r"""Importing this file must **not** initialize CUDA context. test_distributed relies on this assumption to properly run. This means that when this is imported no CUDA calls shall be made, including torch.cuda.device_count(), etc. torch.testing._internal.common_cuda.py can freely initialize CUDA context when imported. """ import sys import os import platform import re import gc import types import inspect import argparse import unittest import warnings import random import contextlib import socket import subprocess import time from collections import OrderedDict from contextlib import contextmanager from functools import wraps from itertools import product from copy import deepcopy from numbers import Number import tempfile import json if sys.version_info[0] == 2: from urllib2 import urlopen # noqa f811 else: from urllib.request import urlopen import __main__ import errno from torch.testing._internal import expecttest import torch import torch.cuda from torch._utils_internal import get_writable_path from torch._six import string_classes, inf import torch.backends.cudnn import torch.backends.mkl from enum import Enum from torch.autograd import gradcheck from torch.autograd.gradcheck import gradgradcheck torch.backends.disable_global_flags() IS_SANDCASTLE = os.getenv('SANDCASTLE') == '1' or os.getenv('TW_JOB_USER') == 'sandcastle' class ProfilingMode(Enum): LEGACY = 1 SIMPLE = 2 PROFILING = 3 @contextmanager def enable_profiling_mode(): if GRAPH_EXECUTOR == ProfilingMode.PROFILING: old_prof_exec_state = torch._C._jit_set_profiling_executor(True) old_prof_mode_state = torch._C._jit_set_profiling_mode(True) try: yield finally: if GRAPH_EXECUTOR == ProfilingMode.PROFILING: torch._C._jit_set_profiling_executor(old_prof_exec_state) torch._C._jit_set_profiling_mode(old_prof_mode_state) func_call = torch._C.ScriptFunction.__call__ meth_call = torch._C.ScriptMethod.__call__ def prof_callable(callable, *args, **kwargs): if 'profile_and_replay' in kwargs: del kwargs['profile_and_replay'] if GRAPH_EXECUTOR == ProfilingMode.PROFILING: with enable_profiling_mode(): callable(*args, **kwargs) return callable(*args, **kwargs) return callable(*args, **kwargs) def prof_func_call(*args, **kwargs): return prof_callable(func_call, *args, **kwargs) def prof_meth_call(*args, **kwargs): return prof_callable(meth_call, *args, **kwargs) torch._C.ScriptFunction.__call__ = prof_func_call torch._C.ScriptMethod.__call__ = prof_meth_call parser = argparse.ArgumentParser(add_help=False) parser.add_argument('--subprocess', action='store_true', help='whether to run each test in a subprocess') parser.add_argument('--seed', type=int, default=1234) parser.add_argument('--accept', action='store_true') parser.add_argument('--ge_config', type=str) GRAPH_EXECUTOR = ProfilingMode.SIMPLE if IS_SANDCASTLE else ProfilingMode.PROFILING args, remaining = parser.parse_known_args() if args.ge_config == 'legacy': GRAPH_EXECUTOR = ProfilingMode.LEGACY elif args.ge_config == 'simple': GRAPH_EXECUTOR = ProfilingMode.SIMPLE TEST_IN_SUBPROCESS = args.subprocess SEED = args.seed if not expecttest.ACCEPT: expecttest.ACCEPT = args.accept UNITTEST_ARGS = [sys.argv[0]] + remaining torch.manual_seed(SEED) def shell(command, cwd=None): sys.stdout.flush() sys.stderr.flush() # The following cool snippet is copied from Py3 core library subprocess.call # only the with # 1. `except KeyboardInterrupt` block added for SIGINT handling. # 2. In Py2, subprocess.Popen doesn't return a context manager, so we do # `p.wait()` in a `final` block for the code to be portable. # # https://github.com/python/cpython/blob/71b6c1af727fbe13525fb734568057d78cea33f3/Lib/subprocess.py#L309-L323 assert not isinstance(command, torch._six.string_classes), "Command to shell should be a list or tuple of tokens" p = subprocess.Popen(command, universal_newlines=True, cwd=cwd) try: return p.wait() except KeyboardInterrupt: # Give `p` a chance to handle KeyboardInterrupt. Without this, # `pytest` can't print errors it collected so far upon KeyboardInterrupt. exit_status = p.wait(timeout=5) if exit_status is not None: return exit_status else: p.kill() raise except: # noqa E722, copied from python core library p.kill() raise finally: # Always call p.wait() to ensure exit p.wait() # Used to run the same test with different tensor types def repeat_test_for_types(dtypes): def repeat_helper(f): @wraps(f) def call_helper(self, *args): for dtype in dtypes: if PY34: with TestCase.subTest(self, dtype=dtype): f(self, *args, dtype=dtype) else: f(self, *args, dtype=dtype) return call_helper return repeat_helper # Environment variable `IS_PYTORCH_CI` is set in `.jenkins/common.sh`. IS_PYTORCH_CI = bool(os.environ.get('IS_PYTORCH_CI')) IN_CIRCLECI = bool(os.environ.get('IN_CIRCLECI')) TEST_REPORT_SOURCE_OVERRIDE = os.environ.get('TEST_REPORT_SOURCE_OVERRIDE') PY3 = sys.version_info > (3, 0) PY34 = sys.version_info >= (3, 4) def run_tests(argv=UNITTEST_ARGS): if TEST_IN_SUBPROCESS: suite = unittest.TestLoader().loadTestsFromModule(__main__) test_cases = [] def add_to_test_cases(suite_or_case): if isinstance(suite_or_case, unittest.TestCase): test_cases.append(suite_or_case) else: for element in suite_or_case: add_to_test_cases(element) add_to_test_cases(suite) failed_tests = [] for case in test_cases: test_case_full_name = case.id().split('.', 1)[1] exitcode = shell([sys.executable] + argv + [test_case_full_name]) if exitcode != 0: failed_tests.append(test_case_full_name) assert len(failed_tests) == 0, "{} unit test(s) failed:\n\t{}".format( len(failed_tests), '\n\t'.join(failed_tests)) else: if IN_CIRCLECI: # import here so that non-CI doesn't need xmlrunner installed import xmlrunner # allow users to override the test file location. We need this # because the distributed tests run the same test file multiple # times with different configurations. if TEST_REPORT_SOURCE_OVERRIDE is not None: test_source = TEST_REPORT_SOURCE_OVERRIDE else: test_source = 'python-unittest' test_report_path = os.path.join('test-reports', test_source) if PY3: os.makedirs(test_report_path, exist_ok=True) else: if not os.path.exists(test_report_path): os.makedirs(test_report_path) unittest.main(argv=argv, testRunner=xmlrunner.XMLTestRunner(output=test_report_path)) else: unittest.main(argv=argv) IS_WINDOWS = sys.platform == "win32" IS_MACOS = sys.platform == "darwin" IS_PPC = platform.machine() == "ppc64le" if IS_WINDOWS: @contextmanager def TemporaryFileName(): # Ideally we would like to not have to manually delete the file, but NamedTemporaryFile # opens the file, and it cannot be opened multiple times in Windows. To support Windows, # close the file after creation and try to remove it manually f = tempfile.NamedTemporaryFile(delete=False) try: f.close() yield f.name finally: os.unlink(f.name) else: @contextmanager # noqa: T484 def TemporaryFileName(): with tempfile.NamedTemporaryFile() as f: yield f.name def _check_module_exists(name): r"""Returns if a top-level module with :attr:`name` exists *without** importing it. This is generally safer than try-catch block around a `import X`. It avoids third party libraries breaking assumptions of some of our tests, e.g., setting multiprocessing start method when imported (see librosa/#747, torchvision/#544). """ if not PY3: # Python 2 import imp try: imp.find_module(name) return True except ImportError: return False elif not PY34: # Python [3, 3.4) import importlib loader = importlib.find_loader(name) return loader is not None else: # Python >= 3.4 import importlib import importlib.util spec = importlib.util.find_spec(name) return spec is not None TEST_NUMPY = _check_module_exists('numpy') TEST_SCIPY = _check_module_exists('scipy') TEST_MKL = torch.backends.mkl.is_available() TEST_NUMBA = _check_module_exists('numba') # Skip the test until issue #28313 gets fixed on Py2. TEST_DILL = _check_module_exists('dill') and PY3 # On Py2, importing librosa 0.6.1 triggers a TypeError (if using newest joblib) # see librosa/librosa#729. # TODO: allow Py2 when librosa 0.6.2 releases TEST_LIBROSA = _check_module_exists('librosa') and PY3 # Python 2.7 doesn't have spawn NO_MULTIPROCESSING_SPAWN = os.environ.get('NO_MULTIPROCESSING_SPAWN', '0') == '1' or sys.version_info[0] == 2 TEST_WITH_ASAN = os.getenv('PYTORCH_TEST_WITH_ASAN', '0') == '1' TEST_WITH_TSAN = os.getenv('PYTORCH_TEST_WITH_TSAN', '0') == '1' TEST_WITH_UBSAN = os.getenv('PYTORCH_TEST_WITH_UBSAN', '0') == '1' TEST_WITH_ROCM = os.getenv('PYTORCH_TEST_WITH_ROCM', '0') == '1' # Enables tests that are slow to run (disabled by default) TEST_WITH_SLOW = os.getenv('PYTORCH_TEST_WITH_SLOW', '0') == '1' # Disables non-slow tests (these tests enabled by default) # This is usually used in conjunction with TEST_WITH_SLOW to # run *only* slow tests. (I could have done an enum, but # it felt a little awkward. TEST_SKIP_FAST = os.getenv('PYTORCH_TEST_SKIP_FAST', '0') == '1' if TEST_NUMPY: import numpy ALL_TENSORTYPES = [torch.float, torch.double, torch.half] # bfloat16 bringup is currently only available on ROCm # ALL_TENSORTYPES2 will eventually be unified with ALL_TENSORTYPES # when bfloat16 bringup is complete on all platforms if TEST_WITH_ROCM: ALL_TENSORTYPES2 = [torch.float, torch.double, torch.half, torch.bfloat16] else: ALL_TENSORTYPES2 = ALL_TENSORTYPES def skipIfRocm(fn): @wraps(fn) def wrapper(*args, **kwargs): if TEST_WITH_ROCM: raise unittest.SkipTest("test doesn't currently work on the ROCm stack") else: fn(*args, **kwargs) return wrapper def skipIfCompiledWithoutNumpy(fn): # Even if the numpy module is present, if `USE_NUMPY=0` is used during the # build, numpy tests will fail numpy_support = TEST_NUMPY if numpy_support: try: # The numpy module is present, verify that PyTorch is compiled with # numpy support torch.from_numpy(numpy.array([2, 2])) except RuntimeError: numpy_support = False @wraps(fn) def wrapper(*args, **kwargs): if not numpy_support: raise unittest.SkipTest("PyTorch was compiled without numpy support") else: fn(*args, **kwargs) return wrapper def _test_function(fn, device): def run_test_function(self): return fn(self, device) return run_test_function def skipIfNoLapack(fn): @wraps(fn) def wrapper(*args, **kwargs): if not torch._C.has_lapack: raise unittest.SkipTest('PyTorch compiled without Lapack') else: fn(*args, **kwargs) return wrapper def skipIfNotRegistered(op_name, message): """Wraps the decorator to hide the import of the `core`. Args: op_name: Check if this op is registered in `core._REGISTERED_OPERATORS`. message: message to fail with. Usage: @skipIfNotRegistered('MyOp', 'MyOp is not linked!') This will check if 'MyOp' is in the caffe2.python.core """ try: from caffe2.python import core skipper = unittest.skipIf(op_name not in core._REGISTERED_OPERATORS, message) except ImportError: skipper = unittest.skip("Cannot import `caffe2.python.core`") return skipper def slowTest(fn): @wraps(fn) def wrapper(*args, **kwargs): if not TEST_WITH_SLOW: raise unittest.SkipTest("test is slow; run with PYTORCH_TEST_WITH_SLOW to enable test") else: fn(*args, **kwargs) wrapper.__dict__['slow_test'] = True return wrapper def skipCUDAMemoryLeakCheckIf(condition): def dec(fn): if getattr(fn, '_do_cuda_memory_leak_check', True): # if current True fn._do_cuda_memory_leak_check = not condition return fn return dec def skipCUDANonDefaultStreamIf(condition): def dec(fn): if getattr(fn, '_do_cuda_non_default_stream', True): # if current True fn._do_cuda_non_default_stream = not condition return fn return dec def suppress_warnings(fn): @wraps(fn) def wrapper(*args, **kwargs): with warnings.catch_warnings(): warnings.simplefilter("ignore") fn(*args, **kwargs) return wrapper def get_cpu_type(type_name): module, name = type_name.rsplit('.', 1) assert module == 'torch.cuda' return getattr(torch, name) def get_gpu_type(type_name): if isinstance(type_name, type): type_name = '{}.{}'.format(type_name.__module__, type_name.__name__) module, name = type_name.rsplit('.', 1) assert module == 'torch' return getattr(torch.cuda, name) def to_gpu(obj, type_map=None): if type_map is None: type_map = {} if isinstance(obj, torch.Tensor): assert obj.is_leaf t = type_map.get(obj.type(), get_gpu_type(obj.type())) with torch.no_grad(): res = obj.clone().type(t) res.requires_grad = obj.requires_grad return res elif torch.is_storage(obj): return obj.new().resize_(obj.size()).copy_(obj) elif isinstance(obj, list): return [to_gpu(o, type_map) for o in obj] elif isinstance(obj, tuple): return tuple(to_gpu(o, type_map) for o in obj) else: return deepcopy(obj) def get_function_arglist(func): if sys.version_info > (3,): return inspect.getfullargspec(func).args else: return inspect.getargspec(func).args def set_rng_seed(seed): torch.manual_seed(seed) random.seed(seed) if TEST_NUMPY: numpy.random.seed(seed) @contextlib.contextmanager def freeze_rng_state(): rng_state = torch.get_rng_state() if torch.cuda.is_available(): cuda_rng_state = torch.cuda.get_rng_state() yield if torch.cuda.is_available(): torch.cuda.set_rng_state(cuda_rng_state) torch.set_rng_state(rng_state) def iter_indices(tensor): if tensor.dim() == 0: return range(0) if tensor.dim() == 1: return range(tensor.size(0)) return product(*(range(s) for s in tensor.size())) def is_iterable(obj): try: iter(obj) return True except TypeError: return False class CudaNonDefaultStream(): def __enter__(self): # Before starting CUDA test save currently active streams on all # CUDA devices and set new non default streams to all CUDA devices # to ensure CUDA tests do not use default stream by mistake. beforeDevice = torch.cuda.current_device() self.beforeStreams = [] for d in range(torch.cuda.device_count()): self.beforeStreams.append(torch.cuda.current_stream(d)) deviceStream = torch.cuda.Stream(device=d) torch._C._cuda_setStream(deviceStream._cdata) torch._C._cuda_setDevice(beforeDevice) def __exit__(self, exec_type, exec_value, traceback): # After completing CUDA test load previously active streams on all # CUDA devices. beforeDevice = torch.cuda.current_device() for d in range(torch.cuda.device_count()): torch._C._cuda_setStream(self.beforeStreams[d]._cdata) torch._C._cuda_setDevice(beforeDevice) class CudaMemoryLeakCheck(): def __init__(self, testcase, name=None): self.name = testcase.id() if name is None else name self.testcase = testcase # initialize context & RNG to prevent false positive detections # when the test is the first to initialize those from torch.testing._internal.common_cuda import initialize_cuda_context_rng initialize_cuda_context_rng() @staticmethod def get_cuda_memory_usage(): # we don't need CUDA synchronize because the statistics are not tracked at # actual freeing, but at when marking the block as free. num_devices = torch.cuda.device_count() gc.collect() return tuple(torch.cuda.memory_allocated(i) for i in range(num_devices)) def __enter__(self): self.befores = self.get_cuda_memory_usage() def __exit__(self, exec_type, exec_value, traceback): # Don't check for leaks if an exception was thrown if exec_type is not None: return afters = self.get_cuda_memory_usage() for i, (before, after) in enumerate(zip(self.befores, afters)): if not TEST_WITH_ROCM: self.testcase.assertEqual( before, after, '{} leaked {} bytes CUDA memory on device {}'.format( self.name, after - before, i)) else: # TODO: Investigate ROCm memory leaking. if before != after: warnings.warn('{} leaked {} bytes ROCm memory on device {}'.format( self.name, after - before, i), RuntimeWarning) # "min_satisfying_examples" setting has been deprecated in hypythesis # 3.56.0 and removed in hypothesis 4.x try: import hypothesis if hypothesis.version.__version_info__ >= (3, 56, 0): hypothesis.settings.register_profile( "pytorch_ci", hypothesis.settings( derandomize=True, suppress_health_check=[hypothesis.HealthCheck.too_slow], database=None, max_examples=100, verbosity=hypothesis.Verbosity.normal)) hypothesis.settings.register_profile( "dev", hypothesis.settings( suppress_health_check=[hypothesis.HealthCheck.too_slow], database=None, max_examples=10, verbosity=hypothesis.Verbosity.normal)) hypothesis.settings.register_profile( "debug", hypothesis.settings( suppress_health_check=[hypothesis.HealthCheck.too_slow], database=None, max_examples=1000, verbosity=hypothesis.Verbosity.verbose)) else: hypothesis.settings.register_profile( "pytorch_ci", hypothesis.settings( derandomize=True, suppress_health_check=[hypothesis.HealthCheck.too_slow], database=None, max_examples=100, min_satisfying_examples=1, verbosity=hypothesis.Verbosity.normal)) hypothesis.settings.register_profile( "dev", hypothesis.settings( suppress_health_check=[hypothesis.HealthCheck.too_slow], database=None, max_examples=10, min_satisfying_examples=1, verbosity=hypothesis.Verbosity.normal)) hypothesis.settings.register_profile( "debug", hypothesis.settings( suppress_health_check=[hypothesis.HealthCheck.too_slow], database=None, max_examples=1000, min_satisfying_examples=1, verbosity=hypothesis.Verbosity.verbose)) hypothesis.settings.load_profile( "pytorch_ci" if IS_PYTORCH_CI else os.getenv('PYTORCH_HYPOTHESIS_PROFILE', 'dev') ) except ImportError: print('Fail to import hypothesis in common_utils, tests are not derandomized') disabled_test_from_issues = None def check_disabled(test_name): global disabled_test_from_issues if disabled_test_from_issues is None: disabled_test_from_issues = {} def read_and_process(): url = 'https://raw.githubusercontent.com/zdevito/pytorch_disabled_tests/master/result.json' contents = urlopen(url, timeout=1).read().decode('utf-8') the_response = json.loads(contents) for item in the_response['items']: title = item['title'] key = 'DISABLED ' if title.startswith(key): test_name = title[len(key):].strip() disabled_test_from_issues[test_name] = item['html_url'] if not IS_SANDCASTLE and os.getenv("PYTORCH_RUN_DISABLED_TESTS", "0") != "1": try: read_and_process() except Exception: print("Couldn't download test skip set, leaving all tests enabled...") if test_name in disabled_test_from_issues: raise unittest.SkipTest( "Test is disabled because an issue exists disabling it: {}".format(disabled_test_from_issues[test_name]) + " To enable set the environment variable PYTORCH_RUN_DISABLED_TESTS=1") class TestCase(expecttest.TestCase): precision = 1e-5 maxDiff = None _do_cuda_memory_leak_check = False _do_cuda_non_default_stream = False def __init__(self, method_name='runTest'): super(TestCase, self).__init__(method_name) test_method = getattr(self, method_name) # Wraps the tested method if we should do CUDA memory check. self._do_cuda_memory_leak_check &= getattr(test_method, '_do_cuda_memory_leak_check', True) # FIXME: figure out the flaky -1024 anti-leaks on windows. See #8044 if self._do_cuda_memory_leak_check and not IS_WINDOWS: self.wrap_with_cuda_policy(method_name, self.assertLeaksNoCudaTensors) # Wraps the tested method if we should enforce non default CUDA stream. self._do_cuda_non_default_stream &= getattr(test_method, '_do_cuda_non_default_stream', True) if self._do_cuda_non_default_stream and not IS_WINDOWS and not TEST_WITH_ROCM: self.wrap_with_cuda_policy(method_name, self.enforceNonDefaultStream) def assertLeaksNoCudaTensors(self, name=None): name = self.id() if name is None else name return CudaMemoryLeakCheck(self, name) def enforceNonDefaultStream(self): return CudaNonDefaultStream() def wrap_with_cuda_policy(self, method_name, policy): test_method = getattr(self, method_name) # the import below may initialize CUDA context, so we do it only if # self._do_cuda_memory_leak_check or self._do_cuda_non_default_stream # is True. from torch.testing._internal.common_cuda import TEST_CUDA fullname = self.id().lower() # class_name.method_name if TEST_CUDA and ('gpu' in fullname or 'cuda' in fullname): setattr(self, method_name, self.wrap_method_with_cuda_policy(test_method, policy)) def wrap_method_with_cuda_policy(self, method, policy): # Assumes that `method` is the tested function in `self`. # NOTE: Python Exceptions (e.g., unittest.Skip) keeps objects in scope # alive, so this cannot be done in setUp and tearDown because # tearDown is run unconditionally no matter whether the test # passes or not. For the same reason, we can't wrap the `method` # call in try-finally and always do the check. @wraps(method) def wrapper(self, *args, **kwargs): with policy(): method(*args, **kwargs) return types.MethodType(wrapper, self) def wrap_with_cuda_memory_check(self, method): return self.wrap_method_with_cuda_policy(method, self.assertLeaksNoCudaTensors) def setUp(self): if TEST_SKIP_FAST: if not getattr(self, self._testMethodName).__dict__.get('slow_test', False): raise unittest.SkipTest("test is fast; we disabled it with PYTORCH_TEST_SKIP_FAST") check_disabled(str(self)) set_rng_seed(SEED) def assertTensorsSlowEqual(self, x, y, prec=None, message=''): max_err = 0 self.assertEqual(x.size(), y.size()) for index in iter_indices(x): max_err = max(max_err, abs(x[index] - y[index])) self.assertLessEqual(max_err, prec, message) def genSparseTensor(self, size, sparse_dim, nnz, is_uncoalesced, device='cpu'): # Assert not given impossible combination, where the sparse dims have # empty numel, but nnz > 0 makes the indices containing values. assert all(size[d] > 0 for d in range(sparse_dim)) or nnz == 0, 'invalid arguments' v_size = [nnz] + list(size[sparse_dim:]) v = torch.randn(*v_size, device=device) i = torch.rand(sparse_dim, nnz, device=device) i.mul_(torch.tensor(size[:sparse_dim]).unsqueeze(1).to(i)) i = i.to(torch.long) if is_uncoalesced: v = torch.cat([v, torch.randn_like(v)], 0) i = torch.cat([i, i], 1) x = torch.sparse_coo_tensor(i, v, torch.Size(size)) if not is_uncoalesced: x = x.coalesce() else: # FIXME: `x` is a sparse view of `v`. Currently rebase_history for # sparse views is not implemented, so this workaround is # needed for inplace operations done on `x`, e.g., copy_(). # Remove after implementing something equivalent to CopySlice # for sparse views. # NOTE: We do clone() after detach() here because we need to be able to change size/storage of x afterwards x = x.detach().clone() return x, x._indices().clone(), x._values().clone() def safeToDense(self, t): r = self.safeCoalesce(t) return r.to_dense() def safeCoalesce(self, t): tc = t.coalesce() self.assertEqual(tc.to_dense(), t.to_dense()) self.assertTrue(tc.is_coalesced()) # Our code below doesn't work when nnz is 0, because # then it's a 0D tensor, not a 2D tensor. if t._nnz() == 0: self.assertEqual(t._indices(), tc._indices()) self.assertEqual(t._values(), tc._values()) return tc value_map = {} for idx, val in zip(t._indices().t(), t._values()): idx_tup = tuple(idx.tolist()) if idx_tup in value_map: value_map[idx_tup] += val else: value_map[idx_tup] = val.clone() if isinstance(val, torch.Tensor) else val new_indices = sorted(list(value_map.keys())) new_values = [value_map[idx] for idx in new_indices] if t._values().ndimension() < 2: new_values = t._values().new(new_values) else: new_values = torch.stack(new_values) new_indices = t._indices().new(new_indices).t() tg = t.new(new_indices, new_values, t.size()) self.assertEqual(tc._indices(), tg._indices()) self.assertEqual(tc._values(), tg._values()) if t.is_coalesced(): self.assertEqual(tc._indices(), t._indices()) self.assertEqual(tc._values(), t._values()) return tg def assertEqual(self, x, y, prec=None, message='', allow_inf=False): if isinstance(prec, str) and message == '': message = prec prec = None if prec is None: prec = self.precision if isinstance(x, torch.Tensor) and isinstance(y, Number): self.assertEqual(x.item(), y, prec=prec, message=message, allow_inf=allow_inf) elif isinstance(y, torch.Tensor) and isinstance(x, Number): self.assertEqual(x, y.item(), prec=prec, message=message, allow_inf=allow_inf) elif isinstance(x, torch.Tensor) and isinstance(y, numpy.bool_): self.assertEqual(x.item(), y, prec=prec, message=message, allow_inf=allow_inf) elif isinstance(y, torch.Tensor) and isinstance(x, numpy.bool_): self.assertEqual(x, y.item(), prec=prec, message=message, allow_inf=allow_inf) elif isinstance(x, torch.Tensor) and isinstance(y, torch.Tensor): def assertTensorsEqual(a, b): super(TestCase, self).assertEqual(a.size(), b.size(), message) if a.numel() > 0: if (a.device.type == 'cpu' and (a.dtype == torch.float16 or a.dtype == torch.bfloat16)): # CPU half and bfloat16 tensors don't have the methods we need below a = a.to(torch.float32) if (a.device.type == 'cuda' and a.dtype == torch.bfloat16): # CUDA bfloat16 tensors don't have the methods we need below a = a.to(torch.float32) b = b.to(a) if (a.dtype == torch.bool) != (b.dtype == torch.bool): raise TypeError("Was expecting both tensors to be bool type.") else: if a.dtype == torch.bool and b.dtype == torch.bool: # we want to respect precision but as bool doesn't support subtraction, # boolean tensor has to be converted to int a = a.to(torch.int) b = b.to(torch.int) diff = a - b if a.is_floating_point(): # check that NaNs are in the same locations nan_mask = torch.isnan(a) self.assertTrue(torch.equal(nan_mask, torch.isnan(b)), message) diff[nan_mask] = 0 # inf check if allow_inf=True if allow_inf: inf_mask = torch.isinf(a) inf_sign = inf_mask.sign() self.assertTrue(torch.equal(inf_sign, torch.isinf(b).sign()), message) diff[inf_mask] = 0 # TODO: implement abs on CharTensor (int8) if diff.is_signed() and diff.dtype != torch.int8: diff = diff.abs() max_err = diff.max() self.assertLessEqual(max_err, prec, message) super(TestCase, self).assertEqual(x.is_sparse, y.is_sparse, message) super(TestCase, self).assertEqual(x.is_quantized, y.is_quantized, message) if x.is_sparse: x = self.safeCoalesce(x) y = self.safeCoalesce(y) assertTensorsEqual(x._indices(), y._indices()) assertTensorsEqual(x._values(), y._values()) elif x.is_quantized and y.is_quantized: self.assertEqual(x.qscheme(), y.qscheme(), prec=prec, message=message, allow_inf=allow_inf) if x.qscheme() == torch.per_tensor_affine: self.assertEqual(x.q_scale(), y.q_scale(), prec=prec, message=message, allow_inf=allow_inf) self.assertEqual(x.q_zero_point(), y.q_zero_point(), prec=prec, message=message, allow_inf=allow_inf) elif x.qscheme() == torch.per_channel_affine: self.assertEqual(x.q_per_channel_scales(), y.q_per_channel_scales(), prec=prec, message=message, allow_inf=allow_inf) self.assertEqual(x.q_per_channel_zero_points(), y.q_per_channel_zero_points(), prec=prec, message=message, allow_inf=allow_inf) self.assertEqual(x.q_per_channel_axis(), y.q_per_channel_axis(), prec=prec, message=message) self.assertEqual(x.dtype, y.dtype) self.assertEqual(x.int_repr().to(torch.int32), y.int_repr().to(torch.int32), prec=prec, message=message, allow_inf=allow_inf) else: assertTensorsEqual(x, y) elif isinstance(x, string_classes) and isinstance(y, string_classes): super(TestCase, self).assertEqual(x, y, message) elif type(x) == set and type(y) == set: super(TestCase, self).assertEqual(x, y, message) elif isinstance(x, dict) and isinstance(y, dict): if isinstance(x, OrderedDict) and isinstance(y, OrderedDict): self.assertEqual(x.items(), y.items(), prec=prec, message=message, allow_inf=allow_inf) else: self.assertEqual(set(x.keys()), set(y.keys()), prec=prec, message=message, allow_inf=allow_inf) key_list = list(x.keys()) self.assertEqual([x[k] for k in key_list], [y[k] for k in key_list], prec=prec, message=message, allow_inf=allow_inf) elif is_iterable(x) and is_iterable(y): super(TestCase, self).assertEqual(len(x), len(y), message) for x_, y_ in zip(x, y): self.assertEqual(x_, y_, prec=prec, message=message, allow_inf=allow_inf) elif isinstance(x, bool) and isinstance(y, bool): super(TestCase, self).assertEqual(x, y, message) elif isinstance(x, Number) and isinstance(y, Number): if abs(x) == inf or abs(y) == inf: if allow_inf: super(TestCase, self).assertEqual(x, y, message) else: self.fail("Expected finite numeric values - x={}, y={}".format(x, y)) return super(TestCase, self).assertLessEqual(abs(x - y), prec, message) else: super(TestCase, self).assertEqual(x, y, message) def assertAlmostEqual(self, x, y, places=None, msg=None, delta=None, allow_inf=None): prec = delta if places: prec = 10**(-places) self.assertEqual(x, y, prec, msg, allow_inf) def assertNotEqual(self, x, y, prec=None, message=''): if isinstance(prec, str) and message == '': message = prec prec = None if prec is None: prec = self.precision if isinstance(x, torch.Tensor) and isinstance(y, torch.Tensor): if x.size() != y.size(): super(TestCase, self).assertNotEqual(x.size(), y.size()) self.assertGreater(x.numel(), 0) y = y.type_as(x) y = y.cuda(device=x.get_device()) if x.is_cuda else y.cpu() nan_mask = x != x if torch.equal(nan_mask, y != y): diff = x - y if diff.is_signed(): diff = diff.abs() diff[nan_mask] = 0 # Use `item()` to work around: # https://github.com/pytorch/pytorch/issues/22301 max_err = diff.max().item() self.assertGreaterEqual(max_err, prec, message) elif type(x) == str and type(y) == str: super(TestCase, self).assertNotEqual(x, y) elif is_iterable(x) and is_iterable(y): super(TestCase, self).assertNotEqual(x, y) else: try: self.assertGreaterEqual(abs(x - y), prec, message) return except (TypeError, AssertionError): pass super(TestCase, self).assertNotEqual(x, y, message) def assertObjectIn(self, obj, iterable): for elem in iterable: if id(obj) == id(elem): return raise AssertionError("object not found in iterable") # TODO: Support context manager interface # NB: The kwargs forwarding to callable robs the 'subname' parameter. # If you need it, manually apply your callable in a lambda instead. def assertExpectedRaises(self, exc_type, callable, *args, **kwargs): subname = None if 'subname' in kwargs: subname = kwargs['subname'] del kwargs['subname'] try: callable(*args, **kwargs) except exc_type as e: self.assertExpected(str(e), subname) return # Don't put this in the try block; the AssertionError will catch it self.fail(msg="Did not raise when expected to") def assertNotWarn(self, callable, msg=''): r""" Test if :attr:`callable` does not raise a warning. """ with self._reset_warning_registry(), warnings.catch_warnings(record=True) as ws: warnings.simplefilter("always") # allow any warning to be raised callable() self.assertTrue(len(ws) == 0, msg) def assertWarns(self, callable, msg=''): r""" Test if :attr:`callable` raises a warning. """ with self._reset_warning_registry(), warnings.catch_warnings(record=True) as ws: warnings.simplefilter("always") # allow any warning to be raised callable() self.assertTrue(len(ws) > 0, msg) def assertWarnsRegex(self, callable, regex, msg=''): r""" Test if :attr:`callable` raises any warning with message that contains the regex pattern :attr:`regex`. """ with self._reset_warning_registry(), warnings.catch_warnings(record=True) as ws: warnings.simplefilter("always") # allow any warning to be raised callable() self.assertTrue(len(ws) > 0, msg) found = any(re.search(regex, str(w.message)) is not None for w in ws) self.assertTrue(found, msg) @contextmanager def maybeWarnsRegex(self, category, regex=''): """Context manager for code that *may* warn, e.g. ``TORCH_WARN_ONCE``. This filters expected warnings from the test log and fails the test if any unexpected warnings are caught. """ with self._reset_warning_registry(), warnings.catch_warnings(record=True) as ws: warnings.simplefilter("always") # allow any warning to be raised # Ignore expected warnings warnings.filterwarnings("ignore", message=regex, category=category) try: yield finally: if len(ws) != 0: msg = 'Caught unexpected warnings:\n' for w in ws: msg += warnings.formatwarning( w.message, w.category, w.filename, w.lineno, w.line) msg += '\n' self.fail(msg) @contextmanager def _reset_warning_registry(self): r""" warnings.catch_warnings() in Python 2 misses already registered warnings. We need to manually clear the existing warning registries to ensure catching warnings in a scope. """ # Python 3 has no problem. if sys.version_info >= (3,): yield return # Backup and clear all existing warning registries. backup = {} for name, mod in list(sys.modules.items()): try: reg = mod.__warningregistry__ except AttributeError: continue else: backup[name] = reg.copy() reg.clear() yield # Restore backed up warning registries. for name, reg_orig in backup.items(): try: mod = sys.modules[name] except KeyError: continue try: reg = mod.__warningregistry__ except AttributeError: mod.__warningregistry__ = reg_orig else: reg.clear() reg.update(reg_orig) def assertExpected(self, s, subname=None): r""" Test that a string matches the recorded contents of a file derived from the name of this test and subname. This file is placed in the 'expect' directory in the same directory as the test script. You can automatically update the recorded test output using --accept. If you call this multiple times in a single function, you must give a unique subname each time. """ if not (isinstance(s, str) or (sys.version_info[0] == 2 and isinstance(s, unicode))): raise TypeError("assertExpected is strings only") def remove_prefix(text, prefix): if text.startswith(prefix): return text[len(prefix):] return text # NB: we take __file__ from the module that defined the test # class, so we place the expect directory where the test script # lives, NOT where test/common_utils.py lives. This doesn't matter in # PyTorch where all test scripts are in the same directory as # test/common_utils.py, but it matters in onnx-pytorch module_id = self.__class__.__module__ munged_id = remove_prefix(self.id(), module_id + ".") test_file = os.path.realpath(sys.modules[module_id].__file__) expected_file = os.path.join(os.path.dirname(test_file), "expect", munged_id) subname_output = "" if subname: expected_file += "-" + subname subname_output = " ({})".format(subname) expected_file += ".expect" expected = None def accept_output(update_type): print("Accepting {} for {}{}:\n\n{}".format(update_type, munged_id, subname_output, s)) with open(expected_file, 'w') as f: f.write(s) try: with open(expected_file) as f: expected = f.read() except IOError as e: if e.errno != errno.ENOENT: raise elif expecttest.ACCEPT: return accept_output("output") else: raise RuntimeError( ("I got this output for {}{}:\n\n{}\n\n" "No expect file exists; to accept the current output, run:\n" "python {} {} --accept").format(munged_id, subname_output, s, __main__.__file__, munged_id)) # a hack for JIT tests if IS_WINDOWS: expected = re.sub(r'CppOp\[(.+?)\]', 'CppOp[]', expected) s = re.sub(r'CppOp\[(.+?)\]', 'CppOp[]', s) if expecttest.ACCEPT: if expected != s: return accept_output("updated output") else: if hasattr(self, "assertMultiLineEqual"): # Python 2.7 only # NB: Python considers lhs "old" and rhs "new". self.assertMultiLineEqual(expected, s) else: self.assertEqual(s, expected) def assertExpectedStripMangled(self, s, subname=None): s = re.sub(r'__torch__[^ ]+', '', s) self.assertExpected(s, subname) # returns captured stderr @staticmethod def runWithPytorchAPIUsageStderr(code): import subprocess env = os.environ.copy() env["PYTORCH_API_USAGE_STDERR"] = "1" pipes = subprocess.Popen( [sys.executable, '-c', code], stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env) return pipes.communicate()[1].decode('ascii') if sys.version_info < (3, 2): # assertRegexpMatches renamed to assertRegex in 3.2 assertRegex = unittest.TestCase.assertRegexpMatches # assertRaisesRegexp renamed to assertRaisesRegex in 3.2 assertRaisesRegex = unittest.TestCase.assertRaisesRegexp if sys.version_info < (3, 5): # assertNotRegexpMatches renamed to assertNotRegex in 3.5 assertNotRegex = unittest.TestCase.assertNotRegexpMatches def download_file(url, binary=True): if sys.version_info < (3,): from urlparse import urlsplit import urllib2 request = urllib2 error = urllib2 else: from urllib.parse import urlsplit from urllib import request, error filename = os.path.basename(urlsplit(url)[2]) data_dir = get_writable_path(os.path.join(os.path.dirname(__file__), 'data')) path = os.path.join(data_dir, filename) if os.path.exists(path): return path try: data = request.urlopen(url, timeout=15).read() with open(path, 'wb' if binary else 'w') as f: f.write(data) return path except error.URLError: msg = "could not download test file '{}'".format(url) warnings.warn(msg, RuntimeWarning) raise unittest.SkipTest(msg) def find_free_port(): sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) sock.bind(('localhost', 0)) sockname = sock.getsockname() sock.close() return sockname[1] def retry_on_address_already_in_use_error(func): """Reruns a test if it sees "Address already in use" error.""" @wraps(func) def wrapper(*args, **kwargs): tries_remaining = 10 while True: try: return func(*args, **kwargs) except RuntimeError as error: if str(error) == "Address already in use": tries_remaining -= 1 if tries_remaining == 0: raise time.sleep(random.random()) continue raise return wrapper # Methods for matrix generation # Used in test_autograd.py and test_torch.py def prod_single_zero(dim_size): result = torch.randn(dim_size, dim_size) result[0, 1] = 0 return result def random_square_matrix_of_rank(l, rank, dtype=torch.double, device='cpu'): assert rank <= l A = torch.randn(l, l, dtype=dtype, device=device) u, s, v = A.svd() for i in range(l): if i >= rank: s[i] = 0 elif s[i] == 0: s[i] = 1 return u.mm(torch.diag(s)).mm(v.transpose(0, 1)) def random_symmetric_matrix(l, *batches, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') A = torch.randn(*(batches + (l, l)), dtype=dtype, device=device) A = (A + A.transpose(-2, -1)).div_(2) return A def random_symmetric_psd_matrix(l, *batches, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') A = torch.randn(*(batches + (l, l)), dtype=dtype, device=device) return torch.matmul(A, A.transpose(-2, -1)) def random_symmetric_pd_matrix(matrix_size, *batch_dims, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') A = torch.randn(*(batch_dims + (matrix_size, matrix_size)), dtype=dtype, device=device) return torch.matmul(A, A.transpose(-2, -1)) \ + torch.eye(matrix_size, dtype=dtype, device=device) * 1e-5 def make_nonzero_det(A, sign=None, min_singular_value=0.1): u, s, v = A.svd() s.clamp_(min=min_singular_value) A = torch.matmul(u, torch.matmul(torch.diag_embed(s), v.transpose(-2, -1))) det = A.det() if sign is not None: if A.dim() == 2: det = det.item() if (det < 0) ^ (sign < 0): A[0, :].neg_() else: cond = ((det < 0) ^ (sign < 0)).nonzero() if cond.size(0) > 0: for i in range(cond.size(0)): A[list(cond[i])][0, :].neg_() return A def random_fullrank_matrix_distinct_singular_value(matrix_size, *batch_dims, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') silent = kwargs.get("silent", False) if silent and not torch._C.has_lapack: return torch.ones(matrix_size, matrix_size, dtype=dtype, device=device) A = torch.randn(batch_dims + (matrix_size, matrix_size), dtype=dtype, device=device) u, _, v = A.svd() s = torch.arange(1., matrix_size + 1, dtype=dtype, device=device).mul_(1.0 / (matrix_size + 1)).diag() return u.matmul(s.expand(batch_dims + (matrix_size, matrix_size)).matmul(v.transpose(-2, -1))) def random_matrix(rows, columns, *batch_dims, **kwargs): dtype = kwargs.get('dtype', torch.double) device = kwargs.get('device', 'cpu') silent = kwargs.get("silent", False) singular = kwargs.get("singular", False) if silent and not torch._C.has_lapack: return torch.ones(rows, columns, dtype=dtype, device=device) A = torch.randn(batch_dims + (rows, columns), dtype=dtype, device=device) u, _, v = A.svd(some=False) s = torch.zeros(rows, columns, dtype=dtype, device=device) k = min(rows, columns) for i in range(k): s[i, i] = (i + 1) / (k + 1) if singular: # make matrix singular s[k - 1, k - 1] = 0 if k > 2: # increase the order of singularity so that the pivoting # in LU factorization will be non-trivial s[0, 0] = 0 return u.matmul(s.expand(batch_dims + (rows, columns)).matmul(v.transpose(-2, -1))) def brute_pdist(inp, p=2): """Computes the same as torch.pdist using primitives""" n = inp.shape[-2] k = n * (n - 1) // 2 if k == 0: # torch complains about empty indices return torch.empty(inp.shape[:-2] + (0,), dtype=inp.dtype, device=inp.device) square = torch.norm(inp[..., None, :] - inp[..., None, :, :], p=p, dim=-1) unroll = square.view(square.shape[:-2] + (n * n,)) inds = torch.ones(k, dtype=torch.int) inds[torch.arange(n - 1, 1, -1, dtype=torch.int).cumsum(0)] += torch.arange(2, n, dtype=torch.int) return unroll[..., inds.cumsum(0)] def brute_cdist(x, y, p=2): r1 = x.shape[-2] r2 = y.shape[-2] if r1 == 0 or r2 == 0: return torch.empty(r1, r2, device=x.device) return torch.norm(x[..., None, :] - y[..., None, :, :], p=p, dim=-1) def do_test_dtypes(self, dtypes, layout, device): for dtype in dtypes: if dtype != torch.float16: out = torch.zeros((2, 3), dtype=dtype, layout=layout, device=device) self.assertIs(dtype, out.dtype) self.assertIs(layout, out.layout) self.assertEqual(device, out.device) def do_test_empty_full(self, dtypes, layout, device): shape = torch.Size([2, 3]) def check_value(tensor, dtype, layout, device, value, requires_grad): self.assertEqual(shape, tensor.shape) self.assertIs(dtype, tensor.dtype) self.assertIs(layout, tensor.layout) self.assertEqual(tensor.requires_grad, requires_grad) if tensor.is_cuda and device is not None: self.assertEqual(device, tensor.device) if value is not None: fill = tensor.new(shape).fill_(value) self.assertEqual(tensor, fill) def get_int64_dtype(dtype): module = '.'.join(str(dtype).split('.')[1:-1]) if not module: return torch.int64 return operator.attrgetter(module)(torch).int64 default_dtype = torch.get_default_dtype() check_value(torch.empty(shape), default_dtype, torch.strided, -1, None, False) check_value(torch.full(shape, -5), default_dtype, torch.strided, -1, None, False) for dtype in dtypes: for rg in {dtype.is_floating_point, False}: int64_dtype = get_int64_dtype(dtype) v = torch.empty(shape, dtype=dtype, device=device, layout=layout, requires_grad=rg) check_value(v, dtype, layout, device, None, rg) out = v.new() check_value(torch.empty(shape, out=out, device=device, layout=layout, requires_grad=rg), dtype, layout, device, None, rg) check_value(v.new_empty(shape), dtype, layout, device, None, False) check_value(v.new_empty(shape, dtype=int64_dtype, device=device, requires_grad=False), int64_dtype, layout, device, None, False) check_value(torch.empty_like(v), dtype, layout, device, None, False) check_value(torch.empty_like(v, dtype=int64_dtype, layout=layout, device=device, requires_grad=False), int64_dtype, layout, device, None, False) if dtype is not torch.float16 and layout != torch.sparse_coo: fv = 3 v = torch.full(shape, fv, dtype=dtype, layout=layout, device=device, requires_grad=rg) check_value(v, dtype, layout, device, fv, rg) check_value(v.new_full(shape, fv + 1), dtype, layout, device, fv + 1, False) out = v.new() check_value(torch.full(shape, fv + 2, out=out, device=device, layout=layout, requires_grad=rg), dtype, layout, device, fv + 2, rg) check_value(v.new_full(shape, fv + 3, dtype=int64_dtype, device=device, requires_grad=False), int64_dtype, layout, device, fv + 3, False) check_value(torch.full_like(v, fv + 4), dtype, layout, device, fv + 4, False) check_value(torch.full_like(v, fv + 5, dtype=int64_dtype, layout=layout, device=device, requires_grad=False), int64_dtype, layout, device, fv + 5, False) THESE_TAKE_WAY_TOO_LONG = { 'test_Conv3d_groups', 'test_conv_double_backward', 'test_conv_double_backward_groups', 'test_Conv3d_dilated', 'test_Conv3d_stride_padding', 'test_Conv3d_dilated_strided', 'test_Conv3d', 'test_Conv2d_dilated', 'test_ConvTranspose3d_dilated', 'test_ConvTranspose2d_dilated', 'test_snli', 'test_Conv2d', 'test_Conv2d_padding', 'test_ConvTranspose2d_no_bias', 'test_ConvTranspose2d', 'test_ConvTranspose3d', 'test_Conv2d_no_bias', 'test_matmul_4d_4d', 'test_multinomial_invalid_probs', } running_script_path = None def set_running_script_path(): global running_script_path try: running_file = os.path.abspath(os.path.realpath(sys.argv[0])) if running_file.endswith('.py'): # skip if the running file is not a script running_script_path = running_file except Exception: pass def check_test_defined_in_running_script(test_case): if running_script_path is None: return test_case_class_file = os.path.abspath(os.path.realpath(inspect.getfile(test_case.__class__))) assert test_case_class_file == running_script_path, "Class of loaded TestCase \"{}\" " \ "is not defined in the running script \"{}\", but in \"{}\". Did you " \ "accidentally import a unittest.TestCase from another file?".format( test_case.id(), running_script_path, test_case_class_file) def load_tests(loader, tests, pattern): set_running_script_path() test_suite = unittest.TestSuite() for test_group in tests: for test in test_group: check_test_defined_in_running_script(test) test_suite.addTest(test) return test_suite def _assertGradAndGradgradChecks(test_case, apply_fn, inputs): # call assert function rather than returning a bool since it's nicer # if we get whether this failed on the gradcheck or the gradgradcheck. test_case.assertTrue(gradcheck(apply_fn, inputs)) test_case.assertTrue(gradgradcheck(apply_fn, inputs)) dtype2prec = {torch.float: 1e-5, torch.double: 1e-5, torch.half: 1e-2, torch.bfloat16: 1e-1}