op(operators=['_fake_quantize_per_tensor_affine_cachemask_tensor_qparams'], input=(torch.tensor([1.0]), torch.tensor([0], dtype=torch.int), torch.tensor([1]), 0, 255), names=('output', 'mask',), hasout=False), op(operators=['_fused_moving_avg_obs_fq_helper'], input=(torch.tensor([1]), torch.tensor([1]), torch.tensor([0.1]), torch.tensor([0.1]), torch.tensor([0.1]), torch.tensor([1]), 0.01, 0, 255, 0), names=('output', 'mask',), hasout=False), op(operators=['_linalg_det'], input=(), names=('result', 'LU', 'pivots'), hasout=True), op(operators=['aminmax'], input=(), names=('min', 'max'), hasout=True), op(operators=['_lu_with_info'], input=(), names=('LU', 'pivots', 'info'), hasout=False), ] def get_func(f): "Return either torch.f or torch.linalg.f, where 'f' is a string" mod = torch if f.startswith('linalg_'): mod = torch.linalg f = f[7:] if f.startswith('_'): mod = torch._VF return getattr(mod, f, None) def check_namedtuple(tup, names): "Check that the namedtuple 'tup' has the given names" for i, name in enumerate(names): self.assertIs(getattr(tup, name), tup[i]) def check_torch_return_type(f, names): """ Check that the return_type exists in torch.return_types and they can constructed. """ return_type = getattr(torch.return_types, f) inputs = [torch.randn(()) for _ in names] self.assertEqual(type(return_type(inputs)), return_type) for op in operators: for f in op.operators: # 1. check the namedtuple returned by calling torch.f func = get_func(f) if func: ret1 = func(a, *op.input) check_namedtuple(ret1, op.names) check_torch_return_type(f, op.names) # # 2. check the out= variant, if it exists if func and op.hasout: ret2 = func(a, *op.input, out=tuple(ret1)) check_namedtuple(ret2, op.names) check_torch_return_type(f + "_out", op.names) # # 3. check the Tensor.f method, if it exists meth = getattr(a, f, None) if meth: ret3 = meth(*op.input) check_namedtuple(ret3, op.names) all_covered_operators = {x for y in operators for x in y.operators} self.assertEqual(all_operators_with_namedtuple_return, all_covered_operators, textwrap.dedent(''' The set of covered operators does not match the `all_operators_with_namedtuple_return` of test_namedtuple_return_api.py. Do you forget to add test for that operator? ''')) if __name__ == '__main__': run_tests()