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test_torch.py
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# -*- coding: utf-8 -*-
# Owner(s): ["module: tests"]
import torch
import torch.utils.data
import numpy as np
import contextlib
import gc
import io
import inspect
import itertools
import math
import random
import re
import copy
import os
import tempfile
import unittest
import warnings
import types
import pickle
import textwrap
import subprocess
import weakref
import sys
from torch.utils.dlpack import from_dlpack, to_dlpack
from torch._six import inf, nan, string_classes
from itertools import product, combinations, permutations
from functools import partial
from torch import multiprocessing as mp
from torch.testing import make_tensor
from torch.testing._internal.common_utils import (
TestCase, TEST_WITH_ROCM, run_tests,
IS_WINDOWS, IS_FILESYSTEM_UTF8_ENCODING, NO_MULTIPROCESSING_SPAWN,
do_test_dtypes, IS_SANDCASTLE, IS_FBCODE, IS_REMOTE_GPU, load_tests, slowTest,
skipCUDAMemoryLeakCheckIf, BytesIOContext, noarchTest,
skipIfRocm, skipIfNoSciPy, TemporaryFileName, TemporaryDirectoryName,
wrapDeterministicFlagAPITest, DeterministicGuard, CudaSyncGuard,
skipIfNotRegistered, bytes_to_scalar)
from multiprocessing.reduction import ForkingPickler
from torch.testing._internal.common_device_type import (
expectedFailureMeta,
expectedFailureXLA,
instantiate_device_type_tests,
skipCUDAVersionIn,
onlyCUDA, onlyCPU,
dtypes, dtypesIfCUDA, dtypesIfCPU, deviceCountAtLeast,
skipMeta,
PYTORCH_CUDA_MEMCHECK, largeTensorTest, onlyNativeDeviceTypes,
expectedAlertNondeterministic, get_all_device_types, skipXLA)
from typing import Dict, List, Tuple
import torch.backends.quantized
import torch.testing._internal.data
from torch.testing._internal.common_cuda import tf32_on_and_off, tf32_is_not_fp32
from torch.testing._internal.common_dtype import (
get_all_fp_dtypes, get_all_int_dtypes, get_all_math_dtypes, get_all_dtypes, get_all_complex_dtypes
)
# Protects against includes accidentally setting the default dtype
assert torch.get_default_dtype() is torch.float32
# load_tests from torch.testing._internal.common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings
load_tests = load_tests
AMPERE_OR_ROCM = TEST_WITH_ROCM or tf32_is_not_fp32()
# Wrap base test class into a class to hide it from testing
# See https://stackoverflow.com/a/25695512
class AbstractTestCases:
# This is intentionally prefixed by an underscore. Otherwise pytest will try to
# run its methods as test cases.
class _TestTorchMixin(TestCase):
def _make_tensors(self, shape, val_range=(-100, 100), use_floating=True, use_integral=True,
use_complex=False) -> Dict[str, List[torch.Tensor]]:
float_types = [torch.double,
torch.float]
int_types = [torch.int64,
torch.int32,
torch.int16]
complex_types = [torch.complex64,
torch.complex128]
def make_contiguous(shape, dtype) -> torch.Tensor:
if dtype in float_types:
val = torch.randn(shape, dtype=dtype)
val = val * ((val_range[1] - val_range[0]) / (math.pi * 2.0))
val = val + ((val_range[1] - val_range[0]) / 2.0)
val = torch.clamp(val, min=val_range[0], max=val_range[1])
return val
result = torch.zeros(shape, dtype=dtype)
result.apply_(lambda x: random.randint(val_range[0], val_range[1]))
return result
def make_non_contiguous(shape, dtype) -> torch.Tensor:
contig = make_contiguous(shape, dtype)
non_contig = torch.empty(shape + (2, 2), dtype=dtype)[..., 0]
non_contig = non_contig.select(-1, -1)
non_contig.copy_(contig)
self.assertFalse(non_contig.is_contiguous())
return non_contig
def make_contiguous_slice(size, dtype) -> torch.Tensor:
contig = make_contiguous((1, size), dtype)
non_contig = contig[:1, 1:size - 1]
self.assertTrue(non_contig.is_contiguous())
return contig
types = []
if use_floating:
types += float_types
if use_integral:
types += int_types
if use_complex:
types += complex_types
tensors: Dict[str, List[torch.Tensor]] = {"cont": [], "noncont": [], "slice": []}
for dtype in types:
tensors["cont"].append(make_contiguous(shape, dtype))
tensors["noncont"].append(make_non_contiguous(shape, dtype))
tensors["slice"].append(make_contiguous_slice(sum(list(shape)), dtype))
return tensors
def test_dir(self):
dir(torch)
def test_wildcard_import(self):
exec('from torch import *')
@wrapDeterministicFlagAPITest
def test_deterministic_flag(self):
for deterministic, warn_only in product([True, False], [True, False]):
torch.use_deterministic_algorithms(deterministic, warn_only=warn_only)
self.assertEqual(deterministic, torch.are_deterministic_algorithms_enabled())
self.assertEqual(warn_only, torch.is_deterministic_algorithms_warn_only_enabled())
if deterministic:
if warn_only:
debug_mode = 1
else:
debug_mode = 2
else:
debug_mode = 0
self.assertEqual(debug_mode, torch.get_deterministic_debug_mode())
for debug_mode in [0, 1, 2]:
torch.set_deterministic_debug_mode(debug_mode)
self.assertEqual(debug_mode, torch.get_deterministic_debug_mode())
deterministic = debug_mode in [1, 2]
warn_only = debug_mode == 1
self.assertEqual(deterministic, torch.are_deterministic_algorithms_enabled())
self.assertEqual(warn_only, torch.is_deterministic_algorithms_warn_only_enabled())
for debug_mode, debug_mode_str in [(0, 'default'), (1, 'warn'), (2, 'error')]:
torch.set_deterministic_debug_mode(debug_mode_str)
self.assertEqual(debug_mode, torch.get_deterministic_debug_mode())
with self.assertRaisesRegex(
TypeError,
r"_set_deterministic_algorithms\(\): argument 'mode' \(position 1\) must be bool, not int"):
torch.use_deterministic_algorithms(1)
with self.assertRaisesRegex(
TypeError,
r"_set_deterministic_algorithms\(\): argument 'warn_only' must be bool, not int"):
torch.use_deterministic_algorithms(False, warn_only=1)
def test_type_conversion_via_dtype_name(self):
x = torch.tensor([1])
self.assertEqual(x.byte().dtype, torch.uint8)
self.assertEqual(x.bool().dtype, torch.bool)
self.assertEqual(x.char().dtype, torch.int8)
self.assertEqual(x.double().dtype, torch.float64)
self.assertEqual(x.float().dtype, torch.float32)
self.assertEqual(x.half().dtype, torch.float16)
self.assertEqual(x.int().dtype, torch.int32)
self.assertEqual(x.bfloat16().dtype, torch.bfloat16)
cfloat = x.cfloat()
self.assertEqual(cfloat.dtype, torch.complex64)
self.assertEqual(cfloat.real, x.float())
self.assertEqual(cfloat.imag, torch.zeros_like(cfloat.imag))
cdouble = x.cdouble()
self.assertEqual(cdouble.dtype, torch.complex128)
self.assertEqual(cdouble.real, x.double())
self.assertEqual(cdouble.imag, torch.zeros_like(cdouble.imag))
def test_doc_template(self) -> None:
from torch._torch_docs import __file__ as doc_file
from torch._torch_docs import multi_dim_common, single_dim_common, factory_common_args, factory_like_common_args
with open(doc_file, "r", encoding="utf-8") as f:
doc_strs = f.read()
for doc_str in re.findall(r'add_docstr\((.*?),.*?("""|\'\'\')(.*?)("""|\'\'\')\)', doc_strs, re.MULTILINE | re.DOTALL):
for common_args in [multi_dim_common, single_dim_common, factory_common_args, factory_like_common_args]:
for k, v in common_args.items():
self.assertNotIn(v, doc_str[2], 'The argument description "{}" in {} can be '
'replaced by {{{}}}'.format(v, doc_str[0], k))
def test_doc(self):
checked_types = (types.MethodType, types.FunctionType,
types.BuiltinFunctionType, types.BuiltinMethodType)
def test_namespace(ns, *skips):
if isinstance(ns, object):
ns_name = ns.__class__.__name__
else:
ns_name = ns.__name__
skip_regexes = []
for r in skips:
if isinstance(r, string_classes):
skip_regexes.append(re.compile('^{}$'.format(re.escape(r))))
else:
skip_regexes.append(r)
for name in dir(ns):
if name.startswith('_'):
continue
if name in ['real', 'imag']:
y = torch.randn(1, dtype=torch.cfloat)
var = getattr(y, name)
elif name in ["H", "mT", "mH"]:
y = torch.randn(1, 1)
var = getattr(y, name)
else:
var = getattr(ns, name)
if not isinstance(var, checked_types):
continue
doc = var.__doc__
has_doc = doc is not None and len(doc.strip()) > 0
full_name = ns_name + '.' + name
if any(r.match(name) for r in skip_regexes):
self.assertFalse(has_doc,
'New docs have been added for {}, please remove '
'it from the skipped list in TestTorch.test_doc'.format(full_name))
else:
self.assertTrue(has_doc, '{} is missing documentation'.format(full_name))
# FIXME: All of the following should be marked as expected failures
# so that it is easier to tell when missing has been added.
# FIXME: fix all the skipped ones below!
test_namespace(torch.randn(1),
'as_strided_',
re.compile('^clamp_(min|max)_?$'),
'is_distributed',
'is_nonzero',
'is_same_size',
'log_softmax',
'map2_',
'new',
'reinforce',
'relu',
'relu_',
'prelu',
'resize',
'resize_as',
'softmax',
'split_with_sizes',
'unsafe_split_with_sizes',
'_autocast_to_fp16',
'_autocast_to_fp32',
)
test_namespace(torch.nn)
test_namespace(torch.nn.functional, 'assert_int_or_pair')
# TODO: add torch.* tests when we have proper namespacing on ATen functions
# test_namespace(torch)
def test_ort_error(self):
with self.assertRaisesRegex(RuntimeError,
"Could not run 'aten::empty.memory_format' with arguments from the 'ORT' backend"):
torch.zeros(1, device=torch.device('ort'))
def test_has_storage(self):
self.assertIsNotNone(torch.tensor([]).storage())
self.assertIsNotNone(torch.empty(0).storage())
self.assertIsNotNone(torch.tensor([]).clone().storage())
self.assertIsNotNone(torch.tensor([0, 0, 0]).nonzero().storage())
self.assertIsNotNone(torch.tensor([]).new().storage())
def test_where_invalid_device(self):
if torch.cuda.is_available():
for devices in [('cpu', 'cuda', 'cuda'), ('cuda', 'cpu', 'cpu'),
('cuda', 'cpu', 'cuda'), ('cpu', 'cuda', 'cpu')]:
condition = torch.rand(16, device=devices[0])
x = torch.rand(16, device=devices[1])
y = torch.rand(16, device=devices[2])
with self.assertRaisesRegex(RuntimeError,
"Expected condition, x and y to be on the same device"):
torch.where(condition, x, y)
def test_where_bool_tensor(self):
for d in get_all_device_types():
a = torch.tensor([True, False], device=d)
res = torch.where(a > 0)
self.assertEqual(1, len(res))
def test_where_tensor(self):
def rand_tensor(size, dtype, device):
if dtype.is_floating_point or dtype.is_complex:
return torch.rand(size=size, dtype=dtype, device=device)
elif dtype == torch.uint8:
return torch.randint(1, 5, size=size, dtype=dtype, device=device)
elif dtype == torch.bool:
return torch.randint(0, 1, size=size, dtype=dtype, device=device).bool()
else:
return torch.randint(-5, 5, size=size, dtype=dtype, device=device)
def get_tensor(size, dtype, device, contiguous):
if not contiguous and len(size) < 2:
raise RuntimeError("Unable to generate non contiguous tensor with size < 2")
t = rand_tensor(size, dtype, device)
if contiguous:
return t
else:
return t.transpose(0, 1)
height = 5
width = 5
for device in get_all_device_types():
for dt1 in get_all_dtypes():
for dt2 in get_all_dtypes():
for contiguous in [True, False]:
x1 = get_tensor((height, width), dt1, device, contiguous)
x2 = get_tensor((height, width), dt2, device, contiguous)
if dt1 != dt2:
self.assertRaisesRegex(RuntimeError, "expected scalar type", lambda: torch.where(x1 == 1, x1, x2))
else:
if x1.is_floating_point():
condition = (x1 < 0.5)
elif x1.is_complex():
condition = (x1.abs() < 0.5)
else:
condition = (x1 == 1)
expected = condition.to(x1.dtype) * x1 + (~condition).to(x2.dtype) * x2
result = torch.where(condition, x1, x2)
self.assertEqual(expected, result)
def test_dtypes(self):
all_dtypes = get_all_dtypes()
do_test_dtypes(self, all_dtypes, torch.strided, torch.device('cpu'))
if torch.cuda.is_available():
all_dtypes.remove(torch.bfloat16) # Remove once _th_zero_ is enabled on cuda for bfloat16
do_test_dtypes(self, all_dtypes, torch.strided, torch.device('cuda:0'))
def test_copy_dtypes(self):
all_dtypes = get_all_dtypes()
for dtype in all_dtypes:
copied_dtype = copy.deepcopy(dtype)
self.assertIs(dtype, copied_dtype)
def test_copy_transpose(self):
x = torch.arange(100 * 100, dtype=torch.float).reshape(100, 100).t()
y = torch.empty(100, 100, dtype=torch.float)
y.copy_(x)
self.assertEqual(y[:, 0], range(100))
self.assertEqual(y[:, 40], range(4000, 4100))
y = torch.empty(100, 100, dtype=torch.double)
y.copy_(x)
self.assertEqual(y[:, 0], range(100))
self.assertEqual(y[:, 40], range(4000, 4100))
# Validates regression reported in https://github.com/pytorch/pytorch/issues/45269
x = torch.arange(100 * 100).reshape(100, 100).to(dtype=torch.cfloat).t()
y = torch.empty(100, 100, dtype=torch.cfloat)
y.copy_(x)
self.assertEqual(y[:, 0], range(100))
self.assertEqual(y[:, 40], range(4000, 4100))
# Verifies the bugfix for https://github.com/pytorch/pytorch/issues/64358
def test_copy_transpose_2d_broadcast(self):
# The shape (60, 60) is chosen because of
# `MIN_SZ = 60 * 60` in `copy_transpose_valid` from aten/src/ATen/native/Copy.cpp
A = torch.randn(60, 60)
A.copy_(torch.tensor([[1.]]))
self.assertEqual(A, torch.ones(60, 60))
def test_device(self):
cpu = torch.device('cpu')
self.assertEqual('cpu', str(cpu))
self.assertEqual('cpu', cpu.type)
self.assertEqual(None, cpu.index)
cpu0 = torch.device('cpu:0')
self.assertEqual('cpu:0', str(cpu0))
self.assertEqual('cpu', cpu0.type)
self.assertEqual(0, cpu0.index)
cpu0 = torch.device('cpu', 0)
self.assertEqual('cpu:0', str(cpu0))
self.assertEqual('cpu', cpu0.type)
self.assertEqual(0, cpu0.index)
cuda = torch.device('cuda')
self.assertEqual('cuda', str(cuda))
self.assertEqual('cuda', cuda.type)
self.assertEqual(None, cuda.index)
cuda1 = torch.device('cuda:1')
self.assertEqual('cuda:1', str(cuda1))
self.assertEqual('cuda', cuda1.type)
self.assertEqual(1, cuda1.index)
cuda1 = torch.device('cuda', 1)
self.assertEqual('cuda:1', str(cuda1))
self.assertEqual('cuda', cuda1.type)
self.assertEqual(1, cuda1.index)
cuda90 = torch.device('cuda', 90)
self.assertEqual('cuda:90', str(cuda90))
self.assertEqual('cuda', cuda90.type)
self.assertEqual(90, cuda90.index)
self.assertRaises(RuntimeError, lambda: torch.device('cpu:-1'))
self.assertRaises(RuntimeError, lambda: torch.device('cuda:-1'))
self.assertRaises(RuntimeError, lambda: torch.device('cuda:2 '))
self.assertRaises(RuntimeError, lambda: torch.device('cuda: 2'))
self.assertRaises(RuntimeError, lambda: torch.device('cuda:2 2'))
self.assertRaises(RuntimeError, lambda: torch.device('cuda:2.'))
self.assertRaises(RuntimeError, lambda: torch.device('cuda:2?'))
self.assertRaises(RuntimeError, lambda: torch.device('cuda:?2'))
self.assertRaises(RuntimeError, lambda: torch.device('cuda:'))
self.assertRaises(RuntimeError, lambda: torch.device('cuda:2.232'))
self.assertRaises(RuntimeError, lambda: torch.device('cuda:2 cuda:3'))
self.assertRaises(RuntimeError, lambda: torch.device('cuda:2+cuda:3'))
self.assertRaises(RuntimeError, lambda: torch.device('cuda:2cuda:3'))
self.assertRaises(RuntimeError, lambda: torch.device(-1))
self.assertRaises(RuntimeError, lambda: torch.device('other'))
self.assertRaises(RuntimeError, lambda: torch.device('other:0'))
device_set = {'cpu', 'cpu:0', 'cuda', 'cuda:0', 'cuda:1', 'cuda:10', 'cuda:100'}
device_hash_set = set()
for device in list(device_set):
device_hash_set.add(hash(torch.device(device)))
self.assertEqual(len(device_set), len(device_hash_set))
def get_expected_device_repr(device):
if device.index is not None:
return "device(type='{type}', index={index})".format(
type=device.type, index=device.index)
return "device(type='{type}')".format(type=device.type)
for device in device_set:
dev = torch.device(device)
self.assertEqual(repr(dev), get_expected_device_repr(dev))
def test_to(self):
def test_copy_behavior(t, non_blocking=False):
self.assertIs(t, t.to(t, non_blocking=non_blocking))
self.assertIs(t, t.to(t.dtype, non_blocking=non_blocking))
self.assertIs(t, t.to(torch.empty_like(t), non_blocking=non_blocking))
self.assertIsNot(t, t.to(t, non_blocking=non_blocking, copy=True))
self.assertIsNot(t, t.to(t.dtype, non_blocking=non_blocking, copy=True))
self.assertIsNot(t, t.to(torch.empty_like(t), non_blocking=non_blocking, copy=True))
devices = [t.device]
if t.device.type == 'cuda':
if t.device.index == -1:
devices.append('cuda:{}'.format(torch.cuda.current_device()))
elif t.device.index == torch.cuda.current_device():
devices.append('cuda')
for device in devices:
self.assertIs(t, t.to(device, non_blocking=non_blocking))
self.assertIs(t, t.to(device, t.dtype, non_blocking=non_blocking))
self.assertIsNot(t, t.to(device, non_blocking=non_blocking, copy=True))
self.assertIsNot(t, t.to(device, t.dtype, non_blocking=non_blocking, copy=True))
a = torch.tensor(5)
test_copy_behavior(a)
self.assertEqual(a.device, a.to('cpu').device)
self.assertEqual(a.device, a.to('cpu', dtype=torch.float32).device)
self.assertIs(torch.float32, a.to('cpu', dtype=torch.float32).dtype)
self.assertEqual(a.device, a.to(torch.float32).device)
self.assertIs(torch.float32, a.to(dtype=torch.float32).dtype)
self.assertEqual(a.data_ptr(), a.to('cpu').data_ptr())
self.assertEqual(a.data_ptr(), a.to(dtype=a.dtype, device=a.device, copy=False).data_ptr())
self.assertEqual(a.data_ptr(), a.to('cpu', copy=False).data_ptr())
self.assertNotEqual(a.data_ptr(), a.to('cpu', copy=True).data_ptr())
if torch.cuda.is_available():
for non_blocking in [True, False]:
for cuda in ['cuda', 'cuda:0' if torch.cuda.device_count() == 1 else 'cuda:1']:
b = torch.tensor(5., device=cuda)
test_copy_behavior(b, non_blocking)
self.assertEqual(b.device, b.to(cuda, non_blocking=non_blocking).device)
self.assertEqual(a.device, b.to('cpu', non_blocking=non_blocking).device)
self.assertEqual(b.device, a.to(cuda, non_blocking=non_blocking).device)
self.assertIs(torch.int32, b.to('cpu', dtype=torch.int32, non_blocking=non_blocking).dtype)
self.assertEqual(a.device, b.to('cpu', dtype=torch.int32, non_blocking=non_blocking).device)
self.assertIs(torch.int32, b.to(dtype=torch.int32).dtype)
self.assertEqual(b.device, b.to(dtype=torch.int32).device)
def test_to_with_tensor(self):
a = torch.tensor(5)
self.assertEqual(a.device, a.to(a).device)
if torch.cuda.is_available():
for non_blocking in [True, False]:
for cuda in ['cuda', 'cuda:0' if torch.cuda.device_count() == 1 else 'cuda:1']:
b = torch.tensor(5., device=cuda)
self.assertEqual(b.device, b.to(b, non_blocking=non_blocking).device)
self.assertEqual(a.device, b.to(a, non_blocking=non_blocking).device)
self.assertEqual(b.device, a.to(b, non_blocking=non_blocking).device)
def test_as_subclass(self):
class SubTensor(torch.Tensor):
member_var = object()
t0 = torch.tensor(0)
t1 = torch.tensor([1, 2])
t2 = torch.tensor([[3, 4], [5, 6]])
s0 = t0.as_subclass(SubTensor)
s1 = t1.as_subclass(SubTensor)
s2 = t2.as_subclass(SubTensor)
# Check that the correct type is returned.
self.assertTrue(type(s0) is SubTensor)
self.assertTrue(type(s1) is SubTensor)
self.assertTrue(type(s2) is SubTensor)
# Check that the data is equal.
self.assertEqual(t0, s0)
self.assertEqual(t1, s1)
self.assertEqual(t2, s2)
t0[()] = 1
t1[1] = 3
t2[1, 1] = 7
# Check that the data is equal even after modification.
self.assertEqual(t0, s0)
self.assertEqual(t1, s1)
self.assertEqual(t2, s2)
# Check that member variables are passed through.
self.assertTrue(s0.member_var is SubTensor.member_var)
self.assertTrue(s1.member_var is SubTensor.member_var)
self.assertTrue(s2.member_var is SubTensor.member_var)
# Test that autograd is propagated.
t = torch.tensor(5, dtype=torch.float32, requires_grad=True)
# Run a calculation on the tensor.
exp_t = torch.exp(t)
# Cast exp_t to a subclass.
exp_s = exp_t.as_subclass(SubTensor)
# Make sure that t.grad was initially None
self.assertTrue(t.grad is None)
# Run the autograd calculation.
exp_s.backward()
# Make sure autograd was propagated to the original tensor
# declared with requires_grad.
self.assertTrue(t.grad is not None)
# Make sure invalid subclasses raise nice errors
class BadSubTensor():
member_var = object()
err_msg = "Creating a Tensor subclass from a class that does not inherit from Tensor"
with self.assertRaisesRegex(RuntimeError, err_msg):
s0 = t0.as_subclass(BadSubTensor)
def test_type(self):
x = torch.randn(3, 3).double()
self.assertEqual(x.type('torch.FloatTensor').dtype, torch.float32)
self.assertEqual(x.type(torch.FloatTensor).dtype, torch.float32)
self.assertEqual(x.int().type(torch.Tensor).dtype, torch.get_default_dtype())
self.assertEqual(x.type(torch.int32).dtype, torch.int32)
def test_qengine(self):
qengines = torch.backends.quantized.supported_engines
original_qe = torch.backends.quantized.engine
for qe in qengines:
torch.backends.quantized.engine = qe
assert torch.backends.quantized.engine == qe, 'qengine not set successfully'
torch.backends.quantized.engine = original_qe
def _spawn_method(self, method, arg):
try:
mp.set_start_method('spawn')
except RuntimeError:
pass
with mp.Pool(1) as pool:
out: list = pool.map(method, [arg])
self.assertTrue(out[0])
@staticmethod
def _test_multinomial_invalid_probs(probs):
try:
# n_sample = 1 is a special case, test n_sample=2 which is more general
torch.multinomial(probs.to('cpu'), 2)
return False # Should not be reached
except RuntimeError as e:
return 'probability tensor contains either `inf`, `nan` or element < 0' in str(e)
@slowTest
@unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \
don't support multiprocessing with spawn start method")
@unittest.skipIf(IS_WINDOWS, 'FIXME: CUDA OOM error on Windows')
def test_multinomial_invalid_probs(self):
test_method = AbstractTestCases._TestTorchMixin._test_multinomial_invalid_probs
self._spawn_method(test_method, torch.tensor([1., -1., 1.]))
self._spawn_method(test_method, torch.tensor([1., inf, 1.]))
self._spawn_method(test_method, torch.tensor([1., -inf, 1.]))
self._spawn_method(test_method, torch.tensor([1., 1., nan]))
def test_copy_broadcast(self):
torch.zeros(5, 6).copy_(torch.zeros(6))
self.assertRaises(RuntimeError, lambda: torch.zeros(5, 6).copy_(torch.zeros(30)))
def test_copy_many_to_one(self):
# Testing in-place copy where it attempt to write from many memory
# storage to a single storage would cause RuntimeError to be thrown
self.assertRaises(RuntimeError, lambda: torch.zeros(1, 6).expand(5, 6).copy_(torch.zeros(5, 6)))
def test_slice(self):
empty = torch.empty(0, 4)
x = torch.arange(0., 16).view(4, 4)
self.assertEqual(x[:], x)
self.assertEqual(x[:4], x)
# start and stop are clamped to the size of dim
self.assertEqual(x[:5], x)
# if start >= stop then the result is empty
self.assertEqual(x[2:1], empty)
self.assertEqual(x[2:2], empty)
# out of bounds is also empty
self.assertEqual(x[10:12], empty)
# additional correctness checks
self.assertEqual(x[:1].tolist(), [[0, 1, 2, 3]])
self.assertEqual(x[:-3].tolist(), [[0, 1, 2, 3]])
self.assertEqual(x[:, -2:3].tolist(), [[2], [6], [10], [14]])
self.assertEqual(x[0:-1:2].tolist(), [[0, 1, 2, 3], [8, 9, 10, 11]])
@unittest.skip("Not implemented yet")
def test_conv2(self):
x = torch.rand(math.floor(torch.uniform(50, 100)), math.floor(torch.uniform(50, 100)))
k = torch.rand(math.floor(torch.uniform(10, 20)), math.floor(torch.uniform(10, 20)))
imvc = torch.conv2(x, k)
imvc2 = torch.conv2(x, k, 'V')
imfc = torch.conv2(x, k, 'F')
ki = k.clone()
ks = k.storage()
kis = ki.storage()
for i in range(ks.size() - 1, 0, -1):
kis[ks.size() - i + 1] = ks[i]
# for i=ks.size(), 1, -1 do kis[ks.size()-i+1]=ks[i] end
imvx = torch.xcorr2(x, ki)
imvx2 = torch.xcorr2(x, ki, 'V')
imfx = torch.xcorr2(x, ki, 'F')
self.assertEqual(imvc, imvc2, atol=0, rtol=0, msg='torch.conv2')
self.assertEqual(imvc, imvx, atol=0, rtol=0, msg='torch.conv2')
self.assertEqual(imvc, imvx2, atol=0, rtol=0, msg='torch.conv2')
self.assertEqual(imfc, imfx, atol=0, rtol=0, msg='torch.conv2')
self.assertLessEqual(math.abs(x.dot(x) - torch.xcorr2(x, x)[0][0]), 1e-10, 'torch.conv2')
xx = torch.empty(2, x.size(1), x.size(2))
xx[1].copy_(x)
xx[2].copy_(x)
kk = torch.empty(2, k.size(1), k.size(2))
kk[1].copy_(k)
kk[2].copy_(k)
immvc = torch.conv2(xx, kk)
immvc2 = torch.conv2(xx, kk, 'V')
immfc = torch.conv2(xx, kk, 'F')
self.assertEqual(immvc[0], immvc[1], atol=0, rtol=0, msg='torch.conv2')
self.assertEqual(immvc[0], imvc, atol=0, rtol=0, msg='torch.conv2')
self.assertEqual(immvc2[0], imvc2, atol=0, rtol=0, msg='torch.conv2')
self.assertEqual(immfc[0], immfc[1], atol=0, rtol=0, msg='torch.conv2')
self.assertEqual(immfc[0], imfc, atol=0, rtol=0, msg='torch.conv2')
@unittest.skip("Not implemented yet")
def test_conv3(self):
x = torch.rand(math.floor(torch.uniform(20, 40)),
math.floor(torch.uniform(20, 40)),
math.floor(torch.uniform(20, 40)))
k = torch.rand(math.floor(torch.uniform(5, 10)),
math.floor(torch.uniform(5, 10)),
math.floor(torch.uniform(5, 10)))
imvc = torch.conv3(x, k)
imvc2 = torch.conv3(x, k, 'V')
imfc = torch.conv3(x, k, 'F')
ki = k.clone()
ks = k.storage()
kis = ki.storage()
for i in range(ks.size() - 1, 0, -1):
kis[ks.size() - i + 1] = ks[i]
imvx = torch.xcorr3(x, ki)
imvx2 = torch.xcorr3(x, ki, 'V')
imfx = torch.xcorr3(x, ki, 'F')
self.assertEqual(imvc, imvc2, atol=0, rtol=0, msg='torch.conv3')
self.assertEqual(imvc, imvx, atol=0, rtol=0, msg='torch.conv3')
self.assertEqual(imvc, imvx2, atol=0, rtol=0, msg='torch.conv3')
self.assertEqual(imfc, imfx, atol=0, rtol=0, msg='torch.conv3')
self.assertLessEqual(math.abs(x.dot(x) - torch.xcorr3(x, x)[0][0][0]), 4e-10, 'torch.conv3')
xx = torch.empty(2, x.size(1), x.size(2), x.size(3))
xx[1].copy_(x)
xx[2].copy_(x)
kk = torch.empty(2, k.size(1), k.size(2), k.size(3))
kk[1].copy_(k)
kk[2].copy_(k)
immvc = torch.conv3(xx, kk)
immvc2 = torch.conv3(xx, kk, 'V')
immfc = torch.conv3(xx, kk, 'F')
self.assertEqual(immvc[0], immvc[1], atol=0, rtol=0, msg='torch.conv3')
self.assertEqual(immvc[0], imvc, atol=0, rtol=0, msg='torch.conv3')
self.assertEqual(immvc2[0], imvc2, atol=0, rtol=0, msg='torch.conv3')
self.assertEqual(immfc[0], immfc[1], atol=0, rtol=0, msg='torch.conv3')
self.assertEqual(immfc[0], imfc, atol=0, rtol=0, msg='torch.conv3')
@unittest.skip("Not implemented yet")
def _test_conv_corr_eq(self, fn, fn_2_to_3):
ix = math.floor(random.randint(20, 40))
iy = math.floor(random.randint(20, 40))
iz = math.floor(random.randint(20, 40))
kx = math.floor(random.randint(5, 10))
ky = math.floor(random.randint(5, 10))
kz = math.floor(random.randint(5, 10))
x = torch.rand(ix, iy, iz)
k = torch.rand(kx, ky, kz)
o3 = fn(x, k)
o32 = torch.zeros(o3.size())
fn_2_to_3(x, k, o3, o32)
self.assertEqual(o3, o32)
@unittest.skip("Not implemented yet")
def test_xcorr3_xcorr2_eq(self):
def reference(x, k, o3, o32):
for i in range(o3.size(1)):
for j in range(k.size(1)):
o32[i].add(torch.xcorr2(x[i + j - 1], k[j]))
self._test_conv_corr_eq(torch.xcorr3, reference)
@unittest.skip("Not implemented yet")
def test_xcorr3_xcorr2_eq_full(self):
def reference(x, k, o3, o32):
for i in range(x.size(1)):
for j in range(k.size(1)):
o32[i].add(torch.xcorr2(x[i], k[k.size(1) - j + 1], 'F'))
self._test_conv_corr_eq(lambda x, k: torch.xcorr3(x, k, 'F'), reference)
@unittest.skip("Not implemented yet")
def test_conv3_conv2_eq_valid(self):
def reference(x, k, o3, o32):
for i in range(o3.size(1)):
for j in range(k.size(1)):
o32[i].add(torch.conv2(x[i + j - 1], k[k.size(1) - j + 1]))
self._test_conv_corr_eq(torch.conv3, reference)
@unittest.skip("Not implemented yet")
def test_fconv3_fconv2_eq(self):
def reference(x, k, o3, o32):
for i in range(o3.size(1)):
for j in range(k.size(1)):
o32[i + j - 1].add(torch.conv2(x[i], k[j], 'F'))
self._test_conv_corr_eq(lambda x, k: torch.conv3(x, k, 'F'), reference)
def test_dtype_is_signed(self):
for dtype in get_all_dtypes():
self.assertEqual(dtype.is_signed, torch.is_signed(torch.tensor(0, dtype=dtype)))
self.assertRaisesRegex(RuntimeError, 'not supported for quantized', lambda: torch.quint8.is_signed)
self.assertRaisesRegex(RuntimeError, 'not supported for quantized', lambda: torch.qint8.is_signed)
self.assertRaisesRegex(RuntimeError, 'not supported for quantized', lambda: torch.qint32.is_signed)
def test_RNGState(self):
state = torch.get_rng_state()
stateCloned = state.clone()
before = torch.rand(1000)
self.assertEqual(state.ne(stateCloned).long().sum(), 0, atol=0, rtol=0)
torch.set_rng_state(state)
after = torch.rand(1000)
self.assertEqual(before, after, atol=0, rtol=0)
def test_RNGStateAliasing(self):
# Fork the random number stream at this point
gen = torch.Generator()
gen.set_state(torch.get_rng_state())
self.assertEqual(gen.get_state(), torch.get_rng_state())
target_value = torch.rand(1000)
# Dramatically alter the internal state of the main generator
_ = torch.rand(100000)
forked_value = torch.rand(1000, generator=gen)
self.assertEqual(target_value, forked_value, atol=0, rtol=0, msg="RNG has not forked correctly.")
def test_RNG_after_pickle(self):
torch.random.manual_seed(100)
before = torch.rand(10)
torch.random.manual_seed(100)
buf = io.BytesIO()
tensor = torch.tensor([1, 2, 3])
ForkingPickler(buf, pickle.HIGHEST_PROTOCOL).dump(tensor)
after = torch.rand(10)
self.assertEqual(before, after, atol=0, rtol=0)
def test_boxMullerState(self):
torch.manual_seed(123)
odd_number = 101
seeded = torch.randn(odd_number)
state = torch.get_rng_state()
midstream = torch.randn(odd_number)
torch.set_rng_state(state)
repeat_midstream = torch.randn(odd_number)
torch.manual_seed(123)
reseeded = torch.randn(odd_number)
self.assertEqual(midstream, repeat_midstream, atol=0, rtol=0,
msg='get_rng_state/set_rng_state not generating same sequence of normally distributed numbers')
self.assertEqual(seeded, reseeded, atol=0, rtol=0,
msg='repeated calls to manual_seed not generating same sequence of normally distributed numbers')
def test_manual_seed(self):
rng_state = torch.get_rng_state()
torch.manual_seed(2)
x = torch.randn(100)
self.assertEqual(torch.initial_seed(), 2)
torch.manual_seed(2)
y = torch.randn(100)
self.assertEqual(x, y)
max_int64 = 0x7fff_ffff_ffff_ffff
min_int64 = -max_int64 - 1
max_uint64 = 0xffff_ffff_ffff_ffff
# Check all boundary cases of valid seed value inputs
test_cases = [
# (seed, expected_initial_seed)
# Positive seeds should be unchanged
(max_int64, max_int64),
(max_int64 + 1, max_int64 + 1),
(max_uint64, max_uint64),
(0, 0),
# Negative seeds wrap around starting from the largest seed value
(-1, max_uint64),
(min_int64, max_int64 + 1)
]
for seed, expected_initial_seed in test_cases:
torch.manual_seed(seed)
actual_initial_seed = torch.initial_seed()
msg = "expected initial_seed() = %x after calling manual_seed(%x), but got %x instead" % (
expected_initial_seed, seed, actual_initial_seed)
self.assertEqual(expected_initial_seed, actual_initial_seed, msg=msg)
for invalid_seed in [min_int64 - 1, max_uint64 + 1]:
with self.assertRaisesRegex(RuntimeError, r'Overflow when unpacking long'):
torch.manual_seed(invalid_seed)
torch.set_rng_state(rng_state)
def test_numel(self):
b = torch.ByteTensor(3, 100, 100)
self.assertEqual(b.nelement(), 3 * 100 * 100)
self.assertEqual(b.numel(), 3 * 100 * 100)
def test_empty_storage_view(self):
# we should be able to "modify" slices of a 0-element
# array without an error being raised due to
# trying to resize its storage
t = torch.from_numpy(np.empty((0, 4)))
t[:, 1::2] *= 1
def test_newaxis_numpy_comparison(self):
def run_test(tensor, *idx):
npt = tensor.numpy()
self.assertEqual(tensor[idx], npt[idx])
# 1D Tensor Tests
x = torch.arange(0, 10)
cases = [
[None],
[None, None],
[Ellipsis, None],
[None, Ellipsis],
[2, None],
[None, 2],
[Ellipsis, None, 2],
[Ellipsis, 2, None],
[2, Ellipsis, None],
[2, None, Ellipsis],
[None, 2, Ellipsis],
[None, Ellipsis, 2],
]
for case in cases:
run_test(x, *case)
# 2D Tensor Tests
x = torch.arange(0, 12).view(3, 4)
cases = [
[None],
[None, None],
[None, None, None],
[Ellipsis, None],
[Ellipsis, None, None],
[None, Ellipsis],
[None, Ellipsis, None],
[None, None, Ellipsis],
[2, None],
[2, None, Ellipsis],
[2, Ellipsis, None],
[None, 2, Ellipsis],
[Ellipsis, 2, None],
[Ellipsis, None, 2],
[None, Ellipsis, 2],
[1, 2, None],
[1, 2, Ellipsis, None],
[1, Ellipsis, 2, None],
[Ellipsis, 1, None, 2],
[Ellipsis, 1, 2, None],
[1, None, 2, Ellipsis],
[None, 1, Ellipsis, 2],
[None, 1, 2, Ellipsis],
]
for case in cases:
run_test(x, *case)
def _consecutive(self, size, start=1):
sequence = torch.ones(torch.tensor(size).prod(0)).cumsum(0)
sequence.add_(start - 1)
return sequence.resize_(*size)
def test_newindex(self):
reference = self._consecutive((3, 3, 3))
# This relies on __index__() being correct - but we have separate tests for that
def checkPartialAssign(index):
reference = torch.zeros(3, 3, 3)
reference[index] = self._consecutive((3, 3, 3))[index]
self.assertEqual(reference[index], self._consecutive((3, 3, 3))[index], atol=0, rtol=0)
reference[index] = 0
self.assertEqual(reference, torch.zeros(3, 3, 3), atol=0, rtol=0)
checkPartialAssign(0)
checkPartialAssign(1)
checkPartialAssign(2)
checkPartialAssign((0, 1))
checkPartialAssign((1, 2))
checkPartialAssign((0, 2))
checkPartialAssign(torch.LongTensor((0, 2)))
with self.assertRaises(IndexError):
reference[1, 1, 1, 1] = 1
with self.assertRaises(IndexError):
reference[1, 1, 1, (1, 1)] = 1
with self.assertRaises(IndexError):
reference[3, 3, 3, 3, 3, 3, 3, 3] = 1
with self.assertRaises(IndexError):
reference[0.0] = 1
with self.assertRaises(TypeError):
reference[0.0:2.0] = 1
with self.assertRaises(IndexError):
reference[0.0, 0.0:2.0] = 1
with self.assertRaises(IndexError):
reference[0.0, :, 0.0:2.0] = 1
with self.assertRaises(IndexError):
reference[0.0, ..., 0.0:2.0] = 1
with self.assertRaises(IndexError):
reference[0.0, :, 0.0] = 1
def test_index_add(self):
for device in get_all_device_types():
for dest_contig, src_contig, index_contig in product([True, False], repeat=3):
for other_sizes in ((), (4, 5)):
for dtype in [torch.int, torch.long]:
num_copy, num_dest = 3, 3
dest = torch.randn(num_dest, *other_sizes, device=device)
if not dest_contig:
dest = torch.testing.make_non_contiguous(dest)
src = torch.randn(num_copy, *other_sizes, device=device)
if not src_contig:
src = torch.testing.make_non_contiguous(src)
idx = torch.randperm(num_dest, dtype=dtype, device=device).narrow(0, 0, num_copy)
if not index_contig:
idx = torch.testing.make_non_contiguous(idx)
# index_add_ without alpha argument