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android / platform / external / pytorch / 3df79f403e / . / test / test_torch.py
blob: c9c41ff652bd1edcc87b9b61bd323f924688a134 [file] [log] [blame]
import sys
import io
import os
import math
import random
import operator
import copy
import shutil
import torch
import torch.cuda
import tempfile
import unittest
import warnings
import pickle
import gzip
import types
import re
from torch._utils_internal import get_file_path, get_file_path_2
from torch.utils.dlpack import from_dlpack, to_dlpack
from torch._utils import _rebuild_tensor
from torch._six import inf, nan, string_classes
from itertools import product, combinations
from functools import reduce
from torch import multiprocessing as mp
from common_utils import TestCase, iter_indices, TEST_NUMPY, TEST_SCIPY, TEST_MKL, \
TEST_LIBROSA, run_tests, download_file, skipIfNoLapack, suppress_warnings, \
IS_WINDOWS, PY3, NO_MULTIPROCESSING_SPAWN, skipIfRocm, do_test_dtypes, do_test_empty_full, \
IS_SANDCASTLE, load_tests
from multiprocessing.reduction import ForkingPickler
# load_tests from common_utils is used to automatically filter tests for
# sharding on sandcastle. This line silences flake warnings
load_tests = load_tests
if TEST_NUMPY:
import numpy as np
if TEST_SCIPY:
from scipy import signal
if TEST_LIBROSA:
import librosa
SIZE = 100
can_retrieve_source = True
with warnings.catch_warnings(record=True) as warns:
with tempfile.NamedTemporaryFile() as checkpoint:
x = torch.save(torch.nn.Module(), checkpoint)
for warn in warns:
if "Couldn't retrieve source code" in warn.message.args[0]:
can_retrieve_source = False
break
class FilelikeMock(object):
def __init__(self, data, has_fileno=True, has_readinto=False):
if has_readinto:
setattr(self, 'readinto', self.readinto_opt)
if has_fileno:
# Python 2's StringIO.StringIO has no fileno attribute.
# This is used to test that.
setattr(self, 'fileno', self.fileno_opt)
self.calls = set([])
self.bytesio = io.BytesIO(data)
def trace(fn, name):
def result(*args, **kwargs):
self.calls.add(name)
return fn(*args, **kwargs)
return result
for attr in ['read', 'readline', 'seek', 'tell', 'write', 'flush']:
traced_fn = trace(getattr(self.bytesio, attr), attr)
setattr(self, attr, traced_fn)
def fileno_opt(self):
raise io.UnsupportedOperation('Not a real file')
def readinto_opt(self, view):
self.calls.add('readinto')
return self.bytesio.readinto(view)
def was_called(self, name):
return name in self.calls
class BytesIOContext(io.BytesIO):
def __enter__(self):
return self
def __exit__(self, *args):
pass
# This is intentionally prefixed by an underscore. Otherwise pytest will try to
# run its methods as test cases.
class _TestTorchMixin(object):
def _check_sum_dim(tensors, dim):
for tensor in tensors:
expected = tensor.numpy().sum(dim)
actual = tensor.sum(dim)
self.assertEqual(expected.shape, actual.shape)
if actual.dtype == torch.float:
self.assertTrue(np.allclose(expected, actual.numpy(), rtol=1e-03, atol=1e-05))
else:
self.assertTrue(np.allclose(expected, actual.numpy()))
def _make_tensors(self, shape, val_range=(-100, 100), use_floating=True, use_integral=True):
float_types = [torch.double,
torch.float]
int_types = [torch.int64,
torch.int32,
torch.int16]
def make_contiguous(shape, dtype):
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):
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):
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
tensors = {"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_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('^{}$'.format(re.escape(r))))
else:
skip_regexes.append(r)
for name in dir(ns):
if name.startswith('_'):
continue
var = getattr(ns, name)
if not isinstance(var, checked_types):
continue
doc = var.__doc__
has_doc = doc is not None and len(doc.strip()) > 0
full_name = ns_name + '.' + name
if any(r.match(name) for r in skip_regexes):
self.assertFalse(has_doc,
'New docs have been added for {}, please remove '
'it from the skipped list in TestTorch.test_doc'.format(full_name))
else:
self.assertTrue(has_doc, '{} is missing documentation'.format(full_name))
# FIXME: fix all the skipped ones below!
test_namespace(torch.randn(1),
'as_strided',
'as_strided_',
re.compile('^clamp_(min|max)_?$'),
'coalesce',
'index_put',
'is_coalesced',
'is_distributed',
'is_floating_point',
'is_complex',
'is_nonzero',
'is_same_size',
'is_signed',
'isclose',
'lgamma',
'lgamma_',
'log_softmax',
'map2_',
'new',
'pin_memory',
'polygamma',
'polygamma_',
'record_stream',
'reinforce',
'relu',
'relu_',
'prelu',
'resize',
'resize_as',
'smm',
'softmax',
'split_with_sizes',
'sspaddmm',
'storage_type',
'tan',
'to_dense',
'sparse_resize_',
'sparse_resize_and_clear_',
)
test_namespace(torch.nn)
test_namespace(torch.nn.functional, 'assert_int_or_pair', 'bilinear', 'feature_alpha_dropout')
# TODO: add torch.* tests when we have proper namespacing on ATen functions
# test_namespace(torch)
def test_dot(self):
types = {
'torch.DoubleTensor': 1e-8,
'torch.FloatTensor': 1e-4,
}
for tname, _prec in types.items():
v1 = torch.randn(100).type(tname)
v2 = torch.randn(100).type(tname)
res1 = torch.dot(v1, v2)
res2 = 0
for i, j in zip(v1, v2):
res2 += i * j
self.assertEqual(res1, res2)
out = torch.randn(()).type(tname)
torch.dot(v1, v2, out=out)
self.assertEqual(res1, out)
# Test 0-strided
for tname, _prec in types.items():
v1 = torch.randn(1).type(tname).expand(100)
v2 = torch.randn(100).type(tname)
res1 = torch.dot(v1, v2)
res2 = 0
for i, j in zip(v1, v2):
res2 += i * j
self.assertEqual(res1, res2)
out = torch.randn(()).type(tname)
torch.dot(v1, v2, out=out)
self.assertEqual(res1, out)
def test_ger(self):
types = {
'torch.DoubleTensor': 1e-8,
'torch.FloatTensor': 1e-4,
}
for tname, _prec in types.items():
v1 = torch.randn(100).type(tname)
v2 = torch.randn(100).type(tname)
res1 = torch.ger(v1, v2)
res2 = torch.zeros(100, 100).type(tname)
for i in range(100):
for j in range(100):
res2[i, j] = v1[i] * v2[j]
self.assertEqual(res1, res2)
# Test 0-strided
for tname, _prec in types.items():
v1 = torch.randn(1).type(tname).expand(100)
v2 = torch.randn(100).type(tname)
res1 = torch.ger(v1, v2)
res2 = torch.zeros(100, 100).type(tname)
for i in range(100):
for j in range(100):
res2[i, j] = v1[i] * v2[j]
self.assertEqual(res1, res2)
def test_addr(self):
types = {
'torch.DoubleTensor': 1e-8,
'torch.FloatTensor': 1e-4,
}
def run_test(m, v1, v2, m_transform=lambda x: x):
m = m_transform(m.clone())
ref = m.clone()
torch.addr(m, v1, v2, out=m)
for i in range(m.size(0)):
for j in range(m.size(1)):
ref[i, j] += v1[i] * v2[j]
self.assertEqual(m, ref)
for tname, _prec in types.items():
for h, w in [(100, 110), (1, 20), (200, 2)]:
m = torch.randn(h, w).type(tname)
v1 = torch.randn(h).type(tname)
v2 = torch.randn(w).type(tname)
run_test(m, v1, v2)
# test transpose
run_test(m, v2, v1, lambda x: x.transpose(0, 1))
# test 0 strided
v1 = torch.randn(1).type(tname).expand(h)
run_test(m, v1, v2)
run_test(m, v2, v1, lambda x: x.transpose(0, 1))
def test_addmv(self):
types = {
'torch.DoubleTensor': 1e-8,
'torch.FloatTensor': 1e-4,
}
for tname, _prec in types.items():
t = torch.randn(10).type(tname)
m = torch.randn(10, 100).type(tname)
v = torch.randn(100).type(tname)
res1 = torch.addmv(t, m, v)
res2 = torch.zeros(10).type(tname)
res2 += t
for i in range(10):
for j in range(100):
res2[i] += m[i, j] * v[j]
self.assertEqual(res1, res2)
# Test 0-strided
for tname, _prec in types.items():
t = torch.randn(1).type(tname).expand(10)
m = torch.randn(10, 1).type(tname).expand(10, 100)
v = torch.randn(100).type(tname)
res1 = torch.addmv(t, m, v)
res2 = torch.zeros(10).type(tname)
res2 += t
for i in range(10):
for j in range(100):
res2[i] += m[i, j] * v[j]
self.assertEqual(res1, res2)
def test_addmm(self):
types = {
'torch.DoubleTensor': 1e-8,
'torch.FloatTensor': 1e-4,
}
for tname, _prec in types.items():
M = torch.randn(10, 25).type(tname)
m1 = torch.randn(10, 50).type(tname)
m2 = torch.randn(50, 25).type(tname)
res1 = torch.addmm(M, m1, m2)
res2 = torch.zeros(10, 25).type(tname)
res2 += M
for i in range(10):
for j in range(25):
for k in range(50):
res2[i, j] += m1[i, k] * m2[k, j]
self.assertEqual(res1, res2)
# Test 0-strided
for tname, _prec in types.items():
M = torch.randn(10, 1).type(tname).expand(10, 25)
m1 = torch.randn(10, 1).type(tname).expand(10, 50)
m2 = torch.randn(50, 25).type(tname)
res1 = torch.addmm(M, m1, m2)
res2 = torch.zeros(10, 25).type(tname)
res2 += M
for i in range(10):
for j in range(25):
for k in range(50):
res2[i, j] += m1[i, k] * m2[k, j]
self.assertEqual(res1, res2)
def test_allclose(self):
x = torch.tensor([1.0, 2.0, 3.0])
y = torch.tensor([1.01, 2.01, 3.01])
self.assertTrue(torch.allclose(x, y, rtol=0, atol=0.02))
self.assertTrue(torch.allclose(x, y, rtol=0.01, atol=0.0))
self.assertFalse(torch.allclose(x, y))
self.assertTrue(torch.allclose(torch.tensor([0.0]), torch.tensor([1e-8])))
x = torch.tensor([2.0, 3.0, nan])
y = torch.tensor([2.01, 3.01, nan])
self.assertFalse(torch.allclose(x, y, rtol=1e-2))
self.assertTrue(torch.allclose(x, y, rtol=1e-2, equal_nan=True))
self.assertFalse(torch.allclose(x, y, rtol=1e-3, equal_nan=True))
inf_t = torch.tensor([inf])
self.assertTrue(torch.allclose(inf_t, inf_t))
self.assertTrue(torch.allclose(-inf_t, -inf_t))
self.assertFalse(torch.allclose(inf_t, -inf_t))
self.assertFalse(torch.allclose(inf_t, torch.tensor([1e20])))
self.assertFalse(torch.allclose(-inf_t, torch.tensor([-1e20])))
def test_linear_algebra_scalar_raises(self):
m = torch.randn(5, 5)
v = torch.randn(5)
s = torch.tensor(7)
self.assertRaises(RuntimeError, lambda: torch.mv(m, s))
self.assertRaises(RuntimeError, lambda: torch.addmv(v, m, s))
self.assertRaises(RuntimeError, lambda: torch.ger(v, s))
self.assertRaises(RuntimeError, lambda: torch.ger(s, v))
self.assertRaises(RuntimeError, lambda: torch.addr(m, v, s))
self.assertRaises(RuntimeError, lambda: torch.addr(m, s, v))
def _test_math(self, torchfn, mathfn, input=None, test_expand=False):
if input is None:
input = []
input.append(list(range(-5, 5)))
input.append([0 for x in range(-5, 5)])
input.append([x + 1e-6 for x in range(-5, 5)])
# Some vectorized implementations don't support large ranges
input.append([x + 1e10 for x in range(-5, 5)])
input.append([x - 1e10 for x in range(-5, 5)])
input.append(torch.randn(10).tolist())
input.append((torch.randn(10) + 1e6).tolist())
input.append([math.pi * (x / 2) for x in range(-5, 5)])
def compare_reference(input, dtype):
input = torch.tensor(input, dtype=dtype)
res1 = torchfn(input.clone())
res2 = input.clone().apply_(lambda x: mathfn(x))
torch.testing.assert_allclose(res1, res2)
# compare against the reference math function
compare_reference(input, torch.double)
compare_reference(input, torch.float)
def check_non_contiguous(shape, dtype):
contig = torch.randn(shape, dtype=dtype)
non_contig = torch.empty(shape + (2,), dtype=dtype)[..., 0]
non_contig.copy_(contig)
self.assertFalse(non_contig.is_contiguous())
self.assertEqual(torchfn(contig), torchfn(non_contig), 'non-contiguous')
# compare application against contiguous vs. non-contiguous
check_non_contiguous((5, 7), torch.double)
check_non_contiguous((1024,), torch.double)
check_non_contiguous((5, 7), torch.float)
check_non_contiguous((1024,), torch.float)
def check_non_contiguous_index(dtype):
contig = torch.randn((2, 2, 1, 2), dtype=dtype)
non_contig = contig[:, 1, ...]
contig = non_contig.clone()
self.assertFalse(non_contig.is_contiguous())
self.assertEqual(torchfn(contig), torchfn(non_contig), 'non-contiguous index')
check_non_contiguous_index(torch.float)
check_non_contiguous_index(torch.double)
def check_non_contiguous_expand(shape, dtype):
contig = torch.randn(shape, dtype=dtype)
non_contig = contig.clone().expand(3, -1, -1)
self.assertFalse(non_contig.is_contiguous())
contig = torchfn(contig)
non_contig = torchfn(non_contig)
for i in range(3):
self.assertEqual(contig, non_contig[i], 'non-contiguous expand[' + str(i) + ']')
# Expand is not defined for in-place operations
if test_expand:
# The size 1 case is special as it leads to 0 stride and needs to persists
check_non_contiguous_expand((1, 3), torch.double)
check_non_contiguous_expand((1, 7), torch.double)
check_non_contiguous_expand((5, 7), torch.float)
# If size(dim) == 1, stride(dim) is not defined.
# The code needs to be able to handle this
def check_contiguous_size1(dtype):
contig = torch.randn((5, 100), dtype=dtype)
contig = contig[:1, :50]
contig2 = torch.empty(contig.size(), dtype=dtype)
contig2.copy_(contig)
self.assertTrue(contig.is_contiguous())
self.assertTrue(contig2.is_contiguous())
self.assertEqual(torchfn(contig), torchfn(contig2), 'contiguous size1')
check_contiguous_size1(torch.double)
check_contiguous_size1(torch.float)
def check_contiguous_size1_largedim(dtype):
contig = torch.randn((5, 2, 3, 1, 4, 5, 3, 2, 1, 2, 3, 4), dtype=dtype)
contig = contig[:1, :, :, :, :, :, :, :, :, :, :, :]
contig2 = torch.empty(contig.size(), dtype=dtype)
contig2.copy_(contig)
self.assertTrue(contig.is_contiguous())
self.assertTrue(contig2.is_contiguous())
self.assertEqual(torchfn(contig), torchfn(contig2), 'contiguous size1')
check_contiguous_size1_largedim(torch.double)
check_contiguous_size1_largedim(torch.float)
def check_large(dtype):
input = torch.randn(1024, 512, dtype=dtype)
actual = torchfn(input)
expected = torch.stack([torchfn(slice) for slice in input])
self.assertEqual(actual, expected, 'large')
# compare large tensor vs. repeated small applications to expose
# possible parallelism bugs.
check_large(torch.double)
check_large(torch.float)
def __test_math_by_name(self, function_name, mathfn, selffn):
mathfn = getattr(math, mathfn)
if selffn:
def torchfn(x):
return getattr(x, function_name)()
else:
torchfn = getattr(torch, function_name)
self._test_math(torchfn, mathfn, test_expand=(not selffn))
def _test_math_by_name(self, function_name, test_self=True):
if test_self:
self.__test_math_by_name(function_name + "_", function_name, True)
self.__test_math_by_name(function_name, function_name, False)
def test_sin(self):
self._test_math_by_name('sin')
def test_sinh(self):
def sinh(x):
try:
return math.sinh(x)
except OverflowError:
return inf if x > 0 else -inf
self._test_math(torch.sinh, sinh)
def test_lgamma(self):
def lgamma(x):
if x <= 0 and x == int(x):
return inf
return math.lgamma(x)
self._test_math(torch.lgamma, lgamma)
@unittest.skipIf(not TEST_SCIPY, "Scipy not found")
def test_mvlgamma(self):
from scipy.special import multigammaln
for d in range(1, 5):
input = torch.empty(10).uniform_(d, 10)
res_torch = torch.mvlgamma(input, d)
res_scipy = multigammaln(input.numpy(), d)
self.assertEqual(res_torch.numpy(), res_scipy)
def test_mvlgamma_argcheck(self):
def run_test(d):
input = torch.linspace((d - 2) / 2, 10, 10)
torch.mvlgamma(input, d)
with self.assertRaisesRegex(RuntimeError, "Condition for computing multivariate log-gamma not met"):
run_test(3)
def _digamma_input(self, test_poles=True):
input = []
input.append((torch.randn(10).abs() + 1e-4).tolist())
input.append((torch.randn(10).abs() + 1e6).tolist())
zeros = torch.linspace(-9.5, -0.5, 10)
input.append(zeros.tolist())
input.append((zeros - 0.49).tolist())
input.append((zeros + 0.49).tolist())
input.append((zeros + (torch.rand(10) * 0.99) - 0.5).tolist())
if test_poles:
input.append([-0.999999994, -1.999999994, -2.0000000111,
-100.99999994, -1931.99999994, 0.000000111,
-0.000000111, 0, -2, -329])
return input
@unittest.skipIf(not TEST_SCIPY, "Scipy not found")
def test_digamma(self):
from scipy.special import digamma
# scipy 1.1.0 changed when it returns +/-inf vs. NaN
def torch_digamma_without_inf(inp):
res = torch.digamma(inp)
res[(res == -inf) | (res == inf)] = nan
return res
def scipy_digamma_without_inf(inp):
res = digamma(inp)
if np.isscalar(res):
return res if np.isfinite(res) else nan
res[np.isinf(res)] = nan
return res
self._test_math(torch_digamma_without_inf, scipy_digamma_without_inf, self._digamma_input())
@unittest.skipIf(not TEST_SCIPY, "Scipy not found")
def test_polygamma(self):
from scipy.special import polygamma
for n in [0, 1]:
self._test_math(lambda x: torch.polygamma(n, x),
lambda x: polygamma(n, x).item(),
self._digamma_input(test_poles=False))
def test_asin(self):
self._test_math(torch.asin, lambda x: math.asin(x) if abs(x) <= 1 else nan)
def test_cos(self):
self._test_math_by_name('cos')
def test_cosh(self):
def cosh(x):
try:
return math.cosh(x)
except OverflowError:
# Return inf on overflow.
# See http://en.cppreference.com/w/cpp/numeric/math/cosh
return inf
self._test_math(torch.cosh, cosh)
def test_acos(self):
self._test_math(torch.acos, lambda x: math.acos(x) if abs(x) <= 1 else nan)
def test_tan(self):
self._test_math_by_name('tan')
def test_tanh(self):
self._test_math_by_name('tanh')
def test_atan(self):
self._test_math_by_name('atan')
def test_log(self):
def log(x):
if x == 0:
return -inf
elif x < 0:
return nan
return math.log(x)
self._test_math(torch.log, log)
def test_log10(self):
def log10(x):
if x == 0:
return -inf
elif x < 0:
return nan
return math.log10(x)
self._test_math(torch.log10, log10)
def test_log1p(self):
def log1p(x):
if x == -1:
return -inf
elif x < -1:
return nan
return math.log1p(x)
self._test_math(torch.log1p, log1p)
def test_log2(self):
def log2(x):
if x == 0:
return -inf
elif x < 0:
return nan
try:
return math.log2(x)
except AttributeError:
return math.log(x, 2)
self._test_math(torch.log2, log2)
def test_sqrt(self):
self._test_math(torch.sqrt, lambda x: math.sqrt(x) if x >= 0 else nan)
def test_erf(self):
self._test_math_by_name('erf')
def test_erfc(self):
self._test_math_by_name('erfc')
def test_erfinv(self):
def checkType(tensor):
inputValues = torch.randn(4, 4, out=tensor()).clamp(-2., 2.)
self.assertEqual(tensor(inputValues).erf().erfinv(), tensor(inputValues))
# test inf
self.assertTrue(torch.equal(tensor([-1, 1]).erfinv(), tensor([-inf, inf])))
# test nan
self.assertEqual(tensor([-2, 2]).erfinv(), tensor([nan, nan]))
checkType(torch.FloatTensor)
checkType(torch.DoubleTensor)
def test_exp(self):
def exp(x):
try:
return math.exp(x)
except OverflowError:
return inf
self._test_math(torch.exp, exp)
def test_expm1(self):
def expm1(x):
try:
return math.expm1(x)
except OverflowError:
return inf
self._test_math(torch.expm1, expm1)
def test_floor(self):
self._test_math_by_name('floor')
def test_ceil(self):
self._test_math_by_name('ceil')
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_ceil_out_cpu_cuda(self):
a = torch.randn(1)
b = torch.randn(1, device="cuda")
self.assertRaises(RuntimeError, lambda: torch.ceil(a, out=b))
def test_rsqrt(self):
def rsqrt(x):
if x == 0:
return inf
elif x < 0:
return nan
return 1.0 / math.sqrt(x)
self._test_math(torch.rsqrt, rsqrt)
def test_sigmoid(self):
# TODO: why not simulate math.sigmoid like with rsqrt?
inputValues = [-1000, -1, 0, 0.5, 1, 2, 1000]
expectedOutput = [0.0000, 0.2689, 0.5, 0.6225, 0.7311, 0.8808, 1.000]
precision_4dps = 0.0002
def checkType(tensor):
self.assertEqual(tensor(inputValues).sigmoid(), tensor(expectedOutput), precision_4dps)
checkType(torch.FloatTensor)
checkType(torch.DoubleTensor)
def test_frac(self):
self._test_math(torch.frac, lambda x: math.fmod(x, 1))
def test_trunc(self):
self._test_math(torch.trunc, lambda x: x - math.fmod(x, 1))
def test_round(self):
self._test_math(torch.round, round)
def test_has_storage(self):
self.assertIsNotNone(torch.Tensor().storage())
self.assertIsNotNone(torch.Tensor(0).storage())
self.assertIsNotNone(torch.Tensor([]).storage())
self.assertIsNotNone(torch.Tensor().clone().storage())
self.assertIsNotNone(torch.Tensor([0, 0, 0]).nonzero().storage())
self.assertIsNotNone(torch.Tensor().new().storage())
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_has_storage_numpy(self):
for dtype in [np.float32, np.float64, np.int64,
np.int32, np.int16, np.uint8]:
arr = np.array([1], dtype=dtype)
self.assertIsNotNone(torch.FloatTensor(arr).storage())
self.assertIsNotNone(torch.DoubleTensor(arr).storage())
self.assertIsNotNone(torch.IntTensor(arr).storage())
self.assertIsNotNone(torch.LongTensor(arr).storage())
self.assertIsNotNone(torch.ByteTensor(arr).storage())
if torch.cuda.is_available():
self.assertIsNotNone(torch.cuda.FloatTensor(arr).storage())
self.assertIsNotNone(torch.cuda.DoubleTensor(arr).storage())
self.assertIsNotNone(torch.cuda.IntTensor(arr).storage())
self.assertIsNotNone(torch.cuda.LongTensor(arr).storage())
self.assertIsNotNone(torch.cuda.ByteTensor(arr).storage())
def _testSelection(self, torchfn, mathfn):
# contiguous
m1 = torch.randn(100, 100)
res1 = torchfn(m1)
res2 = m1[0, 0]
for i, j in iter_indices(m1):
res2 = mathfn(res2, m1[i, j])
self.assertEqual(res1, res2)
# non-contiguous
m1 = torch.randn(10, 10, 10)
m2 = m1[:, 4]
res1 = torchfn(m2)
res2 = m2[0, 0]
for i, j in iter_indices(m2):
res2 = mathfn(res2, m2[i][j])
self.assertEqual(res1, res2)
# with indices
m1 = torch.randn(100, 100)
res1val, res1ind = torchfn(m1, 1, False)
res2val = m1[:, 0:1].clone().squeeze()
res2ind = res1ind.clone().fill_(0)
for i, j in iter_indices(m1):
if mathfn(res2val[i], m1[i, j]) != res2val[i]:
res2val[i] = m1[i, j]
res2ind[i] = j
maxerr = 0
for i in range(res1val.size(0)):
maxerr = max(maxerr, abs(res1val[i] - res2val[i]))
self.assertEqual(res1ind[i], res2ind[i])
self.assertLessEqual(abs(maxerr), 1e-5)
# NaNs
for index in (0, 4, 99):
m1 = torch.randn(100)
m1[index] = nan
res1val, res1ind = torch.max(m1, 0)
self.assertTrue(math.isnan(res1val))
self.assertEqual(res1ind, index)
res1val = torchfn(m1)
self.assertTrue(math.isnan(res1val))
def test_max(self):
self._testSelection(torch.max, max)
@staticmethod
def _test_max_with_inf(self, dtypes=(torch.float, torch.double), device='cpu'):
for dtype in dtypes:
a = torch.tensor([[-inf, -inf, inf, 3], [inf, inf, -inf, -1]], dtype=dtype, device=device)
self.assertTrue(torch.all(torch.max(a, dim=1)[0] == inf).item())
self.assertTrue(torch.max(a).item() == inf)
def test_max_with_inf(self):
self._test_max_with_inf(self)
def test_min(self):
self._testSelection(torch.min, min)
@staticmethod
def _test_min_with_inf(self, dtypes=(torch.float, torch.double), device='cpu'):
for dtype in dtypes:
a = torch.tensor([[-inf, -inf, inf, 3], [inf, inf, -inf, -1]], dtype=dtype, device=device)
self.assertTrue(torch.all(torch.min(a, dim=1)[0] == (-inf)).item())
self.assertTrue(torch.min(a).item() == -inf)
def test_min_with_inf(self):
self._test_min_with_inf(self)
@staticmethod
def _test_norm(self, device):
# full reduction
x = torch.randn(5, device=device)
xn = x.cpu().numpy()
for p in [0, 1, 2, 3, 4, inf, -inf]:
res = x.norm(p).item()
expected = np.linalg.norm(xn, p)
self.assertEqual(res, expected, "full reduction failed for {}-norm".format(p))
# one dimension
x = torch.randn(5, 5, device=device)
xn = x.cpu().numpy()
for p in [0, 1, 2, 3, 4, inf, -inf]:
res = x.norm(p, 1).cpu().numpy()
expected = np.linalg.norm(xn, p, 1)
self.assertEqual(res.shape, expected.shape)
self.assertTrue(np.allclose(res, expected), "dim reduction failed for {}-norm".format(p))
# matrix norm
for p in ['fro', 'nuc']:
res = x.norm(p).cpu().numpy()
expected = np.linalg.norm(xn, p)
self.assertEqual(res.shape, expected.shape)
self.assertTrue(np.allclose(res, expected), "dim reduction failed for {}-norm".format(p))
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
@skipIfNoLapack
def test_norm(self):
self._test_norm(self, device='cpu')
@staticmethod
def _test_dist(self, device):
def run_test(x, y):
for p in [0, 1, 2, 3, 4, inf, -inf]:
dist_xy = torch.dist(x, y, p)
dist_xy_norm = torch.norm(x - y, p)
self.assertEqual(dist_xy, dist_xy_norm)
run_test(torch.randn(5, device=device), torch.randn(5, device=device))
x = torch.zeros(3, device=device)
y = torch.zeros(3, device=device)
y[1] = 1.
run_test(x, y)
def test_dist(self):
self._test_dist(self, device='cpu')
def test_dim_reduction_uint8_overflow(self):
example = [[-1, 2, 1], [5, 3, 6]]
x = torch.tensor(example, dtype=torch.uint8)
self.assertEqual(x.sum(dtype=torch.uint8).item(), 16)
self.assertEqual(x.sum(0, dtype=torch.uint8), torch.FloatTensor([4, 5, 7]))
self.assertEqual(x.sum(1, dtype=torch.uint8), torch.FloatTensor([2, 14]))
y = torch.tensor(example, dtype=torch.uint8)
torch.sum(x, 0, out=y)
self.assertEqual(x.sum(0, dtype=torch.uint8), y)
@staticmethod
def _test_dim_reduction(self, cast):
example = [[-1, 2, 1], [5, 3, 6]]
types = [torch.double,
torch.float,
torch.int64,
torch.int32,
torch.int16]
# This won't test for 256bit instructions, since we usually
# only work on 1 cacheline (1024bit) at a time and these
# examples aren't big enough to trigger that.
for dtype in types:
x = cast(torch.tensor(example, dtype=dtype))
self.assertEqual(x.sum().item(), 16)
self.assertEqual(x.sum(0), torch.FloatTensor([4, 5, 7]))
self.assertEqual(x.sum(1), torch.FloatTensor([2, 14]))
y = cast(torch.tensor(example, dtype=dtype))
torch.sum(x, 0, out=y)
self.assertEqual(x.sum(0), y)
# Mean not supported for Int types
for dtype in types[:2]:
x = cast(torch.tensor(example, dtype=dtype))
self.assertEqual(x.mean().item(), 16.0 / 6)
self.assertEqual(x.mean(0), torch.FloatTensor([2.0, 2.5, 7.0 / 2]))
self.assertEqual(x.mean(1), torch.FloatTensor([2.0 / 3, 14.0 / 3]))
self.assertEqual(x.mean(), x.mean((0, 1)))
for dtype in types:
x = cast(torch.tensor(example, dtype=dtype))
self.assertEqual(x.prod().item(), -180)
self.assertEqual(x.prod(0), torch.FloatTensor([-5, 6, 6]))
self.assertEqual(x.prod(1), torch.FloatTensor([-2, 90]))
for dtype in types:
x = cast(torch.tensor(example, dtype=dtype))
self.assertEqual(x.max().item(), 6)
self.assertEqual(x.max(0), (torch.FloatTensor([5, 3, 6]), torch.FloatTensor([1, 1, 1])))
self.assertEqual(x.max(1), (torch.FloatTensor([2, 6]), torch.FloatTensor([1, 2])))
for dtype in types:
x = cast(torch.tensor(example, dtype=dtype))
self.assertEqual(x.min().item(), -1)
self.assertEqual(x.min(0), (torch.FloatTensor([-1, 2, 1]), torch.FloatTensor([0, 0, 0])))
self.assertEqual(x.min(1), (torch.FloatTensor([-1, 3]), torch.FloatTensor([0, 1])))
for dtype in types:
x = cast(torch.tensor(example, dtype=dtype))
self.assertEqual(x.argmax().item(), 5)
self.assertEqual(x.argmax(dim=0), torch.FloatTensor([1, 1, 1]))
self.assertEqual(x.argmax(dim=1), torch.FloatTensor([1, 2]))
self.assertEqual(x.argmax(dim=0, keepdim=True), torch.FloatTensor([[1, 1, 1]]))
# test that non-contiguous tensors work
self.assertEqual(x[:, :2].argmax().item(), 2)
for dtype in types:
x = cast(torch.tensor(example, dtype=dtype))
self.assertEqual(x.argmin().item(), 0)
self.assertEqual(x.argmin(dim=0), torch.FloatTensor([0, 0, 0]))
self.assertEqual(x.argmin(dim=1), torch.FloatTensor([0, 1]))
self.assertEqual(x.argmin(dim=1, keepdim=True), torch.FloatTensor([[0], [1]]))
# test that non-contiguous tensors work
self.assertEqual(x[:, :2].argmin().item(), 0)
dim_red_fns = [
"mean", "median", "mode", "norm", "prod",
"std", "sum", "var", "max", "min"]
def normfn_attr(t, dim, keepdim=False, out=None):
attr = getattr(torch, "norm")
return attr(t, 2, dim, keepdim, out=out)
for fn_name in dim_red_fns:
fn_attr = getattr(torch, fn_name) if fn_name != "norm" else normfn_attr
def fn(x, dim, keepdim=False, out=None):
ans = fn_attr(x, dim, keepdim=keepdim, out=out)
return ans if not isinstance(ans, tuple) else ans[0]
def fn_tuple(x, dim, keepdim=False, out=None):
return fn_attr(x, dim, keepdim=keepdim, out=out)
def test_multidim(x, dim):
self.assertEqual(fn(x, dim).unsqueeze(dim), fn(x, dim, keepdim=True))
self.assertEqual(x.ndimension() - 1, fn(x, dim).ndimension())
self.assertEqual(x.ndimension(), fn(x, dim, keepdim=True).ndimension())
# general case
x = cast(torch.randn(3, 4, 5))
dim = random.randint(0, 2)
test_multidim(x, dim)
# check 1-d behavior
x = cast(torch.randn(1))
dim = 0
self.assertEqual(fn(x, dim).shape, tuple())
self.assertEqual(fn(x, dim, keepdim=True).shape, (1,))
# check reducing of a singleton dimension
dims = [3, 4, 5]
singleton_dim = random.randint(0, 2)
dims[singleton_dim] = 1
x = cast(torch.randn(dims))
test_multidim(x, singleton_dim)
# check reducing with output kwargs
if fn_name in ['median', 'mode', 'max', 'min']:
y = cast(torch.randn(5, 3))
values = cast(torch.randn(5, 3))
indices = cast(torch.zeros(5, 3).long() - 1)
fn_tuple(y, 1, keepdim=False, out=(values[:, 1], indices[:, 1]))
values_expected, indices_expected = fn_tuple(y, 1, keepdim=False)
self.assertEqual(values[:, 1], values_expected,
'{} values with out= kwarg'.format(fn_name))
self.assertEqual(indices[:, 1], indices_expected,
'{} indices with out= kwarg'.format(fn_name))
continue
x = cast(torch.randn(5, 3))
y = cast(torch.randn(5, 3))
fn(y, 1, keepdim=False, out=x[:, 1])
expected = fn(y, 1, keepdim=False)
self.assertEqual(x[:, 1], expected, '{} with out= kwarg'.format(fn_name))
def test_dim_reduction(self):
self._test_dim_reduction(self, lambda t: t)
@skipIfRocm
def test_reduction_empty(self):
fns_to_test = [
# name, function, identity
('max', lambda *args, **kwargs: torch.max(*args, **kwargs), None),
('kthvalue', lambda *args, **kwargs: torch.kthvalue(*args, k=1, **kwargs), None),
('argmax', lambda *args, **kwargs: torch.argmax(*args, **kwargs), None),
('min', lambda *args, **kwargs: torch.min(*args, **kwargs), None),
('argmin', lambda *args, **kwargs: torch.argmin(*args, **kwargs), None),
('mode', lambda *args, **kwargs: torch.mode(*args, **kwargs), None),
('median', lambda *args, **kwargs: torch.median(*args, **kwargs), None),
('prod', lambda *args, **kwargs: torch.prod(*args, **kwargs), 1),
('sum', lambda *args, **kwargs: torch.sum(*args, **kwargs), 0),
('norm', lambda *args, **kwargs: torch.norm(*args, p=2, **kwargs), 0),
('mean', lambda *args, **kwargs: torch.mean(*args, **kwargs), nan),
('var', lambda *args, **kwargs: torch.var(*args, **kwargs), nan),
('std', lambda *args, **kwargs: torch.std(*args, **kwargs), nan),
('logsumexp', lambda *args, **kwargs: torch.logsumexp(*args, **kwargs), -inf),
]
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
shape = (2, 0, 4)
for device in devices:
x = torch.randn(shape, device=device)
for item in fns_to_test:
name, fn, identity = item
if identity is None:
ident_err = 'does not have an identity'
self.assertRaisesRegex(RuntimeError, ident_err, lambda: fn(x, dim=2))
self.assertRaisesRegex(RuntimeError, ident_err, lambda: fn(x, dim=2, keepdim=True))
self.assertRaisesRegex(RuntimeError, ident_err, lambda: fn(x, dim=1))
self.assertRaisesRegex(RuntimeError, ident_err, lambda: fn(x, dim=1, keepdim=True))
else:
self.assertEqual(torch.empty((2, 0), device=device), fn(x, dim=2))
self.assertEqual(torch.empty((2, 0, 1), device=device), fn(x, dim=2, keepdim=True))
# assertEqual doesn't work with inf, -inf, nan and two tensors.
check = (torch.testing.assert_allclose if math.isnan(identity) or math.isinf(identity) else
self.assertEqual)
check(torch.full((2, 4), identity, device=device), fn(x, dim=1))
check(torch.full((2, 1, 4), identity, device=device), fn(x, dim=1, keepdim=True))
try:
check(torch.full((), identity, device=device), fn(x))
except TypeError as err:
# ignore if there is no allreduce.
self.assertTrue('required positional arguments: "dim"' in str(err))
# any
xb = x.to(torch.uint8)
yb = x.to(torch.uint8)
self.assertEqual((2, 0), xb.any(2).shape)
self.assertEqual((2, 0, 1), xb.any(2, keepdim=True).shape)
self.assertEqual(torch.zeros((2, 4), device=device), xb.any(1))
self.assertEqual(torch.zeros((2, 1, 4), device=device), xb.any(1, keepdim=True))
self.assertEqual(torch.zeros((), device=device), xb.any())
# all
self.assertEqual((2, 0), xb.all(2).shape)
self.assertEqual((2, 0, 1), xb.all(2, keepdim=True).shape)
self.assertEqual(torch.ones((2, 4), device=device), xb.all(1))
self.assertEqual(torch.ones((2, 1, 4), device=device), xb.all(1, keepdim=True))
self.assertEqual(torch.ones((), device=device), xb.all())
@skipIfRocm
def test_pairwise_distance_empty(self):
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
shape = (2, 0)
x = torch.randn(shape, device=device)
y = torch.randn(shape, device=device)
self.assertEqual(torch.zeros(2, device=device), torch.pairwise_distance(x, y))
self.assertEqual(torch.zeros((2, 1), device=device), torch.pairwise_distance(x, y, keepdim=True))
shape = (0, 2)
x = torch.randn(shape, device=device)
y = torch.randn(shape, device=device)
self.assertEqual(torch.zeros(0, device=device), torch.pairwise_distance(x, y))
self.assertEqual(torch.zeros((0, 1), device=device), torch.pairwise_distance(x, y, keepdim=True))
def test_pdist_empty(self):
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
shape = (0, 2)
x = torch.randn(shape, device=device)
self.assertEqual(torch.empty(0, device=device), torch.pdist(x))
shape = (1, 2)
x = torch.randn(shape, device=device)
self.assertEqual(torch.empty(0, device=device), torch.pdist(x))
shape = (3, 0)
x = torch.randn(shape, device=device)
self.assertEqual(torch.zeros(3, device=device), torch.pdist(x))
@unittest.skipIf(not TEST_SCIPY, "Scipy not found")
def test_pdist_scipy(self):
from scipy.spatial.distance import pdist
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
for shape in [(4, 5), (3, 2), (2, 1)]:
for p in [0, 1, 2, 3, 1.5, 2.5, float('inf')]:
for trans in [False, True]:
x = torch.randn(shape, device=device)
if trans:
x.transpose_(0, 1)
actual = torch.pdist(x, p=p)
# pdist doesn't handle 0 or inf norm properly
if p == 0:
expected = pdist(x.cpu(), 'hamming') * x.shape[1]
elif p == float('inf'):
expected = pdist(x.cpu(), lambda a, b: np.abs(a - b).max())
else:
expected = pdist(x.cpu(), 'minkowski', p=p)
self.assertEqual(expected.shape, actual.shape)
self.assertTrue(np.allclose(expected, actual.cpu().numpy()))
@unittest.skipIf(not TEST_SCIPY, "Scipy not found")
def test_logsumexp(self):
from scipy.special import logsumexp
a = torch.randn(5, 4)
a[0, 0] = inf
a[1, :] = -inf
actual = a.logsumexp(1)
expected = logsumexp(a.numpy(), 1)
self.assertEqual(expected.shape, actual.shape)
self.assertTrue(np.allclose(expected, actual.numpy()))
# check that out is actually inplace
b = torch.zeros(5, 2)
c = b[:, 0]
torch.logsumexp(a, 1, out=c)
self.assertTrue(np.allclose(expected, b[:, 0].numpy()))
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_cpu_parallel(self):
# To use parallel branches we'll need to compare on tensors
# that are relatively large. Even if this is run on a single
# core machine these tests will still give you signal on
# the correctness
def _run_test(size):
for dim in range(len(size) + 1):
nv = np.round(np.random.rand(*size)) # 0s and 1s
tv = torch.from_numpy(nv)
# Parallelisim is only used if numel is
# larger than grainsize defined in Parallel.h
self.assertTrue(tv.numel() > 32768)
if dim == len(size):
nvs = nv.sum()
tvs = tv.sum()
else:
nvs = nv.sum(dim)
tvs = tv.sum(dim)
diff = np.abs(nvs - tvs.numpy()).sum()
self.assertEqual(diff, 0)
_run_test([2, 3, 3, 3, 3, 2, 2, 3, 2, 3, 2, 3, 3])
_run_test([4, 4, 4, 4, 4, 4, 4, 4, 4, 4])
_run_test([1, 32 * 8 * 32 * 8])
_run_test([1, 32770])
def _testCSelection(self, torchfn, mathfn):
# Two tensors
size = (100, 100)
a = torch.rand(*size)
b = torch.rand(*size)
c = torchfn(a, b)
expected_c = torch.zeros(*size)
expected_c.map2_(a, b, lambda _, a, b: mathfn(a, b))
self.assertEqual(expected_c, c, 0)
def test_max_elementwise(self):
self._testCSelection(torch.max, max)
def test_min_elementwise(self):
self._testCSelection(torch.min, min)
def test_lerp(self):
def TH_lerp(a, b, weight):
return a + weight * (b - a)
size = (100, 100)
a = torch.rand(*size)
b = torch.rand(*size)
w = random.random()
result = torch.lerp(a, b, w)
expected = a.clone()
expected.map2_(a, b, lambda _, a, b: TH_lerp(a, b, w))
self.assertEqual(result, expected)
def test_all_any(self):
def test(size):
x = torch.ones(*size).byte()
self.assertTrue(x.all())
self.assertTrue(x.any())
x[3] = 0
self.assertFalse(x.all())
self.assertTrue(x.any())
x.zero_()
self.assertFalse(x.all())
self.assertFalse(x.any())
x.fill_(2)
self.assertTrue(x.all())
self.assertTrue(x.any())
test((10,))
test((5, 5))
def test_all_any_empty(self):
x = torch.ByteTensor()
self.assertTrue(x.all())
self.assertFalse(x.any())
def test_all_any_with_dim(self):
def test(x):
r1 = x.prod(dim=0, keepdim=False).byte()
r2 = x.all(dim=0, keepdim=False)
self.assertEqual(r1.shape, r2.shape)
self.assertTrue((r1 == r2).all())
r3 = x.sum(dim=1, keepdim=True).clamp(0, 1).byte()
r4 = x.any(dim=1, keepdim=True)
self.assertEqual(r3.shape, r4.shape)
self.assertTrue((r3 == r4).all())
test(torch.ByteTensor([[0, 0, 0],
[0, 0, 1],
[0, 1, 1],
[1, 1, 1]]))
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_all_any_empty_cuda(self):
x = torch.cuda.ByteTensor()
self.assertTrue(x.all())
self.assertFalse(x.any())
def test_mv(self):
m1 = torch.randn(100, 100)
v1 = torch.randn(100)
res1 = torch.mv(m1, v1)
res2 = res1.clone().zero_()
for i, j in iter_indices(m1):
res2[i] += m1[i][j] * v1[j]
self.assertEqual(res1, res2)
def test_add(self):
# [res] torch.add([res,] tensor1, tensor2)
m1 = torch.randn(100, 100)
v1 = torch.randn(100)
# contiguous
res1 = torch.add(m1[4], v1)
res2 = res1.clone().zero_()
for i in range(m1.size(1)):
res2[i] = m1[4, i] + v1[i]
self.assertEqual(res1, res2)
m1 = torch.randn(100, 100)
v1 = torch.randn(100)
# non-contiguous
res1 = torch.add(m1[:, 4], v1)
res2 = res1.clone().zero_()
for i in range(m1.size(0)):
res2[i] = m1[i, 4] + v1[i]
self.assertEqual(res1, res2)
# [res] torch.add([res,] tensor, value)
m1 = torch.randn(10, 10)
# contiguous
res1 = m1.clone()
res1[3].add_(2)
res2 = m1.clone()
for i in range(m1.size(1)):
res2[3, i] = res2[3, i] + 2
self.assertEqual(res1, res2)
# non-contiguous
m1 = torch.randn(10, 10)
res1 = m1.clone()
res1[:, 3].add_(2)
res2 = m1.clone()
for i in range(m1.size(0)):
res2[i, 3] = res2[i, 3] + 2
self.assertEqual(res1, res2)
# inter-type
m1 = torch.randn(10, 10)
self.assertEqual(m1 + 3, m1 + torch.tensor(3))
self.assertEqual(3 + m1, torch.tensor(3) + m1)
one = torch.tensor(1, dtype=torch.uint8)
self.assertEqual(torch.add(one, 1), 2)
self.assertEqual(torch.add(one, 1).dtype, torch.uint8)
# contiguous + non-contiguous
m1 = torch.randn(10, 10)
m2 = torch.randn(10, 10).t()
res = m1 + m2
self.assertTrue(res.is_contiguous())
self.assertEqual(res, m1 + m2.contiguous())
# 1d + empty
m1 = torch.tensor([1.0], dtype=torch.float)
m2 = torch.tensor([], dtype=torch.float)
self.assertEqual(m1 + m2, [])
# [res] torch.add([res,] tensor1, value, tensor2)
def test_csub(self):
# with a tensor
a = torch.randn(100, 90)
b = a.clone().normal_()
res_add = torch.add(a, -1, b)
res_csub = a.clone()
res_csub.sub_(b)
self.assertEqual(res_add, res_csub)
# with a scalar
a = torch.randn(100, 100)
scalar = 123.5
res_add = torch.add(a, -scalar)
res_csub = a.clone()
res_csub.sub_(scalar)
self.assertEqual(res_add, res_csub)
@staticmethod
def _test_neg(self, cast):
float_types = [torch.DoubleTensor, torch.FloatTensor, torch.LongTensor]
int_types = [torch.IntTensor, torch.ShortTensor, torch.ByteTensor,
torch.CharTensor]
for t in float_types + int_types:
if t in float_types:
a = cast(torch.randn(100, 90).type(t))
else:
a = cast(torch.randint(-128, 128, (100, 90), dtype=t.dtype))
zeros = cast(torch.Tensor().type(t)).resize_as_(a).zero_()
if t == torch.ByteTensor:
res_add = torch.add(zeros, a, alpha=255)
else:
res_add = torch.add(zeros, a, alpha=-1)
res_neg = a.clone()
res_neg.neg_()
self.assertEqual(res_neg, res_add)
# test out of place as well
res_neg_out_place = a.clone().neg()
self.assertEqual(res_neg_out_place, res_add)
# test via __neg__ operator
res_neg_op = -a.clone()
self.assertEqual(res_neg_op, res_add)
def test_neg(self):
self._test_neg(self, lambda t: t)
def test_reciprocal(self):
a = torch.randn(100, 89)
res_div = 1 / a
res_reciprocal = a.clone()
res_reciprocal.reciprocal_()
self.assertEqual(res_reciprocal, res_div)
def test_mul(self):
m1 = torch.randn(10, 10)
res1 = m1.clone()
res1[:, 3].mul_(2)
res2 = m1.clone()
for i in range(res1.size(0)):
res2[i, 3] = res2[i, 3] * 2
self.assertEqual(res1, res2)
def test_div(self):
m1 = torch.randn(10, 10)
res1 = m1.clone()
res1[:, 3].div_(2)
res2 = m1.clone()
for i in range(m1.size(0)):
res2[i, 3] = res2[i, 3] / 2
self.assertEqual(res1, res2)
def test_floordiv(self):
for dtype in torch.testing.get_all_dtypes():
if dtype is torch.float16:
continue
x = torch.randn(100).mul(10).to(dtype)
y = x // 3
self.assertEqual(y.dtype, x.dtype)
z = torch.tensor([math.trunc(v.item() / 3.) for v in x], dtype=y.dtype)
self.assertEqual(y, z)
def test_rdiv(self):
for dtype in torch.testing.get_all_dtypes():
if dtype is torch.float16:
continue
x = torch.rand(100).add(1).mul(4).to(dtype)
y = 30 / x
if dtype.is_floating_point:
z = torch.tensor([30 / v.item() for v in x], dtype=dtype)
else:
z = torch.tensor([math.trunc(30. / v.item()) for v in x], dtype=dtype)
self.assertEqual(y, z)
def test_fmod(self):
m1 = torch.Tensor(10, 10).uniform_(-10., 10.)
res1 = m1.clone()
q = 2.1
res1[:, 3].fmod_(q)
res2 = m1.clone()
for i in range(m1.size(1)):
res2[i, 3] = math.fmod(res2[i, 3], q)
self.assertEqual(res1, res2)
def test_remainder(self):
# Check the Floating point case, both tensor and scalar overloads
for use_item in [True, False]:
m1 = torch.Tensor(10, 10).uniform_(-10., 10.)
res1 = m1.clone()
res2 = m1.clone()
qs = torch.arange(-5.1, 4.1)
# Check the case where the divisor is a simple float
for col_idx, q in enumerate(qs):
# Reference
for i in range(m1.size(0)):
res2[i, col_idx] = res2[i, col_idx] % q
# To test
res1[:, col_idx].remainder_(q if not use_item else q.item())
self.assertEqual(res1, res2)
# Check the case where the divisor is a tensor
res1 = m1.clone()
res1.remainder_(qs.unsqueeze(0).expand_as(res1))
self.assertEqual(res1, res2)
# Check the LongTensor case, both tensor and scalar overloads
for use_item in [True, False]:
long_m1 = torch.LongTensor(10, 10).random_(-10, 10)
long_res1 = long_m1.clone()
long_res2 = long_m1.clone()
long_qs = torch.arange(-5, 5)
long_qs[5] = 5 # Can't handle the divisor=0 case
for col_idx, long_q in enumerate(long_qs):
# Reference
for i in range(long_m1.size(0)):
long_res2[i, col_idx] = long_res2[i, col_idx] % long_q
# To test
long_res1[:, col_idx].remainder_(long_q if not use_item else long_q.item())
self.assertEqual(long_res1, long_res2)
# Divisor is a tensor case
long_res1 = long_m1.clone()
long_res1.remainder_(long_qs.unsqueeze(0).expand_as(long_res1))
@staticmethod
def _test_remainder_overflow(self, dtype, device):
# Check Integer Overflows
x = torch.tensor(23500, dtype=dtype, device=device)
q = 392486996410368
self.assertEqual(x % q, x)
self.assertEqual(-x % q, q - x)
self.assertEqual(x % -q, x - q)
self.assertEqual(-x % -q, -x)
def test_remainder_overflow(self):
self._test_remainder_overflow(self, dtype=torch.int64, device='cpu')
def test_mm(self):
# helper function
def matrixmultiply(mat1, mat2):
n = mat1.size(0)
m = mat1.size(1)
p = mat2.size(1)
res = torch.zeros(n, p)
for i, j in iter_indices(res):
res[i, j] = sum(mat1[i, k] * mat2[k, j] for k in range(m))
return res
# contiguous case
n, m, p = 10, 10, 5
mat1 = torch.randn(n, m)
mat2 = torch.randn(m, p)
res = torch.mm(mat1, mat2)
res2 = matrixmultiply(mat1, mat2)
self.assertEqual(res, res2)
# non contiguous case 1
n, m, p = 10, 10, 5
mat1 = torch.randn(n, m)
mat2 = torch.randn(p, m).t()
res = torch.mm(mat1, mat2)
res2 = matrixmultiply(mat1, mat2)
self.assertEqual(res, res2)
# non contiguous case 2
n, m, p = 10, 10, 5
mat1 = torch.randn(m, n).t()
mat2 = torch.randn(m, p)
res = torch.mm(mat1, mat2)
res2 = matrixmultiply(mat1, mat2)
self.assertEqual(res, res2)
# non contiguous case 3
n, m, p = 10, 10, 5
mat1 = torch.randn(m, n).t()
mat2 = torch.randn(p, m).t()
res = torch.mm(mat1, mat2)
res2 = matrixmultiply(mat1, mat2)
self.assertEqual(res, res2)
# test with zero stride
n, m, p = 10, 10, 5
mat1 = torch.randn(n, m)
mat2 = torch.randn(m, 1).expand(m, p)
res = torch.mm(mat1, mat2)
res2 = matrixmultiply(mat1, mat2)
self.assertEqual(res, res2)
@staticmethod
def _test_btrifact(self, cast):
a = torch.FloatTensor((((1.3722, -0.9020),
(1.8849, 1.9169)),
((0.7187, -1.1695),
(-0.0139, 1.3572)),
((-1.6181, 0.7148),
(1.3728, 0.1319))))
a = cast(a)
a_LU, pivots = a.btrifact()
a_LU_, pivots_, info_ = a.btrifact_with_info()
self.assertEqual(a_LU, a_LU_)
self.assertEqual(pivots, pivots_)
self.assertEqual(info_.abs().sum(), 0)
P, a_L, a_U = torch.btriunpack(a_LU, pivots)
a_ = torch.bmm(P, torch.bmm(a_L, a_U))
self.assertEqual(a_, a)
@skipIfNoLapack
def test_btrifact(self):
self._test_btrifact(self, lambda t: t)
@staticmethod
def _test_btrisolve(self, cast):
a = torch.FloatTensor((((1.3722, -0.9020),
(1.8849, 1.9169)),
((0.7187, -1.1695),
(-0.0139, 1.3572)),
((-1.6181, 0.7148),
(1.3728, 0.1319))))
b = torch.FloatTensor(((4.02, 6.19),
(-1.56, 4.00),
(9.81, -4.09)))
a, b = cast(a), cast(b)
LU_data, pivots, info = a.btrifact_with_info()
self.assertEqual(info.abs().sum(), 0)
x = torch.btrisolve(b, LU_data, pivots)
b_ = torch.bmm(a, x.unsqueeze(2)).squeeze()
self.assertEqual(b_, b)
@skipIfNoLapack
def test_btrisolve(self):
self._test_btrisolve(self, lambda t: t)
def test_bmm(self):
num_batches = 10
M, N, O = 23, 8, 12
b1 = torch.randn(num_batches, M, N)
b2 = torch.randn(num_batches, N, O)
res = torch.bmm(b1, b2)
for i in range(num_batches):
r = torch.mm(b1[i], b2[i])
self.assertEqual(r, res[i])
if torch.cuda.is_available():
# check that mixed arguments are rejected
self.assertRaises(RuntimeError, lambda: torch.bmm(b1, b2.cuda()))
self.assertRaises(RuntimeError, lambda: torch.bmm(b1.cuda(), b2))
def test_addbmm(self):
# num_batches = 10
# M, N, O = 12, 8, 5
num_batches = 2
M, N, O = 2, 3, 4
b1 = torch.randn(num_batches, M, N)
b2 = torch.randn(num_batches, N, O)
res = torch.bmm(b1, b2)
res2 = torch.Tensor().resize_as_(res[0]).zero_()
res2.addbmm_(b1, b2)
self.assertEqual(res2, res.sum(0, False))
res2.addbmm_(1, b1, b2)
self.assertEqual(res2, res.sum(0, False) * 2)
res2.addbmm_(1., .5, b1, b2)
self.assertEqual(res2, res.sum(0, False) * 2.5)
res3 = torch.addbmm(1, res2, 0, b1, b2)
self.assertEqual(res3, res2)
res4 = torch.addbmm(1, res2, .5, b1, b2)
self.assertEqual(res4, res.sum(0, False) * 3)
res5 = torch.addbmm(0, res2, 1, b1, b2)
self.assertEqual(res5, res.sum(0, False))
res6 = torch.addbmm(.1, res2, .5, b1, b2)
self.assertEqual(res6, res2 * .1 + (res.sum(0) * .5))
def test_baddbmm(self):
num_batches = 10
M, N, O = 12, 8, 5
b1 = torch.randn(num_batches, M, N)
b2 = torch.randn(num_batches, N, O)
res = torch.bmm(b1, b2)
res2 = torch.Tensor().resize_as_(res).zero_()
res2.baddbmm_(b1, b2)
self.assertEqual(res2, res)
res2.baddbmm_(1, b1, b2)
self.assertEqual(res2, res * 2)
res2.baddbmm_(1, .5, b1, b2)
self.assertEqual(res2, res * 2.5)
res3 = torch.baddbmm(1, res2, 0, b1, b2)
self.assertEqual(res3, res2)
res4 = torch.baddbmm(1, res2, .5, b1, b2)
self.assertEqual(res4, res * 3)
res5 = torch.baddbmm(0, res2, 1, b1, b2)
self.assertEqual(res5, res)
res6 = torch.baddbmm(.1, res2, .5, b1, b2)
self.assertEqual(res6, res2 * .1 + res * .5)
def test_clamp(self):
m1 = torch.rand(100).mul(5).add(-2.5) # uniform in [-2.5, 2.5]
# just in case we're extremely lucky.
min_val = -1
max_val = 1
m1[1] = min_val
m1[2] = max_val
res1 = m1.clone()
res1.clamp_(min_val, max_val)
res2 = m1.clone()
for i in iter_indices(res2):
res2[i] = max(min_val, min(max_val, res2[i]))
self.assertEqual(res1, res2)
out = m1.clone()
torch.clamp(m1, min=min_val, max=max_val, out=out)
self.assertEqual(out, res1)
res1 = torch.clamp(m1, min=min_val)
res2 = m1.clone()
for i in iter_indices(res2):
res2[i] = max(min_val, res2[i])
self.assertEqual(res1, res2)
torch.clamp(m1, min=min_val, out=out)
self.assertEqual(out, res1)
res1 = torch.clamp(m1, max=max_val)
res2 = m1.clone()
for i in iter_indices(res2):
res2[i] = min(max_val, res2[i])
self.assertEqual(res1, res2)
torch.clamp(m1, max=max_val, out=out)
self.assertEqual(out, res1)
error_msg = 'At least one of \'min\' or \'max\' must not be None'
with self.assertRaisesRegex(RuntimeError, error_msg):
m1.clamp()
with self.assertRaisesRegex(RuntimeError, error_msg):
m1.clamp_()
def test_pow(self):
# [res] torch.pow([res,] x)
# pow has dedicated implementation for different exponents
for exponent in [-2, -1, -0.5, 0.5, 1, 2, 3, 4]:
# base - tensor, exponent - number
# contiguous
m1 = torch.rand(100, 100) + 0.5
res1 = torch.pow(m1[4], exponent)
res2 = res1.clone().zero_()
for i in range(res2.size(0)):
res2[i] = math.pow(m1[4][i], exponent)
self.assertEqual(res1, res2)
# non-contiguous
m1 = torch.rand(100, 100) + 0.5
res1 = torch.pow(m1[:, 4], exponent)
res2 = res1.clone().zero_()
for i in range(res2.size(0)):
res2[i] = math.pow(m1[i, 4], exponent)
self.assertEqual(res1, res2)
# base - number, exponent - tensor
# contiguous
m1 = torch.randn(100, 100)
res1 = torch.pow(3, m1[4])
res2 = res1.clone().zero_()
for i in range(res2.size(0)):
res2[i] = math.pow(3, m1[4, i])
self.assertEqual(res1, res2)
# non-contiguous
m1 = torch.randn(100, 100)
res1 = torch.pow(3, m1[:, 4])
res2 = res1.clone().zero_()
for i in range(res2.size(0)):
res2[i] = math.pow(3, m1[i][4])
self.assertEqual(res1, res2)
def test_rpow(self):
m = torch.randn(10, 10)
self.assertEqual(torch.pow(2, m), 2**m)
@staticmethod
def _test_int_pow(self, cast):
if not TEST_NUMPY:
return
import numpy as np
def check_against_np(tensor, exp):
tensor_np = tensor.cpu().numpy()
exp_np = exp if isinstance(exp, int) else exp.cpu().numpy()
expected = torch.LongTensor(tensor_np ** exp_np).type_as(tensor)
self.assertEqual(torch.pow(tensor, exp), expected)
self.assertEqual(tensor.pow(exp), torch.pow(tensor, exp))
typecasts = [
lambda x: x.long(),
lambda x: x.short(),
lambda x: x.byte(),
]
if not IS_WINDOWS:
typecasts.append(lambda x: x.int())
shape = (11, 5)
tensor = cast(torch.LongTensor(shape).random_(-10, 10))
exps = [0, 1, 2, 5, cast(torch.LongTensor(shape).random_(0, 20))]
for typecast in typecasts:
for exp in exps:
t = typecast(tensor)
e = exp if isinstance(exp, int) else typecast(exp)
check_against_np(t, e)
def test_int_pow(self):
self._test_int_pow(self, lambda x: x)
def _test_cop(self, torchfn, mathfn):
def reference_implementation(res2):
for i, j in iter_indices(sm1):
idx1d = i * sm1.size(0) + j
res2[i, j] = mathfn(sm1[i, j], sm2[idx1d])
return res2
# contiguous
m1 = torch.randn(10, 10, 10)
m2 = torch.randn(10, 10 * 10)
sm1 = m1[4]
sm2 = m2[4]
res1 = torchfn(sm1, sm2.view(10, 10))
res2 = reference_implementation(res1.clone())
self.assertEqual(res1, res2)
# non-contiguous
m1 = torch.randn(10, 10, 10)
m2 = torch.randn(10 * 10, 10 * 10)
sm1 = m1[:, 4]
sm2 = m2[:, 4]
# view as sm1.size()
sm2.set_(sm2.storage(), sm2.storage_offset(), sm1.size(), (sm2.stride()[0] * 10, sm2.stride()[0]))
res1 = torchfn(sm1, sm2)
# reference_implementation assumes 1-d sm2
sm2.set_(sm2.storage(), sm2.storage_offset(), m2[:, 4].size(), m2[:, 4].stride())
res2 = reference_implementation(res1.clone())
self.assertEqual(res1, res2)
def test_cdiv(self):
self._test_cop(torch.div, lambda x, y: x / y)
def test_cfmod(self):
self._test_cop(torch.fmod, math.fmod)
def test_cremainder(self):
self._test_cop(torch.remainder, lambda x, y: x % y)
def test_cmul(self):
self._test_cop(torch.mul, lambda x, y: x * y)
def test_cpow(self):
self._test_cop(torch.pow, lambda x, y: nan if x < 0 else math.pow(x, y))
@unittest.skipIf(not TEST_NUMPY, 'Numpy not found')
def test_einsum(self):
# test cases taken from https://gist.github.com/rockt/15ee013889d65342088e9260a377dc8f
x = torch.randn(5)
y = torch.randn(7)
A = torch.randn(3, 5)
B = torch.randn(2, 5)
C = torch.randn(2, 3, 5)
D = torch.randn(2, 5, 7)
E = torch.randn(7, 9)
F = torch.randn(2, 3, 5, 7)
G = torch.randn(7, 11, 13)
H = torch.randn(4, 4)
I = torch.randn(3, 4, 4)
l = torch.randn(5, 10)
r = torch.randn(5, 20)
w = torch.randn(30, 10, 20)
test_list = [
# -- Vector
("i->", x), # sum
("i,i->", x, x), # dot
("i,i->i", x, x), # vector element-wise mul
("i,j->ij", x, y), # outer
# -- Matrix
("ij->ji", A), # transpose
("ij->j", A), # row sum
("ij->i", A), # col sum
("ij,ij->ij", A, A), # matrix element-wise mul
("ij,j->i", A, x), # matrix vector multiplication
("ij,kj->ik", A, B), # matmul
("ij,ab->ijab", A, E), # matrix outer product
# -- Tensor
("aij,ajk->aik", C, D), # batch matmul
("ijk,jk->i", C, A), # tensor matrix contraction
("aij,jk->aik", D, E), # tensor matrix contraction
("abcd,dfg->abcfg", F, G), # tensor tensor contraction
("ijk,jk->ik", C, A), # tensor matrix contraction with double indices
("ijk,jk->ij", C, A), # tensor matrix contraction with double indices
("ijk,ik->j", C, B), # non contiguous
("ijk,ik->jk", C, B), # non contiguous with double indices
# -- Diagonal
("ii", H), # trace
("ii->i", H), # diagonal
# -- Ellipsis
("i...->...", H),
("ki,...k->i...", A.t(), B),
("k...,jk", A.t(), B),
("...ii->...i", I), # batch diagonal
# -- Other
("bn,anm,bm->ba", l, w, r), # as torch.bilinear
("... ii->...i ", I), # batch diagonal with spaces
]
for test in test_list:
actual = torch.einsum(test[0], test[1:])
expected = np.einsum(test[0], *[t.numpy() for t in test[1:]])
self.assertEqual(expected.shape, actual.shape, test[0])
self.assertTrue(np.allclose(expected, actual.numpy()), test[0])
# test vararg
actual2 = torch.einsum(test[0], *test[1:])
self.assertEqual(expected.shape, actual2.shape, test[0])
self.assertTrue(np.allclose(expected, actual2.numpy()), test[0])
def do_einsum(*args):
return torch.einsum(test[0], args)
# FIXME: following test cases fail gradcheck
if test[0] not in {"i,i->", "i,i->i", "ij,ij->ij"}:
gradcheck_inps = tuple(t.detach().requires_grad_() for t in test[1:])
self.assertTrue(torch.autograd.gradcheck(do_einsum, gradcheck_inps))
self.assertTrue(A._version == 0) # check that we do not use inplace ops
def test_sum_all(self):
def check_sum_all(tensor):
pylist = tensor.reshape(-1).tolist()
self.assertEqual(tensor.sum(), sum(pylist))
check_sum_all(torch.tensor([1, 2, 3, 4, 5]))
check_sum_all(torch.randn(200000))
check_sum_all(torch.randn(2000, 2)[:, 0])
def _assert_matches_numpy(self, t, n):
self.assertEqual(n.shape, t.shape)
if t.dtype == torch.float:
self.assertTrue(np.allclose(n, t.numpy(), rtol=1e-03, atol=1e-05,
equal_nan=True))
else:
self.assertTrue(np.allclose(n, t.numpy(), equal_nan=True))
def _test_dim_ops(self, pytorch_op, numpy_op,
use_floating=True, use_integral=True):
def do_one(tensors_dict, dim):
for category, tensors in tensors_dict.items():
if category == "slice":
dim = 0
for tensor in tensors:
# we have no control over NumPy warnings...
with warnings.catch_warnings():
warnings.simplefilter("ignore")
expected = numpy_op(tensor.numpy(), dim)
actual = pytorch_op(tensor, dim)
self._assert_matches_numpy(actual, expected)
do_one(self._make_tensors((5, 400000), use_floating=use_floating,
use_integral=use_integral), 1)
do_one(self._make_tensors((3, 5, 7), use_floating=use_floating,
use_integral=use_integral), 0)
do_one(self._make_tensors((3, 5, 7), use_floating=use_floating,
use_integral=use_integral), 1)
do_one(self._make_tensors((3, 5, 7), use_floating=use_floating,
use_integral=use_integral), 2)
do_one(self._make_tensors((100000, ), use_floating=use_floating,
use_integral=use_integral), -1)
do_one(self._make_tensors((50, 50, 50), use_floating=use_floating,
use_integral=use_integral), 0)
do_one(self._make_tensors((50, 50, 50), use_floating=use_floating,
use_integral=use_integral), 1)
do_one(self._make_tensors((50, 50, 50), use_floating=use_floating,
use_integral=use_integral), 2)
do_one(self._make_tensors((50, 50, 50), use_floating=use_floating,
use_integral=use_integral), (1, 2))
do_one(self._make_tensors((50, 50, 50), use_floating=use_floating,
use_integral=use_integral), (1, -1))
do_one(self._make_tensors((50, 50, 50), use_floating=use_floating,
use_integral=use_integral), (0, 2))
do_one(self._make_tensors((50, 50, 50), use_floating=use_floating,
use_integral=use_integral), (0, 2, 1))
@unittest.skipIf(not TEST_NUMPY, 'Numpy not found')
def test_sum_dim(self):
self._test_dim_ops(
lambda t, d: t.sum(d),
lambda n, d: n.sum(d))
@unittest.skipIf(not TEST_NUMPY, 'Numpy not found')
def test_mean_dim(self):
self._test_dim_ops(
lambda t, d: t.mean(d),
lambda n, d: n.mean(d),
use_integral=False)
@unittest.skipIf(not TEST_NUMPY, 'Numpy not found')
def test_std_dim(self):
for unbiased in [False, True]:
self._test_dim_ops(
lambda t, d: t.std(d, unbiased=unbiased),
lambda n, d: n.std(d, ddof=1 if unbiased else 0),
use_integral=False)
def test_sum_out(self):
x = torch.rand(100, 100)
res1 = torch.sum(x, 1)
res2 = torch.Tensor()
torch.sum(x, 1, out=res2)
self.assertEqual(res1, res2)
x = torch.rand(100, 100, 100)
res1 = x.sum(2).sum(1)
res2 = torch.Tensor()
torch.sum(x, (2, 1), out=res2)
self.assertEqual(res1, res2)
# TODO: these tests only check if it's possible to pass a return value
# it'd be good to expand them
def test_prod(self):
x = torch.rand(100, 100)
res1 = torch.prod(x, 1)
res2 = torch.Tensor()
torch.prod(x, 1, out=res2)
self.assertEqual(res1, res2)
def test_cumsum(self):
x = torch.rand(100, 100)
res1 = torch.cumsum(x, 1)
res2 = torch.Tensor()
torch.cumsum(x, 1, out=res2)
self.assertEqual(res1, res2)
def test_cumprod(self):
x = torch.rand(100, 100)
res1 = torch.cumprod(x, 1)
res2 = torch.Tensor()
torch.cumprod(x, 1, out=res2)
self.assertEqual(res1, res2)
def _test_reduce_integer_upcast(self, fn, has_out=True):
shape = (3, 4, 5)
reduced_shape = fn(torch.ones(shape)).shape
def _test_out(dtype, other_dtype):
out = torch.ones(reduced_shape, dtype=dtype)
result = fn(x, out=out)
self.assertIs(out.dtype, result.dtype)
self.assertEqual(fn(x.type(dtype)), result)
result = fn(x, out=out, dtype=dtype)
self.assertIs(out.dtype, result.dtype)
self.assertEqual(fn(x.type(dtype)), result)
# 'out' is favored over dtype, check error
self.assertRaises(RuntimeError, lambda: fn(x, out=out, dtype=other_dtype))
for dtype in [dtype for dtype in torch.testing.get_all_dtypes() if dtype != torch.float16]:
x = torch.ones(shape, dtype=dtype)
expected_dtype = dtype if dtype.is_floating_point else torch.int64
self.assertIs(expected_dtype, fn(x).dtype)
self.assertEqual(fn(x.type(expected_dtype)), fn(x))
if dtype.is_floating_point:
other_dtype = torch.float32 if dtype == torch.float64 else torch.float64
else:
other_dtype = torch.int32 if dtype != torch.int32 else torch.int16
self.assertIs(other_dtype, fn(x, dtype=other_dtype).dtype)
self.assertEqual(fn(x.type(other_dtype)), fn(x, dtype=other_dtype))
# test mixed int/float
mixed_dtype = torch.int32 if dtype.is_floating_point else torch.float32
self.assertIs(mixed_dtype, fn(x, dtype=mixed_dtype).dtype)
self.assertEqual(fn(x.type(mixed_dtype)), fn(x, dtype=mixed_dtype))
if has_out:
_test_out(dtype, other_dtype)
_test_out(dtype, mixed_dtype)
def test_sum_integer_upcast(self):
self._test_reduce_integer_upcast(lambda x, **kwargs: torch.sum(x, **kwargs), False)
self._test_reduce_integer_upcast(lambda x, **kwargs: torch.sum(x, 0, **kwargs))
def test_prod_integer_upcast(self):
self._test_reduce_integer_upcast(lambda x, **kwargs: torch.prod(x, **kwargs), False)
self._test_reduce_integer_upcast(lambda x, **kwargs: torch.prod(x, 0, **kwargs))
def test_cumsum_integer_upcast(self):
self._test_reduce_integer_upcast(lambda x, **kwargs: torch.cumsum(x, 0, **kwargs))
def test_cumprod_integer_upcast(self):
self._test_reduce_integer_upcast(lambda x, **kwargs: torch.cumprod(x, 0, **kwargs))
def test_cross(self):
x = torch.rand(100, 3, 100)
y = torch.rand(100, 3, 100)
res1 = torch.cross(x, y)
res2 = torch.Tensor()
torch.cross(x, y, out=res2)
self.assertEqual(res1, res2)
def test_zeros(self):
res1 = torch.zeros(100, 100)
res2 = torch.Tensor()
torch.zeros(100, 100, out=res2)
self.assertEqual(res1, res2)
def test_zeros_like(self):
expected = torch.zeros(100, 100)
res1 = torch.zeros_like(expected)
self.assertEqual(res1, expected)
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_zeros_like_cuda(self):
expected = torch.zeros(100, 100).cuda()
res1 = torch.zeros_like(expected)
self.assertEqual(res1, expected)
@unittest.skipIf(torch.cuda.device_count() < 2, 'only one GPU detected')
def test_zeros_like_multiple_device(self):
expected = torch.zeros(100, 100).cuda()
x = torch.cuda.FloatTensor(100, 100, device=1)
output = torch.zeros_like(x)
self.assertEqual(output, expected)
def test_zeros_out(self):
shape = (3, 4)
out = torch.zeros(shape)
torch.zeros(shape, out=out)
# change the dtype, layout, device
self.assertRaises(RuntimeError, lambda: torch.zeros(shape, dtype=torch.int64, out=out))
self.assertRaises(RuntimeError, lambda: torch.zeros(shape, layout=torch.sparse_coo, out=out))
if torch.cuda.is_available():
self.assertRaises(RuntimeError, lambda: torch.zeros(shape, device='cuda', out=out))
# leave them the same
self.assertEqual(torch.zeros(shape), torch.zeros(shape, dtype=out.dtype, out=out))
self.assertEqual(torch.zeros(shape), torch.zeros(shape, layout=torch.strided, out=out))
self.assertEqual(torch.zeros(shape), torch.zeros(shape, device='cpu', out=out))
def test_histc(self):
x = torch.Tensor((2, 4, 2, 2, 5, 4))
y = torch.histc(x, 5, 1, 5) # nbins, min, max
z = torch.Tensor((0, 3, 0, 2, 1))
self.assertEqual(y, z)
def test_ones(self):
res1 = torch.ones(100, 100)
res2 = torch.Tensor()
torch.ones(100, 100, out=res2)
self.assertEqual(res1, res2)
def test_ones_like(self):
expected = torch.ones(100, 100)
res1 = torch.ones_like(expected)
self.assertEqual(res1, expected)
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_ones_like_cuda(self):
expected = torch.ones(100, 100).cuda()
res1 = torch.ones_like(expected)
self.assertEqual(res1, expected)
@unittest.skipIf(torch.cuda.device_count() < 2, 'only one GPU detected')
def test_ones_like_multiple_device(self):
expected = torch.ones(100, 100).cuda()
x = torch.cuda.FloatTensor(100, 100, device=1)
output = torch.ones_like(x)
self.assertEqual(output, expected)
def test_dtypes(self):
all_dtypes = torch.testing.get_all_dtypes()
do_test_dtypes(self, all_dtypes, torch.strided, torch.device('cpu'))
if torch.cuda.is_available():
do_test_dtypes(self, all_dtypes, torch.strided, torch.device('cuda:0'))
def test_copy_dtypes(self):
all_dtypes = torch.testing.get_all_dtypes()
for dtype in all_dtypes:
copied_dtype = copy.deepcopy(dtype)
self.assertIs(dtype, copied_dtype)
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)
self.assertRaises(RuntimeError, lambda: torch.device('cpu:-1'))
self.assertRaises(RuntimeError, lambda: torch.device('cpu:1'))
self.assertRaises(RuntimeError, lambda: torch.device('cpu', -1))
self.assertRaises(RuntimeError, lambda: torch.device('cpu', 1))
self.assertRaises(RuntimeError, lambda: torch.device('cuda:-1'))
self.assertRaises(RuntimeError, lambda: torch.device('cuda', -1))
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 test_tensor_device(self):
def assertEqual(device_str, fn):
self.assertEqual(torch.device(device_str), fn().device)
self.assertEqual(device_str, str(fn().device))
assertEqual('cpu', lambda: torch.tensor(5))
assertEqual('cpu', lambda: torch.ones((2, 3), dtype=torch.float32, device='cpu'))
# NOTE: 'cpu' is the canonical representation of 'cpu:0', but 'cuda:X' is the canonical
# representation of cuda devices.
assertEqual('cpu', lambda: torch.ones((2, 3), dtype=torch.float32, device='cpu:0'))
assertEqual('cpu', lambda: torch.tensor(torch.ones((2, 3), dtype=torch.float32), device='cpu:0'))
if TEST_NUMPY:
assertEqual('cpu', lambda: torch.tensor(np.random.randn(2, 3), device='cpu'))
if torch.cuda.is_available():
assertEqual('cuda:0', lambda: torch.tensor(5).cuda(0))
assertEqual('cuda:0', lambda: torch.tensor(5).cuda('cuda:0'))
self.assertRaises(RuntimeError, lambda: torch.tensor(5).cuda('cpu'))
self.assertRaises(RuntimeError, lambda: torch.tensor(5).cuda('cpu:0'))
assertEqual('cuda:0', lambda: torch.tensor(5, dtype=torch.int64, device=0))
assertEqual('cuda:0', lambda: torch.tensor(5, dtype=torch.int64, device='cuda:0'))
assertEqual('cuda:' + str(torch.cuda.current_device()),
lambda: torch.tensor(5, dtype=torch.int64, device='cuda'))
assertEqual('cuda:0', lambda: torch.tensor(torch.ones((2, 3), dtype=torch.float32), device='cuda:0'))
if TEST_NUMPY:
assertEqual('cuda:0', lambda: torch.tensor(np.random.randn(2, 3), device='cuda:0'))
if torch.cuda.device_count() > 1:
assertEqual('cuda:1', lambda: torch.tensor(5).cuda(1))
assertEqual('cuda:1', lambda: torch.tensor(5).cuda('cuda:1'))
assertEqual('cuda:1', lambda: torch.tensor(5, dtype=torch.int64, device=1))
assertEqual('cuda:1', lambda: torch.tensor(5, dtype=torch.int64, device='cuda:1'))
assertEqual('cuda:1', lambda: torch.tensor(torch.ones((2, 3), dtype=torch.float32), device='cuda:1'))
if TEST_NUMPY:
assertEqual('cuda:1', lambda: torch.tensor(np.random.randn(2, 3), device='cuda:1'))
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)
@skipIfRocm
def test_empty_full(self):
do_test_empty_full(self, torch.testing.get_all_dtypes(), torch.strided, torch.device('cpu'))
if torch.cuda.device_count() > 0:
do_test_empty_full(self, torch.testing.get_all_dtypes(), torch.strided, None)
do_test_empty_full(self, torch.testing.get_all_dtypes(), torch.strided, torch.device('cuda:0'))
def test_dtype_out_match(self):
d = torch.autograd.Variable(torch.DoubleTensor(2, 3))
self.assertRaises(RuntimeError, lambda: torch.zeros((2, 3), out=d, dtype=torch.float32))
def test_constructor_dtypes(self):
default_type = torch.Tensor().type()
self.assertIs(torch.Tensor().dtype, torch.get_default_dtype())
self.assertIs(torch.uint8, torch.ByteTensor.dtype)
self.assertIs(torch.float32, torch.FloatTensor.dtype)
self.assertIs(torch.float64, torch.DoubleTensor.dtype)
torch.set_default_tensor_type('torch.FloatTensor')
self.assertIs(torch.float32, torch.get_default_dtype())
self.assertIs(torch.FloatStorage, torch.Storage)
torch.set_default_dtype(torch.float64)
self.assertIs(torch.float64, torch.get_default_dtype())
self.assertIs(torch.DoubleStorage, torch.Storage)
torch.set_default_tensor_type(torch.FloatTensor)
self.assertIs(torch.float32, torch.get_default_dtype())
self.assertIs(torch.FloatStorage, torch.Storage)
if torch.cuda.is_available():
torch.set_default_tensor_type(torch.cuda.FloatTensor)
self.assertIs(torch.float32, torch.get_default_dtype())
self.assertIs(torch.float32, torch.cuda.FloatTensor.dtype)
self.assertIs(torch.cuda.FloatStorage, torch.Storage)
torch.set_default_dtype(torch.float64)
self.assertIs(torch.float64, torch.get_default_dtype())
self.assertIs(torch.cuda.DoubleStorage, torch.Storage)
# don't support integral or sparse default types.
self.assertRaises(TypeError, lambda: torch.set_default_tensor_type('torch.IntTensor'))
self.assertRaises(TypeError, lambda: torch.set_default_dtype(torch.int64))
# don't allow passing dtype to set_default_tensor_type
self.assertRaises(TypeError, lambda: torch.set_default_tensor_type(torch.float32))
torch.set_default_tensor_type(default_type)
def test_constructor_device_legacy(self):
self.assertRaises(RuntimeError, lambda: torch.FloatTensor(device='cuda'))
self.assertRaises(RuntimeError, lambda: torch.FloatTensor(torch.Size([2, 3, 4]), device='cuda'))
self.assertRaises(RuntimeError, lambda: torch.FloatTensor((2.0, 3.0), device='cuda'))
self.assertRaises(RuntimeError, lambda: torch.Tensor(device='cuda'))
self.assertRaises(RuntimeError, lambda: torch.Tensor(torch.Size([2, 3, 4]), device='cuda'))
self.assertRaises(RuntimeError, lambda: torch.Tensor((2.0, 3.0), device='cuda'))
x = torch.randn((3,), device='cpu')
self.assertRaises(RuntimeError, lambda: x.new(device='cuda'))
self.assertRaises(RuntimeError, lambda: x.new(torch.Size([2, 3, 4]), device='cuda'))
self.assertRaises(RuntimeError, lambda: x.new((2.0, 3.0), device='cuda'))
if torch.cuda.is_available():
self.assertRaises(RuntimeError, lambda: torch.cuda.FloatTensor(device='cpu'))
self.assertRaises(RuntimeError, lambda: torch.cuda.FloatTensor(torch.Size([2, 3, 4]), device='cpu'))
self.assertRaises(RuntimeError, lambda: torch.cuda.FloatTensor((2.0, 3.0), device='cpu'))
default_type = torch.Tensor().type()
torch.set_default_tensor_type(torch.cuda.FloatTensor)
self.assertRaises(RuntimeError, lambda: torch.Tensor(device='cpu'))
self.assertRaises(RuntimeError, lambda: torch.Tensor(torch.Size([2, 3, 4]), device='cpu'))
self.assertRaises(RuntimeError, lambda: torch.Tensor((2.0, 3.0), device='cpu'))
torch.set_default_tensor_type(torch.cuda.FloatTensor)
torch.set_default_tensor_type(default_type)
x = torch.randn((3,), device='cuda')
self.assertRaises(RuntimeError, lambda: x.new(device='cpu'))
self.assertRaises(RuntimeError, lambda: x.new(torch.Size([2, 3, 4]), device='cpu'))
self.assertRaises(RuntimeError, lambda: x.new((2.0, 3.0), device='cpu'))
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_tensor_factory(self):
expected = torch.Tensor([1, 1])
# test data
res1 = torch.tensor([1, 1])
self.assertEqual(res1, expected)
res1 = torch.tensor([1, 1], dtype=torch.int)
self.assertEqual(res1, expected)
self.assertIs(torch.int, res1.dtype)
# test copy
res2 = torch.tensor(expected)
self.assertEqual(res2, expected)
res2[1] = 2
self.assertEqual(expected, torch.ones_like(expected))
res2 = torch.tensor(expected, dtype=torch.int)
self.assertEqual(res1, expected)
self.assertIs(torch.int, res1.dtype)
# test copy with numpy
if TEST_NUMPY:
for dtype in [np.float64, np.int64, np.int8, np.uint8]:
a = np.array([5.]).astype(dtype)
res1 = torch.tensor(a)
self.assertEqual(5., res1[0].item())
a[0] = 7.
self.assertEqual(5., res1[0].item())
def test_tensor_factory_copy_var(self):
def check_copy(copy, is_leaf, requires_grad, data_ptr=None):
if data_ptr is None:
data_ptr = copy.data_ptr
self.assertEqual(copy.data, source.data)
self.assertTrue(copy.is_leaf == is_leaf)
self.assertTrue(copy.requires_grad == requires_grad)
self.assertTrue(copy.data_ptr == data_ptr)
source = torch.randn(5, 5, dtype=torch.double, requires_grad=True)
# test torch.tensor()
check_copy(torch.tensor(source), True, False)
check_copy(torch.tensor(source, requires_grad=False), True, False)
check_copy(torch.tensor(source, requires_grad=True), True, True)
# test tensor.new_tensor()
copy = torch.randn(1)
check_copy(copy.new_tensor(source), True, False)
check_copy(copy.new_tensor(source, requires_grad=False), True, False)
check_copy(copy.new_tensor(source, requires_grad=True), True, True)
# test torch.as_tensor()
check_copy(torch.as_tensor(source), source.is_leaf, source.requires_grad, source.data_ptr) # not copy
check_copy(torch.as_tensor(source, dtype=torch.float), False, True) # copy and keep the graph
def test_tensor_factory_type_inference(self):
def test_inference(default_dtype):
saved_dtype = torch.get_default_dtype()
torch.set_default_dtype(default_dtype)
self.assertIs(default_dtype, torch.tensor(()).dtype)
self.assertIs(default_dtype, torch.tensor(5.).dtype)
self.assertIs(torch.int64, torch.tensor(5).dtype)
self.assertIs(torch.uint8, torch.tensor(True).dtype)
self.assertIs(torch.int32, torch.tensor(5, dtype=torch.int32).dtype)
self.assertIs(default_dtype, torch.tensor(((7, 5), (9, 5.))).dtype)
self.assertIs(default_dtype, torch.tensor(((5., 5), (3, 5))).dtype)
self.assertIs(torch.int64, torch.tensor(((5, 3), (3, 5))).dtype)
if TEST_NUMPY:
self.assertIs(torch.float64, torch.tensor(np.array(())).dtype)
self.assertIs(torch.float64, torch.tensor(np.array(5.)).dtype)
if np.array(5).dtype == np.int64: # np long, which can be 4 bytes (e.g. on windows)
self.assertIs(torch.int64, torch.tensor(np.array(5)).dtype)
else:
self.assertIs(torch.int32, torch.tensor(np.array(5)).dtype)
self.assertIs(torch.uint8, torch.tensor(np.array(3, dtype=np.uint8)).dtype)
self.assertIs(default_dtype, torch.tensor(((7, np.array(5)), (np.array(9), 5.))).dtype)
self.assertIs(torch.float64, torch.tensor(((7, 5), (9, np.array(5.)))).dtype)
self.assertIs(torch.int64, torch.tensor(((5, np.array(3)), (np.array(3), 5))).dtype)
torch.set_default_dtype(saved_dtype)
test_inference(torch.float64)
test_inference(torch.float32)
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_tensor_factory_cuda_type_inference(self):
saved_type = torch.Tensor().type()
torch.set_default_tensor_type(torch.cuda.DoubleTensor)
torch.set_default_dtype(torch.float32)
self.assertIs(torch.float32, torch.tensor(0.).dtype)
self.assertEqual(torch.device('cuda:0'), torch.tensor(0.).device)
torch.set_default_dtype(torch.float64)
self.assertIs(torch.float64, torch.tensor(0.).dtype)
self.assertEqual(torch.device('cuda:0'), torch.tensor(0.).device)
torch.set_default_tensor_type(saved_type)
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_tensor_factory_cuda_type(self):
saved_type = torch.Tensor().type()
torch.set_default_tensor_type(torch.cuda.FloatTensor)
x = torch.zeros((5, 5))
self.assertIs(torch.float32, x.dtype)
self.assertTrue(x.is_cuda)
torch.set_default_tensor_type(torch.cuda.DoubleTensor)
x = torch.zeros((5, 5))
self.assertIs(torch.float64, x.dtype)
self.assertTrue(x.is_cuda)
torch.set_default_tensor_type(saved_type)
@skipIfRocm
def test_tensor_factories_empty(self):
# ensure we can create empty tensors from each factory function
shapes = [(5, 0, 1), (0,), (0, 0, 1, 0, 2, 0, 0)]
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
for shape in shapes:
self.assertEqual(shape, torch.zeros(shape, device=device).shape)
self.assertEqual(shape, torch.zeros_like(torch.zeros(shape, device=device)).shape)
self.assertEqual(shape, torch.empty(shape, device=device).shape)
self.assertEqual(shape, torch.empty_like(torch.zeros(shape, device=device)).shape)
self.assertEqual(shape, torch.full(shape, 3, device=device).shape)
self.assertEqual(shape, torch.full_like(torch.zeros(shape, device=device), 3).shape)
self.assertEqual(shape, torch.ones(shape, device=device).shape)
self.assertEqual(shape, torch.ones_like(torch.zeros(shape, device=device)).shape)
self.assertEqual(shape, torch.rand(shape, device=device).shape)
self.assertEqual(shape, torch.rand_like(torch.zeros(shape, device=device)).shape)
self.assertEqual(shape, torch.randn(shape, device=device).shape)
self.assertEqual(shape, torch.randn_like(torch.zeros(shape, device=device)).shape)
self.assertEqual(shape, torch.randint(6, shape, device=device).shape)
self.assertEqual(shape, torch.randint_like(torch.zeros(shape, device=device), 6).shape)
self.assertEqual((0,), torch.arange(0, device=device).shape)
self.assertEqual((0, 0), torch.eye(0, device=device).shape)
self.assertEqual((0, 0), torch.eye(0, 0, device=device).shape)
self.assertEqual((5, 0), torch.eye(5, 0, device=device).shape)
self.assertEqual((0, 5), torch.eye(0, 5, device=device).shape)
self.assertEqual((0,), torch.linspace(1, 1, 0, device=device).shape)
self.assertEqual((0,), torch.logspace(1, 1, 0, device=device).shape)
self.assertEqual((0,), torch.randperm(0, device=device).shape)
self.assertEqual((0,), torch.bartlett_window(0, device=device).shape)
self.assertEqual((0,), torch.bartlett_window(0, periodic=False, device=device).shape)
self.assertEqual((0,), torch.hamming_window(0, device=device).shape)
self.assertEqual((0,), torch.hann_window(0, device=device).shape)
self.assertEqual((1, 1, 0), torch.tensor([[[]]], device=device).shape)
self.assertEqual((1, 1, 0), torch.as_tensor([[[]]], device=device).shape)
def test_new_tensor(self):
expected = torch.autograd.Variable(torch.ByteTensor([1, 1]))
# test data
res1 = expected.new_tensor([1, 1])
self.assertEqual(res1, expected)
res1 = expected.new_tensor([1, 1], dtype=torch.int)
self.assertEqual(res1, expected)
self.assertIs(torch.int, res1.dtype)
# test copy
res2 = expected.new_tensor(expected)
self.assertEqual(res2, expected)
res2[1] = 2
self.assertEqual(expected, torch.ones_like(expected))
res2 = expected.new_tensor(expected, dtype=torch.int)
self.assertEqual(res2, expected)
self.assertIs(torch.int, res2.dtype)
# test copy with numpy
if TEST_NUMPY:
a = np.array([5.])
res1 = torch.tensor(a)
res1 = res1.new_tensor(a)
self.assertEqual(5., res1[0].item())
a[0] = 7.
self.assertEqual(5., res1[0].item())
if torch.cuda.device_count() >= 2:
expected = expected.cuda(1)
res1 = expected.new_tensor([1, 1])
self.assertEqual(res1.get_device(), expected.get_device())
res1 = expected.new_tensor([1, 1], dtype=torch.int)
self.assertIs(torch.int, res1.dtype)
self.assertEqual(res1.get_device(), expected.get_device())
res2 = expected.new_tensor(expected)
self.assertEqual(res2.get_device(), expected.get_device())
res2 = expected.new_tensor(expected, dtype=torch.int)
self.assertIs(torch.int, res1.dtype)
self.assertEqual(res2.get_device(), expected.get_device())
res2 = expected.new_tensor(expected, dtype=torch.int, device=0)
self.assertIs(torch.int, res1.dtype)
self.assertEqual(res2.get_device(), 0)
res1 = expected.new_tensor(1)
self.assertEqual(res1.get_device(), expected.get_device())
res1 = expected.new_tensor(1, dtype=torch.int)
self.assertIs(torch.int, res1.dtype)
self.assertEqual(res1.get_device(), expected.get_device())
def test_as_tensor(self):
# from python data
x = [[0, 1], [2, 3]]
self.assertEqual(torch.tensor(x), torch.as_tensor(x))
self.assertEqual(torch.tensor(x, dtype=torch.float32), torch.as_tensor(x, dtype=torch.float32))
# python data with heterogeneous types
z = [0, 'torch']
with self.assertRaisesRegex(TypeError, "invalid data type"):
torch.tensor(z)
torch.as_tensor(z)
# python data with self-referential lists
z = [0]
z += [z]
with self.assertRaisesRegex(TypeError, "self-referential lists are incompatible"):
torch.tensor(z)
torch.as_tensor(z)
z = [[1, 2], z]
with self.assertRaisesRegex(TypeError, "self-referential lists are incompatible"):
torch.tensor(z)
torch.as_tensor(z)
# from tensor (doesn't copy unless type is different)
y = torch.tensor(x)
self.assertIs(y, torch.as_tensor(y))
self.assertIsNot(y, torch.as_tensor(y, dtype=torch.float32))
if torch.cuda.is_available():
self.assertIsNot(y, torch.as_tensor(y, device='cuda'))
y_cuda = y.to('cuda')
self.assertIs(y_cuda, torch.as_tensor(y_cuda))
self.assertIs(y_cuda, torch.as_tensor(y_cuda, device='cuda'))
if TEST_NUMPY:
# doesn't copy
for dtype in [np.float64, np.int64, np.int8, np.uint8]:
n = np.random.rand(5, 6).astype(dtype)
n_astensor = torch.as_tensor(n)
self.assertEqual(torch.tensor(n), n_astensor)
n_astensor[0][0] = 25.7
self.assertEqual(torch.tensor(n), n_astensor)
# changing dtype causes copy
n = np.random.rand(5, 6).astype(np.float32)
n_astensor = torch.as_tensor(n, dtype=torch.float64)
self.assertEqual(torch.tensor(n, dtype=torch.float64), n_astensor)
n_astensor[0][1] = 250.8
self.assertNotEqual(torch.tensor(n, dtype=torch.float64), n_astensor)
# changing device causes copy
if torch.cuda.is_available():
n = np.random.randn(5, 6)
n_astensor = torch.as_tensor(n, device='cuda')
self.assertEqual(torch.tensor(n, device='cuda'), n_astensor)
n_astensor[0][2] = 250.9
self.assertNotEqual(torch.tensor(n, device='cuda'), n_astensor)
def test_diag(self):
x = torch.rand(100, 100)
res1 = torch.diag(x)
res2 = torch.Tensor()
torch.diag(x, out=res2)
self.assertEqual(res1, res2)
@staticmethod
def _test_diagonal(self, dtype, device):
x = torch.randn((100, 100), dtype=dtype, device=device)
result = torch.diagonal(x)
expected = torch.diag(x)
self.assertEqual(result, expected)
x = torch.randn((100, 100), dtype=dtype, device=device)
result = torch.diagonal(x, 17)
expected = torch.diag(x, 17)
self.assertEqual(result, expected)
def test_diagonal(self):
self._test_diagonal(self, dtype=torch.float32, device='cpu')
@unittest.skipIf(not TEST_NUMPY, 'Numpy not found')
def test_diagonal_multidim(self):
x = torch.randn(10, 11, 12, 13)
xn = x.numpy()
for args in [(2, 2, 3),
(2,),
(-2, 1, 2),
(0, -2, -1)]:
result = torch.diagonal(x, *args)
expected = xn.diagonal(*args)
self.assertEqual(expected.shape, result.shape)
self.assertTrue(np.allclose(expected, result.numpy()))
# test non-continguous
xp = x.permute(1, 2, 3, 0)
result = torch.diagonal(xp, 0, -2, -1)
expected = xp.numpy().diagonal(0, -2, -1)
self.assertEqual(expected.shape, result.shape)
self.assertTrue(np.allclose(expected, result.numpy()))
@staticmethod
def _test_diag_embed(self, dtype, device):
x = torch.arange(3 * 4, dtype=dtype, device=device).view(3, 4)
result = torch.diag_embed(x)
expected = torch.stack([torch.diag(r) for r in x], 0)
self.assertEqual(result, expected)
result = torch.diag_embed(x, offset=1, dim1=0, dim2=2)
expected = torch.stack([torch.diag(r, 1) for r in x], 1)
self.assertEqual(result, expected)
def test_diag_embed(self):
self._test_diag_embed(self, dtype=torch.float32, device='cpu')
@staticmethod
def _test_diagflat(self, dtype, device):
# Basic sanity test
x = torch.randn((100,), dtype=dtype, device=device)
result = torch.diagflat(x)
expected = torch.diag(x)
self.assertEqual(result, expected)
# Test offset
x = torch.randn((100,), dtype=dtype, device=device)
result = torch.diagflat(x, 17)
expected = torch.diag(x, 17)
self.assertEqual(result, expected)
# Test where input has more than one dimension
x = torch.randn((2, 3, 4), dtype=dtype, device=device)
result = torch.diagflat(x)
expected = torch.diag(x.contiguous().view(-1))
self.assertEqual(result, expected)
# Noncontig input
x = torch.randn((2, 3, 4), dtype=dtype, device=device).transpose(2, 0)
self.assertFalse(x.is_contiguous())
result = torch.diagflat(x)
expected = torch.diag(x.contiguous().view(-1))
self.assertEqual(result, expected)
def test_diagflat(self):
self._test_diagflat(self, dtype=torch.float32, device='cpu')
def test_eye(self):
res1 = torch.eye(100, 100)
res2 = torch.Tensor()
torch.eye(100, 100, out=res2)
self.assertEqual(res1, res2)
def test_renorm(self):
m1 = torch.randn(10, 5)
res1 = torch.Tensor()
def renorm(matrix, value, dim, max_norm):
m1 = matrix.transpose(dim, 0).contiguous()
# collapse non-dim dimensions.
m2 = m1.clone().resize_(m1.size(0), int(math.floor(m1.nelement() / m1.size(0))))
norms = m2.norm(value, 1, True)
# clip
new_norms = norms.clone()
new_norms[torch.gt(norms, max_norm)] = max_norm
new_norms.div_(norms.add_(1e-7))
# renormalize
m1.mul_(new_norms.expand_as(m1))
return m1.transpose(dim, 0)
# note that the axis fed to torch.renorm is different (2~=1)
maxnorm = m1.norm(2, 1).mean()
m2 = renorm(m1, 2, 1, maxnorm)
m1.renorm_(2, 1, maxnorm)
self.assertEqual(m1, m2, 1e-5)
self.assertEqual(m1.norm(2, 0), m2.norm(2, 0), 1e-5)
m1 = torch.randn(3, 4, 5)
m2 = m1.transpose(1, 2).contiguous().clone().resize_(15, 4)
maxnorm = m2.norm(2, 0).mean()
m2 = renorm(m2, 2, 1, maxnorm)
m1.renorm_(2, 1, maxnorm)
m3 = m1.transpose(1, 2).contiguous().clone().resize_(15, 4)
self.assertEqual(m3, m2)
self.assertEqual(m3.norm(2, 0), m2.norm(2, 0))
@staticmethod
def _test_renorm_ps(self, device):
# full reduction
x = torch.randn(5, 5)
xn = x.numpy()
for p in [1, 2, 3, 4, inf]:
res = x.renorm(p, 1, 1)
expected = x / x.norm(p, 0, keepdim=True).clamp(min=1)
self.assertEqual(res.numpy(), expected.numpy(), "renorm failed for {}-norm".format(p))
def test_renorm_ps(self):
self._test_renorm_ps(self, device='cpu')
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_renorm_ps_cuda(self):
self._test_renorm_ps(self, device='cuda')
@staticmethod
def _test_multinomial(self, type):
def make_prob_dist(shape, is_contiguous):
if is_contiguous:
return type(*shape).uniform_()
elif len(shape) == 1:
return type(*(shape + [5])).uniform_()[:, 2]
else:
# num dim = 2
new_shape = [2, shape[1], 7, 1, shape[0], 1, 10]
prob_dist = type(*new_shape).uniform_()
prob_dist = prob_dist.transpose(1, 4)
prob_dist = prob_dist[1, :, 5, 0, :, 0, 4]
assert not prob_dist.is_contiguous() # sanity check
return prob_dist
for is_contiguous in (True, False):
# with replacement
n_row = 3
for n_col in range(4, 5 + 1):
prob_dist = make_prob_dist([n_row, n_col], is_contiguous)
# indices that shouldn't be sampled (<0 means none)
zero_prob_indices = torch.LongTensor(n_row).random_(-2, n_col).tolist()
for i, j in enumerate(zero_prob_indices):
if j >= 0:
prob_dist[i, j] = 0
n_sample = n_col * 3
sample_indices = torch.multinomial(prob_dist, n_sample, True)
self.assertEqual(prob_dist.dim(), 2)
self.assertEqual(sample_indices.size(1), n_sample)
for i in range(n_row):
zero_prob_idx = zero_prob_indices[i]
if zero_prob_idx < 0:
continue
for j in range(n_sample):
self.assertNotEqual(sample_indices[i, j], zero_prob_idx,
"sampled an index with zero probability")
# without replacement
n_row = 3
for n_col in range(2, 10 + 1, 2):
prob_dist = make_prob_dist([n_row, n_col], is_contiguous)
# indices that shouldn't be sampled (<0 means none)
zero_prob_indices = torch.LongTensor(n_row).random_(-1, n_col).tolist()
for i, j in enumerate(zero_prob_indices):
if j >= 0:
prob_dist[i, j] = 0
n_sample = max(1, n_col - 2)
sample_indices = torch.multinomial(prob_dist, n_sample, False)
self.assertEqual(prob_dist.dim(), 2)
self.assertEqual(sample_indices.size(1), n_sample)
for i in range(n_row):
row_samples = {}
zero_prob_idx = zero_prob_indices[i]
for j in range(n_sample):
sample_idx = sample_indices[i, j]
if zero_prob_idx >= 0:
self.assertNotEqual(sample_idx, zero_prob_idx,
"sampled an index with zero probability")
self.assertNotIn(sample_idx, row_samples, "sampled an index twice")
row_samples[sample_idx] = True
# vector
n_col = 4
prob_dist = make_prob_dist([n_col], is_contiguous).fill_(1)
zero_prob_idx = 1 # index that shouldn't be sampled
prob_dist[zero_prob_idx] = 0
n_sample = 20
sample_indices = torch.multinomial(prob_dist, n_sample, True)
for sample_index in sample_indices:
self.assertNotEqual(sample_index, zero_prob_idx, "sampled an index with zero probability")
s_dim = sample_indices.dim()
self.assertEqual(sample_indices.dim(), 1, "wrong number of dimensions")
self.assertEqual(prob_dist.dim(), 1, "wrong number of prob_dist dimensions")
self.assertEqual(sample_indices.size(0), n_sample, "wrong number of samples")
def test_multinomial(self):
self._test_multinomial(self, torch.FloatTensor)
def _spawn_method(self, method, arg):
try:
mp.set_start_method('spawn')
except RuntimeError:
pass
with mp.Pool(1) as pool:
self.assertTrue(pool.map(method, [arg]))
@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 'invalid multinomial distribution' in str(e)
@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')
@unittest.skipIf(not PY3,
"spawn start method is not supported in Python 2, \
but we need it for for testing failure case for CPU RNG on Windows")
def test_multinomial_invalid_probs(self):
test_method = _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]))
self._spawn_method(test_method, torch.Tensor([0, 1, 0]))
@suppress_warnings
def test_range(self):
res1 = torch.range(0, 1)
res2 = torch.Tensor()
torch.range(0, 1, out=res2)
self.assertEqual(res1, res2, 0)
# Check range for non-contiguous tensors.
x = torch.zeros(2, 3)
torch.range(0, 3, out=x.narrow(1, 1, 2))
res2 = torch.Tensor(((0, 0, 1), (0, 2, 3)))
self.assertEqual(x, res2, 1e-16)
# Check negative
res1 = torch.Tensor((1, 0))
res2 = torch.Tensor()
torch.range(1, 0, -1, out=res2)
self.assertEqual(res1, res2, 0)
# Equal bounds
res1 = torch.ones(1)
res2 = torch.Tensor()
torch.range(1, 1, -1, out=res2)
self.assertEqual(res1, res2, 0)
torch.range(1, 1, 1, out=res2)
self.assertEqual(res1, res2, 0)
# FloatTensor
res1 = torch.range(0.6, 0.9, 0.1, out=torch.FloatTensor())
self.assertEqual(res1.size(0), 4)
res1 = torch.range(1, 10, 0.3, out=torch.FloatTensor())
self.assertEqual(res1.size(0), 31)
# DoubleTensor
res1 = torch.range(0.6, 0.9, 0.1, out=torch.DoubleTensor())
self.assertEqual(res1.size(0), 4)
res1 = torch.range(1, 10, 0.3, out=torch.DoubleTensor())
self.assertEqual(res1.size(0), 31)
def test_range_warning(self):
with warnings.catch_warnings(record=True) as w:
torch.range(0, 10)
self.assertEqual(len(w), 1)
def test_arange(self):
res1 = torch.arange(0, 1)
res2 = torch.Tensor()
torch.arange(0, 1, out=res2)
self.assertEqual(res1, res2, 0)
# Check arange with only one argument
res1 = torch.arange(10)
res2 = torch.arange(0, 10)
self.assertEqual(res1, res2, 0)
# Check arange for non-contiguous tensors.
x = torch.zeros(2, 3)
torch.arange(0, 4, out=x.narrow(1, 1, 2))
res2 = torch.Tensor(((0, 0, 1), (0, 2, 3)))
self.assertEqual(x, res2, 1e-16)
# Check negative
res1 = torch.Tensor((1, 0))
res2 = torch.Tensor()
torch.arange(1, -1, -1, out=res2)
self.assertEqual(res1, res2, 0)
# Equal bounds
res1 = torch.ones(1)
res2 = torch.Tensor()
torch.arange(1, 0, -1, out=res2)
self.assertEqual(res1, res2, 0)
torch.arange(1, 2, 1, out=res2)
self.assertEqual(res1, res2, 0)
# FloatTensor
res1 = torch.arange(0.6, 0.89, 0.1, out=torch.FloatTensor())
self.assertEqual(res1, [0.6, 0.7, 0.8])
res1 = torch.arange(1, 10, 0.3, out=torch.FloatTensor())
self.assertEqual(res1.size(0), 30)
self.assertEqual(res1[0], 1)
self.assertEqual(res1[29], 9.7)
# DoubleTensor
res1 = torch.arange(0.6, 0.89, 0.1, out=torch.DoubleTensor())
self.assertEqual(res1, [0.6, 0.7, 0.8])
res1 = torch.arange(1, 10, 0.3, out=torch.DoubleTensor())
self.assertEqual(res1.size(0), 30)
self.assertEqual(res1[0], 1)
self.assertEqual(res1[29], 9.7)
# Check that it's exclusive
r = torch.arange(0, 5)
self.assertEqual(r.min(), 0)
self.assertEqual(r.max(), 4)
self.assertEqual(r.numel(), 5)
r = torch.arange(0, 5, 2)
self.assertEqual(r.min(), 0)
self.assertEqual(r.max(), 4)
self.assertEqual(r.numel(), 3)
r1 = torch.arange(0, 5 + 1e-6)
r2 = torch.arange(0, 5)
r3 = torch.arange(0, 5 - 1e-6)
self.assertEqual(r1[:-1], r2, 0)
self.assertEqual(r2, r3, 0)
r1 = torch.arange(10, -1 + 1e-6, -1)
r2 = torch.arange(10, -1, -1)
r3 = torch.arange(10, -1 - 1e-6, -1)
self.assertEqual(r1, r2, 0)
self.assertEqual(r2, r3[:-1], 0)
msg = "unsupported range"
self.assertRaisesRegex(RuntimeError, msg, lambda: torch.arange(0, float('inf')))
self.assertRaisesRegex(RuntimeError, msg, lambda: torch.arange(float('inf')))
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
self.assertRaisesRegex(RuntimeError, msg, lambda: torch.arange(-5, float('nan'), device=device))
# check with step size
self.assertRaisesRegex(RuntimeError, msg, lambda: torch.arange(0, float('-inf'), -1, device=device))
self.assertRaisesRegex(RuntimeError, msg, lambda: torch.arange(0, float('inf'), device=device))
self.assertRaisesRegex(RuntimeError, msg, lambda: torch.arange(float('-inf'), 10, device=device))
self.assertRaisesRegex(RuntimeError, msg, lambda: torch.arange(float('nan'), 10, device=device))
self.assertRaisesRegex(RuntimeError, msg, lambda: torch.arange(float('inf'), device=device))
self.assertRaisesRegex(RuntimeError, msg, lambda: torch.arange(float('nan'), device=device))
self.assertRaisesRegex(
RuntimeError, "overflow",
lambda: torch.arange(1.175494351e-38, 3.402823466e+38, device=device))
def test_arange_inference(self):
saved_dtype = torch.get_default_dtype()
torch.set_default_dtype(torch.float32)
# end only
self.assertIs(torch.float32, torch.arange(1.).dtype)
self.assertIs(torch.float32, torch.arange(torch.tensor(1.)).dtype)
self.assertIs(torch.float32, torch.arange(torch.tensor(1., dtype=torch.float64)).dtype)
self.assertIs(torch.int64, torch.arange(1).dtype)
self.assertIs(torch.int64, torch.arange(torch.tensor(1)).dtype)
self.assertIs(torch.int64, torch.arange(torch.tensor(1, dtype=torch.int16)).dtype)
# start, end, [step]
self.assertIs(torch.float32, torch.arange(1., 3).dtype)
self.assertIs(torch.float32, torch.arange(torch.tensor(1., dtype=torch.float64), 3).dtype)
self.assertIs(torch.float32, torch.arange(1, 3.).dtype)
self.assertIs(torch.float32, torch.arange(torch.tensor(1, dtype=torch.int16), torch.tensor(3.)).dtype)
self.assertIs(torch.float32, torch.arange(1, 3, 1.).dtype)
self.assertIs(torch.float32,
torch.arange(torch.tensor(1),
torch.tensor(3, dtype=torch.int16),
torch.tensor(1., dtype=torch.float64)).dtype)
self.assertIs(torch.int64, torch.arange(1, 3).dtype)
self.assertIs(torch.int64, torch.arange(torch.tensor(1), 3).dtype)
self.assertIs(torch.int64, torch.arange(torch.tensor(1), torch.tensor(3, dtype=torch.int16)).dtype)
self.assertIs(torch.int64, torch.arange(1, 3, 1).dtype)
self.assertIs(torch.int64,
torch.arange(torch.tensor(1),
torch.tensor(3),
torch.tensor(1, dtype=torch.int16)).dtype)
torch.set_default_dtype(saved_dtype)
def test_randint_inference(self):
size = (2, 1)
for args in [(3,), (1, 3)]: # (low,) and (low, high)
self.assertIs(torch.int64, torch.randint(*args, size=size).dtype)
self.assertIs(torch.int64, torch.randint(*args, size=size, layout=torch.strided).dtype)
self.assertIs(torch.int64, torch.randint(*args, size=size, generator=torch.default_generator).dtype)
self.assertIs(torch.float32, torch.randint(*args, size=size, dtype=torch.float32).dtype)
out = torch.empty(size, dtype=torch.float32)
self.assertIs(torch.float32, torch.randint(*args, size=size, out=out).dtype)
self.assertIs(torch.float32, torch.randint(*args, size=size, out=out, dtype=torch.float32).dtype)
out = torch.empty(size, dtype=torch.int64)
self.assertIs(torch.int64, torch.randint(*args, size=size, out=out).dtype)
self.assertIs(torch.int64, torch.randint(*args, size=size, out=out, dtype=torch.int64).dtype)
@staticmethod
def _select_broadcastable_dims(dims_full=None):
# select full dimensionality
if dims_full is None:
dims_full = []
ndims = random.randint(1, 4)
dims_full = [random.randint(1, 8) for _ in range(ndims)]
else:
ndims = len(dims_full)
# select actual dimensions for ops:
# larger: full ndims, individual sizes may be reduced
# smaller: possibly reduced ndims, sizes may be reduced
smaller_ndims = random.randint(1, ndims)
dims_small = []
dims_large = []
for i in range(ndims - 1, -1, -1):
j = random.randint(1, 3)
if j == 1: # no reduced singleton dimension
ds = dims_full[i]
dl = dims_full[i]
elif j == 2: # larger may have reduced singleton dimension
ds = dims_full[i]
dl = 1 if len(dims_small) < smaller_ndims else dims_full[i]
elif j == 3: # smaller may have reduced singleton dimension
ds = 1
dl = dims_full[i]
dims_large = [dl] + dims_large
if len(dims_small) < smaller_ndims:
dims_small = [ds] + dims_small
return (dims_small, dims_large, dims_full)
@staticmethod
def _test_broadcast(self, cast):
# all functions
fns = {
"dist", "atan2", "pow", "lerp", "add",
"sub", "mul", "div", "fmod", "remainder",
"eq", "ge", "gt", "le", "lt", "max", "min", "ne",
"addcdiv", "addcmul", "masked_scatter", "masked_select", "masked_fill",
"map", "map2", "copy"
}
# functions with three tensor arguments
fns_3_args = {"addcdiv", "addcmul", "map2"}
for fn in fns:
(dims_small, dims_large, dims_full) = self._select_broadcastable_dims()
small = cast(torch.randn(*dims_small).float())
large = cast(torch.randn(*dims_large).float())
small_expanded = small.expand(*dims_full)
large_expanded = large.expand(*dims_full)
small2 = None
small2_expanded = None
if fn in fns_3_args:
# create another smaller tensor
(dims_small2, _, _) = self._select_broadcastable_dims(dims_full)
small2 = cast(torch.randn(*dims_small2).float())
small2_expanded = small2.expand(*dims_full)
if small.is_cuda and fn in ['map', 'map2']:
# map and map2 are not implementd on CUDA tensors
continue
# TODO: fix masked_scatter and masked_fill broadcasting
if hasattr(large_expanded, fn) and fn not in ['masked_scatter', 'masked_fill']:
# run through tensor versions of functions
# and verify fully expanded inputs give same results
expanded = {large: large_expanded, small: small_expanded, small2: small2_expanded}
def tensorfn(myfn, t1, t2):
if fn == "lerp":
return myfn(t1, 0.5)
elif fn == "masked_select":
return myfn(t1 < 0)
elif fn in fns_3_args:
return myfn(1, t1, t2)
else:
return myfn(t1)
# test various orders
for first, second, third in [(large, small, small2), (small, large, small2),
(small2, small, large), (small2, large, small)]:
if first is None:
break # ignore last iter when small2 is None
method_expanded = getattr(expanded[first], fn)
method = getattr(first, fn)
r1 = tensorfn(method_expanded, expanded[second], expanded[third])
r2 = tensorfn(method, second, third)
self.assertEqual(r1, r2)
# now for torch. versions of functions
if hasattr(torch, fn):
fntorch = getattr(torch, fn)
expanded = {large: large_expanded, small: small_expanded, small2: small2_expanded}
def torchfn(t1, t2, t3):
if fn == "lerp":
return fntorch(t1, t2, 0.5)
elif fn == "masked_select":
return fntorch(t1, t2 < 0)
elif fn == "masked_scatter":
return fntorch(t1, t2 < 0.5, cast(torch.arange(1, t1.nelement() + 1).float()))
elif fn == "masked_fill":
return fntorch(t1, t2 < 0.5, 1.0)
elif fn in fns_3_args:
return fntorch(t1, 1.0, t2, t3)
else:
return fntorch(t1, t2)
# test various orders
for first, second, third in [(large, small, small2), (small, large, small2),
(small2, small, large), (small2, large, small)]:
if first is None:
break # ignore last iter when small2 is None
r1 = torchfn(expanded[first], expanded[second], expanded[third])
r2 = torchfn(first, second, third)
self.assertEqual(r1, r2)
# now for in place functions
# in-place tensor is not broadcastable; test only guaranteed
# to work by broadcasting other argument(s)
if not hasattr(large_expanded, fn + "_"):
continue
# need to clone largeExpanded so we can reuse, since functions are in-place
large_expanded_clone = large_expanded.clone()
def tensorfn_inplace(t0, t1, t2=None):
t0_fn = getattr(t0, fn + "_")
if fn == "lerp":
return t0_fn(t1, 0.5)
elif fn == "masked_scatter":
return t0_fn(t1 < 0.5, cast(torch.arange(1, t0.nelement() + 1).float()))
elif fn == "masked_fill":
return t0_fn(t1 < 0.5, 1.0)
elif fn == "map":
return t0_fn(t1, lambda x, y: x + y)
elif fn == "map2":
return t0_fn(t1, t2, lambda x, y, z: x + y + z)
elif fn in fns_3_args:
return t0_fn(1.0, t1, t2)
else:
return t0_fn(t1)
r1 = tensorfn_inplace(large_expanded, small_expanded, small2_expanded)
r2 = tensorfn_inplace(large_expanded_clone, small, small2)
# in-place pointwise operations don't actually work if the in-place
# tensor is 0-strided (numpy has the same issue)
if (0 not in large_expanded.stride() and 0 not in large_expanded_clone.stride()):
self.assertEqual(r1, r2)
def broadcastable(t0, t1, t2=None):
try:
t1.expand_as(t0)
if t2 is not None:
t2.expand_as(t0)
except RuntimeError:
return False
return True
def _test_in_place_broadcastable(t0, t1, t2=None):
if not broadcastable(t0, t1, t2):
same_size = t0.numel() == t1.numel() and (t0.numel() == t2.numel() if t2 is not None else True)
if not same_size:
self.assertRaises(RuntimeError, lambda: tensorfn_inplace(t0, t1, t2))
else:
tensorfn_inplace(t0, t1, t2)
if fn not in fns_3_args:
_test_in_place_broadcastable(small, large_expanded)
_test_in_place_broadcastable(small, large)
else:
_test_in_place_broadcastable(small2, small_expanded, large_expanded)
_test_in_place_broadcastable(small2, small, large)
def test_broadcast(self):
self._test_broadcast(self, lambda t: t)
def test_broadcast_empty(self):
# empty + empty
self.assertRaises(RuntimeError, lambda: torch.randn(5, 0) + torch.randn(0, 5))
self.assertEqual(torch.randn(5, 0), torch.randn(0) + torch.randn(5, 0))
self.assertEqual(torch.randn(5, 0, 0), torch.randn(0) + torch.randn(5, 0, 1))
# scalar + empty
self.assertEqual(torch.randn(5, 0, 6), torch.randn(()) + torch.randn(5, 0, 6))
# non-empty, empty
self.assertEqual(torch.randn(0), torch.randn(0) + torch.randn(1))
self.assertEqual(torch.randn(0, 7, 0, 6, 5, 0, 7),
torch.randn(0, 7, 0, 6, 5, 0, 1) + torch.randn(1, 1, 5, 1, 7))
self.assertRaises(RuntimeError, lambda: torch.randn(7, 0) + torch.randn(2, 1))
def test_broadcast_tensors(self):
x0 = torch.randn(2, 1, 3)
x1 = torch.randn(3)
x2 = torch.randn(3, 1)
expected_size = (2, 3, 3)
y0, y1, y2 = torch.broadcast_tensors(x0, x1, x2)
self.assertTrue(y0.size() == expected_size)
self.assertTrue(y1.size() == expected_size)
self.assertTrue(y2.size() == expected_size)
@staticmethod
def _test_contiguous(self, cast):
x = cast(torch.randn(1, 16, 5, 5))
self.assertTrue(x.is_contiguous())
stride = list(x.stride())
stride[0] = 20
# change the stride in dimension 0. the tensor is still contiguous because size[0] is 1
x.set_(x.storage(), 0, x.size(), stride)
self.assertTrue(x.is_contiguous())
def test_contiguous(self):
return self._test_contiguous(self, lambda t: t)
def test_empty_tensor_props(self):
sizes = [(0,), (0, 3), (5, 0), (5, 0, 3, 0, 2), (0, 3, 0, 2), (0, 5, 0, 2, 0)]
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for size in sizes:
for device in devices:
x = torch.empty(tuple(size), device=device)
self.assertEqual(size, x.shape)
self.assertTrue(x.is_contiguous())
size_ones_instead_of_zeros = (x if x != 0 else 1 for x in size)
y = torch.empty(tuple(size_ones_instead_of_zeros), device=device)
self.assertEqual(x.stride(), y.stride())
def test_scalars_as_floats(self):
"zero-dim variables that don't require grad should bind to scalar arguments"
x = torch.tensor(2.)
y = torch.tensor(3.)
# 3 + (3 * 3) * 2
self.assertEqual(y.addcmul(y, y, value=x), 21)
x = torch.tensor(2., requires_grad=True)
self.assertRaises(Exception, lambda: y.addcmul(y, y, value=x))
@staticmethod
def _test_broadcast_fused_matmul(self, cast):
fns = ["baddbmm", "addbmm", "addmm", "addmv", "addr"]
for fn in fns:
batch_dim = random.randint(1, 8)
n_dim = random.randint(1, 8)
m_dim = random.randint(1, 8)
p_dim = random.randint(1, 8)
def dims_full_for_fn():
if fn == "baddbmm":
return ([batch_dim, n_dim, p_dim], [batch_dim, n_dim, m_dim], [batch_dim, m_dim, p_dim])
elif fn == "addbmm":
return ([n_dim, p_dim], [batch_dim, n_dim, m_dim], [batch_dim, m_dim, p_dim])
elif fn == "addmm":
return ([n_dim, p_dim], [n_dim, m_dim], [m_dim, p_dim])
elif fn == "addmv":
return ([n_dim], [n_dim, m_dim], [m_dim])
elif fn == "addr":
return ([n_dim, m_dim], [n_dim], [m_dim])
else:
raise AssertionError("unknown function")
(t0_dims_full, t1_dims, t2_dims) = dims_full_for_fn()
(t0_dims_small, _, _) = self._select_broadcastable_dims(t0_dims_full)
t0_small = cast(torch.randn(*t0_dims_small).float())
t1 = cast(torch.randn(*t1_dims).float())
t2 = cast(torch.randn(*t2_dims).float())
t0_full = cast(t0_small.expand(*t0_dims_full))
fntorch = getattr(torch, fn)
r0 = fntorch(t0_small, t1, t2)
r1 = fntorch(t0_full, t1, t2)
self.assertEqual(r0, r1)
def test_broadcast_fused_matmul(self):
self._test_broadcast_fused_matmul(self, lambda t: t)
@staticmethod
def _test_broadcast_batched_matmul(self, cast):
n_dim = random.randint(1, 8)
m_dim = random.randint(1, 8)
p_dim = random.randint(1, 8)
full_batch_dims = [random.randint(1, 3) for i in range(random.randint(1, 3))]
(batch_dims_small, _, _) = self._select_broadcastable_dims(full_batch_dims)
def verify_batched_matmul(full_lhs, one_dimensional):
if not one_dimensional:
lhs_dims = [n_dim, m_dim]
rhs_dims = [m_dim, p_dim]
result_dims = [n_dim, p_dim]
else:
lhs_dims = [n_dim, m_dim] if full_lhs else [m_dim]
rhs_dims = [m_dim, p_dim] if not full_lhs else [m_dim]
result_dims = [n_dim] if full_lhs else [p_dim]
lhs_mat_dims = lhs_dims if len(lhs_dims) != 1 else [1, m_dim]
rhs_mat_dims = rhs_dims if len(rhs_dims) != 1 else [m_dim, 1]
full_mat_dims = lhs_mat_dims if full_lhs else rhs_mat_dims
dim0_dims = rhs_dims if full_lhs else lhs_dims
small_dims = batch_dims_small + (rhs_mat_dims if full_lhs else lhs_mat_dims)
small = cast(torch.randn(*(small_dims)).float())
dim0 = cast(torch.randn(*(dim0_dims)).float())
full = cast(torch.randn(*(full_batch_dims + full_mat_dims)).float())
if not one_dimensional:
(lhsTensors, rhsTensors) = ((full,), (small, dim0)) if full_lhs else ((small, dim0), (full,))
else:
(lhsTensors, rhsTensors) = ((full,), (dim0,)) if full_lhs else ((dim0,), (full,))
def maybe_squeeze_result(l, r, result):
if len(lhs_dims) == 1 and l.dim() != 1:
return result.squeeze(-2)
elif len(rhs_dims) == 1 and r.dim() != 1:
return result.squeeze(-1)
else:
return result
for lhs in lhsTensors:
lhs_expanded = lhs.expand(*(torch.Size(full_batch_dims) + torch.Size(lhs_mat_dims)))
lhs_expanded_matmul_fn = getattr(lhs_expanded, "matmul")
for rhs in rhsTensors:
rhs_expanded = ((rhs if len(rhs_dims) != 1 else rhs.unsqueeze(-1)).
expand(*(torch.Size(full_batch_dims) + torch.Size(rhs_mat_dims))))
truth = maybe_squeeze_result(lhs_expanded, rhs_expanded, lhs_expanded_matmul_fn(rhs_expanded))
for l in (lhs, lhs_expanded):
for r in (rhs, rhs_expanded):
l_matmul_fn = getattr(l, "matmul")
result = maybe_squeeze_result(l, r, l_matmul_fn(r))
self.assertEqual(truth, result)
# test torch.matmul function as well
torch_result = maybe_squeeze_result(l, r, torch.matmul(l, r))
self.assertEqual(truth, torch_result)
# test torch.matmul with out
out = torch.zeros_like(torch_result)
torch.matmul(l, r, out=out)
self.assertEqual(truth, maybe_squeeze_result(l, r, out))
# compare to bmm
bmm_result = (torch.bmm(lhs_expanded.contiguous().view(-1, *lhs_mat_dims),
rhs_expanded.contiguous().view(-1, *rhs_mat_dims)))
self.assertEqual(truth.view(-1, *result_dims), bmm_result.view(-1, *result_dims))
for indices in product((True, False), repeat=2):
verify_batched_matmul(*indices)
def test_broadcast_batched_matmul(self):
self._test_broadcast_batched_matmul(self, lambda t: t)
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_randperm(self):
_RNGState = torch.get_rng_state()
res1 = torch.randperm(100)
res2 = torch.LongTensor()
torch.set_rng_state(_RNGState)
torch.randperm(100, out=res2)
self.assertEqual(res1, res2, 0)
# randperm of 0 elements is an empty tensor
res1 = torch.randperm(0)
res2 = torch.LongTensor(5)
torch.randperm(0, out=res2)
self.assertEqual(res1.numel(), 0)
self.assertEqual(res2.numel(), 0)
def test_random(self):
# This test is flaky with p<=(2/(ub-lb))^200=6e-36
t = torch.FloatTensor(200)
lb = 1
ub = 4
t.fill_(-1)
t.random_(lb, ub)
self.assertEqual(t.min(), lb)
self.assertEqual(t.max(), ub - 1)
t.fill_(-1)
t.random_(ub)
self.assertEqual(t.min(), 0)
self.assertEqual(t.max(), ub - 1)
@staticmethod
def _test_random_neg_values(self, use_cuda=False):
signed_types = ['torch.DoubleTensor', 'torch.FloatTensor', 'torch.LongTensor',
'torch.IntTensor', 'torch.ShortTensor']
for tname in signed_types:
res = torch.rand(SIZE, SIZE).type(tname)
if use_cuda:
res = res.cuda()
res.random_(-10, -1)
self.assertLessEqual(res.max().item(), 9)
self.assertGreaterEqual(res.min().item(), -10)
def test_random_neg_values(self):
self._test_random_neg_values(self)
def assertIsOrdered(self, order, x, mxx, ixx, task):
SIZE = 4
if order == 'descending':
def check_order(a, b):
return a >= b
elif order == 'ascending':
def check_order(a, b):
return a <= b
else:
error('unknown order "{}", must be "ascending" or "descending"'.format(order))
are_ordered = True
for j, k in product(range(SIZE), range(1, SIZE)):
self.assertTrue(check_order(mxx[j][k - 1], mxx[j][k]),
'torch.sort ({}) values unordered for {}'.format(order, task))
seen = set()
indicesCorrect = True
size = x.size(x.dim() - 1)
for k in range(size):
seen.clear()
for j in range(size):
self.assertEqual(x[k][ixx[k][j]], mxx[k][j],
'torch.sort ({}) indices wrong for {}'.format(order, task))
seen.add(ixx[k][j])
self.assertEqual(len(seen), size)
def test_sort(self):
SIZE = 4
x = torch.rand(SIZE, SIZE)
res1val, res1ind = torch.sort(x)
# Test use of result tensor
res2val = torch.Tensor()
res2ind = torch.LongTensor()
torch.sort(x, out=(res2val, res2ind))
self.assertEqual(res1val, res2val, 0)
self.assertEqual(res1ind, res2ind, 0)
self.assertEqual(torch.argsort(x), res1ind)
self.assertEqual(x.argsort(), res1ind)
# Test sorting of random numbers
self.assertIsOrdered('ascending', x, res2val, res2ind, 'random')
# Test simple sort
self.assertEqual(
torch.sort(torch.Tensor((50, 40, 30, 20, 10)))[0],
torch.Tensor((10, 20, 30, 40, 50)),
0
)
# Test that we still have proper sorting with duplicate keys
x = torch.floor(torch.rand(SIZE, SIZE) * 10)
torch.sort(x, out=(res2val, res2ind))
self.assertIsOrdered('ascending', x, res2val, res2ind, 'random with duplicate keys')
# DESCENDING SORT
x = torch.rand(SIZE, SIZE)
res1val, res1ind = torch.sort(x, x.dim() - 1, True)
# Test use of result tensor
res2val = torch.Tensor()
res2ind = torch.LongTensor()
torch.sort(x, x.dim() - 1, True, out=(res2val, res2ind))
self.assertEqual(res1val, res2val, 0)
self.assertEqual(res1ind, res2ind, 0)
self.assertEqual(torch.argsort(x, x.dim() - 1, True), res1ind)
self.assertEqual(x.argsort(x.dim() - 1, True), res1ind)
# Test sorting of random numbers
self.assertIsOrdered('descending', x, res2val, res2ind, 'random')
# Test simple sort task
self.assertEqual(
torch.sort(torch.Tensor((10, 20, 30, 40, 50)), 0, True)[0],
torch.Tensor((50, 40, 30, 20, 10)),
0
)
# Test that we still have proper sorting with duplicate keys
self.assertIsOrdered('descending', x, res2val, res2ind, 'random with duplicate keys')
@unittest.skipIf(not TEST_NUMPY, 'Numpy not found')
def test_tensordot(self):
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for d in devices:
a = torch.arange(60., device=d).reshape(3, 4, 5)
b = torch.arange(24., device=d).reshape(4, 3, 2)
c = torch.tensordot(a, b, dims=([1, 0], [0, 1])).cpu()
cn = torch.from_numpy(np.tensordot(a.cpu().numpy(), b.cpu().numpy(),
axes=([1, 0], [0, 1])))
self.assertEqual(c, cn)
a = torch.randn(2, 3, 4, 5, device=d)
b = torch.randn(4, 5, 6, 7, device=d)
c = torch.tensordot(a, b, dims=2).cpu()
cn = torch.from_numpy(np.tensordot(a.cpu().numpy(), b.cpu().numpy(),
axes=2))
self.assertEqual(c, cn)
c = torch.tensordot(a, b).cpu()
cn = torch.from_numpy(np.tensordot(a.cpu().numpy(), b.cpu().numpy()))
self.assertEqual(c, cn)
def test_topk(self):
def topKViaSort(t, k, dim, dir):
sorted, indices = t.sort(dim, dir)
return sorted.narrow(dim, 0, k), indices.narrow(dim, 0, k)
def compareTensors(t, res1, ind1, res2, ind2, dim):
# Values should be exactly equivalent
self.assertEqual(res1, res2, 0)
# Indices might differ based on the implementation, since there is
# no guarantee of the relative order of selection
if not ind1.eq(ind2).all():
# To verify that the indices represent equivalent elements,
# gather from the input using the topk indices and compare against
# the sort indices
vals = t.gather(dim, ind2)
self.assertEqual(res1, vals, 0)
def compare(t, k, dim, dir):
topKVal, topKInd = t.topk(k, dim, dir, True)
sortKVal, sortKInd = topKViaSort(t, k, dim, dir)
compareTensors(t, sortKVal, sortKInd, topKVal, topKInd, dim)
t = torch.rand(random.randint(1, SIZE),
random.randint(1, SIZE),
random.randint(1, SIZE))
for _kTries in range(3):
for _dimTries in range(3):
for transpose in (True, False):
for dir in (True, False):
testTensor = t
if transpose:
dim1 = random.randrange(t.ndimension())
dim2 = dim1
while dim1 == dim2:
dim2 = random.randrange(t.ndimension())
testTensor = t.transpose(dim1, dim2)
dim = random.randrange(testTensor.ndimension())
k = random.randint(1, testTensor.size(dim))
compare(testTensor, k, dim, dir)
def test_topk_arguments(self):
q = torch.randn(10, 2, 10)
# Make sure True isn't mistakenly taken as the 2nd dimension (interpreted as 1)
self.assertRaises(TypeError, lambda: q.topk(4, True))
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_topk_noncontiguous_gpu(self):
t = torch.randn(20, device="cuda")[::2]
top1, idx1 = t.topk(5)
top2, idx2 = t.contiguous().topk(5)
self.assertEqual(top1, top2)
self.assertEqual(idx1, idx2)
def test_kthvalue(self):
SIZE = 50
x = torch.rand(SIZE, SIZE, SIZE)
x0 = x.clone()
k = random.randint(1, SIZE)
res1val, res1ind = torch.kthvalue(x, k, keepdim=False)
res2val, res2ind = torch.sort(x)
self.assertEqual(res1val[:, :], res2val[:, :, k - 1], 0)
self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], 0)
# test use of result tensors
k = random.randint(1, SIZE)
res1val = torch.Tensor()
res1ind = torch.LongTensor()
torch.kthvalue(x, k, keepdim=False, out=(res1val, res1ind))
res2val, res2ind = torch.sort(x)
self.assertEqual(res1val[:, :], res2val[:, :, k - 1], 0)
self.assertEqual(res1ind[:, :], res2ind[:, :, k - 1], 0)
# test non-default dim
k = random.randint(1, SIZE)
res1val, res1ind = torch.kthvalue(x, k, 0, keepdim=False)
res2val, res2ind = torch.sort(x, 0)
self.assertEqual(res1val, res2val[k - 1], 0)
self.assertEqual(res1ind, res2ind[k - 1], 0)
# non-contiguous
y = x.narrow(1, 0, 1)
y0 = y.contiguous()
k = random.randint(1, SIZE)
res1val, res1ind = torch.kthvalue(y, k)
res2val, res2ind = torch.kthvalue(y0, k)
self.assertEqual(res1val, res2val, 0)
self.assertEqual(res1ind, res2ind, 0)
# check that the input wasn't modified
self.assertEqual(x, x0, 0)
# simple test case (with repetitions)
y = torch.Tensor((3, 5, 4, 1, 1, 5))
self.assertEqual(torch.kthvalue(y, 3)[0], 3, 0)
self.assertEqual(torch.kthvalue(y, 2)[0], 1, 0)
def test_median(self):
for size in (155, 156):
x = torch.rand(size, size)
x0 = x.clone()
nelem = x.nelement()
res1val = torch.median(x)
res2val, _ = torch.sort(x.view(nelem))
ind = int(math.floor((nelem + 1) / 2) - 1)
self.assertEqual(res2val[ind], res1val, 0)
res1val, res1ind = torch.median(x, dim=1, keepdim=False)
res2val, res2ind = torch.sort(x)
ind = int(math.floor((size + 1) / 2) - 1)
self.assertEqual(res2val.select(1, ind), res1val, 0)
self.assertEqual(res2val.select(1, ind), res1val, 0)
# Test use of result tensor
res2val = torch.Tensor()
res2ind = torch.LongTensor()
torch.median(x, dim=-1, keepdim=False, out=(res2val, res2ind))
self.assertEqual(res2val, res1val, 0)
self.assertEqual(res2ind, res1ind, 0)
# Test non-default dim
res1val, res1ind = torch.median(x, 0, keepdim=False)
res2val, res2ind = torch.sort(x, 0)
self.assertEqual(res1val, res2val[ind], 0)
self.assertEqual(res1ind, res2ind[ind], 0)
# input unchanged
self.assertEqual(x, x0, 0)
def test_mode(self):
x = torch.arange(1., SIZE * SIZE + 1).clone().resize_(SIZE, SIZE)
x[:2] = 1
x[:, :2] = 1
x0 = x.clone()
# Pre-calculated results.
res1val = torch.Tensor(SIZE).fill_(1)
# The indices are the position of the last appearance of the mode element.
res1ind = torch.LongTensor(SIZE).fill_(1)
res1ind[0] = SIZE - 1
res1ind[1] = SIZE - 1
res2val, res2ind = torch.mode(x, keepdim=False)
self.assertEqual(res1val, res2val, 0)
self.assertEqual(res1ind, res2ind, 0)
# Test use of result tensor
res2val = torch.Tensor()
res2ind = torch.LongTensor()
torch.mode(x, keepdim=False, out=(res2val, res2ind))
self.assertEqual(res1val, res2val, 0)
self.assertEqual(res1ind, res2ind, 0)
# Test non-default dim
res2val, res2ind = torch.mode(x, 0, False)
self.assertEqual(res1val, res2val, 0)
self.assertEqual(res1ind, res2ind, 0)
# input unchanged
self.assertEqual(x, x0, 0)
def test_tril(self):
x = torch.rand(SIZE, SIZE)
res1 = torch.tril(x)
res2 = torch.Tensor()
torch.tril(x, out=res2)
self.assertEqual(res1, res2, 0)
def _compare_trilu_indices(self, row, col, offset=0, dtype=torch.long):
if row == 0 or col == 0:
# have to handle this separately as tril and triu does not take
# empty matrix as input
self.assertEqual(
torch.empty(0, 2, dtype=dtype).transpose(0, 1),
torch.tril_indices(row, col, offset, dtype=dtype))
self.assertEqual(
torch.empty(0, 2, dtype=dtype).transpose(0, 1),
torch.triu_indices(row, col, offset, dtype=dtype))
else:
self.assertEqual(
torch.ones(row, col, dtype=dtype)
.tril(offset).nonzero().transpose(0, 1),
torch.tril_indices(row, col, offset, dtype=dtype))
self.assertEqual(
torch.ones(row, col, dtype=dtype)
.triu(offset).nonzero().transpose(0, 1),
torch.triu_indices(row, col, offset, dtype=dtype))
def test_tril_and_triu_indices(self):
self._compare_trilu_indices(1, 1)
self._compare_trilu_indices(3, 3)
self._compare_trilu_indices(3, 3, offset=1)
self._compare_trilu_indices(3, 3, offset=2)
self._compare_trilu_indices(3, 3, offset=200)
self._compare_trilu_indices(3, 3, offset=-1)
self._compare_trilu_indices(3, 3, offset=-2)
self._compare_trilu_indices(3, 3, offset=-200)
self._compare_trilu_indices(0, 3, offset=0)
self._compare_trilu_indices(0, 3, offset=1)
self._compare_trilu_indices(0, 3, offset=-1)
self._compare_trilu_indices(3, 0, offset=0)
self._compare_trilu_indices(3, 0, offset=1)
self._compare_trilu_indices(3, 0, offset=-1)
self._compare_trilu_indices(0, 0, offset=0)
self._compare_trilu_indices(0, 0, offset=1)
self._compare_trilu_indices(0, 0, offset=-1)
self._compare_trilu_indices(3, 6, offset=0)
self._compare_trilu_indices(3, 6, offset=1)
self._compare_trilu_indices(3, 6, offset=3)
self._compare_trilu_indices(3, 6, offset=9)
self._compare_trilu_indices(3, 6, offset=-1)
self._compare_trilu_indices(3, 6, offset=-3)
self._compare_trilu_indices(3, 6, offset=-9)
self._compare_trilu_indices(6, 3, offset=0)
self._compare_trilu_indices(6, 3, offset=1)
self._compare_trilu_indices(6, 3, offset=3)
self._compare_trilu_indices(6, 3, offset=9)
self._compare_trilu_indices(6, 3, offset=-1)
self._compare_trilu_indices(6, 3, offset=-3)
self._compare_trilu_indices(6, 3, offset=-9)
self._compare_trilu_indices(258, 253, offset=1, dtype=torch.float32)
self._compare_trilu_indices(257, 258, offset=1, dtype=torch.float64)
self._compare_trilu_indices(258, 258, offset=1, dtype=torch.short)
self._compare_trilu_indices(3, 513, offset=1, dtype=torch.long)
self._compare_trilu_indices(513, 3, offset=1, dtype=torch.int)
self._compare_trilu_indices(513, 0, offset=1, dtype=torch.double)
x = torch.ones(
3, 3, dtype=torch.long, device='cpu', layout=torch.strided)
l = x.tril(0).nonzero().transpose(0, 1)
u = x.triu(0).nonzero().transpose(0, 1)
self.assertEqual(l, torch.tril_indices(3, 3))
self.assertEqual(l, torch.tril_indices(3, 3, device='cpu'))
self.assertEqual(
l, torch.tril_indices(3, 3, device='cpu', layout=torch.strided))
self.assertEqual(u, torch.triu_indices(3, 3))
self.assertEqual(u, torch.triu_indices(3, 3, device='cpu'))
self.assertEqual(
u, torch.triu_indices(3, 3, device='cpu', layout=torch.strided))
self.assertRaises(
RuntimeError,
lambda: torch.triu_indices(
1, 1, device='cpu', layout=torch.sparse_coo))
self.assertRaises(
RuntimeError,
lambda: torch.tril_indices(
1, 1, device='cpu', layout=torch.sparse_coo))
def test_triu(self):
x = torch.rand(SIZE, SIZE)
res1 = torch.triu(x)
res2 = torch.Tensor()
torch.triu(x, out=res2)
self.assertEqual(res1, res2, 0)
def test_cat(self):
SIZE = 10
for dim in range(-3, 3):
pos_dim = dim if dim >= 0 else 3 + dim
x = torch.rand(13, SIZE, SIZE).transpose(0, pos_dim)
y = torch.rand(17, SIZE, SIZE).transpose(0, pos_dim)
z = torch.rand(19, SIZE, SIZE).transpose(0, pos_dim)
res1 = torch.cat((x, y, z), dim)
self.assertEqual(res1.narrow(pos_dim, 0, 13), x, 0)
self.assertEqual(res1.narrow(pos_dim, 13, 17), y, 0)
self.assertEqual(res1.narrow(pos_dim, 30, 19), z, 0)
x = torch.randn(20, SIZE, SIZE)
self.assertEqual(torch.cat(torch.split(x, 7)), x)
self.assertEqual(torch.cat(torch.chunk(x, 7)), x)
y = torch.randn(1, SIZE, SIZE)
z = torch.cat([x, y])
self.assertEqual(z.size(), (21, SIZE, SIZE))
self.assertRaises(RuntimeError, lambda: torch.cat([]))
self.assertRaisesRegex(TypeError, 'got None', lambda: torch.cat([x, None]))
def test_cat_bad_input_sizes(self):
x = torch.randn(2, 1)
y = torch.randn(2, 1, 1)
z = torch.randn(2, 1, 1)
self.assertRaises(RuntimeError, lambda: torch.cat([x, y, z]))
x = torch.randn(2, 1, 2)
y = torch.randn(2, 1, 1)
z = torch.randn(2, 2, 1)
self.assertRaises(RuntimeError, lambda: torch.cat([x, y, z], dim=1))
def test_cat_scalars(self):
x = torch.tensor(0)
y = torch.tensor(1)
with self.assertRaisesRegex(RuntimeError, 'zero-dimensional.*cannot be concatenated'):
torch.cat([x, y])
@staticmethod
def _test_cat_empty_legacy(self, use_cuda=False):
# FIXME: this is legacy behavior and should be removed
# when we support empty tensors with arbitrary sizes
dtype = torch.float32
device = 'cuda' if use_cuda else 'cpu'
x = torch.randn((4, 3, 32, 32), dtype=dtype, device=device)
empty = torch.randn((0,), dtype=dtype, device=device)
res1 = torch.cat([x, empty], dim=1)
res2 = torch.cat([empty, x], dim=1)
self.assertEqual(res1, res2)
conv = torch.nn.Conv2d(3, 3, kernel_size=1).float()
if use_cuda:
conv = conv.cuda()
res1 = torch.cat([conv(x), empty], dim=1)
res2 = torch.cat([empty, conv(x)], dim=1)
self.assertEqual(res1, res2)
res1 = torch.cat([empty, empty], dim=1)
self.assertEqual(res1, empty)
with self.assertRaisesRegex(RuntimeError,
'expected a non-empty list of Tensors'):
torch.cat([], dim=1)
def test_cat_empty_legacy(self):
self._test_cat_empty_legacy(self)
@staticmethod
def _test_cat_empty(self, use_cuda=False):
dtype = torch.float32
device = 'cuda' if use_cuda else 'cpu'
x = torch.randn((4, 3, 32, 32), dtype=dtype, device=device)
empty = torch.randn((4, 0, 32, 32), dtype=dtype, device=device)
res1 = torch.cat([x, empty], dim=1)
res2 = torch.cat([empty, x], dim=1)
self.assertEqual(res1, res2)
conv = torch.nn.Conv2d(3, 3, kernel_size=1).float()
if use_cuda:
conv = conv.cuda()
res1 = torch.cat([conv(x), empty], dim=1)
res2 = torch.cat([empty, conv(x)], dim=1)
self.assertEqual(res1, res2)
res1 = torch.cat([empty, empty], dim=1)
self.assertEqual(res1, empty)
# check non-legacy-behavior (sizes don't match)
empty = torch.randn((4, 0, 31, 32), dtype=dtype, device=device)
self.assertRaises(RuntimeError, lambda: torch.cat([x, empty], dim=1))
self.assertRaises(RuntimeError, lambda: torch.cat([empty, x], dim=1))
# check non-legacy-behavior (dimensions don't match)
empty = torch.randn((4, 0), dtype=dtype, device=device)
self.assertRaises(RuntimeError, lambda: torch.cat([x, empty], dim=1))
self.assertRaises(RuntimeError, lambda: torch.cat([empty, x], dim=1))
def test_cat_empty(self):
self._test_cat_empty(self)
def test_narrow(self):
x = torch.Tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
self.assertEqual(x.narrow(0, 0, 1), torch.Tensor([[0, 1, 2]]))
self.assertEqual(x.narrow(0, 0, 2), torch.Tensor([[0, 1, 2], [3, 4, 5]]))
self.assertEqual(x.narrow(0, 1, 1), torch.Tensor([[3, 4, 5]]))
self.assertEqual(x.narrow(0, -1, 1), torch.Tensor([[6, 7, 8]]))
self.assertEqual(x.narrow(0, -2, 2), torch.Tensor([[3, 4, 5], [6, 7, 8]]))
self.assertEqual(x.narrow(0, -3, 3), torch.Tensor([[0, 1, 2], [3, 4, 5], [6, 7, 8]]))
self.assertEqual(x.narrow(-1, -1, 1), torch.Tensor([[2], [5], [8]]))
self.assertEqual(x.narrow(-2, -1, 1), torch.Tensor([[6, 7, 8]]))
def test_narrow_empty(self):
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
x = torch.randn(2, 3, 4, device=device)
for d in range(x.dim()):
y = x.narrow(d, x.size(d), 0)
sz = list(x.size())
sz[d] = 0
self.assertEqual(sz, y.size())
def test_stack(self):
x = torch.rand(2, 3, 4)
y = torch.rand(2, 3, 4)
z = torch.rand(2, 3, 4)
for dim in range(4):
res = torch.stack((x, y, z), dim)
res_neg = torch.stack((x, y, z), dim - 4)
expected_size = x.size()[:dim] + (3,) + x.size()[dim:]
self.assertEqual(res, res_neg)
self.assertEqual(res.size(), expected_size)
self.assertEqual(res.select(dim, 0), x, 0)
self.assertEqual(res.select(dim, 1), y, 0)
self.assertEqual(res.select(dim, 2), z, 0)
def test_stack_out(self):
x = torch.rand(2, 3, 4)
y = torch.rand(2, 3, 4)
z = torch.rand(2, 3, 4)
for dim in range(4):
expected_size = x.size()[:dim] + (3,) + x.size()[dim:]
res_out = x.new(expected_size)
res_neg_out = x.new(expected_size)
res_out_dp = res_out.data_ptr()
res_out_neg_dp = res_neg_out.data_ptr()
torch.stack((x, y, z), dim, out=res_out)
torch.stack((x, y, z), dim - 4, out=res_neg_out)
self.assertEqual(res_out, res_neg_out)
self.assertEqual(res_out.size(), expected_size)
self.assertEqual(res_out_dp, res_out.data_ptr())
self.assertEqual(res_out_neg_dp, res_neg_out.data_ptr())
self.assertEqual(res_out.select(dim, 0), x, 0)
self.assertEqual(res_out.select(dim, 1), y, 0)
self.assertEqual(res_out.select(dim, 2), z, 0)
def test_unbind(self):
x = torch.rand(2, 3, 4, 5)
for dim in range(4):
res = torch.unbind(x, dim)
res2 = x.unbind(dim)
self.assertEqual(x.size(dim), len(res))
self.assertEqual(x.size(dim), len(res2))
for i in range(dim):
self.assertEqual(x.select(dim, i), res[i])
self.assertEqual(x.select(dim, i), res2[i])
def test_linspace(self):
_from = random.random()
to = _from + random.random()
res1 = torch.linspace(_from, to, 137)
res2 = torch.Tensor()
torch.linspace(_from, to, 137, out=res2)
self.assertEqual(res1, res2, 0)
self.assertRaises(RuntimeError, lambda: torch.linspace(0, 1, -1))
self.assertEqual(torch.linspace(0, 1, 1), torch.zeros(1), 0)
# Check linspace for generating with start > end.
self.assertEqual(torch.linspace(2, 0, 3), torch.Tensor((2, 1, 0)), 0)
# Check linspace for non-contiguous tensors.
x = torch.zeros(2, 3)
y = torch.linspace(0, 3, 4, out=x.narrow(1, 1, 2))
self.assertEqual(x, torch.Tensor(((0, 0, 1), (0, 2, 3))), 0)
def test_logspace(self):
_from = random.random()
to = _from + random.random()
res1 = torch.logspace(_from, to, 137)
res2 = torch.Tensor()
torch.logspace(_from, to, 137, out=res2)
self.assertEqual(res1, res2, 0)
self.assertRaises(RuntimeError, lambda: torch.logspace(0, 1, -1))
self.assertEqual(torch.logspace(0, 1, 1), torch.ones(1), 0)
# Check logspace_ for generating with start > end.
self.assertEqual(torch.logspace(1, 0, 2), torch.Tensor((10, 1)), 0)
# Check logspace_ for non-contiguous tensors.
x = torch.zeros(2, 3)
y = torch.logspace(0, 3, 4, out=x.narrow(1, 1, 2))
self.assertEqual(x, torch.Tensor(((0, 1, 10), (0, 100, 1000))), 0)
def test_rand(self):
torch.manual_seed(123456)
res1 = torch.rand(SIZE, SIZE)
res2 = torch.Tensor()
torch.manual_seed(123456)
torch.rand(SIZE, SIZE, out=res2)
self.assertEqual(res1, res2)
def test_randint(self):
torch.manual_seed(123456)
res1 = torch.randint(0, 6, (SIZE, SIZE))
res2 = torch.Tensor()
torch.manual_seed(123456)
torch.randint(0, 6, (SIZE, SIZE), out=res2)
torch.manual_seed(123456)
res3 = torch.randint(6, (SIZE, SIZE))
res4 = torch.Tensor()
torch.manual_seed(123456)
torch.randint(6, (SIZE, SIZE), out=res4)
self.assertEqual(res1, res2)
self.assertEqual(res1, res3)
self.assertEqual(res1, res4)
self.assertEqual(res2, res3)
self.assertEqual(res2, res4)
self.assertEqual(res3, res4)
res1 = res1.view(-1)
high = (res1 < 6).type(torch.LongTensor)
low = (res1 >= 0).type(torch.LongTensor)
tensorSize = res1.size()[0]
assert(tensorSize == high.sum())
assert(tensorSize == low.sum())
def test_randn(self):
torch.manual_seed(123456)
res1 = torch.randn(SIZE, SIZE)
res2 = torch.Tensor()
torch.manual_seed(123456)
torch.randn(SIZE, SIZE, out=res2)
self.assertEqual(res1, res2)
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].data.tolist(), [[0, 1, 2, 3]])
self.assertEqual(x[:-3].data.tolist(), [[0, 1, 2, 3]])
self.assertEqual(x[:, -2:3].data.tolist(), [[2], [6], [10], [14]])
self.assertEqual(x[0:-1:2].data.tolist(), [[0, 1, 2, 3], [8, 9, 10, 11]])
def test_is_signed(self):
self.assertEqual(torch.IntTensor(5).is_signed(), True)
self.assertEqual(torch.ByteTensor(5).is_signed(), False)
self.assertEqual(torch.CharTensor(5).is_signed(), True)
self.assertEqual(torch.FloatTensor(5).is_signed(), True)
self.assertEqual(torch.HalfTensor(10).is_signed(), True)
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_is_signed_cuda(self):
self.assertEqual(torch.cuda.IntTensor(5).is_signed(), True)
self.assertEqual(torch.cuda.ByteTensor(5).is_signed(), False)
self.assertEqual(torch.cuda.CharTensor(5).is_signed(), True)
self.assertEqual(torch.cuda.FloatTensor(5).is_signed(), True)
self.assertEqual(torch.cuda.HalfTensor(10).is_signed(), True)
@skipIfNoLapack
def test_gesv(self):
a = torch.Tensor(((6.80, -2.11, 5.66, 5.97, 8.23),
(-6.05, -3.30, 5.36, -4.44, 1.08),
(-0.45, 2.58, -2.70, 0.27, 9.04),
(8.32, 2.71, 4.35, -7.17, 2.14),
(-9.67, -5.14, -7.26, 6.08, -6.87))).t()
b = torch.Tensor(((4.02, 6.19, -8.22, -7.57, -3.03),
(-1.56, 4.00, -8.67, 1.75, 2.86),
(9.81, -4.09, -4.57, -8.61, 8.99))).t()
res1 = torch.gesv(b, a)[0]
self.assertLessEqual(b.dist(torch.mm(a, res1)), 1e-12)
ta = torch.Tensor()
tb = torch.Tensor()
res2 = torch.gesv(b, a, out=(tb, ta))[0]
res3 = torch.gesv(b, a, out=(b, a))[0]
self.assertEqual(res1, tb)
self.assertEqual(res1, b)
self.assertEqual(res1, res2)
self.assertEqual(res1, res3)
# test reuse
res1 = torch.gesv(b, a)[0]
ta = torch.Tensor()
tb = torch.Tensor()
torch.gesv(b, a, out=(tb, ta))[0]
self.assertEqual(res1, tb)
torch.gesv(b, a, out=(tb, ta))[0]
self.assertEqual(res1, tb)
@staticmethod
def _test_gesv_batched(self, cast):
from common_utils import random_fullrank_matrix_distinct_singular_value
# test against gesv: one batch
A = cast(random_fullrank_matrix_distinct_singular_value(5, 1))
b = cast(torch.randn(1, 5, 10))
x_exp, LU_exp = torch.gesv(b.squeeze(0), A.squeeze(0))
x, LU = torch.gesv(b, A)
self.assertEqual(x, x_exp.unsqueeze(0))
self.assertEqual(LU, LU_exp.unsqueeze(0))
# test against gesv in a loop: four batches
A = cast(random_fullrank_matrix_distinct_singular_value(5, 4))
b = cast(torch.randn(4, 5, 10))
x_exp_list = list()
LU_exp_list = list()
for i in range(4):
x_exp, LU_exp = torch.gesv(b[i], A[i])
x_exp_list.append(x_exp)
LU_exp_list.append(LU_exp)
x_exp = torch.stack(x_exp_list)
LU_exp = torch.stack(LU_exp_list)
x, LU = torch.gesv(b, A)
self.assertEqual(x, x_exp)
self.assertEqual(LU, LU_exp)
# basic correctness test
A = cast(random_fullrank_matrix_distinct_singular_value(5, 3))
b = cast(torch.randn(3, 5, 10))
x, LU = torch.gesv(b, A)
self.assertEqual(torch.matmul(A, x), b)
# Test non-contiguous inputs.
if not TEST_NUMPY:
return
import numpy
from numpy.linalg import solve
A = cast(random_fullrank_matrix_distinct_singular_value(2, 2)).permute(1, 0, 2)
b = cast(torch.randn(2, 2, 2)).permute(2, 1, 0)
x, _ = torch.gesv(b, A)
x_exp = torch.Tensor(solve(A.cpu().numpy(), b.cpu().numpy()))
self.assertEqual(x.data, cast(x_exp))
@skipIfNoLapack
def test_gesv_batched(self):
self._test_gesv_batched(self, lambda t: t)
@staticmethod
def _test_gesv_batched_dims(self, cast):
if not TEST_NUMPY:
return
from numpy.linalg import solve
from common_utils import random_fullrank_matrix_distinct_singular_value
# test against numpy.linalg.solve
A = cast(random_fullrank_matrix_distinct_singular_value(4, 2, 1, 3))
b = cast(torch.randn(2, 1, 3, 4, 6))
x, _ = torch.gesv(b, A)
x_exp = torch.Tensor(solve(A.cpu().numpy(), b.cpu().numpy()))
self.assertEqual(x.data, cast(x_exp))
# test column major format
A = cast(random_fullrank_matrix_distinct_singular_value(4, 2, 1, 3)).transpose(-2, -1)
b = cast(torch.randn(2, 1, 3, 6, 4)).transpose(-2, -1)
assert not A.is_contiguous()
assert not b.is_contiguous()
x, _ = torch.gesv(b, A)
x_exp = torch.Tensor(solve(A.cpu().numpy(), b.cpu().numpy()))
self.assertEqual(x.data, cast(x_exp))
# broadcasting b
A = cast(random_fullrank_matrix_distinct_singular_value(4, 2, 1, 3))
b = cast(torch.randn(4, 6))
x, _ = torch.gesv(b, A)
x_exp = torch.Tensor(solve(A.cpu().numpy(), b.cpu().numpy()))
self.assertEqual(x.data, cast(x_exp))
# broadcasting A
A = cast(random_fullrank_matrix_distinct_singular_value(4))
b = cast(torch.randn(2, 1, 3, 4, 2))
x, _ = torch.gesv(b, A)
x_exp = torch.Tensor(solve(A.cpu().numpy(), b.cpu().numpy()))
self.assertEqual(x.data, cast(x_exp))
# broadcasting both A & b
A = cast(random_fullrank_matrix_distinct_singular_value(4, 1, 3, 1))
b = cast(torch.randn(2, 1, 3, 4, 5))
x, _ = torch.gesv(b, A)
x_exp = torch.Tensor(solve(A.cpu().numpy(), b.cpu().numpy()))
self.assertEqual(x.data, cast(x_exp))
@skipIfNoLapack
def test_gesv_batched_dims(self):
self._test_gesv_batched_dims(self, lambda t: t)
@skipIfNoLapack
def test_qr(self):
# Since the QR decomposition is unique only up to the signs of the rows of
# R, we must ensure these are positive before doing the comparison.
def canonicalize(q, r):
d = r.diag().sign().diag()
return torch.mm(q, d), torch.mm(d, r)
def canon_and_check(q, r, expected_q, expected_r):
q_canon, r_canon = canonicalize(q, r)
expected_q_canon, expected_r_canon = canonicalize(expected_q, expected_r)
self.assertEqual(q_canon, expected_q_canon)
self.assertEqual(r_canon, expected_r_canon)
def check_qr(a, expected_q, expected_r):
# standard invocation
q, r = torch.qr(a)
canon_and_check(q, r, expected_q, expected_r)
# in-place
q, r = torch.Tensor(), torch.Tensor()
torch.qr(a, out=(q, r))
canon_and_check(q, r, expected_q, expected_r)
# manually calculate qr using geqrf and orgqr
m = a.size(0)
n = a.size(1)
k = min(m, n)
result, tau = torch.geqrf(a)
self.assertEqual(result.size(0), m)
self.assertEqual(result.size(1), n)
self.assertEqual(tau.size(0), k)
r = torch.triu(result.narrow(0, 0, k))
q = torch.orgqr(result, tau)
q, r = q.narrow(1, 0, k), r
canon_and_check(q, r, expected_q, expected_r)
# check square case
a = torch.Tensor(((1, 2, 3), (4, 5, 6), (7, 8, 10)))
expected_q = torch.Tensor((
(-1.230914909793328e-01, 9.045340337332914e-01, 4.082482904638621e-01),
(-4.923659639173310e-01, 3.015113445777629e-01, -8.164965809277264e-01),
(-8.616404368553292e-01, -3.015113445777631e-01, 4.082482904638634e-01)))
expected_r = torch.Tensor((
(-8.124038404635959e+00, -9.601136296387955e+00, -1.193987e+01),
(0.000000000000000e+00, 9.045340337332926e-01, 1.507557e+00),
(0.000000000000000e+00, 0.000000000000000e+00, 4.082483e-01)))
check_qr(a, expected_q, expected_r)
# check rectangular thin
a = torch.Tensor((
(1, 2, 3),
(4, 5, 6),
(7, 8, 9),
(10, 11, 13),
))
expected_q = torch.Tensor((
(-0.0776150525706334, -0.833052161400748, 0.3651483716701106),
(-0.3104602102825332, -0.4512365874254053, -0.1825741858350556),
(-0.5433053679944331, -0.0694210134500621, -0.7302967433402217),
(-0.7761505257063329, 0.3123945605252804, 0.5477225575051663)
))
expected_r = torch.Tensor((
(-12.8840987267251261, -14.5916298832790581, -17.0753115655393231),
(0, -1.0413152017509357, -1.770235842976589),
(0, 0, 0.5477225575051664)
))
check_qr(a, expected_q, expected_r)
# check rectangular fat
a = torch.Tensor((
(1, 2, 3, 4),
(5, 6, 7, 8),
(9, 10, 11, 13)
))
expected_q = torch.Tensor((
(-0.0966736489045663, 0.907737593658436, 0.4082482904638653),
(-0.4833682445228317, 0.3157348151855452, -0.8164965809277254),
(-0.870062840141097, -0.2762679632873518, 0.4082482904638621)
))
expected_r = torch.Tensor((
(-1.0344080432788603e+01, -1.1794185166357092e+01,
-1.3244289899925587e+01, -1.5564457473635180e+01),
(0.0000000000000000e+00, 9.4720444555662542e-01,
1.8944088911132546e+00, 2.5653453733825331e+00),
(0.0000000000000000e+00, 0.0000000000000000e+00,
1.5543122344752192e-15, 4.0824829046386757e-01)
))
check_qr(a, expected_q, expected_r)
# check big matrix
a = torch.randn(1000, 1000)
q, r = torch.qr(a)
a_qr = torch.mm(q, r)
self.assertEqual(a, a_qr, prec=1e-3)
@skipIfNoLapack
def test_ormqr(self):
mat1 = torch.randn(10, 10)
mat2 = torch.randn(10, 10)
q, r = torch.qr(mat1)
m, tau = torch.geqrf(mat1)
res1 = torch.mm(q, mat2)
res2 = torch.ormqr(m, tau, mat2)
self.assertEqual(res1, res2)
res1 = torch.mm(mat2, q)
res2 = torch.ormqr(m, tau, mat2, False)
self.assertEqual(res1, res2)
res1 = torch.mm(q.t(), mat2)
res2 = torch.ormqr(m, tau, mat2, True, True)
self.assertEqual(res1, res2)
res1 = torch.mm(mat2, q.t())
res2 = torch.ormqr(m, tau, mat2, False, True)
self.assertEqual(res1, res2)
@staticmethod
def _test_trtrs(self, cast):
a = torch.Tensor(((6.80, -2.11, 5.66, 5.97, 8.23),
(-6.05, -3.30, 5.36, -4.44, 1.08),
(-0.45, 2.58, -2.70, 0.27, 9.04),
(8.32, 2.71, 4.35, -7.17, 2.14),
(-9.67, -5.14, -7.26, 6.08, -6.87))).t()
b = torch.Tensor(((4.02, 6.19, -8.22, -7.57, -3.03),
(-1.56, 4.00, -8.67, 1.75, 2.86),
(9.81, -4.09, -4.57, -8.61, 8.99))).t()
a = cast(a)
b = cast(b)
U = torch.triu(a)
L = torch.tril(a)
# solve Ux = b
x = torch.trtrs(b, U)[0]
self.assertLessEqual(b.dist(torch.mm(U, x)), 1e-12)
x = torch.trtrs(b, U, True, False, False)[0]
self.assertLessEqual(b.dist(torch.mm(U, x)), 1e-12)
# solve Lx = b
x = torch.trtrs(b, L, False)[0]
self.assertLessEqual(b.dist(torch.mm(L, x)), 1e-12)
x = torch.trtrs(b, L, False, False, False)[0]
self.assertLessEqual(b.dist(torch.mm(L, x)), 1e-12)
# solve U'x = b
x = torch.trtrs(b, U, True, True)[0]
self.assertLessEqual(b.dist(torch.mm(U.t(), x)), 1e-12)
x = torch.trtrs(b, U, True, True, False)[0]
self.assertLessEqual(b.dist(torch.mm(U.t(), x)), 1e-12)
# solve U'x = b by manual transposition
y = torch.trtrs(b, U.t(), False, False)[0]
self.assertLessEqual(x.dist(y), 1e-12)
# solve L'x = b
x = torch.trtrs(b, L, False, True)[0]
self.assertLessEqual(b.dist(torch.mm(L.t(), x)), 1e-12)
x = torch.trtrs(b, L, False, True, False)[0]
self.assertLessEqual(b.dist(torch.mm(L.t(), x)), 1e-12)
# solve L'x = b by manual transposition
y = torch.trtrs(b, L.t(), True, False)[0]
self.assertLessEqual(x.dist(y), 1e-12)
# test reuse
res1 = torch.trtrs(b, a)[0]
ta = cast(torch.Tensor())
tb = cast(torch.Tensor())
torch.trtrs(b, a, out=(tb, ta))
self.assertEqual(res1, tb, 0)
tb.zero_()
torch.trtrs(b, a, out=(tb, ta))
self.assertEqual(res1, tb, 0)
@skipIfNoLapack
def test_trtrs(self):
self._test_trtrs(self, lambda t: t)
@skipIfNoLapack
def test_gels(self):
def _test_underdetermined(a, b, expectedNorm):
m = a.size()[0]
n = a.size()[1]
assert(m <= n)
a_copy = a.clone()
b_copy = b.clone()
res1 = torch.gels(b, a)[0]
self.assertEqual(a, a_copy, 0)
self.assertEqual(b, b_copy, 0)
self.assertEqual((torch.mm(a, res1) - b).norm(), expectedNorm, 1e-8)
ta = torch.Tensor()
tb = torch.Tensor()
res2 = torch.gels(b, a, out=(tb, ta))[0]
self.assertEqual(a, a_copy, 0)
self.assertEqual(b, b_copy, 0)
self.assertEqual((torch.mm(a, res1) - b).norm(), expectedNorm, 1e-8)
res3 = torch.gels(b, a, out=(b, a))[0]
self.assertEqual((torch.mm(a_copy, b) - b_copy).norm(), expectedNorm, 1e-8)
self.assertEqual(res1, tb, 0)
self.assertEqual(res1, b, 0)
self.assertEqual(res1, res2, 0)
self.assertEqual(res1, res3, 0)
def _test_overdetermined(a, b, expectedNorm):
m = a.size()[0]
n = a.size()[1]
assert(m > n)
def check_norm(a, b, expected_norm, gels_result):
# Checks |ax - b| and the residual info from the result
n = a.size()[1]
# The first n rows is the least square solution.
# Rows n to m-1 contain residual information.
x = gels_result[:n]
resid_info = gels_result[n:]
resid_norm = (torch.mm(a, x) - b).norm()
self.assertEqual(resid_norm, expectedNorm, 1e-8)
self.assertEqual(resid_info.norm(), resid_norm, 1e-8)
a_copy = a.clone()
b_copy = b.clone()
res1 = torch.gels(b, a)[0]
self.assertEqual(a, a_copy, 0)
self.assertEqual(b, b_copy, 0)
check_norm(a, b, expectedNorm, res1)
ta = torch.Tensor()
tb = torch.Tensor()
res2 = torch.gels(b, a, out=(tb, ta))[0]
self.assertEqual(a, a_copy, 0)
self.assertEqual(b, b_copy, 0)
check_norm(a, b, expectedNorm, res2)
res3 = torch.gels(b, a, out=(b, a))[0]
check_norm(a_copy, b_copy, expectedNorm, res3)
self.assertEqual(res1, tb, 0)
self.assertEqual(res1, b, 0)
self.assertEqual(res1, res2, 0)
self.assertEqual(res1, res3, 0)
# basic test
expectedNorm = 0
a = torch.Tensor(((1.44, -9.96, -7.55, 8.34),
(-7.84, -0.28, 3.24, 8.09),
(-4.39, -3.24, 6.27, 5.28),
(4.53, 3.83, -6.64, 2.06))).t()
b = torch.Tensor(((8.58, 8.26, 8.48, -5.28),
(9.35, -4.43, -0.70, -0.26))).t()
_test_underdetermined(a, b, expectedNorm)
# test overderemined
expectedNorm = 17.390200628863
a = torch.Tensor(((1.44, -9.96, -7.55, 8.34, 7.08, -5.45),
(-7.84, -0.28, 3.24, 8.09, 2.52, -5.70),
(-4.39, -3.24, 6.27, 5.28, 0.74, -1.19),
(4.53, 3.83, -6.64, 2.06, -2.47, 4.70))).t()
b = torch.Tensor(((8.58, 8.26, 8.48, -5.28, 5.72, 8.93),
(9.35, -4.43, -0.70, -0.26, -7.36, -2.52))).t()
_test_overdetermined(a, b, expectedNorm)
# test underdetermined
expectedNorm = 0
a = torch.Tensor(((1.44, -9.96, -7.55),
(-7.84, -0.28, 3.24),
(-4.39, -3.24, 6.27),
(4.53, 3.83, -6.64))).t()
b = torch.Tensor(((8.58, 8.26, 8.48),
(9.35, -4.43, -0.70))).t()
_test_underdetermined(a, b, expectedNorm)
# test reuse
expectedNorm = 0
a = torch.Tensor(((1.44, -9.96, -7.55, 8.34),
(-7.84, -0.28, 3.24, 8.09),
(-4.39, -3.24, 6.27, 5.28),
(4.53, 3.83, -6.64, 2.06))).t()
b = torch.Tensor(((8.58, 8.26, 8.48, -5.28),
(9.35, -4.43, -0.70, -0.26))).t()
ta = torch.Tensor()
tb = torch.Tensor()
torch.gels(b, a, out=(tb, ta))
self.assertEqual((torch.mm(a, tb) - b).norm(), expectedNorm, 1e-8)
torch.gels(b, a, out=(tb, ta))
self.assertEqual((torch.mm(a, tb) - b).norm(), expectedNorm, 1e-8)
torch.gels(b, a, out=(tb, ta))
self.assertEqual((torch.mm(a, tb) - b).norm(), expectedNorm, 1e-8)
@skipIfNoLapack
def test_eig(self):
a = torch.Tensor(((1.96, 0.00, 0.00, 0.00, 0.00),
(-6.49, 3.80, 0.00, 0.00, 0.00),
(-0.47, -6.39, 4.17, 0.00, 0.00),
(-7.20, 1.50, -1.51, 5.70, 0.00),
(-0.65, -6.34, 2.67, 1.80, -7.10))).t().contiguous()
e = torch.eig(a)[0]
ee, vv = torch.eig(a, True)
te = torch.Tensor()
tv = torch.Tensor()
eee, vvv = torch.eig(a, True, out=(te, tv))
self.assertEqual(e, ee, 1e-12)
self.assertEqual(ee, eee, 1e-12)
self.assertEqual(ee, te, 1e-12)
self.assertEqual(vv, vvv, 1e-12)
self.assertEqual(vv, tv, 1e-12)
# test reuse
X = torch.randn(4, 4)
X = torch.mm(X.t(), X)
e, v = torch.zeros(4, 2), torch.zeros(4, 4)
torch.eig(X, True, out=(e, v))
Xhat = torch.mm(torch.mm(v, torch.diag(e.select(1, 0))), v.t())
self.assertEqual(X, Xhat, 1e-8, 'VeV\' wrong')
self.assertFalse(v.is_contiguous(), 'V is contiguous')
torch.eig(X, True, out=(e, v))
Xhat = torch.mm(v, torch.mm(e.select(1, 0).diag(), v.t()))
self.assertEqual(X, Xhat, 1e-8, 'VeV\' wrong')
self.assertFalse(v.is_contiguous(), 'V is contiguous')
# test non-contiguous
X = torch.randn(4, 4)
X = torch.mm(X.t(), X)
e = torch.zeros(4, 2, 2)[:, 1]
v = torch.zeros(4, 2, 4)[:, 1]
self.assertFalse(v.is_contiguous(), 'V is contiguous')
self.assertFalse(e.is_contiguous(), 'E is contiguous')
torch.eig(X, True, out=(e, v))
Xhat = torch.mm(torch.mm(v, torch.diag(e.select(1, 0))), v.t())
self.assertEqual(X, Xhat, 1e-8, 'VeV\' wrong')
@staticmethod
def _test_symeig(self, conv_fn):
xval = conv_fn(torch.rand(100, 3))
cov = torch.mm(xval.t(), xval)
rese = conv_fn(torch.zeros(3))
resv = conv_fn(torch.zeros(3, 3))
# First call to symeig
self.assertTrue(resv.is_contiguous(), 'resv is not contiguous')
torch.symeig(cov.clone(), True, out=(rese, resv))
ahat = torch.mm(torch.mm(resv, torch.diag(rese)), resv.t())
self.assertEqual(cov, ahat, 1e-8, 'VeV\' wrong')
# Second call to symeig
self.assertFalse(resv.is_contiguous(), 'resv is contiguous')
torch.symeig(cov.clone(), True, out=(rese, resv))
ahat = torch.mm(torch.mm(resv, torch.diag(rese)), resv.t())
self.assertEqual(cov, ahat, 1e-8, 'VeV\' wrong')
# test eigenvectors=False
rese2 = conv_fn(torch.zeros(3))
resv2 = conv_fn(torch.randn(3, 3))
expected_resv2 = conv_fn(torch.zeros(3, 3))
torch.symeig(cov.clone(), False, out=(rese2, resv2))
self.assertEqual(rese, rese2)
self.assertEqual(resv2, expected_resv2)
# test non-contiguous
X = conv_fn(torch.rand(5, 5))
X = X.t() * X
e = conv_fn(torch.zeros(4, 2)).select(1, 1)
v = conv_fn(torch.zeros(4, 2, 4))[:, 1]
self.assertFalse(v.is_contiguous(), 'V is contiguous')
self.assertFalse(e.is_contiguous(), 'E is contiguous')
torch.symeig(X, True, out=(e, v))
Xhat = torch.mm(torch.mm(v, torch.diag(e)), v.t())
self.assertEqual(X, Xhat, 1e-8, 'VeV\' wrong')
@skipIfNoLapack
def test_symeig(self):
self._test_symeig(self, lambda x: x)
@skipIfNoLapack
def test_svd(self):
a = torch.Tensor(((8.79, 6.11, -9.15, 9.57, -3.49, 9.84),
(9.93, 6.91, -7.93, 1.64, 4.02, 0.15),
(9.83, 5.04, 4.86, 8.83, 9.80, -8.99),
(5.45, -0.27, 4.85, 0.74, 10.00, -6.02),
(3.16, 7.98, 3.01, 5.80, 4.27, -5.31))).t().clone()
u, s, v = torch.svd(a)
uu = torch.Tensor()
ss = torch.Tensor()
vv = torch.Tensor()
uuu, sss, vvv = torch.svd(a, out=(uu, ss, vv))
self.assertEqual(u, uu, 0, 'torch.svd')
self.assertEqual(u, uuu, 0, 'torch.svd')
self.assertEqual(s, ss, 0, 'torch.svd')
self.assertEqual(s, sss, 0, 'torch.svd')
self.assertEqual(v, vv, 0, 'torch.svd')
self.assertEqual(v, vvv, 0, 'torch.svd')
# test reuse
X = torch.randn(4, 4)
U, S, V = torch.svd(X)
Xhat = torch.mm(U, torch.mm(S.diag(), V.t()))
self.assertEqual(X, Xhat, 1e-8, 'USV\' wrong')
self.assertFalse(U.is_contiguous(), 'U is contiguous')
torch.svd(X, out=(U, S, V))
Xhat = torch.mm(U, torch.mm(S.diag(), V.t()))
self.assertEqual(X, Xhat, 1e-8, 'USV\' wrong')
# test non-contiguous
X = torch.randn(5, 5)
U = torch.zeros(5, 2, 5)[:, 1]
S = torch.zeros(5, 2)[:, 1]
V = torch.zeros(5, 2, 5)[:, 1]
self.assertFalse(U.is_contiguous(), 'U is contiguous')
self.assertFalse(S.is_contiguous(), 'S is contiguous')
self.assertFalse(V.is_contiguous(), 'V is contiguous')
torch.svd(X, out=(U, S, V))
Xhat = torch.mm(U, torch.mm(S.diag(), V.t()))
self.assertEqual(X, Xhat, 1e-8, 'USV\' wrong')
@staticmethod
def _test_svd_no_singularvectors(self, cast):
for size in [(5, 5), (5, 20), (20, 5)]:
a = cast(torch.randn(*size))
u, s_expect, v = torch.svd(a)
u, s_actual, v = torch.svd(a, compute_uv=False)
self.assertEqual(s_expect, s_actual, "Singular values don't match")
@skipIfNoLapack
def test_svd_no_singularvectors(self):
self._test_svd_no_singularvectors(self, lambda t: t)
@staticmethod
def _test_matrix_rank(self, conv_fn):
a = conv_fn(torch.eye(10))
self.assertEqual(torch.matrix_rank(a).item(), 10)
self.assertEqual(torch.matrix_rank(a, True).item(), 10)
a[5, 5] = 0
self.assertEqual(torch.matrix_rank(a).item(), 9)
self.assertEqual(torch.matrix_rank(a, True).item(), 9)
a = conv_fn(torch.randn(24, 42))
self.assertEqual(torch.matrix_rank(a), torch.matrix_rank(a.t()))
aaT = torch.mm(a, a.t())
self.assertEqual(torch.matrix_rank(aaT), torch.matrix_rank(aaT, True))
aTa = torch.mm(a.t(), a)
self.assertEqual(torch.matrix_rank(aTa), torch.matrix_rank(aTa, True))
if TEST_NUMPY:
from numpy.linalg import matrix_rank
a = conv_fn(torch.randn(35, 75))
self.assertEqual(torch.matrix_rank(a).item(), matrix_rank(a.cpu().numpy()))
self.assertEqual(torch.matrix_rank(a, 0.01).item(), matrix_rank(a.cpu().numpy(), 0.01))
aaT = torch.mm(a, a.t())
self.assertEqual(torch.matrix_rank(aaT).item(), matrix_rank(aaT.cpu().numpy()))
self.assertEqual(torch.matrix_rank(aaT, 0.01).item(), matrix_rank(aaT.cpu().numpy(), 0.01))
if np.lib.NumpyVersion(np.__version__) >= '1.14.0':
self.assertEqual(torch.matrix_rank(aaT, True).item(), matrix_rank(aaT.cpu().numpy(), True))
self.assertEqual(torch.matrix_rank(aaT, 0.01, True).item(),
matrix_rank(aaT.cpu().numpy(), 0.01, True))
@skipIfNoLapack
def test_matrix_rank(self):
self._test_matrix_rank(self, lambda x: x)
@staticmethod
def _test_signal_window_functions(self, device='cpu'):
if not TEST_SCIPY:
raise unittest.SkipTest('Scipy not found')
def test(name):
torch_method = getattr(torch, name + '_window')
for size in [1, 2, 5, 10, 50, 100, 1024, 2048]:
for periodic in [True, False]:
res = torch_method(size, periodic=periodic, device=device)
ref = torch.from_numpy(signal.get_window(name, size, fftbins=periodic))
self.assertEqual(res, ref)
with self.assertRaisesRegex(RuntimeError, r'not implemented for sparse types'):
torch_method(3, layout=torch.sparse_coo)
with self.assertRaisesRegex(RuntimeError, r'floating point'):
torch_method(3, dtype=torch.long)
self.assertTrue(torch_method(3, requires_grad=True).requires_grad)
self.assertFalse(torch_method(3).requires_grad)
for window in ['hann', 'hamming', 'bartlett', 'blackman']:
test(window)
def test_signal_window_functions(self):
self._test_signal_window_functions(self)
@staticmethod
def _test_inverse(self, conv_fn):
from common_utils import random_fullrank_matrix_distinct_singular_value
# no batches: 2-D tensors
matrix = conv_fn(random_fullrank_matrix_distinct_singular_value(5))
matrix_inverse = torch.inverse(matrix)
identity = conv_fn(torch.eye(5))
self.assertEqual(identity, torch.mm(matrix, matrix_inverse), 1e-8, 'inverse value')
self.assertEqual(identity, torch.mm(matrix_inverse, matrix), 1e-8, 'inverse value')
matrix_inverse_out = conv_fn(torch.empty(5, 5))
torch.inverse(matrix, out=matrix_inverse_out)
self.assertEqual(matrix_inverse_out, matrix_inverse, 0, 'inverse value in-place')
# second call, now that matrix_inverse_out is transposed
torch.inverse(matrix, out=matrix_inverse_out)
self.assertEqual(matrix_inverse_out, matrix_inverse, 0, 'inverse value in-place')
# one batch
matrix = conv_fn(random_fullrank_matrix_distinct_singular_value(5, 1))
matrix_inverse = torch.inverse(matrix)
expected_inv = matrix.squeeze(0).inverse()
self.assertEqual(matrix_inverse, expected_inv.unsqueeze(0))
# four batches
matrices = conv_fn(random_fullrank_matrix_distinct_singular_value(5, 4))
expected_inv_list = []
for i in range(0, 4):
expected_inv_list.append(torch.inverse(matrices[i]))
expected_inv = torch.stack(expected_inv_list)
matrices_inverse = torch.inverse(matrices)
self.assertEqual(matrices_inverse, expected_inv)
# six batches (2 x 3)
matrices = conv_fn(random_fullrank_matrix_distinct_singular_value(5, 2, 3))
expected_inv_list = []
for mat in matrices.view(-1, 5, 5):
expected_inv_list.append(torch.inverse(mat))
expected_inv = torch.stack(expected_inv_list).view(2, 3, 5, 5)
matrices_inverse = torch.inverse(matrices)
self.assertEqual(matrices_inverse, expected_inv)
# incorrect input test
with self.assertRaisesRegex(RuntimeError, "must be batches of square matrices"):
torch.inverse(torch.randn(2, 3, 4, 3))
# correctness test
matrices = conv_fn(random_fullrank_matrix_distinct_singular_value(5, 3))
matrices_inverse = torch.inverse(matrices)
self.assertEqual(torch.matmul(matrices, matrices_inverse), identity.expand_as(matrices))
self.assertEqual(torch.matmul(matrices_inverse, matrices), identity.expand_as(matrices))
# torch.inverse with out and batches
matrices = conv_fn(random_fullrank_matrix_distinct_singular_value(5, 3))
matrices_inverse = conv_fn(torch.empty(3, 5, 5))
torch.inverse(matrices, out=matrices_inverse)
self.assertEqual(torch.inverse(matrices), matrices_inverse)
# non-contiguous inputs
if not TEST_NUMPY:
return
from numpy.linalg import inv
matrices = conv_fn(random_fullrank_matrix_distinct_singular_value(3, 2)).permute(0, 2, 1)
assert not matrices.is_contiguous()
matrices_inverse = torch.inverse(matrices)
expected_inv = torch.as_tensor(inv(matrices.cpu().numpy()))
self.assertEqual(matrices_inverse, conv_fn(expected_inv))
@skipIfNoLapack
def test_inverse(self):
self._test_inverse(self, lambda t: t)
@staticmethod
def _test_pinverse(self, conv_fn):
def run_test(M):
# Testing against definition for pseudo-inverses
MPI = torch.pinverse(M)
self.assertEqual(M, M.mm(MPI).mm(M), 1e-8, 'pseudo-inverse condition 1')
self.assertEqual(MPI, MPI.mm(M).mm(MPI), 1e-8, 'pseudo-inverse condition 2')
self.assertEqual(M.mm(MPI), (M.mm(MPI)).t(), 1e-8, 'pseudo-inverse condition 3')
self.assertEqual(MPI.mm(M), (MPI.mm(M)).t(), 1e-8, 'pseudo-inverse condition 4')
# Square matrix
M = conv_fn(torch.randn(5, 5))
run_test(M)
# Rectangular matrix
M = conv_fn(torch.randn(3, 4))
run_test(M)
# Test inverse and pseudo-inverse for invertible matrix
M = torch.randn(5, 5)
M = conv_fn(M.mm(M.t()))
self.assertEqual(conv_fn(torch.eye(5)), M.pinverse().mm(M), 1e-7, 'pseudo-inverse for invertible matrix')
@skipIfNoLapack
def test_pinverse(self):
self._test_pinverse(self, conv_fn=lambda x: x)
@staticmethod
def _test_matrix_power(self, conv_fn):
def run_test(M, sign=1):
if sign == -1:
M = M.inverse()
MP2 = torch.matrix_power(M, 2)
self.assertEqual(MP2, torch.matmul(M, M))
MP3 = torch.matrix_power(M, 3)
self.assertEqual(MP3, torch.matmul(MP2, M))
MP4 = torch.matrix_power(M, 4)
self.assertEqual(MP4, torch.matmul(MP2, MP2))
MP6 = torch.matrix_power(M, 6)
self.assertEqual(MP6, torch.matmul(MP3, MP3))
MP0 = torch.matrix_power(M, 0)
self.assertEqual(MP0, torch.eye(M.size(-2)).expand_as(M))
# Single matrix
M = conv_fn(torch.randn(5, 5))
run_test(M)
# Batch matrices
M = conv_fn(torch.randn(3, 3, 3))
run_test(M)
# Many batch matrices
M = conv_fn(torch.randn(2, 3, 3, 3))
run_test(M)
# This is for negative powers
from common_utils import random_fullrank_matrix_distinct_singular_value
M = conv_fn(random_fullrank_matrix_distinct_singular_value(5))
run_test(M, sign=-1)
M = conv_fn(random_fullrank_matrix_distinct_singular_value(3, 3))
run_test(M, sign=-1)
M = conv_fn(random_fullrank_matrix_distinct_singular_value(3, 2, 3))
run_test(M, sign=-1)
@skipIfNoLapack
def test_matrix_power(self):
self._test_matrix_power(self, conv_fn=lambda x: x)
@staticmethod
def _test_chain_matmul(self, cast):
def product(matrices):
for mat in matrices[1:]:
matrices[0] = matrices[0].mm(mat)
return matrices[0]
def run_test(p, cast):
matrices = []
for (pi, pi_1) in zip(p[:-1], p[1:]):
matrices.append(cast(torch.randn(pi, pi_1)))
self.assertEqual(torch.chain_matmul(*matrices), product(matrices))
run_test([10, 20, 30, 5], cast)
run_test([15, 5, 10, 20, 25], cast)
@skipIfRocm
def test_chain_matmul(self):
self._test_chain_matmul(self, cast=lambda x: x)
@staticmethod
def _test_det_logdet_slogdet(self, conv_fn):
def reference_det(M):
# naive row reduction
M = M.clone()
l = M.size(0)
multiplier = 1
for i in range(l):
if M[i, 0] != 0:
if i != 0:
M[0], M[i] = M[i], M[0]
multiplier = -1
break
else:
return 0
for i in range(1, l):
row = M[i]
for j in range(i):
row -= row[j] / M[j, j] * M[j]
M[i] = row
return M.diag().prod() * multiplier
def test_single_det(M, target, desc):
det = M.det()
logdet = M.logdet()
sdet, logabsdet = M.slogdet()
self.assertEqual(det, target, 1e-7, '{} (det)'.format(desc))
if det.item() < 0:
self.assertTrue(logdet.item() != logdet.item(), '{} (logdet negative case)'.format(desc))
self.assertTrue(sdet.item() == -1, '{} (slogdet sign negative case)'.format(desc))
self.assertEqual(logabsdet.exp(), det.abs(), 1e-7, '{} (slogdet logabsdet negative case)'.format(desc))
elif det.item() == 0:
self.assertEqual(logdet.exp().item(), 0, 1e-7, '{} (logdet zero case)'.format(desc))
self.assertTrue(sdet.item() == 0, '{} (slogdet sign zero case)'.format(desc))
self.assertEqual(logabsdet.exp().item(), 0, 1e-7, '{} (slogdet logabsdet zero case)'.format(desc))
else:
self.assertEqual(logdet.exp(), det, 1e-7, '{} (logdet positive case)'.format(desc))
self.assertTrue(sdet.item() == 1, '{} (slogdet sign positive case)'.format(desc))
self.assertEqual(logabsdet.exp(), det, 1e-7, '{} (slogdet logabsdet positive case)'.format(desc))
eye = conv_fn(torch.eye(5))
test_single_det(eye, torch.tensor(1, dtype=eye.dtype), 'identity')
def test(M):
assert M.size(0) >= 5, 'this helper fn assumes M to be at least 5x5'
M = conv_fn(M)
M_det = M.det()
ref_M_det = reference_det(M)
test_single_det(M, ref_M_det, 'basic')
if abs(ref_M_det.item()) >= 1e-10: # skip singular
test_single_det(M, M.inverse().det().pow_(-1), 'inverse')
test_single_det(M, M.t().det(), 'transpose')
for x in [0, 2, 4]:
for scale in [-2, -0.1, 0, 10]:
target = M_det * scale
# dim 0
M_clone = M.clone()
M_clone[:, x] *= scale
test_single_det(M_clone, target, 'scale a row')
# dim 1
M_clone = M.clone()
M_clone[x, :] *= scale
test_single_det(M_clone, target, 'scale a column')
for x1, x2 in [(0, 3), (4, 1), (3, 2)]:
assert x1 != x2, 'x1 and x2 needs to be different for this test'
target = M_det.clone().zero_()
# dim 0
M_clone = M.clone()
M_clone[:, x2] = M_clone[:, x1]
test_single_det(M_clone, target, 'two rows are same')
# dim 1
M_clone = M.clone()
M_clone[x2, :] = M_clone[x1, :]
test_single_det(M_clone, target, 'two columns are same')
for scale1, scale2 in [(0.3, -1), (0, 2), (10, 0.1)]:
target = -M_det * scale1 * scale2
# dim 0
M_clone = M.clone()
t = M_clone[:, x1] * scale1
M_clone[:, x1] += M_clone[:, x2] * scale2
M_clone[:, x2] = t
test_single_det(M_clone, target, 'exchanging rows')
# dim 1
M_clone = M.clone()
t = M_clone[x1, :] * scale1
M_clone[x1, :] += M_clone[x2, :] * scale2
M_clone[x2, :] = t
test_single_det(M_clone, target, 'exchanging columns')
def get_random_mat_scale(n):
# For matrices with values i.i.d. with 0 mean, unit variance, and
# subexponential tail, we have:
# E[log det(A^2)] \approx log((n-1)!)
#
# Notice:
# log Var[det(A)] = log E[det(A^2)] >= E[log det(A^2)]
#
# So:
# stddev[det(A)] >= sqrt( (n-1)! )
#
# We use this as an intuitive guideline to scale random generated
# matrices so our closeness tests can work more robustly:
# scale by sqrt( (n-1)! )^(-1/n) = ( (n-1)! )^(-1/(2n))
#
# source: https://arxiv.org/pdf/1112.0752.pdf
return math.factorial(n - 1) ** (-1.0 / (2 * n))
for n in [5, 10, 25]:
scale = get_random_mat_scale(n)
test(torch.randn(n, n) * scale)
r = torch.randn(n, n) * scale
# symmetric psd
test(r.mm(r.t()))
# symmetric pd
r = torch.randn(n, n) * scale
test(r.mm(r.t()) + torch.eye(n) * 1e-6)
# symmetric
r = torch.randn(n, n) * scale
for i in range(n):
for j in range(i):
r[i, j] = r[j, i]
test(r)
# non-contiguous
test((torch.randn(n, n, n + 1) * scale)[:, 2, 1:])
# det = 0
r = torch.randn(n, n) * scale
u, s, v = r.svd()
if reference_det(u) < 0:
u = -u
if reference_det(v) < 0:
v = -v
s[0] *= -1
s[-1] = 0
test(u.mm(s.diag()).mm(v))
@skipIfNoLapack
def test_det_logdet_slogdet(self):
self._test_det_logdet_slogdet(self, lambda x: x)
@staticmethod
def _test_fft_ifft_rfft_irfft(self, device='cpu'):
def _test_complex(sizes, signal_ndim, prepro_fn=lambda x: x):
x = prepro_fn(torch.randn(*sizes, device=device))
for normalized in (True, False):
res = x.fft(signal_ndim, normalized=normalized)
rec = res.ifft(signal_ndim, normalized=normalized)
self.assertEqual(x, rec, 1e-8, 'fft and ifft')
res = x.ifft(signal_ndim, normalized=normalized)
rec = res.fft(signal_ndim, normalized=normalized)
self.assertEqual(x, rec, 1e-8, 'ifft and fft')
def _test_real(sizes, signal_ndim, prepro_fn=lambda x: x):
x = prepro_fn(torch.randn(*sizes, device=device))
signal_numel = 1
signal_sizes = x.size()[-signal_ndim:]
for normalized, onesided in product((True, False), repeat=2):
res = x.rfft(signal_ndim, normalized=normalized, onesided=onesided)
if not onesided: # check Hermitian symmetry
def test_one_sample(res, test_num=10):
idxs_per_dim = [torch.LongTensor(test_num).random_(s).tolist() for s in signal_sizes]
for idx in zip(*idxs_per_dim):
reflected_idx = tuple((s - i) % s for i, s in zip(idx, res.size()))
idx_val = res.__getitem__(idx)
reflected_val = res.__getitem__(reflected_idx)
self.assertEqual(idx_val[0], reflected_val[0], 'rfft hermitian symmetry on real part')
self.assertEqual(idx_val[1], -reflected_val[1], 'rfft hermitian symmetry on imaginary part')
if len(sizes) == signal_ndim:
test_one_sample(res)
else:
output_non_batch_shape = res.size()[-(signal_ndim + 1):]
flatten_batch_res = res.view(-1, *output_non_batch_shape)
nb = flatten_batch_res.size(0)
test_idxs = torch.LongTensor(min(nb, 4)).random_(nb)
for test_idx in test_idxs.tolist():
test_one_sample(flatten_batch_res[test_idx])
# compare with C2C
xc = torch.stack([x, torch.zeros_like(x)], -1)
xc_res = xc.fft(signal_ndim, normalized=normalized)
self.assertEqual(res, xc_res)
test_input_signal_sizes = [signal_sizes]
rec = res.irfft(signal_ndim, normalized=normalized,
onesided=onesided, signal_sizes=signal_sizes)
self.assertEqual(x, rec, 1e-8, 'rfft and irfft')
if not onesided: # check that we can use C2C ifft
rec = res.ifft(signal_ndim, normalized=normalized)
self.assertEqual(x, rec.select(-1, 0), 1e-8, 'twosided rfft and ifft real')
self.assertEqual(rec.select(-1, 1).data.abs().mean(), 0, 1e-8, 'twosided rfft and ifft imaginary')
# contiguous case
_test_real((100,), 1)
_test_real((10, 1, 10, 100), 1)
_test_real((100, 100), 2)
_test_real((2, 2, 5, 80, 60), 2)
_test_real((50, 40, 70), 3)
_test_real((30, 1, 50, 25, 20), 3)
_test_complex((100, 2), 1)
_test_complex((100, 100, 2), 1)
_test_complex((100, 100, 2), 2)
_test_complex((1, 20, 80, 60, 2), 2)
_test_complex((50, 40, 70, 2), 3)
_test_complex((6, 5, 50, 25, 20, 2), 3)
# non-contiguous case
_test_real((165,), 1, lambda x: x.narrow(0, 25, 100)) # input is not aligned to complex type
_test_real((100, 100, 3), 1, lambda x: x[:, :, 0])
_test_real((100, 100), 2, lambda x: x.t())
_test_real((20, 100, 10, 10), 2, lambda x: x.view(20, 100, 100)[:, :60])
_test_real((65, 80, 115), 3, lambda x: x[10:60, 13:53, 10:80])
_test_real((30, 20, 50, 25), 3, lambda x: x.transpose(1, 2).transpose(2, 3))
_test_complex((2, 100), 1, lambda x: x.t())
_test_complex((100, 2), 1, lambda x: x.expand(100, 100, 2))
_test_complex((300, 200, 3), 2, lambda x: x[:100, :100, 1:]) # input is not aligned to complex type
_test_complex((20, 90, 110, 2), 2, lambda x: x[:, 5:85].narrow(2, 5, 100))
_test_complex((40, 60, 3, 80, 2), 3, lambda x: x.transpose(2, 0).select(0, 2)[5:55, :, 10:])
_test_complex((30, 55, 50, 22, 2), 3, lambda x: x[:, 3:53, 15:40, 1:21])
# non-contiguous with strides not representable as aligned with complex type
_test_complex((50,), 1, lambda x: x.as_strided([5, 5, 2], [3, 2, 1]))
_test_complex((50,), 1, lambda x: x.as_strided([5, 5, 2], [4, 2, 2]))
_test_complex((50,), 1, lambda x: x.as_strided([5, 5, 2], [4, 3, 1]))
_test_complex((50,), 2, lambda x: x.as_strided([5, 5, 2], [3, 3, 1]))
_test_complex((50,), 2, lambda x: x.as_strided([5, 5, 2], [4, 2, 2]))
_test_complex((50,), 2, lambda x: x.as_strided([5, 5, 2], [4, 3, 1]))
@unittest.skipIf(not TEST_MKL, "PyTorch is built without MKL support")
def test_fft_ifft_rfft_irfft(self):
self._test_fft_ifft_rfft_irfft(self)
@staticmethod
def _test_stft(self, device='cpu'):
if not TEST_LIBROSA:
raise unittest.SkipTest('librosa not found')
def librosa_stft(x, n_fft, hop_length, win_length, window, center):
if window is None:
window = np.ones(n_fft if win_length is None else win_length)
else:
window = window.cpu().numpy()
input_1d = x.dim() == 1
if input_1d:
x = x.view(1, -1)
result = []
for xi in x:
ri = librosa.stft(xi.cpu().numpy(), n_fft, hop_length, win_length, window, center=center)
result.append(torch.from_numpy(np.stack([ri.real, ri.imag], -1)))
result = torch.stack(result, 0)
if input_1d:
result = result[0]
return result
def _test(sizes, n_fft, hop_length=None, win_length=None, win_sizes=None,
center=True, expected_error=None):
x = torch.randn(*sizes, device=device)
if win_sizes is not None:
window = torch.randn(*win_sizes, device=device)
else:
window = None
if expected_error is None:
result = x.stft(n_fft, hop_length, win_length, window, center=center)
ref_result = librosa_stft(x, n_fft, hop_length, win_length, window, center)
self.assertEqual(result, ref_result, 7e-6, 'stft comparison against librosa')
else:
self.assertRaises(expected_error,
lambda: x.stft(n_fft, hop_length, win_length, window, center=center))
for center in [True, False]:
_test((10,), 7, center=center)
_test((10, 4000), 1024, center=center)
_test((10,), 7, 2, center=center)
_test((10, 4000), 1024, 512, center=center)
_test((10,), 7, 2, win_sizes=(7,), center=center)
_test((10, 4000), 1024, 512, win_sizes=(1024,), center=center)
# spectral oversample
_test((10,), 7, 2, win_length=5, center=center)
_test((10, 4000), 1024, 512, win_length=100, center=center)
_test((10, 4, 2), 1, 1, expected_error=RuntimeError)
_test((10,), 11, 1, center=False, expected_error=RuntimeError)
_test((10,), -1, 1, expected_error=RuntimeError)
_test((10,), 3, win_length=5, expected_error=RuntimeError)
_test((10,), 5, 4, win_sizes=(11,), expected_error=RuntimeError)
_test((10,), 5, 4, win_sizes=(1, 1), expected_error=RuntimeError)
def test_stft(self):
self._test_stft(self)
@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, 0, 'torch.conv2')
self.assertEqual(imvc, imvx, 0, 'torch.conv2')
self.assertEqual(imvc, imvx2, 0, 'torch.conv2')
self.assertEqual(imfc, imfx, 0, 'torch.conv2')
self.assertLessEqual(math.abs(x.dot(x) - torch.xcorr2(x, x)[0][0]), 1e-10, 'torch.conv2')
xx = torch.Tensor(2, x.size(1), x.size(2))
xx[1].copy_(x)
xx[2].copy_(x)
kk = torch.Tensor(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], 0, 'torch.conv2')
self.assertEqual(immvc[0], imvc, 0, 'torch.conv2')
self.assertEqual(immvc2[0], imvc2, 0, 'torch.conv2')
self.assertEqual(immfc[0], immfc[1], 0, 'torch.conv2')
self.assertEqual(immfc[0], imfc, 0, '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, 0, 'torch.conv3')
self.assertEqual(imvc, imvx, 0, 'torch.conv3')
self.assertEqual(imvc, imvx2, 0, 'torch.conv3')
self.assertEqual(imfc, imfx, 0, 'torch.conv3')
self.assertLessEqual(math.abs(x.dot(x) - torch.xcorr3(x, x)[0][0][0]), 4e-10, 'torch.conv3')
xx = torch.Tensor(2, x.size(1), x.size(2), x.size(3))
xx[1].copy_(x)
xx[2].copy_(x)
kk = torch.Tensor(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], 0, 'torch.conv3')
self.assertEqual(immvc[0], imvc, 0, 'torch.conv3')
self.assertEqual(immvc2[0], imvc2, 0, 'torch.conv3')
self.assertEqual(immfc[0], immfc[1], 0, 'torch.conv3')
self.assertEqual(immfc[0], imfc, 0, '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(lambda x, k: torch.xcorr3(x, k), 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(lambda x, k: torch.conv3(x, k), 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_logical(self):
x = torch.rand(100, 100) * 2 - 1
xgt = torch.gt(x, 1)
xlt = torch.lt(x, 1)
xeq = torch.eq(x, 1)
xne = torch.ne(x, 1)
neqs = xgt + xlt
all = neqs + xeq
self.assertEqual(neqs.long().sum(), xne.long().sum(), 0)
self.assertEqual(x.nelement(), all.long().sum())
def test_isfinite(self):
x = torch.Tensor([1, inf, 2, -inf, nan, -10])
self.assertEqual(torch.isfinite(x), torch.ByteTensor([1, 0, 1, 0, 0, 1]))
def test_isfinite_int(self):
x = torch.tensor([1, 2, 3])
self.assertEqual(torch.isfinite(x), torch.ByteTensor([1, 1, 1]))
def test_isinf(self):
x = torch.Tensor([1, inf, 2, -inf, nan])
self.assertEqual(torch.isinf(x), torch.ByteTensor([0, 1, 0, 1, 0]))
def test_isnan(self):
x = torch.Tensor([1, nan, 2])
self.assertEqual(torch.isnan(x), torch.ByteTensor([0, 1, 0]))
def test_RNGState(self):
state = torch.get_rng_state()
stateCloned = state.clone()
before = torch.rand(1000)
self.assertEqual(state.ne(stateCloned).long().sum(), 0, 0)
torch.set_rng_state(state)
after = torch.rand(1000)
self.assertEqual(before, after, 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, 0, "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, 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, 0,
'get_rng_state/set_rng_state not generating same sequence of normally distributed numbers')
self.assertEqual(seeded, reseeded, 0,
'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)
torch.set_rng_state(rng_state)
@staticmethod
def _test_cholesky(self, cast):
x = cast(torch.rand(10, 10) + 1e-1)
A = torch.mm(x, x.t())
# default Case
C = torch.cholesky(A)
B = torch.mm(C, C.t())
self.assertEqual(A, B, 1e-14)
# test Upper Triangular
U = torch.cholesky(A, True)
B = torch.mm(U.t(), U)
self.assertEqual(A, B, 1e-14, 'cholesky (upper) did not allow rebuilding the original matrix')
# test Lower Triangular
L = torch.cholesky(A, False)
B = torch.mm(L, L.t())
self.assertEqual(A, B, 1e-14, 'cholesky (lower) did not allow rebuilding the original matrix')
@skipIfNoLapack
def test_cholesky(self):
self._test_cholesky(self, lambda t: t)
@staticmethod
def _test_cholesky_batched(self, cast):
from common_utils import random_symmetric_pd_matrix
def cholesky_test_helper(n, batch_dims, cast, upper):
A = cast(random_symmetric_pd_matrix(n, *batch_dims))
cholesky_exp = torch.stack([m.cholesky(upper=upper) for m in A.reshape(-1, n, n)])
cholesky_exp = cholesky_exp.reshape_as(A)
self.assertEqual(cholesky_exp, torch.cholesky(A, upper=upper))
for upper, batchsize in product([True, False], [(3,), (3, 4), (2, 3, 4)]):
cholesky_test_helper(3, batchsize, cast, upper)
@skipIfNoLapack
def test_cholesky_batched(self):
self._test_cholesky_batched(self, lambda t: t)
@staticmethod
def _test_potrs(self, cast):
a = torch.Tensor(((6.80, -2.11, 5.66, 5.97, 8.23),
(-6.05, -3.30, 5.36, -4.44, 1.08),
(-0.45, 2.58, -2.70, 0.27, 9.04),
(8.32, 2.71, 4.35, -7.17, 2.14),
(-9.67, -5.14, -7.26, 6.08, -6.87))).t()
b = torch.Tensor(((4.02, 6.19, -8.22, -7.57, -3.03),
(-1.56, 4.00, -8.67, 1.75, 2.86),
(9.81, -4.09, -4.57, -8.61, 8.99))).t()
# make sure 'a' is symmetric PSD
a = torch.mm(a, a.t())
a, b = cast(a), cast(b)
# upper Triangular Test
U = torch.cholesky(a, True)
x = torch.potrs(b, U, True)
self.assertLessEqual(b.dist(torch.mm(a, x)), 1e-12)
# lower Triangular Test
L = torch.cholesky(a, False)
x = torch.potrs(b, L, False)
self.assertLessEqual(b.dist(torch.mm(a, x)), 1e-12)
@skipIfNoLapack
def test_potrs(self):
self._test_potrs(self, lambda t: t)
@staticmethod
def _test_potrs_batched(self, cast):
from common_utils import random_symmetric_pd_matrix
def potrs_test_helper(A_dims, b_dims, cast, upper):
A = cast(random_symmetric_pd_matrix(*A_dims))
L = torch.cholesky(A, upper)
b = cast(torch.randn(*b_dims))
return A, L, b
for upper in [True, False]:
# test against potrs: one batch with both choices of upper
A, L, b = potrs_test_helper((5, 1), (1, 5, 10), cast, upper)
x_exp = torch.potrs(b.squeeze(0), L.squeeze(0), upper=upper)
x = torch.potrs(b, L, upper=upper)
self.assertEqual(x, x_exp.unsqueeze(0))
# test against potrs in a loop: four batches with both choices of upper
A, L, b = potrs_test_helper((5, 4), (4, 5, 10), cast, upper)
x_exp_list = list()
for i in range(4):
x_exp = torch.potrs(b[i], L[i], upper=upper)
x_exp_list.append(x_exp)
x_exp = torch.stack(x_exp_list)
x = torch.potrs(b, L, upper=upper)
self.assertEqual(x, x_exp)
# basic correctness test
A, L, b = potrs_test_helper((5, 3), (3, 5, 10), cast, upper)
x = torch.potrs(b, L, upper)
self.assertLessEqual(b.dist(torch.matmul(A, x)), 1e-12)
# Test non-contiguous inputs.
if not TEST_NUMPY:
return
import numpy
from numpy.linalg import solve
A = random_symmetric_pd_matrix(2, 2)
b = torch.randn(2, 2, 2)
x_exp = torch.Tensor(solve(A.permute(0, 2, 1).numpy(), b.permute(2, 1, 0).numpy()))
A = cast(A).permute(0, 2, 1)
b = cast(b).permute(2, 1, 0)
assert not A.is_contiguous() and not b.is_contiguous(), "contiguous inputs"
L = torch.cholesky(A, upper)
x = torch.potrs(b, L, upper=upper)
self.assertEqual(x, cast(x_exp))
@skipIfNoLapack
def test_potrs_batched(self):
self._test_potrs_batched(self, lambda t: t)
@staticmethod
def _test_potrs_batched_dims(self, cast):
if not TEST_NUMPY:
return
from numpy.linalg import solve
from common_utils import random_symmetric_pd_matrix
def run_test(A_dims, b_dims, cast, upper):
A = random_symmetric_pd_matrix(*A_dims)
b = torch.randn(*b_dims)
x_exp = torch.Tensor(solve(A.numpy(), b.numpy()))
A, b = cast(A), cast(b)
L = torch.cholesky(A, upper)
x = torch.potrs(b, L, upper=upper)
self.assertEqual(x, cast(x_exp))
for upper in [True, False]:
# test against numpy.linalg.solve
run_test((4, 2, 1, 3), (2, 1, 3, 4, 6), cast, upper) # no broadcasting
run_test((4, 2, 1, 3), (4, 6), cast, upper) # broadcasting b
run_test((4,), (2, 1, 3, 4, 2), cast, upper) # broadcasting A
run_test((4, 1, 3, 1), (2, 1, 3, 4, 5), cast, upper) # broadcasting A & b
@skipIfNoLapack
def test_potrs_batched_dims(self):
self._test_potrs_batched_dims(self, lambda t: t)
@skipIfNoLapack
def test_potri(self):
a = torch.Tensor(((6.80, -2.11, 5.66, 5.97, 8.23),
(-6.05, -3.30, 5.36, -4.44, 1.08),
(-0.45, 2.58, -2.70, 0.27, 9.04),
(8.32, 2.71, 4.35, -7.17, 2.14),
(-9.67, -5.14, -7.26, 6.08, -6.87))).t()
# make sure 'a' is symmetric PSD
a = torch.mm(a, a.t())
# compute inverse directly
inv0 = torch.inverse(a)
# default case
chol = torch.cholesky(a)
inv1 = torch.potri(chol, False)
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# upper Triangular Test
chol = torch.cholesky(a, True)
inv1 = torch.potri(chol, True)
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# lower Triangular Test
chol = torch.cholesky(a, False)
inv1 = torch.potri(chol, False)
self.assertLessEqual(inv0.dist(inv1), 1e-12)
@skipIfNoLapack
def test_pstrf(self):
def checkPsdCholesky(a, uplo, inplace):
if inplace:
u = torch.empty_like(a)
piv = a.new(a.size(0)).int()
kwargs = {'out': (u, piv)}
else:
kwargs = {}
args = [a]
if uplo is not None:
args += [uplo]
u, piv = torch.pstrf(*args, **kwargs)
if uplo is False:
a_reconstructed = torch.mm(u, u.t())
else:
a_reconstructed = torch.mm(u.t(), u)
piv = piv.long()
a_permuted = a.index_select(0, piv).index_select(1, piv)
self.assertEqual(a_permuted, a_reconstructed, 1e-14)
dimensions = ((5, 1), (5, 3), (5, 5), (10, 10))
for dim in dimensions:
m = torch.Tensor(*dim).uniform_()
a = torch.mm(m, m.t())
# add a small number to the diagonal to make the matrix numerically positive semidefinite
for i in range(m.size(0)):
a[i][i] = a[i][i] + 1e-7
for inplace in (True, False):
for uplo in (None, True, False):
checkPsdCholesky(a, uplo, inplace)
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 _consecutive(self, size, start=1):
sequence = torch.ones(int(torch.Tensor(size).prod(0))).cumsum(0)
sequence.add_(start - 1)
return sequence.resize_(*size)
@staticmethod
def _test_index(self, conv_fn):
def consec(size, start=1):
sequence = torch.ones(int(torch.Tensor(size).prod(0))).cumsum(0)
sequence.add_(start - 1)
return sequence.view(*size)
reference = conv_fn(consec((3, 3, 3)))
# empty tensor indexing
self.assertEqual(reference[conv_fn(torch.LongTensor())], reference.new(0, 3, 3))
self.assertEqual(reference[0], consec((3, 3)), 0)
self.assertEqual(reference[1], consec((3, 3), 10), 0)
self.assertEqual(reference[2], consec((3, 3), 19), 0)
self.assertEqual(reference[0, 1], consec((3,), 4), 0)
self.assertEqual(reference[0:2], consec((2, 3, 3)), 0)
self.assertEqual(reference[2, 2, 2], 27, 0)
self.assertEqual(reference[:], consec((3, 3, 3)), 0)
# indexing with Ellipsis
self.assertEqual(reference[..., 2], torch.Tensor([[3, 6, 9],
[12, 15, 18],
[21, 24, 27]]), 0)
self.assertEqual(reference[0, ..., 2], torch.Tensor([3, 6, 9]), 0)
self.assertEqual(reference[..., 2], reference[:, :, 2], 0)
self.assertEqual(reference[0, ..., 2], reference[0, :, 2], 0)
self.assertEqual(reference[0, 2, ...], reference[0, 2], 0)
self.assertEqual(reference[..., 2, 2, 2], 27, 0)
self.assertEqual(reference[2, ..., 2, 2], 27, 0)
self.assertEqual(reference[2, 2, ..., 2], 27, 0)
self.assertEqual(reference[2, 2, 2, ...], 27, 0)
self.assertEqual(reference[...], reference, 0)
reference_5d = conv_fn(consec((3, 3, 3, 3, 3)))
self.assertEqual(reference_5d[..., 1, 0], reference_5d[:, :, :, 1, 0], 0)
self.assertEqual(reference_5d[2, ..., 1, 0], reference_5d[2, :, :, 1, 0], 0)
self.assertEqual(reference_5d[2, 1, 0, ..., 1], reference_5d[2, 1, 0, :, 1], 0)
self.assertEqual(reference_5d[...], reference_5d, 0)
# LongTensor indexing
reference = conv_fn(consec((5, 5, 5)))
idx = conv_fn(torch.LongTensor([2, 4]))
self.assertEqual(reference[idx], torch.stack([reference[2], reference[4]]))
# TODO: enable one indexing is implemented like in numpy
# self.assertEqual(reference[2, idx], torch.stack([reference[2, 2], reference[2, 4]]))
# self.assertEqual(reference[3, idx, 1], torch.stack([reference[3, 2], reference[3, 4]])[:, 1])
# None indexing
self.assertEqual(reference[2, None], reference[2].unsqueeze(0))
self.assertEqual(reference[2, None, None], reference[2].unsqueeze(0).unsqueeze(0))
self.assertEqual(reference[2:4, None], reference[2:4].unsqueeze(1))
self.assertEqual(reference[None, 2, None, None], reference.unsqueeze(0)[:, 2].unsqueeze(0).unsqueeze(0))
self.assertEqual(reference[None, 2:5, None, None], reference.unsqueeze(0)[:, 2:5].unsqueeze(2).unsqueeze(2))
# indexing 0-length slice
self.assertEqual(torch.empty(0, 5, 5), reference[slice(0)])
self.assertEqual(torch.empty(0, 5), reference[slice(0), 2])
self.assertEqual(torch.empty(0, 5), reference[2, slice(0)])
self.assertEqual(torch.tensor([]), reference[2, 1:1, 2])
# indexing with step
reference = consec((10, 10, 10))
self.assertEqual(reference[1:5:2], torch.stack([reference[1], reference[3]], 0))
self.assertEqual(reference[1:6:2], torch.stack([reference[1], reference[3], reference[5]], 0))
self.assertEqual(reference[1:9:4], torch.stack([reference[1], reference[5]], 0))
self.assertEqual(reference[2:4, 1:5:2], torch.stack([reference[2:4, 1], reference[2:4, 3]], 1))
self.assertEqual(reference[3, 1:6:2], torch.stack([reference[3, 1], reference[3, 3], reference[3, 5]], 0))
self.assertEqual(reference[None, 2, 1:9:4], torch.stack([reference[2, 1], reference[2, 5]], 0).unsqueeze(0))
self.assertEqual(reference[:, 2, 1:6:2],
torch.stack([reference[:, 2, 1], reference[:, 2, 3], reference[:, 2, 5]], 1))
lst = [list(range(i, i + 10)) for i in range(0, 100, 10)]
tensor = conv_fn(torch.DoubleTensor(lst))
for _i in range(100):
idx1_start = random.randrange(10)
idx1_end = idx1_start + random.randrange(1, 10 - idx1_start + 1)
idx1_step = random.randrange(1, 8)
idx1 = slice(idx1_start, idx1_end, idx1_step)
if random.randrange(2) == 0:
idx2_start = random.randrange(10)
idx2_end = idx2_start + random.randrange(1, 10 - idx2_start + 1)
idx2_step = random.randrange(1, 8)
idx2 = slice(idx2_start, idx2_end, idx2_step)
lst_indexed = list(map(lambda l: l[idx2], lst[idx1]))
tensor_indexed = tensor[idx1, idx2]
else:
lst_indexed = lst[idx1]
tensor_indexed = tensor[idx1]
self.assertEqual(torch.DoubleTensor(lst_indexed), tensor_indexed)
self.assertRaises(ValueError, lambda: reference[1:9:0])
self.assertRaises(ValueError, lambda: reference[1:9:-1])
self.assertRaises(IndexError, lambda: reference[1, 1, 1, 1])
self.assertRaises(IndexError, lambda: reference[1, 1, 1, 1:1])
self.assertRaises(IndexError, lambda: reference[3, 3, 3, 3, 3, 3, 3, 3])
self.assertRaises(IndexError, lambda: reference[0.0])
self.assertRaises(TypeError, lambda: reference[0.0:2.0])
self.assertRaises(IndexError, lambda: reference[0.0, 0.0:2.0])
self.assertRaises(IndexError, lambda: reference[0.0, :, 0.0:2.0])
self.assertRaises(IndexError, lambda: reference[0.0, ..., 0.0:2.0])
self.assertRaises(IndexError, lambda: reference[0.0, :, 0.0])
def delitem():
del reference[0]
self.assertRaises(TypeError, delitem)
def test_index(self):
self._test_index(self, lambda x: x)
@staticmethod
def _test_advancedindex(self, conv_fn):
# Tests for Integer Array Indexing, Part I - Purely integer array
# indexing
def consec(size, start=1):
numel = reduce(lambda x, y: x * y, size, 1)
sequence = torch.ones(numel).cumsum(0)
sequence.add_(start - 1)
return sequence.view(*size)
# pick a random valid indexer type
def ri(indices):
choice = random.randint(0, 2)
if choice == 0:
return conv_fn(torch.LongTensor(indices))
elif choice == 1:
return list(indices)
else:
return tuple(indices)
# First, we will test indexing to generate return values
# Case 1: Purely Integer Array Indexing
reference = conv_fn(consec((10,)))
self.assertEqual(reference[[0]], consec((1,)))
self.assertEqual(reference[ri([0]), ], consec((1,)))
self.assertEqual(reference[ri([3]), ], consec((1,), 4))
self.assertEqual(reference[[2, 3, 4]], consec((3,), 3))
self.assertEqual(reference[ri([2, 3, 4]), ], consec((3,), 3))
self.assertEqual(reference[ri([0, 2, 4]), ], torch.Tensor([1, 3, 5]))
# setting values
reference[[0]] = -2
self.assertEqual(reference[[0]], torch.Tensor([-2]))
reference[[0]] = -1
self.assertEqual(reference[ri([0]), ], torch.Tensor([-1]))
reference[[2, 3, 4]] = 4
self.assertEqual(reference[[2, 3, 4]], torch.Tensor([4, 4, 4]))
reference[ri([2, 3, 4]), ] = 3
self.assertEqual(reference[ri([2, 3, 4]), ], torch.Tensor([3, 3, 3]))
reference[ri([0, 2, 4]), ] = conv_fn(torch.Tensor([5, 4, 3]))
self.assertEqual(reference[ri([0, 2, 4]), ], torch.Tensor([5, 4, 3]))
# Tensor with stride != 1
# strided is [1, 3, 5, 7]
reference = conv_fn(consec((10,)))
strided = conv_fn(torch.Tensor())
strided.set_(reference.storage(), storage_offset=0,
size=torch.Size([4]), stride=[2])
self.assertEqual(strided[[0]], torch.Tensor([1]))
self.assertEqual(strided[ri([0]), ], torch.Tensor([1]))
self.assertEqual(strided[ri([3]), ], torch.Tensor([7]))
self.assertEqual(strided[[1, 2]], torch.Tensor([3, 5]))
self.assertEqual(strided[ri([1, 2]), ], torch.Tensor([3, 5]))
self.assertEqual(strided[ri([[2, 1], [0, 3]]), ],
torch.Tensor([[5, 3], [1, 7]]))
# stride is [4, 8]
strided = conv_fn(torch.Tensor())
strided.set_(reference.storage(), storage_offset=4,
size=torch.Size([2]), stride=[4])
self.assertEqual(strided[[0]], torch.Tensor([5]))
self.assertEqual(strided[ri([0]), ], torch.Tensor([5]))
self.assertEqual(strided[ri([1]), ], torch.Tensor([9]))
self.assertEqual(strided[[0, 1]], torch.Tensor([5, 9]))
self.assertEqual(strided[ri([0, 1]), ], torch.Tensor([5, 9]))
self.assertEqual(strided[ri([[0, 1], [1, 0]]), ],
torch.Tensor([[5, 9], [9, 5]]))
# reference is 1 2
# 3 4
# 5 6
reference = conv_fn(consec((3, 2)))
self.assertEqual(reference[ri([0, 1, 2]), ri([0])], torch.Tensor([1, 3, 5]))
self.assertEqual(reference[ri([0, 1, 2]), ri([1])], torch.Tensor([2, 4, 6]))
self.assertEqual(reference[ri([0]), ri([0])], consec((1,)))
self.assertEqual(reference[ri([2]), ri([1])], consec((1,), 6))
self.assertEqual(reference[[ri([0, 0]), ri([0, 1])]], torch.Tensor([1, 2]))
self.assertEqual(reference[[ri([0, 1, 1, 0, 2]), ri([1])]],
torch.Tensor([2, 4, 4, 2, 6]))
self.assertEqual(reference[[ri([0, 0, 1, 1]), ri([0, 1, 0, 0])]],
torch.Tensor([1, 2, 3, 3]))
rows = ri([[0, 0],
[1, 2]])
columns = [0],
self.assertEqual(reference[rows, columns], torch.Tensor([[1, 1],
[3, 5]]))
rows = ri([[0, 0],
[1, 2]])
columns = ri([1, 0])
self.assertEqual(reference[rows, columns], torch.Tensor([[2, 1],
[4, 5]]))
rows = ri([[0, 0],
[1, 2]])
columns = ri([[0, 1],
[1, 0]])
self.assertEqual(reference[rows, columns], torch.Tensor([[1, 2],
[4, 5]]))
# setting values
reference[ri([0]), ri([1])] = -1
self.assertEqual(reference[ri([0]), ri([1])], torch.Tensor([-1]))
reference[ri([0, 1, 2]), ri([0])] = conv_fn(torch.Tensor([-1, 2, -4]))
self.assertEqual(reference[ri([0, 1, 2]), ri([0])], torch.Tensor([-1,
2, -4]))
reference[rows, columns] = conv_fn(torch.Tensor([[4, 6], [2, 3]]))
self.assertEqual(reference[rows, columns],
torch.Tensor([[4, 6], [2, 3]]))
# Verify still works with Transposed (i.e. non-contiguous) Tensors
reference = conv_fn(torch.Tensor([[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11]])).t_()
# Transposed: [[0, 4, 8],
# [1, 5, 9],
# [2, 6, 10],
# [3, 7, 11]]
self.assertEqual(reference[ri([0, 1, 2]), ri([0])], torch.Tensor([0, 1,
2]))
self.assertEqual(reference[ri([0, 1, 2]), ri([1])], torch.Tensor([4, 5,
6]))
self.assertEqual(reference[ri([0]), ri([0])], torch.Tensor([0]))
self.assertEqual(reference[ri([2]), ri([1])], torch.Tensor([6]))
self.assertEqual(reference[[ri([0, 0]), ri([0, 1])]], torch.Tensor([0, 4]))
self.assertEqual(reference[[ri([0, 1, 1, 0, 3]), ri([1])]],
torch.Tensor([4, 5, 5, 4, 7]))
self.assertEqual(reference[[ri([0, 0, 1, 1]), ri([0, 1, 0, 0])]],
torch.Tensor([0, 4, 1, 1]))
rows = ri([[0, 0],
[1, 2]])
columns = [0],
self.assertEqual(reference[rows, columns], torch.Tensor([[0, 0],
[1, 2]]))
rows = ri([[0, 0],
[1, 2]])
columns = ri([1, 0])
self.assertEqual(reference[rows, columns], torch.Tensor([[4, 0],
[5, 2]]))
rows = ri([[0, 0],
[1, 3]])
columns = ri([[0, 1],
[1, 2]])
self.assertEqual(reference[rows, columns], torch.Tensor([[0, 4],
[5, 11]]))
# setting values
reference[ri([0]), ri([1])] = -1
self.assertEqual(reference[ri([0]), ri([1])], torch.Tensor([-1]))
reference[ri([0, 1, 2]), ri([0])] = conv_fn(torch.Tensor([-1, 2, -4]))
self.assertEqual(reference[ri([0, 1, 2]), ri([0])], torch.Tensor([-1,
2, -4]))
reference[rows, columns] = conv_fn(torch.Tensor([[4, 6], [2, 3]]))
self.assertEqual(reference[rows, columns],
torch.Tensor([[4, 6], [2, 3]]))
# stride != 1
# strided is [[1 3 5 7],
# [9 11 13 15]]
reference = conv_fn(torch.arange(0., 24).view(3, 8))
strided = conv_fn(torch.Tensor())
strided.set_(reference.storage(), 1, size=torch.Size([2, 4]),
stride=[8, 2])
self.assertEqual(strided[ri([0, 1]), ri([0])], torch.Tensor([1, 9]))
self.assertEqual(strided[ri([0, 1]), ri([1])], torch.Tensor([3, 11]))
self.assertEqual(strided[ri([0]), ri([0])], torch.Tensor([1]))
self.assertEqual(strided[ri([1]), ri([3])], torch.Tensor([15]))
self.assertEqual(strided[[ri([0, 0]), ri([0, 3])]], torch.Tensor([1, 7]))
self.assertEqual(strided[[ri([1]), ri([0, 1, 1, 0, 3])]],
torch.Tensor([9, 11, 11, 9, 15]))
self.assertEqual(strided[[ri([0, 0, 1, 1]), ri([0, 1, 0, 0])]],
torch.Tensor([1, 3, 9, 9]))
rows = ri([[0, 0],
[1, 1]])
columns = [0],
self.assertEqual(strided[rows, columns], torch.Tensor([[1, 1],
[9, 9]]))
rows = ri([[0, 1],
[1, 0]])
columns = ri([1, 2])
self.assertEqual(strided[rows, columns], torch.Tensor([[3, 13],
[11, 5]]))
rows = ri([[0, 0],
[1, 1]])
columns = ri([[0, 1],
[1, 2]])
self.assertEqual(strided[rows, columns], torch.Tensor([[1, 3],
[11, 13]]))
# setting values
# strided is [[10, 11],
# [17, 18]]
reference = conv_fn(torch.arange(0., 24).view(3, 8))
strided = conv_fn(torch.Tensor())
strided.set_(reference.storage(), 10, size=torch.Size([2, 2]),
stride=[7, 1])
self.assertEqual(strided[ri([0]), ri([1])], torch.Tensor([11]))
strided[ri([0]), ri([1])] = -1
self.assertEqual(strided[ri([0]), ri([1])], torch.Tensor([-1]))
reference = conv_fn(torch.arange(0., 24).view(3, 8))
strided = conv_fn(torch.Tensor())
strided.set_(reference.storage(), 10, size=torch.Size([2, 2]),
stride=[7, 1])
self.assertEqual(strided[ri([0, 1]), ri([1, 0])], torch.Tensor([11,
17]))
strided[ri([0, 1]), ri([1, 0])] = conv_fn(torch.Tensor([-1, 2]))
self.assertEqual(strided[ri([0, 1]), ri([1, 0])], torch.Tensor([-1,
2]))
reference = conv_fn(torch.arange(0., 24).view(3, 8))
strided = conv_fn(torch.Tensor())
strided.set_(reference.storage(), 10, size=torch.Size([2, 2]),
stride=[7, 1])
rows = ri([[0],
[1]])
columns = ri([[0, 1],
[0, 1]])
self.assertEqual(strided[rows, columns],
torch.Tensor([[10, 11], [17, 18]]))
strided[rows, columns] = conv_fn(torch.Tensor([[4, 6], [2, 3]]))
self.assertEqual(strided[rows, columns],
torch.Tensor([[4, 6], [2, 3]]))
# Tests using less than the number of dims, and ellipsis
# reference is 1 2
# 3 4
# 5 6
reference = conv_fn(consec((3, 2)))
self.assertEqual(reference[ri([0, 2]), ], torch.Tensor([[1, 2], [5, 6]]))
self.assertEqual(reference[ri([1]), ...], torch.Tensor([[3, 4]]))
self.assertEqual(reference[..., ri([1])], torch.Tensor([[2], [4], [6]]))
# verify too many indices fails
with self.assertRaises(IndexError):
reference[ri([1]), ri([0, 2]), ri([3])]
# test invalid index fails
reference = conv_fn(torch.empty(10))
# can't test cuda because it is a device assert
if not reference.is_cuda:
for err_idx in (10, -11):
with self.assertRaisesRegex(IndexError, r'out of'):
reference[err_idx]
with self.assertRaisesRegex(RuntimeError, r'out of'):
reference[conv_fn(torch.LongTensor([err_idx]))]
with self.assertRaisesRegex(RuntimeError, r'out of'):
reference[[err_idx]]
if TEST_NUMPY:
# we use numpy to compare against, to verify that our advanced
# indexing semantics are the same, and also for ease of test
# writing
def tensor_indices_to_np(tensor, indices):
# convert the Torch Tensor to a numpy array
if (tensor.is_cuda):
tensor = tensor.cpu()
npt = tensor.numpy()
# convert indices
idxs = tuple(i.tolist() if isinstance(i, torch.LongTensor) else
i for i in indices)
return npt, idxs
def get_numpy(tensor, indices):
npt, idxs = tensor_indices_to_np(tensor, indices)
# index and return as a Torch Tensor
return torch.Tensor(npt[idxs])
def set_numpy(tensor, indices, value):
if not isinstance(value, int):
if value.is_cuda:
value = value.cpu()
value = value.numpy()
npt, idxs = tensor_indices_to_np(tensor, indices)
npt[idxs] = value
return npt
def assert_get_eq(tensor, indexer):
self.assertEqual(tensor[indexer],
conv_fn(get_numpy(tensor, indexer)))
def assert_set_eq(tensor, indexer, val):
pyt = tensor.clone()
numt = tensor.clone()
pyt[indexer] = val
numt = conv_fn(torch.Tensor(set_numpy(numt, indexer, val)))
self.assertEqual(pyt, numt)
def get_set_tensor(indexed, indexer):
set_size = indexed[indexer].size()
set_count = indexed[indexer].numel()
set_tensor = conv_fn(torch.randperm(set_count).view(set_size).double())
return set_tensor
# Tensor is 0 1 2 3 4
# 5 6 7 8 9
# 10 11 12 13 14
# 15 16 17 18 19
reference = conv_fn(torch.arange(0., 20).view(4, 5))
indices_to_test = [
# grab the second, fourth columns
[slice(None), [1, 3]],
# first, third rows,
[[0, 2], slice(None)],
# weird shape
[slice(None), [[0, 1],
[2, 3]]],
# negatives
[[-1], [0]],
[[0, 2], [-1]],
[slice(None), [-1]],
]
# only test dupes on gets
get_indices_to_test = indices_to_test + [[slice(None), [0, 1, 1, 2, 2]]]
for indexer in get_indices_to_test:
assert_get_eq(reference, indexer)
for indexer in indices_to_test:
assert_set_eq(reference, indexer, 44)
assert_set_eq(reference,
indexer,
get_set_tensor(reference, indexer))
reference = conv_fn(torch.arange(0., 160).view(4, 8, 5))
indices_to_test = [
[slice(None), slice(None), [0, 3, 4]],
[slice(None), [2, 4, 5, 7], slice(None)],
[[2, 3], slice(None), slice(None)],
[slice(None), [0, 2, 3], [1, 3, 4]],
[slice(None), [0], [1, 2, 4]],
[slice(None), [0, 1, 3], [4]],
[slice(None), [[0, 1], [1, 0]], [[2, 3]]],
[slice(None), [[0, 1], [2, 3]], [[0]]],
[slice(None), [[5, 6]], [[0, 3], [4, 4]]],
[[0, 2, 3], [1, 3, 4], slice(None)],
[[0], [1, 2, 4], slice(None)],
[[0, 1, 3], [4], slice(None)],
[[[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None)],
[[[0, 1], [1, 0]], [[2, 3]], slice(None)],
[[[0, 1], [2, 3]], [[0]], slice(None)],
[[[2, 1]], [[0, 3], [4, 4]], slice(None)],
[[[2]], [[0, 3], [4, 1]], slice(None)],
# less dim, ellipsis
[[0, 2], ],
[[0, 2], slice(None)],
[[0, 2], Ellipsis],
[[0, 2], slice(None), Ellipsis],
[[0, 2], Ellipsis, slice(None)],
[[0, 2], [1, 3]],
[[0, 2], [1, 3], Ellipsis],
[Ellipsis, [1, 3], [2, 3]],
[Ellipsis, [2, 3, 4]],
[Ellipsis, slice(None), [2, 3, 4]],
[slice(None), Ellipsis, [2, 3, 4]],
# ellipsis counts for nothing
[Ellipsis, slice(None), slice(None), [0, 3, 4]],
[slice(None), Ellipsis, slice(None), [0, 3, 4]],
[slice(None), slice(None), Ellipsis, [0, 3, 4]],
[slice(None), slice(None), [0, 3, 4], Ellipsis],
[Ellipsis, [[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None)],
[[[0, 1], [1, 0]], [[2, 1], [3, 5]], Ellipsis, slice(None)],
[[[0, 1], [1, 0]], [[2, 1], [3, 5]], slice(None), Ellipsis],
]
for indexer in indices_to_test:
assert_get_eq(reference, indexer)
assert_set_eq(reference, indexer, 212)
assert_set_eq(reference,
indexer,
get_set_tensor(reference, indexer))
reference = conv_fn(torch.arange(0., 1296).view(3, 9, 8, 6))
indices_to_test = [
[slice(None), slice(None), slice(None), [0, 3, 4]],
[slice(None), slice(None), [2, 4, 5, 7], slice(None)],
[slice(None), [2, 3], slice(None), slice(None)],
[[1, 2], slice(None), slice(None), slice(None)],
[slice(None), slice(None), [0, 2, 3], [1, 3, 4]],
[slice(None), slice(None), [0], [1, 2, 4]],
[slice(None), slice(None), [0, 1, 3], [4]],
[slice(None), slice(None), [[0, 1], [1, 0]], [[2, 3]]],
[slice(None), slice(None), [[0, 1], [2, 3]], [[0]]],
[slice(None), slice(None), [[5, 6]], [[0, 3], [4, 4]]],
[slice(None), [0, 2, 3], [1, 3, 4], slice(None)],
[slice(None), [0], [1, 2, 4], slice(None)],
[slice(None), [0, 1, 3], [4], slice(None)],
[slice(None), [[0, 1], [3, 4]], [[2, 3], [0, 1]], slice(None)],
[slice(None), [[0, 1], [3, 4]], [[2, 3]], slice(None)],
[slice(None), [[0, 1], [3, 2]], [[0]], slice(None)],
[slice(None), [[2, 1]], [[0, 3], [6, 4]], slice(None)],
[slice(None), [[2]], [[0, 3], [4, 2]], slice(None)],
[[0, 1, 2], [1, 3, 4], slice(None), slice(None)],
[[0], [1, 2, 4], slice(None), slice(None)],
[[0, 1, 2], [4], slice(None), slice(None)],
[[[0, 1], [0, 2]], [[2, 4], [1, 5]], slice(None), slice(None)],
[[[0, 1], [1, 2]], [[2, 0]], slice(None), slice(None)],
[[[2, 2]], [[0, 3], [4, 5]], slice(None), slice(None)],
[[[2]], [[0, 3], [4, 5]], slice(None), slice(None)],
[slice(None), [3, 4, 6], [0, 2, 3], [1, 3, 4]],
[slice(None), [2, 3, 4], [1, 3, 4], [4]],
[slice(None), [0, 1, 3], [4], [1, 3, 4]],
[slice(None), [6], [0, 2, 3], [1, 3, 4]],
[slice(None), [2, 3, 5], [3], [4]],
[slice(None), [0], [4], [1, 3, 4]],
[slice(None), [6], [0, 2, 3], [1]],
[slice(None), [[0, 3], [3, 6]], [[0, 1], [1, 3]], [[5, 3], [1, 2]]],
[[2, 2, 1], [0, 2, 3], [1, 3, 4], slice(None)],
[[2, 0, 1], [1, 2, 3], [4], slice(None)],
[[0, 1, 2], [4], [1, 3, 4], slice(None)],
[[0], [0, 2, 3], [1, 3, 4], slice(None)],
[[0, 2, 1], [3], [4], slice(None)],
[[0], [4], [1, 3, 4], slice(None)],
[[1], [0, 2, 3], [1], slice(None)],
[[[1, 2], [1, 2]], [[0, 1], [2, 3]], [[2, 3], [3, 5]], slice(None)],
# less dim, ellipsis
[Ellipsis, [0, 3, 4]],
[Ellipsis, slice(None), [0, 3, 4]],
[Ellipsis, slice(None), slice(None), [0, 3, 4]],
[slice(None), Ellipsis, [0, 3, 4]],
[slice(None), slice(None), Ellipsis, [0, 3, 4]],
[slice(None), [0, 2, 3], [1, 3, 4]],
[slice(None), [0, 2, 3], [1, 3, 4], Ellipsis],
[Ellipsis, [0, 2, 3], [1, 3, 4], slice(None)],
[[0], [1, 2, 4]],
[[0], [1, 2, 4], slice(None)],
[[0], [1, 2, 4], Ellipsis],
[[0], [1, 2, 4], Ellipsis, slice(None)],
[[1], ],
[[0, 2, 1], [3], [4]],
[[0, 2, 1], [3], [4], slice(None)],
[[0, 2, 1], [3], [4], Ellipsis],
[Ellipsis, [0, 2, 1], [3], [4]],
]
for indexer in indices_to_test:
assert_get_eq(reference, indexer)
assert_set_eq(reference, indexer, 1333)
assert_set_eq(reference,
indexer,
get_set_tensor(reference, indexer))
indices_to_test += [
[slice(None), slice(None), [[0, 1], [1, 0]], [[2, 3], [3, 0]]],
[slice(None), slice(None), [[2]], [[0, 3], [4, 4]]],
]
for indexer in indices_to_test:
assert_get_eq(reference, indexer)
assert_set_eq(reference, indexer, 1333)
def test_advancedindex(self):
self._test_advancedindex(self, lambda x: x)
@staticmethod
def _test_advancedindex_big(self, conv_fn):
reference = conv_fn(torch.arange(0, 123344).int())
self.assertEqual(reference[[0, 123, 44488, 68807, 123343], ],
torch.LongTensor([0, 123, 44488, 68807, 123343]))
def test_advancedindex_big(self):
self._test_advancedindex_big(self, lambda x: x)
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
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 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], 0)
reference[index] = 0
self.assertEqual(reference, torch.zeros(3, 3, 3), 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_copy(self):
num_copy, num_dest = 3, 20
dest = torch.randn(num_dest, 4, 5)
src = torch.randn(num_copy, 4, 5)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_copy_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]] = src[i]
self.assertEqual(dest, dest2, 0)
dest = torch.randn(num_dest)
src = torch.randn(num_copy)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_copy_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]] = src[i]
self.assertEqual(dest, dest2, 0)
def test_index_add(self):
num_copy, num_dest = 3, 3
dest = torch.randn(num_dest, 4, 5)
src = torch.randn(num_copy, 4, 5)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_add_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]] += src[i]
self.assertEqual(dest, dest2)
dest = torch.randn(num_dest)
src = torch.randn(num_copy)
idx = torch.randperm(num_dest).narrow(0, 0, num_copy)
dest2 = dest.clone()
dest.index_add_(0, idx, src)
for i in range(idx.size(0)):
dest2[idx[i]] = dest2[idx[i]] + src[i]
self.assertEqual(dest, dest2)
def test_index_select(self):
src = torch.randn(3, 4, 5)
# Index can be duplicated.
idx = torch.LongTensor([2, 1, 0, 1, 2])
dest = torch.index_select(src, 0, idx)
self.assertEqual(dest.shape, (5, 4, 5))
for i in range(idx.size(0)):
self.assertEqual(dest[i], src[idx[i]])
# Check that 'out' is used correctly.
out = torch.randn(5 * 4 * 5)
dest = torch.index_select(src, 0, idx, out=out.view(5, 4, 5))
self.assertEqual(dest.shape, (5, 4, 5))
for i in range(idx.size(0)):
self.assertEqual(dest[i], src[idx[i]])
out.fill_(0.123)
self.assertEqual(out, dest.view(-1)) # Must point to the same storage.
def test_take(self):
def check(src, idx):
expected = src.contiguous().view(-1).index_select(
0, idx.contiguous().view(-1)).view_as(idx)
actual = src.take(idx)
self.assertEqual(actual.size(), idx.size())
self.assertEqual(expected, actual)
src = torch.randn(2, 3, 5)
idx = torch.LongTensor([[0, 2], [3, 4]])
check(src, idx)
check(src.transpose(1, 2), idx)
def test_take_empty(self):
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
for input_shape in [(0,), (0, 1, 2, 0), (1, 2, 3)]:
for indices_shape in [(0,), (0, 1, 2, 0)]:
input = torch.empty(input_shape, device=device)
indices = torch.empty(indices_shape, dtype=torch.int64, device=device)
self.assertEqual(indices, torch.take(input, indices))
def test_put_(self):
def check(dst, idx, value):
expected = dst.clone().view(-1).index_copy_(
0, idx.contiguous().view(-1), value.contiguous().view(-1))
expected = expected.view_as(dst)
dst.put_(idx, value)
self.assertEqual(expected, dst)
dst = torch.randn(2, 3, 5)
idx = torch.LongTensor([[0, 2], [3, 4]])
values = torch.randn(2, 2)
check(dst, idx, values)
check(dst.transpose(1, 2), idx, values)
def test_put_accumulate(self):
dst = torch.ones(2, 2)
idx = torch.LongTensor([[0, 1], [0, 1]])
src = torch.Tensor([1, 2, 3, 4])
dst.put_(idx, src, accumulate=True)
self.assertEqual(dst.tolist(), [[5, 7], [1, 1]])
def test_put_empty(self):
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
for dst_shape in [(0,), (0, 1, 2, 0), (1, 2, 3)]:
for indices_shape in [(0,), (0, 1, 2, 0)]:
for accumulate in [False, True]:
dst = torch.randn(dst_shape, device=device)
indices = torch.empty(indices_shape, dtype=torch.int64, device=device)
src = torch.randn(indices_shape, device=device)
self.assertEqual(dst, dst.put_(indices, src, accumulate=accumulate))
# Fill idx with valid indices.
@staticmethod
def _fill_indices(self, idx, dim, dim_size, elems_per_row, m, n, o):
for i in range(1 if dim == 0 else m):
for j in range(1 if dim == 1 else n):
for k in range(1 if dim == 2 else o):
ii = [i, j, k]
ii[dim] = slice(0, idx.size(dim) + 1)
idx[tuple(ii)] = torch.randperm(dim_size)[0:elems_per_row]
def test_flatten(self):
src = torch.randn(5, 5, 5, 5)
flat = src.flatten(0, -1)
self.assertEqual(flat.shape, torch.Size([625]))
self.assertEqual(src.view(-1), flat.view(-1))
flat = src.flatten(0, 2)
self.assertEqual(flat.shape, torch.Size([125, 5]))
self.assertEqual(src.view(-1), flat.view(-1))
flat = src.flatten(0, 1)
self.assertEqual(flat.shape, torch.Size([25, 5, 5]))
self.assertEqual(src.view(-1), flat.view(-1))
flat = src.flatten(1, 2)
self.assertEqual(flat.shape, torch.Size([5, 25, 5]))
self.assertEqual(src.view(-1), flat.view(-1))
flat = src.flatten(2, 3)
self.assertEqual(flat.shape, torch.Size([5, 5, 25]))
self.assertEqual(src.view(-1), flat.view(-1))
flat = src.flatten(-2, -1)
self.assertEqual(flat.shape, torch.Size([5, 5, 25]))
self.assertEqual(src.view(-1), flat.view(-1))
flat = src.flatten(2, 2)
self.assertEqual(flat, src)
# out of bounds index
with self.assertRaisesRegex(RuntimeError, 'Dimension out of range'):
src.flatten(5, 10)
# invalid start and end
with self.assertRaisesRegex(RuntimeError, 'start_dim cannot come after end_dim'):
src.flatten(2, 0)
@staticmethod
def _test_gather(self, cast, test_bounds=True):
m, n, o = random.randint(10, 20), random.randint(10, 20), random.randint(10, 20)
elems_per_row = random.randint(1, 10)
dim = random.randrange(3)
src = torch.randn(m, n, o)
idx_size = [m, n, o]
idx_size[dim] = elems_per_row
idx = torch.LongTensor().resize_(*idx_size)
_TestTorchMixin._fill_indices(self, idx, dim, src.size(dim), elems_per_row, m, n, o)
src = cast(src)
idx = cast(idx)
actual = torch.gather(src, dim, idx)
expected = cast(torch.Tensor().resize_(*idx_size))
for i in range(idx_size[0]):
for j in range(idx_size[1]):
for k in range(idx_size[2]):
ii = [i, j, k]
ii[dim] = idx[i, j, k]
expected[i, j, k] = src[tuple(ii)]
self.assertEqual(actual, expected, 0)
if test_bounds:
idx[0][0][0] = 23
self.assertRaises(RuntimeError, lambda: torch.gather(src, dim, idx))
src = cast(torch.randn(3, 4, 5))
expected, idx = src.max(2, True)
expected = cast(expected)
idx = cast(idx)
actual = torch.gather(src, 2, idx)
self.assertEqual(actual, expected, 0)
def test_gather(self):
self._test_gather(self, lambda t: t)
@staticmethod
def _test_scatter_base(self, cast, method, is_scalar=False, test_bounds=True):
m, n, o = random.randint(10, 20), random.randint(10, 20), random.randint(10, 20)
elems_per_row = random.randint(1, 10)
dim = random.randrange(3)
idx_size = [m, n, o]
idx_size[dim] = elems_per_row
idx = cast(torch.LongTensor().resize_(*idx_size))
_TestTorchMixin._fill_indices(self, idx, dim, ([m, n, o])[dim], elems_per_row, m, n, o)
if is_scalar:
src = random.random()
else:
src = cast(torch.Tensor(*idx_size).normal_())
base = cast(torch.randn(m, n, o))
actual = getattr(base.clone(), method)(dim, idx, src)
expected = base.clone()
for i in range(idx_size[0]):
for j in range(idx_size[1]):
for k in range(idx_size[2]):
ii = [i, j, k]
ii[dim] = idx[i, j, k]
if method == 'scatter_' and not is_scalar:
expected[tuple(ii)] = src[i, j, k]
elif method == 'scatter_add_':
expected[tuple(ii)] += src[i, j, k]
else:
expected[tuple(ii)] = src
self.assertEqual(actual, expected, 0)
if test_bounds:
idx[0][0][0] = 34
with self.assertRaises(RuntimeError):
getattr(base.clone(), method)(dim, idx, src)
# test for empty index, should be a no-op
idx = cast(torch.LongTensor())
actual = getattr(base.clone(), method)(dim, idx, src)
self.assertEqual(actual, base, 0)
def test_scatter(self):
self._test_scatter_base(self, lambda t: t, 'scatter_')
def test_scatterAdd(self):
self._test_scatter_base(self, lambda t: t, 'scatter_add_')
def test_scatterFill(self):
self._test_scatter_base(self, lambda t: t, 'scatter_', True)
def test_masked_scatter(self):
num_copy, num_dest = 3, 10
dest = torch.randn(num_dest)
src = torch.randn(num_copy)
mask = torch.ByteTensor((0, 0, 0, 0, 1, 0, 1, 0, 1, 0))
dest2 = dest.clone()
dest.masked_scatter_(mask, src)
j = 0
for i in range(num_dest):
if mask[i]:
dest2[i] = src[j]
j += 1
self.assertEqual(dest, dest2, 0)
# make source bigger than number of 1s in mask
src = torch.randn(num_dest)
dest.masked_scatter_(mask, src)
# make src smaller. this should fail
src = torch.randn(num_copy - 1)
with self.assertRaises(RuntimeError):
dest.masked_scatter_(mask, src)
def test_masked_select(self):
num_src = 10
src = torch.randn(num_src)
mask = torch.rand(num_src).clamp(0, 1).mul(2).floor().byte()
dst = src.masked_select(mask)
dst2 = []
for i in range(num_src):
if mask[i]:
dst2 += [src[i]]
self.assertEqual(dst, torch.Tensor(dst2), 0)
def test_masked_fill(self):
num_dest = 10
dst = torch.randn(num_dest)
mask = torch.rand(num_dest).mul(2).floor().byte()
val = random.random()
dst2 = dst.clone()
dst.masked_fill_(mask, val)
for i in range(num_dest):
if mask[i]:
dst2[i] = val
self.assertEqual(dst, dst2, 0)
# test non-contiguous case
dst = torch.randn(num_dest, num_dest, num_dest).permute((2, 0, 1))
dst2 = dst.clone()
dst.masked_fill_(dst > 0, val)
dst2.masked_fill_(dst2 > 0, val)
self.assertEqual(dst, dst2, 0)
def test_abs(self):
def _test_abs(tensors_dict):
for category, tensors in tensors_dict.items():
for data in tensors:
switch = torch.rand(data.size()).mul(2).floor().mul(2).add(-1).type(data.dtype)
res = torch.mul(data, switch)
self.assertTensorsSlowEqual(res.abs(), data, 1e-16)
max_val = 1000
_test_abs(self._make_tensors((3, 4), val_range=(0, max_val)))
_test_abs(self._make_tensors((3, 5, 7), val_range=(0, max_val)))
_test_abs(self._make_tensors((2, 2, 5, 8, 2, 3), val_range=(0, max_val)))
_test_abs(self._make_tensors((1000, ), val_range=(0, max_val)))
_test_abs(self._make_tensors((10, 10, 10), val_range=(0, max_val)))
# Checking that the right abs function is called for LongTensor
bignumber = 2 ^ 31 + 1
res = torch.LongTensor((-bignumber,))
self.assertGreater(res.abs()[0], 0)
# One of
rec = torch.randn(2, 2, 3, 7, 6, 2).type(torch.float64).clamp(0, 1)
val1 = rec.select(-1, -1).data[0][0][0].sum()
val2 = rec.select(-1, -1).data.abs()[0][0][0].sum()
self.assertEqual(val1, val2, 1e-8, 'absolute value')
def test_hardshrink(self):
data_original = torch.tensor([1, 0.5, 0.3, 0.6]).view(2, 2)
float_types = [
'torch.DoubleTensor',
'torch.FloatTensor'
]
for t in float_types:
data = data_original.type(t)
self.assertEqual(torch.tensor([1, 0.5, 0, 0.6]).view(2, 2), data.hardshrink(0.3))
self.assertEqual(torch.tensor([1, 0, 0, 0.6]).view(2, 2), data.hardshrink(0.5))
# test default lambd=0.5
self.assertEqual(data.hardshrink(), data.hardshrink(0.5))
# test non-contiguous case
self.assertEqual(torch.tensor([1, 0, 0.5, 0.6]).view(2, 2), data.t().hardshrink(0.3))
def test_unbiased(self):
tensor = torch.randn(100)
self.assertEqual(tensor.var(0), tensor.var(0, unbiased=True))
self.assertEqual(tensor.var(), tensor.var(unbiased=True))
self.assertEqual(tensor.var(unbiased=False), tensor.var(0, unbiased=False))
tensor = torch.FloatTensor([1.0, 2.0])
self.assertEqual(tensor.var(unbiased=True), 0.5)
self.assertEqual(tensor.var(unbiased=False), 0.25)
tensor = torch.FloatTensor([1.0, 2.0, 3.0])
self.assertEqual(tensor.var(unbiased=True), 1.0)
self.assertEqual(tensor.var(unbiased=False), 2.0 / 3.0)
tensor = torch.randn(100)
self.assertEqual(tensor.std(0), tensor.std(0, unbiased=True))
self.assertEqual(tensor.std(), tensor.std(unbiased=True))
self.assertEqual(tensor.std(unbiased=False), tensor.std(0, unbiased=False))
def test_var_stability(self):
tensor = torch.FloatTensor([2281.5, 2281.25])
self.assertEqual(tensor.var(dim=0), 0.03125)
self.assertEqual(tensor.var(), 0.03125)
@staticmethod
def _test_view(self, cast):
tensor = cast(torch.rand(15))
template = cast(torch.rand(3, 5))
empty = cast(torch.Tensor())
target = template.size()
self.assertEqual(tensor.view_as(template).size(), target)
self.assertEqual(tensor.view(3, 5).size(), target)
self.assertEqual(tensor.view(torch.Size([3, 5])).size(), target)
self.assertEqual(tensor.view(-1, 5).size(), target)
self.assertEqual(tensor.view(3, -1).size(), target)
tensor_view = tensor.view(5, 3)
tensor_view.fill_(random.uniform(0, 1))
self.assertEqual(empty.view_as(empty), empty)
self.assertEqual(empty.view(0), empty)
self.assertRaises(RuntimeError, lambda: tensor.view(15, 0))
self.assertRaises(RuntimeError, lambda: tensor.view(7, -1))
self.assertRaises(RuntimeError, lambda: tensor.view(15, -1, -1))
# test view when tensor is not contiguous in every dimension, but only
# contiguous dimensions are touched.
tensor = cast(torch.rand(4, 2, 5, 1, 6, 2, 9, 3)).transpose(-1, 2).transpose(-2, 3)
# size: [ 4, 2, 3, 9, 6, 2, 1, 5]
# stride: [3840, 1620, 1, 3, 54, 27, 324, 324]
# contiguous dim chunks: [__________, ____, ____, __________, ____, ____]
# merging 1 to chunk after: [__________, ____, ____, __________, __________]
contig_tensor = tensor.clone()
# [4, 2] => [8, 1]
# [3] => [3]
# [9] => [3, 3]
# [6, 2] => [4, 1, 3]
# [1, 5] => [5]
view_size = [8, 1, 3, 3, 3, 4, 1, 3, 5]
self.assertEqual(tensor.view(*view_size), contig_tensor.view(*view_size))
# [4, 2] => [2, 4]
# [3] => [3]
# [9] => [1, 9]
# [6, 2] => [2, 2, 3]
# [1, 5] => [5, 1]
view_size = [2, 4, 3, 1, 9, 2, 2, 3, 5, 1]
self.assertEqual(tensor.view(*view_size), contig_tensor.view(*view_size))
# adding size 1 dims
view_size = [1, 1, 2, 1, 4, 3, 1, 1, 9, 1, 2, 1, 2, 3, 1, 5, 1, 1]
self.assertEqual(tensor.view(*view_size), contig_tensor.view(*view_size))
# invalid views
self.assertRaises(RuntimeError, lambda: tensor.view(-1))
# crossing [4, 2], [3]
self.assertRaises(RuntimeError, lambda: tensor.view(24, 9, 6, 2, 1, 5))
# crossing [6, 2], [1, 5]
self.assertRaises(RuntimeError, lambda: tensor.view(8, 3, 9, 6, 10))
# crossing [9], [6, 2]
self.assertRaises(RuntimeError, lambda: tensor.view(8, 3, 54, 2, 1, 5))
# view with stride 0 dims
tensor = cast(torch.Tensor(1, 1)).expand(3, 4) # all dims are contiguous
contig_tensor = tensor.clone()
self.assertEqual(tensor.view(-1), contig_tensor.view(-1))
self.assertEqual(tensor.view(1, -1, 1), contig_tensor.view(1, -1, 1))
self.assertEqual(tensor.view(-1, 1), contig_tensor.view(-1, 1))
self.assertEqual(tensor.view(6, 2, 1), contig_tensor.view(6, 2, 1))
self.assertEqual(tensor.view(1, 6, 2, 1), contig_tensor.view(1, 6, 2, 1))
def test_view(self):
_TestTorchMixin._test_view(self, lambda x: x)
def test_view_empty(self):
x = torch.randn(0, 6)
self.assertEqual((1, 0, 6, 1, 1), x.view(1, 0, 6, 1, 1).shape)
def test_reshape(self):
x = torch.randn(3, 3)
self.assertEqual(x.data_ptr(), x.reshape(-1).data_ptr())
self.assertEqual(x.data_ptr(), x.reshape(1, 9, 1).data_ptr())
self.assertEqual(torch.reshape(x, (9,)), x.reshape(9))
self.assertRaises(RuntimeError, lambda: x.reshape(-1, -1))
y = torch.randn(4, 4, 4)[:, 0, :]
self.assertNotEqual(y.data_ptr(), y.reshape(-1).data_ptr())
self.assertEqual(y.contiguous().view(-1), y.reshape(-1))
self.assertEqual(y.reshape(2, 2, 4).data_ptr(), y.data_ptr())
s = torch.randn(())
self.assertEqual(s.data_ptr(), s.reshape(()).data_ptr())
self.assertEqual(s.reshape(-1).shape, (1,))
self.assertRaises(RuntimeError, lambda: s.reshape(2))
empty = torch.tensor([])
self.assertEqual(empty, empty.reshape(-1))
self.assertEqual(empty, empty.reshape([0]))
# TODO: fix these once we have multi-dimensional empty tensors
self.assertEqual(empty.reshape([0, 1]).shape, (0, 1))
self.assertEqual(empty.reshape([1, -1]).shape, (1, 0))
self.assertRaises(RuntimeError, lambda: empty.reshape(1))
x = torch.randn(3, 3)
self.assertEqual(x.data_ptr(), x.reshape_as(torch.rand(9)).data_ptr())
self.assertEqual(x.data_ptr(), x.reshape_as(torch.rand(1, 9, 1)).data_ptr())
self.assertRaises(RuntimeError, lambda: x.reshape_as(torch.rand(10)))
def test_empty_reshape(self):
x = torch.randn(0, 6)
self.assertEqual((1, 0, 6, 1, 1), x.reshape(1, 0, 6, 1, 1).shape)
# should be viewable -- i.e. data_ptr is the same.
self.assertEqual(x.data_ptr(), x.reshape(1, 0, 6, 1, 1).data_ptr())
# match NumPy semantics -- don't infer the size of dimension with a degree of freedom
self.assertRaises(RuntimeError, lambda: x.reshape(0, -1))
@skipIfRocm
def test_tensor_shape_empty(self):
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
x = torch.randn((0, 1, 3, 0), device=device)
# flatten
self.assertEqual((0,), torch.flatten(x, 0, 3).shape)
self.assertEqual((0, 0), torch.flatten(x, 0, 2).shape)
self.assertEqual((0, 3, 0), torch.flatten(x, 1, 2).shape)
# squeeze, unsqueeze
self.assertEqual((0, 1, 1, 3, 0), torch.unsqueeze(x, 1).shape)
self.assertEqual((0, 3, 0), torch.squeeze(x, 1).shape)
self.assertEqual((0, 3, 0), torch.squeeze(x).shape)
# transpose, t
self.assertEqual((0, 0, 3, 1), torch.transpose(x, 1, 3).shape)
y = torch.randn((5, 0), device=device)
self.assertEqual((0, 5), y.t().shape)
# select
self.assertEqual((0, 1, 0), torch.select(x, 2, 2).shape)
# unfold
self.assertEqual((0, 1, 1, 0, 3), x.unfold(2, 3, 2).shape)
y = torch.randn((0, 1, 3), device=device)
self.assertEqual((1, 1, 3, 0), y.unfold(0, 0, 4).shape)
# repeat, permute
self.assertEqual((9, 0, 5, 6, 0), x.repeat(9, 7, 5, 2, 3).shape)
self.assertEqual((3, 0, 0, 1), x.permute(2, 3, 0, 1).shape)
# diagonal, diagflat
self.assertEqual((0,), torch.diagonal(torch.randn((5, 0), device=device)).shape)
self.assertEqual((0,), torch.diagonal(torch.randn((0, 5), device=device)).shape)
# off the end offsets are valid
self.assertEqual((0,), torch.diagonal(torch.randn((5, 0), device=device), offset=1).shape)
self.assertEqual((0,), torch.diagonal(torch.randn((0, 5), device=device), offset=1).shape)
# check non-zero sized offsets off the end
self.assertEqual((5, 6, 0), torch.diagonal(torch.randn((3, 4, 5, 6), device=device), offset=45252).shape)
self.assertEqual((5, 6, 0), torch.diagonal(torch.randn((3, 4, 5, 6), device=device), offset=-45252).shape)
self.assertEqual((0, 0), torch.diagflat(torch.tensor([], device=device)).shape)
self.assertEqual(torch.zeros(1, 1), torch.diagflat(torch.tensor([], device=device), offset=1))
self.assertEqual((0, 0), torch.diagflat(torch.tensor([[]], device=device)).shape)
self.assertEqual(torch.zeros(1, 1), torch.diagflat(torch.tensor([[]], device=device), offset=1))
# stack, split, chunk
self.assertEqual((4, 0, 1, 3, 0), torch.stack((x, x, x, x)).shape)
self.assertEqual([(0, 1, 3, 0)],
[z.shape for z in torch.chunk(x, 1, dim=0)])
self.assertEqual([(0, 1, 3, 0), ] * 3, [z.shape for z in torch.chunk(x, 3, dim=0)])
self.assertEqual([(0, 1, 1, 0), ] * 3, [z.shape for z in torch.chunk(x, 3, dim=2)])
# NOTE: split_with_sizes behaves differently than NumPy in that it
# takes sizes rather than offsets
self.assertEqual([(0, 1, 0, 0), (0, 1, 1, 0), (0, 1, 2, 0)],
[z.shape for z in torch.split(x, (0, 1, 2), dim=2)])
self.assertRaises(RuntimeError, lambda: torch.split(x, 0, dim=1))
# This is strange because the split size is larger than the dim size, but consistent with
# how split handles that case generally (when no 0s are involved).
self.assertEqual([(0, 1, 3, 0)], [z.shape for z in torch.split(x, 1, dim=0)])
self.assertEqual([(0, 1, 3, 0)], [z.shape for z in torch.split(x, 0, dim=0)])
# functions that operate over a dimension but don't reduce.
@skipIfRocm
def test_dim_function_empty(self):
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
shape = (0, 1, 2, 0)
x = torch.randn(shape, device=device)
# size stride
self.assertEqual(0, x.size(3))
self.assertEqual(2, x.size(2))
self.assertEqual(2, x.stride(0))
self.assertEqual(1, x.stride(2))
self.assertEqual(x, torch.nn.functional.glu(x, 0))
self.assertEqual((0, 1, 1, 0), torch.nn.functional.glu(x, 2).shape)
# softmax, logsoftmax
self.assertEqual(x, torch.nn.functional.softmax(x, 0))
self.assertEqual(x, torch.nn.functional.softmax(x, 2))
self.assertEqual(x, torch.nn.functional.log_softmax(x, 0))
self.assertEqual(x, torch.nn.functional.log_softmax(x, 2))
# cumsum, cumprod
self.assertEqual(shape, torch.cumsum(x, 0).shape)
self.assertEqual(shape, torch.cumsum(x, 2).shape)
self.assertEqual(shape, torch.cumprod(x, 0).shape)
self.assertEqual(shape, torch.cumprod(x, 2).shape)
# flip
self.assertEqual(x, x.flip(0))
self.assertEqual(x, x.flip(2))
# roll
self.assertEqual(x, x.roll(0, 1).roll(0, -1))
self.assertEqual(x, x.roll(1, x.size(1)))
self.assertEqual(x, x.roll(1))
self.assertEqual(x, x.roll((1, 1), (3, 1)))
# unbind
self.assertEqual((), x.unbind(0))
self.assertEqual((torch.empty((0, 1, 0), device=device), torch.empty((0, 1, 0), device=device)),
x.unbind(2))
# cross
y = torch.randn((0, 1, 3, 0), device=device)
self.assertEqual(y.shape, torch.cross(y, y).shape)
# renorm
self.assertEqual(shape, torch.renorm(x, 1, 0, 5).shape)
self.assertEqual(shape, torch.renorm(x, 1, 2, 5).shape)
# sort
self.assertEqual([shape, shape], [z.shape for z in torch.sort(x, dim=0)])
self.assertEqual([shape, shape], [z.shape for z in torch.sort(x, dim=2)])
# topk
self.assertEqual([shape, shape], [z.shape for z in torch.topk(x, 0, dim=0)])
self.assertEqual([(0, 1, 1, 0), (0, 1, 1, 0)], [z.shape for z in torch.topk(x, 1, dim=2)])
y = torch.randn((2, 3, 4), device=device)
self.assertEqual([(2, 3, 0), (2, 3, 0)], [z.shape for z in torch.topk(y, 0)])
# gather
self.assertEqual(shape, torch.gather(x, 0, torch.empty(shape, dtype=torch.int64, device=device)).shape)
self.assertEqual(shape, torch.gather(x, 2, torch.empty(shape, dtype=torch.int64, device=device)).shape)
larger_shape = torch.empty((0, 1, 3, 0), dtype=torch.int64, device=device)
self.assertEqual(larger_shape.shape, torch.gather(x, 2, larger_shape).shape)
smaller_shape = torch.empty((0, 1, 0, 0), dtype=torch.int64, device=device)
self.assertEqual(smaller_shape.shape, torch.gather(x, 2, smaller_shape).shape)
y = torch.randn((2, 3, 4), device=device)
self.assertEqual((0, 3, 4),
torch.gather(y, 0, torch.empty((0, 3, 4), dtype=torch.int64, device=device)).shape)
# scatter, scatter_add
for dim in [0, 2]:
y = torch.randn(shape, device=device)
y_src = torch.randn(shape, device=device)
ind = torch.empty(shape, dtype=torch.int64, device=device)
self.assertEqual(shape, y.scatter_(dim, ind, y_src).shape)
self.assertEqual(shape, y.scatter_add_(dim, ind, y_src).shape)
z = torch.randn((2, 3, 4), device=device)
z_src = torch.randn((2, 3, 4), device=device)
self.assertEqual(z, z.scatter_(2, torch.empty((2, 3, 0), dtype=torch.int64, device=device), z_src))
self.assertEqual(z, z.scatter_add_(2, torch.empty((2, 3, 0), dtype=torch.int64, device=device), z_src))
# index_fill, index_copy, index_add
c = x.clone()
c_clone = c.clone()
ind_empty = torch.tensor([], dtype=torch.int64, device=device)
ind_01 = torch.tensor([0, 1], dtype=torch.int64, device=device)
self.assertEqual(c_clone, c.index_fill_(0, ind_empty, -1))
self.assertEqual(c_clone, c.index_fill_(2, ind_empty, -1))
self.assertEqual(c_clone, c.index_fill_(2, torch.tensor([0, 1], dtype=torch.int64, device=device), -1))
self.assertEqual(c_clone, c.index_copy_(0, ind_empty, torch.empty((0, 1, 2, 0), device=device)))
self.assertEqual(c_clone, c.index_copy_(2, ind_empty, torch.empty((0, 1, 0, 0), device=device)))
self.assertEqual(c_clone, c.index_copy_(2, ind_01, torch.empty((0, 1, 2, 0), device=device)))
self.assertEqual(c_clone, c.index_add_(0, ind_empty, torch.empty((0, 1, 2, 0), device=device)))
self.assertEqual(c_clone, c.index_add_(2, ind_empty, torch.empty((0, 1, 0, 0), device=device)))
self.assertEqual(c_clone, c.index_add_(2, ind_01, torch.empty((0, 1, 2, 0), device=device)))
c = torch.randn((0, 1, 2), device=device)
c_clone = c.clone()
self.assertEqual(c_clone, c.index_fill_(0, ind_empty, -1))
self.assertEqual(c_clone, c.index_copy_(0, ind_empty, torch.empty((0, 1, 2), device=device)))
self.assertEqual(c_clone, c.index_add_(0, ind_empty, torch.empty((0, 1, 2), device=device)))
self.assertEqual(c_clone, c.index_fill_(0, ind_empty, -1))
self.assertEqual(c_clone, c.index_copy_(0, ind_empty, torch.empty((0, 1, 2), device=device)))
self.assertEqual(c_clone, c.index_add_(0, ind_empty, torch.empty((0, 1, 2), device=device)))
# index fill/copy/add non-empty
z = torch.randn((2, 3, 4), device=device)
self.assertEqual(z, z.index_fill_(0, ind_empty, -1))
z = torch.randn((2, 3, 4), device=device)
self.assertEqual(z, z.index_copy_(0, ind_empty, torch.empty((0, 3, 4), device=device)))
z = torch.randn((2, 3, 4), device=device)
self.assertEqual(z, z.index_add_(0, ind_empty, torch.empty((0, 3, 4), device=device)))
# index_select
self.assertEqual(x, x.index_select(0, ind_empty))
self.assertEqual((0, 1, 0, 0), x.index_select(2, ind_empty).shape)
self.assertEqual(x, x.index_select(2, ind_01))
z = torch.randn((2, 3, 4), device=device) # non-empty
self.assertEqual((0, 3, 4), z.index_select(0, ind_empty).shape)
c = torch.randn((0, 1, 2), device=device)
self.assertEqual(c, c.index_select(0, ind_empty))
c = torch.randn((0, 1, 2), device=device)
self.assertEqual(c, c.index_select(0, ind_empty))
@skipIfRocm
def test_blas_empty(self):
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
def fn(torchfn, *args):
return torchfn(*tuple(torch.randn(shape, device=device) if isinstance(shape, tuple) else shape
for shape in args))
# mm, addmm
self.assertEqual((0, 0), fn(torch.mm, (0, 0), (0, 0)).shape)
self.assertEqual((0, 5), fn(torch.mm, (0, 0), (0, 5)).shape)
self.assertEqual((5, 0), fn(torch.mm, (5, 0), (0, 0)).shape)
self.assertEqual((3, 0), fn(torch.mm, (3, 2), (2, 0)).shape)
self.assertEqual(torch.zeros((5, 6), device=device), fn(torch.mm, (5, 0), (0, 6)))
self.assertEqual((0, 0), fn(torch.addmm, (0, 0), (0, 0), (0, 0)).shape)
self.assertEqual((5, 6), fn(torch.addmm, (5, 6), (5, 0), (0, 6)).shape)
# mv, addmv
self.assertEqual((0,), fn(torch.mv, (0, 0), (0,)).shape)
self.assertEqual((0,), fn(torch.mv, (0, 2), (2,)).shape)
self.assertEqual(torch.zeros((3,), device=device), fn(torch.mv, (3, 0), (0,)))
self.assertEqual((0,), fn(torch.addmv, (0,), (0, 0), (0,)).shape)
self.assertEqual((3,), fn(torch.addmv, (3,), (3, 0), (0,)).shape)
# ger, addr
self.assertEqual((0, 0), fn(torch.ger, (0,), (0,)).shape)
self.assertEqual((5, 0), fn(torch.ger, (5,), (0,)).shape)
self.assertEqual((0, 4), fn(torch.ger, (0,), (4,)).shape)
self.assertEqual((0, 0), fn(torch.addr, (0, 0), (0,), (0,)).shape)
self.assertEqual((5, 0), fn(torch.addr, (5, 0), (5,), (0,)).shape)
self.assertEqual((0, 4), fn(torch.addr, (0, 4), (0,), (4,)).shape)
# bmm, baddbmm
self.assertEqual((0, 0, 0), fn(torch.bmm, (0, 0, 0), (0, 0, 0)).shape)
self.assertEqual((3, 0, 5), fn(torch.bmm, (3, 0, 0), (3, 0, 5)).shape)
self.assertEqual((0, 5, 6), fn(torch.bmm, (0, 5, 0), (0, 0, 6)).shape)
self.assertEqual(torch.zeros((3, 5, 6), device=device), fn(torch.bmm, (3, 5, 0), (3, 0, 6)))
self.assertEqual((0, 0, 0), fn(torch.baddbmm, (0, 0, 0), (0, 0, 0), (0, 0, 0)).shape)
self.assertEqual((3, 0, 5), fn(torch.baddbmm, (3, 0, 5), (3, 0, 0), (3, 0, 5)).shape)
self.assertEqual((0, 5, 6), fn(torch.baddbmm, (0, 5, 6), (0, 5, 0), (0, 0, 6)).shape)
self.assertEqual((3, 5, 6), fn(torch.baddbmm, (3, 5, 6), (3, 5, 0), (3, 0, 6)).shape)
# addbmm
self.assertEqual((0, 0), fn(torch.addbmm, (0, 0), (0, 0, 0), (0, 0, 0)).shape)
self.assertEqual((0, 5), fn(torch.addbmm, (0, 5), (3, 0, 0), (3, 0, 5)).shape)
self.assertEqual((5, 6), fn(torch.addbmm, (5, 6), (0, 5, 0), (0, 0, 6)).shape)
# matmul
self.assertEqual(torch.tensor(0., device=device), fn(torch.matmul, (0,), (0,)))
self.assertEqual((0, 0), fn(torch.matmul, (0, 0), (0, 0)).shape)
self.assertEqual((0, 0, 0), fn(torch.matmul, (0, 0, 0), (0, 0, 0)).shape)
self.assertEqual((5, 0, 0), fn(torch.matmul, (5, 0, 0), (5, 0, 0)).shape)
self.assertEqual(torch.zeros((5, 3, 4), device=device), fn(torch.matmul, (5, 3, 0), (5, 0, 4)))
# dot
self.assertEqual(torch.tensor(0., device=device), fn(torch.dot, (0,), (0,)))
if torch._C.has_lapack:
# btrifact
A_LU, pivots = fn(torch.btrifact, (0, 5, 5))
self.assertEqual([(0, 5, 5), (0, 5)], [A_LU.shape, pivots.shape])
A_LU, pivots = fn(torch.btrifact, (0, 0, 0))
self.assertEqual([(0, 0, 0), (0, 0)], [A_LU.shape, pivots.shape])
A_LU, pivots = fn(torch.btrifact, (2, 0, 0))
self.assertEqual([(2, 0, 0), (2, 0)], [A_LU.shape, pivots.shape])
@skipIfRocm
def test_blas_alpha_beta_empty(self):
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
# ensure beta is respected
value = 11
input = torch.full((2,), value, device=device)
mat = torch.ones((2, 0), device=device)
vec = torch.ones((0,), device=device)
out = torch.randn((2,), device=device)
alpha = 6
beta = 3
self.assertEqual(torch.full((2,), beta * value, device=device),
torch.addmv(input=input, mat=mat, vec=vec, alpha=alpha, beta=beta))
self.assertEqual(torch.full((2,), beta * value, device=device),
torch.addmv(input=input, mat=mat, vec=vec, alpha=alpha, beta=beta, out=out))
# torch.addmm
input = torch.full((2, 3), value, device=device)
mat2 = torch.ones((0, 3), device=device)
out = torch.randn((2, 3), device=device)
self.assertEqual(torch.full((2, 3), beta * value, device=device),
torch.addmm(input=input, mat1=mat, mat2=mat2, alpha=alpha, beta=beta))
self.assertEqual(torch.full((2, 3), beta * value, device=device),
torch.addmm(input=input, mat1=mat, mat2=mat2, alpha=alpha, beta=beta, out=out))
@skipIfNoLapack
def test_lapack_empty(self):
# FIXME: these are just a selection of LAPACK functions -- we need a general strategy here.
# The LAPACK functions themselves generally do NOT work with zero sized dimensions, although
# numpy/sci often has a direct wrapper (e.g. lu_factor) and a wrapper that "does the right thing"
# (e.g. lu). We often name our functions identically to the lapack function, so it will take work
# to name / migrate-to better wrappers.
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
# need to init cuda to check has_magma
empty = torch.randn((0, 0), device=device)
if device == 'cuda' and not torch.cuda.has_magma:
continue
def fn(torchfn, *args):
return torchfn(*tuple(torch.randn(shape, device=device) if isinstance(shape, tuple) else shape
for shape in args))
# inverse, pinverse
self.assertEqual((0, 0), fn(torch.inverse, (0, 0)).shape)
self.assertEqual((5, 0), fn(torch.pinverse, (0, 5)).shape)
self.assertEqual((0, 5), fn(torch.pinverse, (5, 0)).shape)
self.assertEqual((0, 0), fn(torch.pinverse, (0, 0)).shape)
# svd
self.assertRaises(RuntimeError, lambda: fn(torch.svd, (0, 0)))
# det, logdet, slogdet
self.assertEqual(torch.tensor(1., device=device), fn(torch.det, (0, 0)))
self.assertEqual(torch.tensor(0., device=device), fn(torch.logdet, (0, 0)))
self.assertEqual((torch.tensor(1., device=device), torch.tensor(0., device=device)),
fn(torch.slogdet, (0, 0)))
# eig, symeig
evalues, evectors = fn(torch.eig, (0, 0), True)
self.assertEqual([(0, 2), (0, 0)], [evalues.shape, evectors.shape])
evalues, evectors = fn(torch.symeig, (0, 0), True)
self.assertEqual([(0,), (0, 0)], [evalues.shape, evectors.shape])
# qr, gels
self.assertRaises(RuntimeError, lambda: torch.qr(torch.randn(0, 0)))
self.assertRaises(RuntimeError, lambda: torch.gels(torch.randn(0, 0), torch.randn(0, 0)))
self.assertRaises(RuntimeError, lambda: torch.gels(torch.randn(0,), torch.randn(0, 0)))
def test_expand(self):
tensor = torch.rand(1, 8, 1)
tensor2 = torch.rand(5)
template = torch.rand(4, 8, 5)
target = template.size()
self.assertEqual(tensor.expand_as(template).size(), target)
self.assertEqual(tensor.expand(4, 8, 5).size(), target)
self.assertEqual(tensor.expand(target).size(), target)
self.assertEqual(tensor2.expand_as(template).size(), target)
self.assertEqual(tensor2.expand(4, 8, 5).size(), target)
self.assertEqual(tensor2.expand(target).size(), target)
# test double expand
self.assertEqual(tensor2.expand(1, 5).expand(2, 2, 5), tensor2.repeat(2, 2, 1))
# test non-contiguous
noncontig = torch.randn(5, 2, 1, 3)[:, 0]
self.assertFalse(noncontig.is_contiguous())
self.assertEqual(noncontig.expand(2, 5, 4, 3), noncontig.contiguous().repeat(2, 1, 4, 1))
# make sure it's compatible with unsqueeze
expanded = tensor2.expand(1, 1, 5)
unsqueezed = tensor2.unsqueeze(0).unsqueeze(1)
self.assertEqual(expanded, unsqueezed)
self.assertEqual(expanded.stride(), unsqueezed.stride())
# test -1 as target size
self.assertEqual(tensor.expand(4, -1, 5), tensor.expand(4, 8, 5))
self.assertRaises(RuntimeError, lambda: tensor2.expand(-1, -1))
# test expanding empty to empty
self.assertEqual(torch.zeros(0).expand((0,)), torch.zeros(0))
def test_repeat(self):
initial_shape = (8, 4)
tensor = torch.rand(*initial_shape)
size = (3, 1, 1)
torchSize = torch.Size(size)
target = [3, 8, 4]
self.assertEqual(tensor.repeat(*size).size(), target, 'Error in repeat')
self.assertEqual(tensor.repeat(torchSize).size(), target,
'Error in repeat using LongStorage')
result = tensor.repeat(*size)
self.assertEqual(result.size(), target, 'Error in repeat using result')
result = tensor.repeat(torchSize)
self.assertEqual(result.size(), target, 'Error in repeat using result and LongStorage')
self.assertEqual(result.mean(0).view(8, 4), tensor, 'Error in repeat (not equal)')
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_repeat_tile(self):
initial_shape = (8, 4)
repeats = ((3, 1, 1),
(3, 3, 3),
(1, 2, 1),
(2, 2, 2, 2))
def _generate_noncontiguous_input():
out = np.broadcast_to(np.random.random((1, 4)),
initial_shape)
assert not (out.flags.c_contiguous or out.flags.f_contiguous)
return out
for repeat in repeats:
for tensor in (torch.from_numpy(np.random.random(initial_shape)),
torch.from_numpy(_generate_noncontiguous_input()),):
self.assertEqual(tensor.repeat(*repeat).numpy(),
np.tile(tensor.numpy(), repeat))
def test_is_same_size(self):
t1 = torch.Tensor(3, 4, 9, 10)
t2 = torch.Tensor(3, 4)
t3 = torch.Tensor(1, 9, 3, 3)
t4 = torch.Tensor(3, 4, 9, 10)
self.assertFalse(t1.is_same_size(t2))
self.assertFalse(t1.is_same_size(t3))
self.assertTrue(t1.is_same_size(t4))
def test_is_set_to(self):
t1 = torch.Tensor(3, 4, 9, 10)
t2 = torch.Tensor(3, 4, 9, 10)
t3 = torch.Tensor().set_(t1)
t4 = t3.clone().resize_(12, 90)
self.assertFalse(t1.is_set_to(t2))
self.assertTrue(t1.is_set_to(t3))
self.assertTrue(t3.is_set_to(t1), "is_set_to should be symmetric")
self.assertFalse(t1.is_set_to(t4))
self.assertFalse(torch.Tensor().is_set_to(torch.Tensor()),
"Tensors with no storages should not appear to be set "
"to each other")
def test_tensor_set(self):
t1 = torch.Tensor()
t2 = torch.Tensor(3, 4, 9, 10).uniform_()
t1.set_(t2)
self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
size = torch.Size([9, 3, 4, 10])
t1.set_(t2.storage(), 0, size)
self.assertEqual(t1.size(), size)
t1.set_(t2.storage(), 0, tuple(size))
self.assertEqual(t1.size(), size)
self.assertEqual(t1.stride(), (120, 40, 10, 1))
stride = (10, 360, 90, 1)
t1.set_(t2.storage(), 0, size, stride)
self.assertEqual(t1.stride(), stride)
t1.set_(t2.storage(), 0, size=size, stride=stride)
self.assertEqual(t1.size(), size)
self.assertEqual(t1.stride(), stride)
# test argument names
t1 = torch.Tensor()
# 1. case when source is tensor
t1.set_(source=t2)
self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
# 2. case when source is storage
t1.set_(source=t2.storage())
self.assertEqual(t1.storage()._cdata, t2.storage()._cdata)
# 3. case when source is storage, and other args also specified
t1.set_(source=t2.storage(), storage_offset=0, size=size, stride=stride)
self.assertEqual(t1.size(), size)
self.assertEqual(t1.stride(), stride)
def test_equal(self):
# Contiguous, 1D
t1 = torch.Tensor((3, 4, 9, 10))
t2 = t1.contiguous()
t3 = torch.Tensor((1, 9, 3, 10))
t4 = torch.Tensor((3, 4, 9))
t5 = torch.Tensor()
self.assertTrue(t1.equal(t2))
self.assertFalse(t1.equal(t3))
self.assertFalse(t1.equal(t4))
self.assertFalse(t1.equal(t5))
self.assertTrue(torch.equal(t1, t2))
self.assertFalse(torch.equal(t1, t3))
self.assertFalse(torch.equal(t1, t4))
self.assertFalse(torch.equal(t1, t5))
# Non contiguous, 2D
s = torch.Tensor(((1, 2, 3, 4), (5, 6, 7, 8)))
s1 = s[:, 1:3]
s2 = s1.clone()
s3 = torch.Tensor(((2, 3), (6, 7)))
s4 = torch.Tensor(((0, 0), (0, 0)))
self.assertFalse(s1.is_contiguous())
self.assertTrue(s1.equal(s2))
self.assertTrue(s1.equal(s3))
self.assertFalse(s1.equal(s4))
self.assertTrue(torch.equal(s1, s2))
self.assertTrue(torch.equal(s1, s3))
self.assertFalse(torch.equal(s1, s4))
def test_element_size(self):
byte = torch.ByteStorage().element_size()
char = torch.CharStorage().element_size()
short = torch.ShortStorage().element_size()
int = torch.IntStorage().element_size()
long = torch.LongStorage().element_size()
float = torch.FloatStorage().element_size()
double = torch.DoubleStorage().element_size()
self.assertEqual(byte, torch.ByteTensor().element_size())
self.assertEqual(char, torch.CharTensor().element_size())
self.assertEqual(short, torch.ShortTensor().element_size())
self.assertEqual(int, torch.IntTensor().element_size())
self.assertEqual(long, torch.LongTensor().element_size())
self.assertEqual(float, torch.FloatTensor().element_size())
self.assertEqual(double, torch.DoubleTensor().element_size())
self.assertGreater(byte, 0)
self.assertGreater(char, 0)
self.assertGreater(short, 0)
self.assertGreater(int, 0)
self.assertGreater(long, 0)
self.assertGreater(float, 0)
self.assertGreater(double, 0)
# These tests are portable, not necessarily strict for your system.
self.assertEqual(byte, 1)
self.assertEqual(char, 1)
self.assertGreaterEqual(short, 2)
self.assertGreaterEqual(int, 2)
self.assertGreaterEqual(int, short)
self.assertGreaterEqual(long, 4)
self.assertGreaterEqual(long, int)
self.assertGreaterEqual(double, float)
def test_split(self):
tensor = torch.rand(7, 4)
split_size = 3
dim = 0
target_sizes = ([3, 4], [3, 4], [1, 4])
splits = tensor.split(split_size, dim)
start = 0
for target_size, split in zip(target_sizes, splits):
self.assertEqual(split.size(), target_size)
self.assertEqual(tensor.narrow(dim, start, target_size[dim]), split, 0)
start = start + target_size[dim]
# Variable sections split
tensor = torch.randn(20, 10)
dim = 0
split_sizes = [5, 5, 10]
target_sizes = ([[5, 10], [5, 10], [10, 10]])
splits = tensor.split(split_sizes, dim)
start = 0
for target_size, split in zip(target_sizes, splits):
self.assertEqual(split.size(), target_size)
self.assertEqual(tensor.narrow(dim, start, target_size[dim]), split, 0)
start = start + target_size[dim]
split_sizes = [2, 2, 6]
target_sizes = ([20, 2], [20, 2], [20, 6])
dim = 1
splits = tensor.split(split_sizes, dim)
start = 0
for target_size, split in zip(target_sizes, splits):
self.assertEqual(split.size(), target_size)
self.assertEqual(tensor.narrow(dim, start, target_size[dim]), split, 0)
start = start + target_size[dim]
def test_chunk(self):
tensor = torch.rand(4, 7)
num_chunks = 3
dim = 1
target_sizes = ([4, 3], [4, 3], [4, 1])
splits = tensor.chunk(num_chunks, dim)
start = 0
for target_size, split in zip(target_sizes, splits):
self.assertEqual(split.size(), target_size)
self.assertEqual(tensor.narrow(dim, start, target_size[dim]), split, 0)
start = start + target_size[dim]
# Invalid chunk sizes
error_regex = 'chunk expects.*greater than 0'
with self.assertRaisesRegex(RuntimeError, error_regex):
tensor.chunk(0)
with self.assertRaisesRegex(RuntimeError, error_regex):
tensor.chunk(-2)
def test_tolist(self):
list0D = []
tensor0D = torch.Tensor(list0D)
self.assertEqual(tensor0D.tolist(), list0D)
table1D = [1, 2, 3]
tensor1D = torch.Tensor(table1D)
storage = torch.Storage(table1D)
self.assertEqual(tensor1D.tolist(), table1D)
self.assertEqual(storage.tolist(), table1D)
self.assertEqual(tensor1D.tolist(), table1D)
self.assertEqual(storage.tolist(), table1D)
table2D = [[1, 2], [3, 4]]
tensor2D = torch.Tensor(table2D)
self.assertEqual(tensor2D.tolist(), table2D)
tensor3D = torch.Tensor([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
tensorNonContig = tensor3D.select(1, 1)
self.assertFalse(tensorNonContig.is_contiguous())
self.assertEqual(tensorNonContig.tolist(), [[3, 4], [7, 8]])
def test_permute(self):
orig = [1, 2, 3, 4, 5, 6, 7]
perm = torch.randperm(7).tolist()
x = torch.Tensor(*orig).fill_(0)
new = list(map(lambda x: x - 1, x.permute(*perm).size()))
self.assertEqual(perm, new)
self.assertEqual(x.size(), orig)
@staticmethod
def _test_flip(self, use_cuda=False):
device = torch.device('cuda') if use_cuda else torch.device('cpu')
data = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8], device=device).view(2, 2, 2)
self.assertEqual(torch.tensor([5, 6, 7, 8, 1, 2, 3, 4]).view(2, 2, 2), data.flip(0))
self.assertEqual(torch.tensor([3, 4, 1, 2, 7, 8, 5, 6]).view(2, 2, 2), data.flip(1))
self.assertEqual(torch.tensor([2, 1, 4, 3, 6, 5, 8, 7]).view(2, 2, 2), data.flip(2))
self.assertEqual(torch.tensor([7, 8, 5, 6, 3, 4, 1, 2]).view(2, 2, 2), data.flip(0, 1))
self.assertEqual(torch.tensor([8, 7, 6, 5, 4, 3, 2, 1]).view(2, 2, 2), data.flip(0, 1, 2))
# check for wrap dim
self.assertEqual(torch.tensor([2, 1, 4, 3, 6, 5, 8, 7]).view(2, 2, 2), data.flip(-1))
# check for permute
self.assertEqual(torch.tensor([6, 5, 8, 7, 2, 1, 4, 3]).view(2, 2, 2), data.flip(0, 2))
self.assertEqual(torch.tensor([6, 5, 8, 7, 2, 1, 4, 3]).view(2, 2, 2), data.flip(2, 0))
# not allow flip on the same dim more than once
self.assertRaises(RuntimeError, lambda: data.flip(0, 1, 1))
# not allow empty list as input
self.assertRaises(TypeError, lambda: data.flip())
# not allow size of flip dim > total dims
self.assertRaises(RuntimeError, lambda: data.flip(0, 1, 2, 3))
# not allow dim > max dim
self.assertRaises(RuntimeError, lambda: data.flip(3))
# test for non-contiguous case
expanded_data = torch.arange(1, 4, device=device).view(3, 1).expand(3, 2)
tranposed_data = torch.arange(1, 9, device=device).view(2, 2, 2).transpose(0, 1)
self.assertEqual(torch.tensor([3, 3, 2, 2, 1, 1]).view(3, 2), expanded_data.flip(0))
self.assertEqual(torch.tensor([8, 7, 4, 3, 6, 5, 2, 1]).view(2, 2, 2), tranposed_data.flip(0, 1, 2))
# test for shape
data = torch.randn(2, 3, 4, device=device)
size = [2, 3, 4]
test_dims = []
for i in range(1, 3):
test_dims += combinations(range(len(size)), i)
for ds in test_dims:
self.assertEqual(size, list(data.flip(ds).size()))
# test rectangular case
data = torch.tensor([1, 2, 3, 4, 5, 6]).view(2, 3)
flip0_result = torch.tensor([[4, 5, 6], [1, 2, 3]])
flip1_result = torch.tensor([[3, 2, 1], [6, 5, 4]])
if use_cuda:
data = data.cuda()
flip0_result = flip0_result.cuda()
flip1_result = flip1_result.cuda()
self.assertEqual(flip0_result, data.flip(0))
self.assertEqual(flip1_result, data.flip(1))
# test empty tensor, should just return an empty tensor of the same shape
data = torch.tensor([])
self.assertEqual(data, data.flip(0))
def test_flip(self):
self._test_flip(self, use_cuda=False)
def test_roll(self):
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
numbers = torch.arange(1, 9, device=device)
single_roll = numbers.roll(1, 0)
expected = torch.tensor([8, 1, 2, 3, 4, 5, 6, 7], device=device)
self.assertEqual(single_roll, expected, "{} did not equal expected result".format(single_roll))
roll_backwards = numbers.roll(-2, 0)
expected = torch.tensor([3, 4, 5, 6, 7, 8, 1, 2], device=device)
self.assertEqual(roll_backwards, expected, "{} did not equal expected result".format(roll_backwards))
data = numbers.view(2, 2, 2)
rolled = data.roll(1, 0)
expected = torch.tensor([5, 6, 7, 8, 1, 2, 3, 4], device=device).view(2, 2, 2)
self.assertEqual(expected, rolled, "{} did not equal expected result: {}".format(rolled, expected))
data = data.view(2, 4)
# roll a loop until back where started
loop_rolled = data.roll(2, 0).roll(4, 1)
self.assertEqual(data, loop_rolled, "{} did not equal the original: {}".format(loop_rolled, data))
# multiple inverse loops
self.assertEqual(data, data.roll(-20, 0).roll(-40, 1))
self.assertEqual(torch.tensor([8, 1, 2, 3, 4, 5, 6, 7], device=device), numbers.roll(1, 0))
# test non-contiguous
# strided equivalent to numbers.as_strided(size=(4, 2), stride=(1, 4))
strided = numbers.view(2, 4).transpose(0, 1)
self.assertFalse(strided.is_contiguous(), "this test needs a non-contiguous tensor")
expected = torch.tensor([4, 8, 1, 5, 2, 6, 3, 7]).view(4, 2)
rolled = strided.roll(1, 0)
self.assertEqual(expected, rolled,
"non contiguous tensor rolled to {} instead of {} ".format(rolled, expected))
# test roll with no dimension specified
expected = numbers.roll(1, 0).view(2, 4)
self.assertEqual(expected, data.roll(1), "roll with no dims should flatten and roll.")
self.assertEqual(expected, data.roll(1, dims=None), "roll with no dims should flatten and roll.")
# test roll over multiple dimensions
expected = torch.tensor([[7, 8, 5, 6], [3, 4, 1, 2]], device=device)
double_rolled = data.roll(shifts=(2, -1), dims=(1, 0))
self.assertEqual(double_rolled, expected,
"should be able to roll over two dimensions, got {}".format(double_rolled))
self.assertRaisesRegex(RuntimeError, "required", lambda: data.roll(shifts=(), dims=()))
self.assertRaisesRegex(RuntimeError, "required", lambda: data.roll(shifts=(), dims=1))
# shifts/dims should align
self.assertRaisesRegex(RuntimeError, "align", lambda: data.roll(shifts=(1, 2), dims=(1,)))
self.assertRaisesRegex(RuntimeError, "align", lambda: data.roll(shifts=(1,), dims=(1, 2)))
def test_reversed(self):
val = torch.arange(0, 10)
self.assertEqual(reversed(val), torch.arange(9, -1, -1))
val = torch.arange(1, 10).view(3, 3)
self.assertEqual(reversed(val), torch.tensor([[7, 8, 9], [4, 5, 6], [1, 2, 3]]))
val = torch.tensor(42)
self.assertEqual(reversed(val), torch.tensor(42))
@staticmethod
def _test_rot90(self, use_cuda=False):
device = torch.device("cuda" if use_cuda else "cpu")
data = torch.arange(1, 5, device=device).view(2, 2)
self.assertEqual(torch.tensor([1, 2, 3, 4]).view(2, 2), data.rot90(0, [0, 1]))
self.assertEqual(torch.tensor([2, 4, 1, 3]).view(2, 2), data.rot90(1, [0, 1]))
self.assertEqual(torch.tensor([4, 3, 2, 1]).view(2, 2), data.rot90(2, [0, 1]))
self.assertEqual(torch.tensor([3, 1, 4, 2]).view(2, 2), data.rot90(3, [0, 1]))
# test for default args k=1, dims=[0, 1]
self.assertEqual(data.rot90(), data.rot90(1, [0, 1]))
# test for reversed order of dims
self.assertEqual(data.rot90(3, [0, 1]), data.rot90(1, [1, 0]))
# test for modulo of k
self.assertEqual(data.rot90(5, [0, 1]), data.rot90(1, [0, 1]))
self.assertEqual(data.rot90(3, [0, 1]), data.rot90(-1, [0, 1]))
self.assertEqual(data.rot90(-5, [0, 1]), data.rot90(-1, [0, 1]))
# test for dims out-of-range error
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0, -3]))
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0, 2]))
# test tensor with more than 2D
data = torch.arange(1, 9, device=device).view(2, 2, 2)
self.assertEqual(torch.tensor([2, 4, 1, 3, 6, 8, 5, 7]).view(2, 2, 2), data.rot90(1, [1, 2]))
self.assertEqual(data.rot90(1, [1, -1]), data.rot90(1, [1, 2]))
# test for errors
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0, 3]))
self.assertRaises(RuntimeError, lambda: data.rot90(1, [1, 1]))
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0, 1, 2]))
self.assertRaises(RuntimeError, lambda: data.rot90(1, [0]))
def test_rot90(self):
self._test_rot90(self, use_cuda=False)
def test_storage(self):
v = torch.randn(3, 5)
self.assertEqual(v.storage()[0], v.data[0][0])
self.assertEqual(v.storage()[14], v.data[2][4])
def test_nonzero(self):
num_src = 12
types = [
'torch.ByteTensor',
'torch.CharTensor',
'torch.ShortTensor',
'torch.IntTensor',
'torch.FloatTensor',
'torch.DoubleTensor',
'torch.LongTensor',
]
shapes = [
torch.Size((12,)),
torch.Size((12, 1)),
torch.Size((1, 12)),
torch.Size((6, 2)),
torch.Size((3, 2, 2)),
]
for t in types:
while True:
tensor = torch.rand(num_src).mul(2).floor().type(t)
if tensor.sum() > 0:
break
for shape in shapes:
tensor = tensor.clone().resize_(shape)
dst1 = torch.nonzero(tensor)
dst2 = tensor.nonzero()
dst3 = torch.LongTensor()
torch.nonzero(tensor, out=dst3)
if len(shape) == 1:
dst = []
for i in range(num_src):
if tensor[i] != 0:
dst += [i]
self.assertEqual(dst1.select(1, 0), torch.LongTensor(dst), 0)
self.assertEqual(dst2.select(1, 0), torch.LongTensor(dst), 0)
self.assertEqual(dst3.select(1, 0), torch.LongTensor(dst), 0)
elif len(shape) == 2:
# This test will allow through some False positives. It only checks
# that the elements flagged positive are indeed non-zero.
for i in range(dst1.size(0)):
self.assertNotEqual(tensor[dst1[i, 0], dst1[i, 1]].item(), 0)
elif len(shape) == 3:
# This test will allow through some False positives. It only checks
# that the elements flagged positive are indeed non-zero.
for i in range(dst1.size(0)):
self.assertNotEqual(tensor[dst1[i, 0], dst1[i, 1], dst1[i, 2]].item(), 0)
def test_nonzero_empty(self):
devices = ['cpu'] if not torch.cuda.is_available() else ['cpu', 'cuda']
for device in devices:
x = torch.randn(0, 2, 0, 5, 0, device=device)
y = torch.nonzero(x)
self.assertEqual(0, y.numel())
self.assertEqual(torch.Size([0, 5]), y.shape)
def test_deepcopy(self):
from copy import deepcopy
a = torch.randn(5, 5)
b = torch.randn(5, 5)
c = a.view(25)
q = [a, [a.storage(), b.storage()], b, c]
w = deepcopy(q)
self.assertEqual(w[0], q[0], 0)
self.assertEqual(w[1][0], q[1][0], 0)
self.assertEqual(w[1][1], q[1][1], 0)
self.assertEqual(w[1], q[1], 0)
self.assertEqual(w[2], q[2], 0)
# Check that deepcopy preserves sharing
w[0].add_(1)
for i in range(a.numel()):
self.assertEqual(w[1][0][i], q[1][0][i] + 1)
self.assertEqual(w[3], c + 1)
w[2].sub_(1)
for i in range(a.numel()):
self.assertEqual(w[1][1][i], q[1][1][i] - 1)
def test_deepcopy_scalar(self):
from copy import deepcopy
a = torch.tensor(5)
self.assertEqual(a.size(), deepcopy(a).size())
self.assertEqual(a, deepcopy(a))
def test_deepcopy_parameter(self):
from copy import deepcopy
l = torch.nn.Linear(10, 1)
s = l.state_dict(keep_vars=True)
self.assertEqual(torch.nn.Parameter, type(s['weight']))
self.assertEqual(torch.nn.Parameter, type(s['bias']))
s2 = deepcopy(s)
self.assertEqual(torch.nn.Parameter, type(s2['weight']))
self.assertEqual(torch.nn.Parameter, type(s2['bias']))
def test_copy(self):
from copy import copy
a = torch.randn(5, 5)
a_clone = a.clone()
b = copy(a)
b.fill_(1)
# copy is a shallow copy, only copies the tensor view,
# not the data
self.assertEqual(a, b)
def test_pickle(self):
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
a = torch.randn(5, 5)
serialized = pickle.dumps(a)
b = pickle.loads(serialized)
self.assertEqual(a, b)
def test_pickle_parameter(self):
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
a = torch.nn.Parameter(torch.randn(5, 5))
serialized = pickle.dumps(a)
b = pickle.loads(serialized)
self.assertTrue(isinstance(b, torch.nn.Parameter))
self.assertEqual(a.requires_grad, b.requires_grad)
self.assertEqual(a, b)
def test_pickle_parameter_no_requires_grad(self):
if sys.version_info[0] == 2:
import cPickle as pickle
else:
import pickle
a = torch.nn.Parameter(torch.randn(5, 5), requires_grad=False)
serialized = pickle.dumps(a)
b = pickle.loads(serialized)
self.assertTrue(isinstance(b, torch.nn.Parameter))
self.assertEqual(a.requires_grad, b.requires_grad)
self.assertEqual(a, b)
def test_norm_fastpaths(self):
x = torch.randn(3, 5)
# slow path
result = torch.norm(x, 4.5, 1)
expected = torch.pow(x.abs().pow(4.5).sum(1), 1.0 / 4.5)
self.assertEqual(result, expected)
# fast 0-norm
result = torch.norm(x, 0, 1)
expected = (x != 0).type_as(x).sum(1)
self.assertEqual(result, expected)
# fast 1-norm
result = torch.norm(x, 1, 1)
expected = x.abs().sum(1)
self.assertEqual(result, expected)
# fast 2-norm
result = torch.norm(x, 2, 1)
expected = torch.sqrt(x.pow(2).sum(1))
self.assertEqual(result, expected)
# fast 3-norm
result = torch.norm(x, 3, 1)
expected = torch.pow(x.pow(3).abs().sum(1), 1.0 / 3.0)
self.assertEqual(result, expected)
@staticmethod
def _test_bernoulli(self, t_dtype, p_dtype, device):
for trivial_p in ([0, 1], [1, 0, 1, 1, 0, 1]):
x = torch.tensor(trivial_p, dtype=p_dtype, device=device)
self.assertEqual(x.bernoulli().tolist(), trivial_p)
def isBinary(t):
return torch.ne(t, 0).mul_(torch.ne(t, 1)).sum().item() == 0
p = torch.rand(5, 5, dtype=p_dtype, device=device)
self.assertTrue(isBinary(p.bernoulli()))
p = torch.rand(5, dtype=p_dtype, device=device).expand(5, 5)
self.assertTrue(isBinary(p.bernoulli()))
p = torch.rand(5, 5, dtype=p_dtype, device=device)
torch.bernoulli(torch.rand_like(p), out=p)
self.assertTrue(isBinary(p))
p = torch.rand(5, dtype=p_dtype, device=device).expand(5, 5)
torch.bernoulli(torch.rand_like(p), out=p)
self.assertTrue(isBinary(p))
t = torch.empty(10, 10, dtype=t_dtype, device=device)
t.fill_(2)
t.bernoulli_(0.5)
self.assertTrue(isBinary(t))
p = torch.rand(10, dtype=p_dtype, device=device).expand(10, 10)
t.fill_(2)
t.bernoulli_(p)
self.assertTrue(isBinary(t))
t.fill_(2)
torch.bernoulli(torch.rand_like(t, dtype=p_dtype), out=t)
self.assertTrue(isBinary(t))
t.fill_(2)
t.bernoulli_(torch.rand_like(t, dtype=p_dtype))
self.assertTrue(isBinary(t))
def test_bernoulli(self):
self._test_bernoulli(self, torch.float32, torch.float64, 'cpu')
# test that it works with integral tensors
self._test_bernoulli(self, torch.uint8, torch.float64, 'cpu')
def test_normal(self):
q = torch.Tensor(100, 100)
q.normal_()
self.assertEqual(q.mean(), 0, 0.2)
self.assertEqual(q.std(), 1, 0.2)
q.normal_(2, 3)
self.assertEqual(q.mean(), 2, 0.3)
self.assertEqual(q.std(), 3, 0.3)
q = torch.Tensor(100, 100)
q_row1 = q[0:1].clone()
q[99:100].normal_()
self.assertEqual(q[99:100].mean(), 0, 0.2)
self.assertEqual(q[99:100].std(), 1, 0.2)
self.assertEqual(q[0:1].clone(), q_row1)
mean = torch.Tensor(100, 100)
std = torch.Tensor(100, 100)
mean[:50] = 0
mean[50:] = 1
std[:, :50] = 4
std[:, 50:] = 1
r = torch.normal(mean)
self.assertEqual(r[:50].mean(), 0, 0.2)
self.assertEqual(r[50:].mean(), 1, 0.2)
self.assertEqual(r.std(), 1, 0.2)
r = torch.normal(mean, 3)
self.assertEqual(r[:50].mean(), 0, 0.2)
self.assertEqual(r[50:].mean(), 1, 0.2)
self.assertEqual(r.std(), 3, 0.2)
r = torch.normal(2, std)
self.assertEqual(r.mean(), 2, 0.2)
self.assertEqual(r[:, :50].std(), 4, 0.3)
self.assertEqual(r[:, 50:].std(), 1, 0.2)
r = torch.normal(mean, std)
self.assertEqual(r[:50].mean(), 0, 0.2)
self.assertEqual(r[50:].mean(), 1, 0.2)
self.assertEqual(r[:, :50].std(), 4, 0.3)
self.assertEqual(r[:, 50:].std(), 1, 0.2)
def test_parsing_int64(self):
# accepts integer arguments
x = torch.cumsum(torch.ones(5, 5), 0)
self.assertEqual(x, torch.cumsum(torch.ones(5, 5), torch.tensor(0)))
# doesn't accept floating point variables
self.assertRaises(TypeError, lambda: torch.cumsum(torch.ones(5, 5), torch.tensor(0.)))
def test_parsing_double(self):
# accepts floating point and integer arguments
x = torch.randn(2, 3)
torch.isclose(x, x, 1, 1)
self.assertTrue(torch.isclose(x, x, 1, 1).all())
self.assertTrue(torch.isclose(x, x, 1.5, 1.).all())
# accepts floating point and integer tensors
self.assertTrue(torch.isclose(x, x, torch.tensor(1), torch.tensor(1)).all())
self.assertTrue(torch.isclose(x, x, torch.tensor(1.5), torch.tensor(1.)).all())
# doesn't accept variables with requires_grad
self.assertRaises(TypeError,
lambda: torch.isclose(x, x, torch.tensor(1.5), torch.tensor(1., requires_grad=True)).all())
def test_parsing_intlist(self):
# parse with integer variables
self.assertEqual(torch.Size([3, 4]), torch.ones((torch.tensor(3), torch.tensor(4))).shape)
self.assertEqual(torch.Size([3, 4]), torch.ones(torch.tensor(3), torch.tensor(4)).shape)
# parse with numpy integers
if TEST_NUMPY:
self.assertEqual(torch.Size([3, 4]), torch.ones((np.array(3), np.int64(4))).shape)
self.assertEqual(torch.Size([3, 4]), torch.ones(np.array(3), np.int64(4)).shape)
self.assertEqual(torch.Size([3, 4]), torch.ones((np.int64(3), np.array(4))).shape)
self.assertEqual(torch.Size([3, 4]), torch.ones(np.int64(3), np.array(4)).shape)
# fail parse with float variables
self.assertRaises(TypeError, lambda: torch.ones((torch.tensor(3.), torch.tensor(4))))
# fail parse with numpy floats
if TEST_NUMPY:
self.assertRaises(TypeError, lambda: torch.ones((np.float(3.), torch.tensor(4))))
self.assertRaises(TypeError, lambda: torch.ones((np.array(3.), torch.tensor(4))))
# fail parse with > 1 element variables
self.assertRaises(TypeError, lambda: torch.ones(torch.tensor(3, 3)))
self.assertRaises(TypeError, lambda: torch.ones((torch.tensor(3, 3))))
if TEST_NUMPY:
self.assertRaises(TypeError, lambda: torch.ones(np.array(3, 3)))
self.assertRaises(TypeError, lambda: torch.ones((np.array(3, 3))))
# fail parse with additional positional args after intlist arg
self.assertRaisesRegex(TypeError,
"received an invalid combination of arguments",
lambda: torch.LongTensor((6, 0), 1, 1, 0))
self.assertRaisesRegex(TypeError,
"missing 1 required positional arguments",
lambda: torch.tensor().new_zeros((5, 5), 0))
def _test_serialization_data(self):
a = [torch.randn(5, 5).float() for i in range(2)]
b = [a[i % 2] for i in range(4)] # 0-3
b += [a[0].storage()] # 4
b += [a[0].reshape(-1)[1:4].storage()] # 5
b += [torch.arange(1, 11).int()] # 6
t1 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
t2 = torch.FloatTensor().set_(a[0].reshape(-1)[1:4].clone().storage(), 0, (3,), (1,))
b += [(t1.storage(), t1.storage(), t2.storage())] # 7
b += [a[0].reshape(-1)[0:2].storage()] # 8
return b
def _test_serialization_assert(self, b, c):
self.assertEqual(b, c, 0)
self.assertTrue(isinstance(c[0], torch.FloatTensor))
self.assertTrue(isinstance(c[1], torch.FloatTensor))
self.assertTrue(isinstance(c[2], torch.FloatTensor))
self.assertTrue(isinstance(c[3], torch.FloatTensor))
self.assertTrue(isinstance(c[4], torch.FloatStorage))
c[0].fill_(10)
self.assertEqual(c[0], c[2], 0)
self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), 0)
c[1].fill_(20)
self.assertEqual(c[1], c[3], 0)
# I have to do it in this roundabout fashion, because there's no
# way to slice storages
for i in range(4):
self.assertEqual(c[4][i + 1], c[5][i])
# check that serializing the same storage view object unpickles
# it as one object not two (and vice versa)
views = c[7]
self.assertEqual(views[0]._cdata, views[1]._cdata)
self.assertEqual(views[0], views[2])
self.assertNotEqual(views[0]._cdata, views[2]._cdata)
rootview = c[8]
self.assertEqual(rootview.data_ptr(), c[0].data_ptr())
def test_serialization(self):
# Test serialization with a real file
b = self._test_serialization_data()
for use_name in (False, True):
# Passing filename to torch.save(...) will cause the file to be opened twice,
# which is not supported on Windows
if sys.platform == "win32" and use_name:
continue
with tempfile.NamedTemporaryFile() as f:
handle = f if not use_name else f.name
torch.save(b, handle)
f.seek(0)
c = torch.load(handle)
self._test_serialization_assert(b, c)
# test non-ascii encoding of bytes arrays/strings
# The following bytes are produced by serializing
# [b'\xc5\xbc\xc4\x85\xc4\x85\xc3\xb3\xc5\xbc\xc4\x85\xc5\xbc', torch.zeros(1, dtype=torch.float), 2]
# in Python 2.7.12 and PyTorch 0.4.1, where the first element contains
# bytes of some utf-8 characters (i.e., `utf8_str.encode('utf-8')`).
serialized = (
b'\x80\x02\x8a\nl\xfc\x9cF\xf9 j\xa8P\x19.\x80\x02M\xe9\x03.'
b'\x80\x02}q\x01(U\x10protocol_versionq\x02M\xe9\x03U\n'
b'type_sizesq\x03}q\x04(U\x03intq\x05K\x04U\x05shortq\x06K\x02U'
b'\x04longq\x07K\x04uU\rlittle_endianq\x08\x88u.\x80\x02]q'
b'\x01(U\x0e\xc5\xbc\xc4\x85\xc4\x85\xc3\xb3\xc5\xbc\xc4\x85'
b'\xc5\xbcq\x02ctorch._utils\n_rebuild_tensor_v2\nq\x03((U'
b'\x07storageq\x04ctorch\nFloatStorage\nq\x05U\x0845640624q'
b'\x06U\x03cpuq\x07\x8a\x01\x01NtQK\x00K\x01\x85K\x01\x85'
b'\x89NtRq\x08K\x02e.\x80\x02]q\x01U\x0845640624q\x02a.\x01\x00'
b'\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
)
buf = io.BytesIO(serialized)
utf8_bytes = b'\xc5\xbc\xc4\x85\xc4\x85\xc3\xb3\xc5\xbc\xc4\x85\xc5\xbc'
utf8_str = utf8_bytes.decode('utf-8')
if PY3:
with self.assertRaisesRegex(UnicodeDecodeError, "'ascii' codec can't decode byte"):
loaded = torch.load(buf)
buf.seek(0)
loaded_utf8 = torch.load(buf, encoding='utf-8')
self.assertEqual(loaded_utf8, [utf8_str, torch.zeros(1, dtype=torch.float), 2])
buf.seek(0)
loaded_bytes = torch.load(buf, encoding='bytes')
else:
loaded_bytes = torch.load(buf)
self.assertEqual(loaded_bytes, [utf8_bytes, torch.zeros(1, dtype=torch.float), 2])
def test_serialization_filelike(self):
# Test serialization (load and save) with a filelike object
b = self._test_serialization_data()
with BytesIOContext() as f:
torch.save(b, f)
f.seek(0)
c = torch.load(f)
self._test_serialization_assert(b, c)
def test_serialization_gzip(self):
# Test serialization with gzip file
b = self._test_serialization_data()
f1 = tempfile.NamedTemporaryFile(delete=False)
f2 = tempfile.NamedTemporaryFile(delete=False)
torch.save(b, f1)
with open(f1.name, 'rb') as f_in, gzip.open(f2.name, 'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
with gzip.open(f2.name, 'rb') as f:
c = torch.load(f)
self._test_serialization_assert(b, c)
def test_serialization_offset(self):
a = torch.randn(5, 5)
i = 41
for use_name in (False, True):
# Passing filename to torch.save(...) will cause the file to be opened twice,
# which is not supported on Windows
if sys.platform == "win32" and use_name:
continue
with tempfile.NamedTemporaryFile() as f:
handle = f if not use_name else f.name
pickle.dump(i, f)
torch.save(a, f)
f.seek(0)
j = pickle.load(f)
b = torch.load(f)
self.assertTrue(torch.equal(a, b))
self.assertEqual(i, j)
def test_serialization_offset_filelike(self):
a = torch.randn(5, 5)
i = 41
with BytesIOContext() as f:
pickle.dump(i, f)
torch.save(a, f)
f.seek(0)
j = pickle.load(f)
b = torch.load(f)
self.assertTrue(torch.equal(a, b))
self.assertEqual(i, j)
def test_serialization_offset_gzip(self):
a = torch.randn(5, 5)
i = 41
f1 = tempfile.NamedTemporaryFile(delete=False)
f2 = tempfile.NamedTemporaryFile(delete=False)
with open(f1.name, 'wb') as f:
pickle.dump(i, f)
torch.save(a, f)
with open(f1.name, 'rb') as f_in, gzip.open(f2.name, 'wb') as f_out:
shutil.copyfileobj(f_in, f_out)
with gzip.open(f2.name, 'rb') as f:
j = pickle.load(f)
b = torch.load(f)
self.assertTrue(torch.equal(a, b))
self.assertEqual(i, j)
def test_half_tensor(self):
x = torch.randn(5, 5).float()
y = torch.randn(5, 5).float()
xh, yh = x.half(), y.half()
self.assertEqual(x.half().float(), x, 1e-3)
z = torch.Tensor(5, 5)
self.assertEqual(z.copy_(xh), x, 1e-3)
with tempfile.NamedTemporaryFile() as f:
torch.save(xh, f)
f.seek(0)
xh2 = torch.load(f)
self.assertEqual(xh.float(), xh2.float())
def test_serialize_device(self):
device_str = ['cpu', 'cpu:0', 'cuda', 'cuda:0']
device_obj = [torch.device(d) for d in device_str]
for device in device_obj:
device_copied = copy.deepcopy(device)
self.assertEqual(device, device_copied)
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_half_tensor_cuda(self):
x = torch.randn(5, 5).half()
self.assertEqual(x.cuda(), x)
xc = x.cuda()
with tempfile.NamedTemporaryFile() as f:
torch.save(xc, f)
f.seek(0)
xc2 = torch.load(f)
self.assertIsInstance(xc2, type(xc))
self.assertEqual(xc.float(), xc2.float())
def _test_serialization_cuda(self, filecontext_lambda):
device_count = torch.cuda.device_count()
t0 = torch.cuda.FloatTensor(5).fill_(1)
torch.cuda.set_device(device_count - 1)
tn = torch.cuda.FloatTensor(3).fill_(2)
torch.cuda.set_device(0)
b = (t0, tn)
with filecontext_lambda() as f:
torch.save(b, f)
f.seek(0)
c = torch.load(f)
self.assertEqual(b, c, 0)
u0, un = c
self.assertEqual(u0.get_device(), 0)
self.assertEqual(un.get_device(), device_count - 1)
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_serialization_cuda(self):
self._test_serialization_cuda(tempfile.NamedTemporaryFile)
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_serialization_cuda_filelike(self):
self._test_serialization_cuda(BytesIOContext)
def test_serialization_backwards_compat(self):
a = [torch.arange(1 + i, 26 + i).view(5, 5).float() for i in range(2)]
b = [a[i % 2] for i in range(4)]
b += [a[0].storage()]
b += [a[0].reshape(-1)[1:4].clone().storage()]
path = download_file('https://download.pytorch.org/test_data/legacy_serialized.pt')
c = torch.load(path)
self.assertEqual(b, c, 0)
self.assertTrue(isinstance(c[0], torch.FloatTensor))
self.assertTrue(isinstance(c[1], torch.FloatTensor))
self.assertTrue(isinstance(c[2], torch.FloatTensor))
self.assertTrue(isinstance(c[3], torch.FloatTensor))
self.assertTrue(isinstance(c[4], torch.FloatStorage))
c[0].fill_(10)
self.assertEqual(c[0], c[2], 0)
self.assertEqual(c[4], torch.FloatStorage(25).fill_(10), 0)
c[1].fill_(20)
self.assertEqual(c[1], c[3], 0)
# test some old tensor serialization mechanism
class OldTensorBase(object):
def __init__(self, new_tensor):
self.new_tensor = new_tensor
def __getstate__(self):
return (self.new_tensor.storage(),
self.new_tensor.storage_offset(),
tuple(self.new_tensor.size()),
self.new_tensor.stride())
class OldTensorV1(OldTensorBase):
def __reduce__(self):
return (torch.Tensor, (), self.__getstate__())
class OldTensorV2(OldTensorBase):
def __reduce__(self):
return (_rebuild_tensor, self.__getstate__())
x = torch.randn(30).as_strided([2, 3], [9, 3], 2)
for old_cls in [OldTensorV1, OldTensorV2]:
with tempfile.NamedTemporaryFile() as f:
old_x = old_cls(x)
torch.save(old_x, f)
f.seek(0)
load_x = torch.load(f)
self.assertEqual(x.storage(), load_x.storage())
self.assertEqual(x.storage_offset(), load_x.storage_offset())
self.assertEqual(x.size(), load_x.size())
self.assertEqual(x.stride(), load_x.stride())
# unique_key is necessary because on Python 2.7, if a warning passed to
# the warning module is the same, it is not raised again.
def _test_serialization_container(self, unique_key, filecontext_lambda):
tmpmodule_name = 'tmpmodule{}'.format(unique_key)
def import_module(name, filename):
if sys.version_info >= (3, 5):
import importlib.util
spec = importlib.util.spec_from_file_location(name, filename)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
else:
import imp
module = imp.load_source(name, filename)
sys.modules[module.__name__] = module
return module
with filecontext_lambda() as checkpoint:
fname = get_file_path_2(os.path.dirname(__file__), 'data', 'network1.py')
module = import_module(tmpmodule_name, fname)
torch.save(module.Net(), checkpoint)
# First check that the checkpoint can be loaded without warnings
checkpoint.seek(0)
with warnings.catch_warnings(record=True) as w:
loaded = torch.load(checkpoint)
self.assertTrue(isinstance(loaded, module.Net))
if can_retrieve_source:
self.assertEquals(len(w), 0)
# Replace the module with different source
fname = get_file_path_2(os.path.dirname(__file__), 'data', 'network2.py')
module = import_module(tmpmodule_name, fname)
checkpoint.seek(0)
with warnings.catch_warnings(record=True) as w:
loaded = torch.load(checkpoint)
self.assertTrue(isinstance(loaded, module.Net))
if can_retrieve_source:
self.assertEquals(len(w), 1)
self.assertTrue(w[0].category, 'SourceChangeWarning')
def test_serialization_container(self):
self._test_serialization_container('file', tempfile.NamedTemporaryFile)
def test_serialization_container_filelike(self):
self._test_serialization_container('filelike', BytesIOContext)
def test_serialization_map_location(self):
test_file_path = download_file('https://download.pytorch.org/test_data/gpu_tensors.pt')
def map_location(storage, loc):
return storage
def load_bytes():
with open(test_file_path, 'rb') as f:
return io.BytesIO(f.read())
fileobject_lambdas = [lambda: test_file_path, load_bytes]
cpu_map_locations = [
map_location,
{'cuda:0': 'cpu'},
'cpu',
torch.device('cpu'),
]
gpu_0_map_locations = [
{'cuda:0': 'cuda:0'},
'cuda',
'cuda:0',
torch.device('cuda'),
torch.device('cuda', 0)
]
gpu_last_map_locations = [
'cuda:{}'.format(torch.cuda.device_count() - 1),
]
def check_map_locations(map_locations, tensor_class, intended_device):
for fileobject_lambda in fileobject_lambdas:
for map_location in map_locations:
tensor = torch.load(fileobject_lambda(), map_location=map_location)
self.assertEqual(tensor.device, intended_device)
self.assertIsInstance(tensor, tensor_class)
self.assertEqual(tensor, tensor_class([[1.0, 2.0], [3.0, 4.0]]))
check_map_locations(cpu_map_locations, torch.FloatTensor, torch.device('cpu'))
if torch.cuda.is_available():
check_map_locations(gpu_0_map_locations, torch.cuda.FloatTensor, torch.device('cuda', 0))
check_map_locations(
gpu_last_map_locations,
torch.cuda.FloatTensor,
torch.device('cuda', torch.cuda.device_count() - 1)
)
@unittest.skipIf(torch.cuda.is_available(), "Testing torch.load on CPU-only machine")
@unittest.skipIf(not PY3, "Test tensors were serialized using python 3")
def test_load_nonexistent_device(self):
# Setup: create a serialized file object with a 'cuda:0' restore location
# The following was generated by saving a torch.randn(2, device='cuda') tensor.
serialized = (b'\x80\x02\x8a\nl\xfc\x9cF\xf9 j\xa8P\x19.\x80\x02M\xe9'
b'\x03.\x80\x02}q\x00(X\x10\x00\x00\x00protocol_versionq'
b'\x01M\xe9\x03X\r\x00\x00\x00little_endianq\x02\x88X\n'
b'\x00\x00\x00type_sizesq\x03}q\x04(X\x05\x00\x00\x00shortq'
b'\x05K\x02X\x03\x00\x00\x00intq\x06K\x04X\x04\x00\x00\x00'
b'longq\x07K\x04uu.\x80\x02ctorch._utils\n_rebuild_tensor_v2'
b'\nq\x00((X\x07\x00\x00\x00storageq\x01ctorch\nFloatStorage'
b'\nq\x02X\x0e\x00\x00\x0094919395964320q\x03X\x06\x00\x00'
b'\x00cuda:0q\x04K\x02Ntq\x05QK\x00K\x02\x85q\x06K\x01\x85q'
b'\x07\x89Ntq\x08Rq\t.\x80\x02]q\x00X\x0e\x00\x00\x00'
b'94919395964320q\x01a.\x02\x00\x00\x00\x00\x00\x00\x00\xbb'
b'\x1f\x82\xbe\xea\x81\xd1>')
buf = io.BytesIO(serialized)
error_msg = r'Attempting to deserialize object on a CUDA device'
with self.assertRaisesRegex(RuntimeError, error_msg):
_ = torch.load(buf)
def test_serialization_filelike_api_requirements(self):
filemock = FilelikeMock(b'', has_readinto=False)
tensor = torch.randn(3, 5)
torch.save(tensor, filemock)
expected_superset = set(['write', 'flush'])
self.assertTrue(expected_superset.issuperset(filemock.calls))
# Reset between save and load
filemock.seek(0)
filemock.calls.clear()
_ = torch.load(filemock)
expected_superset = set(['read', 'readline', 'seek', 'tell'])
self.assertTrue(expected_superset.issuperset(filemock.calls))
def _test_serialization_filelike(self, tensor, mock, desc):
f = mock(b'')
torch.save(tensor, f)
f.seek(0)
data = mock(f.read())
msg = 'filelike serialization with {}'
b = torch.load(data)
self.assertTrue(torch.equal(tensor, b), msg.format(desc))
def test_serialization_filelike_missing_attrs(self):
# Test edge cases where filelike objects are missing attributes.
# The Python io docs suggests that these attributes should really exist
# and throw io.UnsupportedOperation, but that isn't always the case.
mocks = [
('no readinto', lambda x: FilelikeMock(x)),
('has readinto', lambda x: FilelikeMock(x, has_readinto=True)),
('no fileno', lambda x: FilelikeMock(x, has_fileno=False)),
]
to_serialize = torch.randn(3, 10)
for desc, mock in mocks:
self._test_serialization_filelike(to_serialize, mock, desc)
def test_serialization_filelike_stress(self):
a = torch.randn(11 * (2 ** 9) + 1, 5 * (2 ** 9))
# This one should call python read multiple times
self._test_serialization_filelike(a, lambda x: FilelikeMock(x, has_readinto=False),
'read() stress test')
self._test_serialization_filelike(a, lambda x: FilelikeMock(x, has_readinto=True),
'readinto() stress test')
def test_serialization_filelike_uses_readinto(self):
# For maximum effiency, when reading a file-like object,
# ensure the C API calls readinto instead of read.
a = torch.randn(5, 4)
f = io.BytesIO()
torch.save(a, f)
f.seek(0)
data = FilelikeMock(f.read(), has_readinto=True)
b = torch.load(data)
self.assertTrue(data.was_called('readinto'))
def test_serialization_storage_slice(self):
# Generated using:
#
# t = torch.zeros(2);
# s1 = t.storage()[:1]
# s2 = t.storage()[1:]
# torch.save((s1, s2), 'foo.ser')
#
# with PyTorch 0.3.1
serialized = (b'\x80\x02\x8a\nl\xfc\x9cF\xf9 j\xa8P\x19.\x80\x02M\xe9\x03'
b'.\x80\x02}q\x00(X\n\x00\x00\x00type_sizesq\x01}q\x02(X\x03'
b'\x00\x00\x00intq\x03K\x04X\x05\x00\x00\x00shortq\x04K\x02X'
b'\x04\x00\x00\x00longq\x05K\x04uX\x10\x00\x00\x00protocol_versionq'
b'\x06M\xe9\x03X\r\x00\x00\x00little_endianq\x07\x88u.\x80\x02'
b'(X\x07\x00\x00\x00storageq\x00ctorch\nFloatStorage\nq\x01X\x0e'
b'\x00\x00\x0094279043900432q\x02X\x03\x00\x00\x00cpuq\x03K\x02'
b'X\x0e\x00\x00\x0094279029750368q\x04K\x00K\x01\x87q\x05tq\x06'
b'Q(h\x00h\x01X\x0e\x00\x00\x0094279043900432q\x07h\x03K\x02X'
b'\x0e\x00\x00\x0094279029750432q\x08K\x01K\x01\x87q\ttq\nQ'
b'\x86q\x0b.\x80\x02]q\x00X\x0e\x00\x00\x0094279043900432q'
b'\x01a.\x02\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00'
b'\x00\x00\x00\x00')
buf = io.BytesIO(serialized)
(s1, s2) = torch.load(buf)
self.assertEqual(s1[0], 0)
self.assertEqual(s2[0], 0)
self.assertEqual(s1.data_ptr() + 4, s2.data_ptr())
def test_load_error_msg(self):
expected_err_msg = (".*You can only torch.load from a file that is seekable. " +
"Please pre-load the data into a buffer like io.BytesIO and " +
"try to load from it instead.")
resource = FilelikeMock(data=b"data")
delattr(resource, "tell")
delattr(resource, "seek")
self.assertRaisesRegex(AttributeError, expected_err_msg, lambda: torch.load(resource))
def test_from_buffer(self):
a = bytearray([1, 2, 3, 4])
self.assertEqual(torch.ByteStorage.from_buffer(a).tolist(), [1, 2, 3, 4])
shorts = torch.ShortStorage.from_buffer(a, 'big')
self.assertEqual(shorts.size(), 2)
self.assertEqual(shorts.tolist(), [258, 772])
ints = torch.IntStorage.from_buffer(a, 'little')
self.assertEqual(ints.size(), 1)
self.assertEqual(ints[0], 67305985)
f = bytearray([0x40, 0x10, 0x00, 0x00])
floats = torch.FloatStorage.from_buffer(f, 'big')
self.assertEqual(floats.size(), 1)
self.assertEqual(floats[0], 2.25)
@unittest.skipIf(IS_WINDOWS, "TODO: need to fix this test case for Windows")
def test_from_file(self):
size = 10000
with tempfile.NamedTemporaryFile() as f:
s1 = torch.FloatStorage.from_file(f.name, True, size)
t1 = torch.FloatTensor(s1).copy_(torch.randn(size))
# check mapping
s2 = torch.FloatStorage.from_file(f.name, True, size)
t2 = torch.FloatTensor(s2)
self.assertEqual(t1, t2, 0)
# check changes to t1 from t2
rnum = random.uniform(-1, 1)
t1.fill_(rnum)
self.assertEqual(t1, t2, 0)
# check changes to t2 from t1
rnum = random.uniform(-1, 1)
t2.fill_(rnum)
self.assertEqual(t1, t2, 0)
def test_print(self):
default_type = torch.Tensor().type()
for t in torch._tensor_classes:
if t == torch.HalfTensor:
continue # HalfTensor does not support fill
if t.is_sparse:
continue
if t.is_cuda and not torch.cuda.is_available():
continue
obj = t(100, 100).fill_(1)
obj.__repr__()
str(obj)
# test half tensor
obj = torch.rand(100, 100, device='cpu').half()
obj.__repr__()
str(obj)
for t in torch._storage_classes:
if t.is_cuda and not torch.cuda.is_available():
continue
obj = t(100).fill_(1)
obj.__repr__()
str(obj)
# test big integer
x = torch.tensor(2341234123412341)
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor(2341234123412341)''')
# test scientific notation
x = torch.tensor([1e28, 1e-28])
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([1.0000e+28, 1.0000e-28])''')
# test no leading space if all elements positive
x = torch.tensor([1, 2])
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([1, 2])''')
# test for leading space if there are negative elements
x = torch.tensor([1, -2])
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([ 1, -2])''')
# test inf and nan
x = torch.tensor([4, inf, 1.5, -inf, 0, nan, 1])
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([4.0000, inf, 1.5000, -inf, 0.0000, nan, 1.0000])''')
# test dtype
torch.set_default_dtype(torch.float)
x = torch.tensor([1e-324, 1e-323, 1e-322, 1e307, 1e308, 1e309], dtype=torch.float64)
self.assertEqual(x.__repr__(), str(x))
expected_str = '''\
tensor([ 0.0000e+00, 9.8813e-324, 9.8813e-323, 1.0000e+307, 1.0000e+308,
inf], dtype=torch.float64)'''
self.assertExpectedInline(str(x), expected_str)
# test changing default dtype
torch.set_default_dtype(torch.float64)
self.assertEqual(x.__repr__(), str(x))
expected_str = '''\
tensor([ 0.0000e+00, 9.8813e-324, 9.8813e-323, 1.0000e+307, 1.0000e+308,
inf])'''
self.assertExpectedInline(str(x), expected_str)
# test summary
x = torch.zeros(10000)
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([0., 0., 0., ..., 0., 0., 0.])''')
# test internal summary function
x = torch.rand(1, 20, 5, 30)
summary = torch._tensor_str.get_summarized_data(x)
self.assertEqual(summary.shape, (1, 6, 5, 6))
first_and_last = [0, 1, 2, -3, -2, -1]
self.assertEqual(summary, x[:, first_and_last][..., first_and_last])
# test device
if torch.cuda.is_available():
x = torch.tensor([123], device='cuda:0')
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([123], device='cuda:0')''')
# test changing default to cuda
torch.set_default_tensor_type(torch.cuda.FloatTensor)
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([123])''')
torch.set_default_tensor_type(default_type)
# test integral floats and requires_grad
x = torch.tensor([123.], requires_grad=True)
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([123.], requires_grad=True)''')
# test non-contiguous print
# sliced tensor should have > PRINT_OPTS.threshold elements
x = torch.ones(100, 2, 2, 10)
y = x.as_strided(size=(100, 2, 10), stride=(2 * 2 * 10, 2 * 10, 1))
self.assertEqual(str(y), y.__repr__())
expected_str = '''\
tensor([[[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.]],
[[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.]],
[[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.]],
...,
[[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.]],
[[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.]],
[[1., 1., 1., ..., 1., 1., 1.],
[1., 1., 1., ..., 1., 1., 1.]]])\
'''
self.assertExpectedInline(str(y), expected_str)
# test print 0-dim tensor: there's no 0-dim in Numpy, we match arrayprint style
x = torch.tensor(0.00002)
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor(2.0000e-05)''')
# [Numpy] test print float in sci_mode when min < 0.0001.
x = torch.tensor([0.00002])
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([2.0000e-05])''')
# [Numpy] test print float in sci_mode when max > 1e8.
# TODO: Pytorch uses fixed precision to print, while Numpy uses dragon4_scientific
# to do automatic trimming and padding.
x = torch.tensor([123456789.])
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([1.2346e+08])''')
# [Numpy] test print float in sci_mode when max / min > 1000.
x = torch.tensor([0.01, 11])
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([1.0000e-02, 1.1000e+01])''')
# [Numpy] test print int max / min > 1000, no sci_mode
x = torch.tensor([1, 1010])
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([ 1, 1010])''')
# [Numpy] test print int > 1e8, no sci_mode
x = torch.tensor([1000000000]) # 1e9
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([1000000000])''')
# [Numpy] test printing float in int_mode
x = torch.tensor([1., 1000.])
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([ 1., 1000.])''')
# [Numpy] test printing float in int_mode in sci format when max / min > 1000.
x = torch.tensor([1., 1010.])
self.assertEqual(x.__repr__(), str(x))
self.assertExpectedInline(str(x), '''tensor([1.0000e+00, 1.0100e+03])''')
def test_sizeof(self):
sizeof_empty = torch.randn(0).storage().__sizeof__()
sizeof_10 = torch.randn(10).storage().__sizeof__()
sizeof_100 = torch.randn(100).storage().__sizeof__()
self.assertEqual((sizeof_100 - sizeof_empty) // (sizeof_10 - sizeof_empty), 10)
self.assertEqual((sizeof_100 - sizeof_empty) % (sizeof_10 - sizeof_empty), 0)
sizeof_empty = torch.randn(0).type(torch.ByteTensor).storage().__sizeof__()
sizeof_10 = torch.randn(10).type(torch.ByteTensor).storage().__sizeof__()
sizeof_100 = torch.randn(100).type(torch.ByteTensor).storage().__sizeof__()
self.assertEqual((sizeof_100 - sizeof_empty) // (sizeof_10 - sizeof_empty), 10)
self.assertEqual((sizeof_100 - sizeof_empty) % (sizeof_10 - sizeof_empty), 0)
def test_unsqueeze(self):
x = torch.randn(2, 3, 4)
y = x.unsqueeze(1)
self.assertEqual(y, x.view(2, 1, 3, 4))
y = x.clone().unsqueeze_(2)
self.assertEqual(y, x.view(2, 3, 1, 4))
x = x[:, 1]
self.assertFalse(x.is_contiguous())
y = x.unsqueeze(1)
self.assertEqual(y, x.contiguous().view(2, 1, 4))
y = x.clone().unsqueeze_(2)
self.assertEqual(y, x.contiguous().view(2, 4, 1))
def test_iter(self):
x = torch.randn(5, 5)
for i, sub in enumerate(x):
self.assertEqual(sub, x[i])
x = torch.Tensor()
self.assertEqual(list(x), [])
def test_accreal_type(self):
x = torch.ones(2, 3, 4)
self.assertIsInstance(x.double().sum().item(), float)
self.assertIsInstance(x.float().sum().item(), float)
self.assertIsInstance(x.long().sum().item(), int)
self.assertIsInstance(x.int().sum().item(), int)
self.assertIsInstance(x.short().sum().item(), int)
self.assertIsInstance(x.char().sum().item(), int)
self.assertIsInstance(x.byte().sum().item(), int)
def test_assertEqual(self):
x = torch.FloatTensor([0])
self.assertEqual(x, 0)
xv = torch.autograd.Variable(x)
self.assertEqual(xv, 0)
self.assertEqual(x, xv)
self.assertEqual(xv, x)
def test_new(self):
x = torch.autograd.Variable(torch.Tensor())
y = torch.autograd.Variable(torch.randn(4, 4))
z = torch.autograd.Variable(torch.IntTensor([1, 2, 3]))
self.assertEqual(x.new().shape, [0])
self.assertEqual(x.new(), x)
self.assertEqual(x.new(1, 2).shape, [1, 2])
self.assertEqual(x.new(torch.Size([3, 4])).shape, [3, 4])
self.assertEqual(x.new([3, 4]).shape, [2])
self.assertEqual(x.new([3, 4]).tolist(), [3, 4])
self.assertEqual(x.new((3, 4)).tolist(), [3, 4])
if TEST_NUMPY:
self.assertEqual(x.new([np.int32(3), np.float64(4)]).tolist(), [3, 4])
self.assertEqual(x.new(np.array((3, 4))).tolist(), [3, 4])
self.assertEqual(x.new([z[2], z[0] + 3]).tolist(), [3, 4])
self.assertEqual(x.new(size=(3, 4)).shape, [3, 4])
self.assertEqual(x.new(tuple()).shape, [0])
self.assertEqual(x.new(y.storage()).data_ptr(), y.data_ptr())
self.assertEqual(x.new(y).data_ptr(), y.data_ptr())
self.assertIsNot(x.new(y), y)
self.assertRaises(TypeError, lambda: x.new(z))
# TypeError would be better
self.assertRaises(RuntimeError, lambda: x.new(z.storage()))
def test_empty_like(self):
x = torch.autograd.Variable(torch.Tensor())
y = torch.autograd.Variable(torch.randn(4, 4))
z = torch.autograd.Variable(torch.IntTensor([1, 2, 3]))
for a in (x, y, z):
self.assertEqual(torch.empty_like(a).shape, a.shape)
self.assertEqual(torch.empty_like(a).type(), a.type())
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
@skipIfRocm
def test_pin_memory(self):
x = torch.randn(3, 5)
self.assertFalse(x.is_pinned())
pinned = x.pin_memory()
self.assertTrue(pinned.is_pinned())
self.assertEqual(pinned, x)
self.assertNotEqual(pinned.data_ptr(), x.data_ptr())
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_numpy_unresizable(self):
x = np.zeros((2, 2))
y = torch.from_numpy(x)
with self.assertRaises(ValueError):
x.resize((5, 5))
z = torch.randn(5, 5)
w = z.numpy()
with self.assertRaises(RuntimeError):
z.resize_(10, 10)
with self.assertRaises(ValueError):
w.resize((10, 10))
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_toNumpy(self):
types = [
'torch.ByteTensor',
'torch.IntTensor',
'torch.HalfTensor',
'torch.FloatTensor',
'torch.DoubleTensor',
'torch.LongTensor',
]
for tp in types:
# 1D
sz = 10
x = torch.randn(sz).mul(255).type(tp)
y = x.numpy()
for i in range(sz):
self.assertEqual(x[i], y[i])
# 1D > 0 storage offset
xm = torch.randn(sz * 2).mul(255).type(tp)
x = xm.narrow(0, sz - 1, sz)
self.assertTrue(x.storage_offset() > 0)
y = x.numpy()
for i in range(sz):
self.assertEqual(x[i], y[i])
def check2d(x, y):
for i in range(sz1):
for j in range(sz2):
self.assertEqual(x[i][j], y[i][j])
# empty
x = torch.Tensor().type(tp)
y = x.numpy()
self.assertEqual(y.size, 0)
# contiguous 2D
sz1 = 3
sz2 = 5
x = torch.randn(sz1, sz2).mul(255).type(tp)
y = x.numpy()
check2d(x, y)
self.assertTrue(y.flags['C_CONTIGUOUS'])
# with storage offset
xm = torch.randn(sz1 * 2, sz2).mul(255).type(tp)
x = xm.narrow(0, sz1 - 1, sz1)
y = x.numpy()
self.assertTrue(x.storage_offset() > 0)
check2d(x, y)
self.assertTrue(y.flags['C_CONTIGUOUS'])
# non-contiguous 2D
x = torch.randn(sz2, sz1).mul(255).type(tp).t()
y = x.numpy()
check2d(x, y)
self.assertFalse(y.flags['C_CONTIGUOUS'])
# with storage offset
xm = torch.randn(sz2 * 2, sz1).mul(255).type(tp)
x = xm.narrow(0, sz2 - 1, sz2).t()
y = x.numpy()
self.assertTrue(x.storage_offset() > 0)
check2d(x, y)
# non-contiguous 2D with holes
xm = torch.randn(sz2 * 2, sz1 * 2).mul(255).type(tp)
x = xm.narrow(0, sz2 - 1, sz2).narrow(1, sz1 - 1, sz1).t()
y = x.numpy()
self.assertTrue(x.storage_offset() > 0)
check2d(x, y)
if tp != 'torch.HalfTensor':
# check writeable
x = torch.randn(3, 4).mul(255).type(tp)
y = x.numpy()
self.assertTrue(y.flags.writeable)
y[0][1] = 3
self.assertTrue(x[0][1] == 3)
y = x.t().numpy()
self.assertTrue(y.flags.writeable)
y[0][1] = 3
self.assertTrue(x[0][1] == 3)
def test_dlpack_conversion(self):
x = torch.randn(1, 2, 3, 4).type('torch.FloatTensor')
z = from_dlpack(to_dlpack(x))
self.assertEqual(z, x)
@unittest.skipIf(not torch.cuda.is_available(), "No CUDA")
def test_dlpack_cuda(self):
x = torch.randn(1, 2, 3, 4).cuda()
z = from_dlpack(to_dlpack(x))
self.assertEqual(z, x)
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_from_numpy(self):
dtypes = [
np.double,
np.float,
np.float16,
np.int64,
np.int32,
np.int16,
np.int8,
np.uint8,
np.longlong,
]
for dtype in dtypes:
array = np.array([1, 2, 3, 4], dtype=dtype)
tensor_from_array = torch.from_numpy(array)
# TODO: change to tensor equality check once HalfTensor
# implements `==`
for i in range(len(array)):
self.assertEqual(tensor_from_array[i], array[i])
# check storage offset
x = np.linspace(1, 125, 125)
x.shape = (5, 5, 5)
x = x[1]
expected = torch.arange(1, 126).view(5, 5, 5)[1]
self.assertEqual(torch.from_numpy(x), expected)
# check noncontiguous
x = np.linspace(1, 25, 25)
x.shape = (5, 5)
expected = torch.arange(1, 26).view(5, 5).t()
self.assertEqual(torch.from_numpy(x.T), expected)
# check noncontiguous with holes
x = np.linspace(1, 125, 125)
x.shape = (5, 5, 5)
x = x[:, 1]
expected = torch.arange(1, 126).view(5, 5, 5)[:, 1]
self.assertEqual(torch.from_numpy(x), expected)
# check zero dimensional
x = np.zeros((0, 2))
self.assertEqual(torch.from_numpy(x).shape, (0, 2))
x = np.zeros((2, 0))
self.assertEqual(torch.from_numpy(x).shape, (2, 0))
# check ill-sized strides raise exception
x = np.array([3., 5., 8.])
x.strides = (3,)
self.assertRaises(ValueError, lambda: torch.from_numpy(x))
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_ctor_with_numpy_array(self):
correct_dtypes = [
np.double,
np.float,
np.float16,
np.int64,
np.int32,
np.int16,
np.int8,
np.uint8,
]
incorrect_byteorder = '>' if sys.byteorder == 'little' else '<'
incorrect_dtypes = map(lambda t: incorrect_byteorder + t, ['d', 'f'])
for dtype in correct_dtypes:
array = np.array([1, 2, 3, 4], dtype=dtype)
# Upcast
tensor = torch.DoubleTensor(array)
for i in range(len(array)):
self.assertEqual(tensor[i], array[i])
if torch.cuda.is_available():
tensor = torch.cuda.DoubleTensor(array)
for i in range(len(array)):
self.assertEqual(tensor[i], array[i])
# Downcast (sometimes)
tensor = torch.FloatTensor(array)
for i in range(len(array)):
self.assertEqual(tensor[i], array[i])
tensor = torch.HalfTensor(array)
for i in range(len(array)):
self.assertEqual(tensor[i], array[i])
if torch.cuda.is_available():
tensor = torch.cuda.FloatTensor(array)
for i in range(len(array)):
self.assertEqual(tensor[i], array[i])
tensor = torch.cuda.HalfTensor(array)
for i in range(len(array)):
self.assertEqual(tensor[i], array[i])
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_ctor_with_numpy_scalar_ctor(self):
dtypes = [
np.double,
np.float,
np.float16,
np.int64,
np.int32,
np.int16,
np.uint8
]
for dtype in dtypes:
self.assertEqual(dtype(42), torch.tensor(dtype(42)).item())
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_numpy_index(self):
i = np.int32([0, 1, 2])
x = torch.randn(5, 5)
for idx in i:
self.assertFalse(isinstance(idx, int))
self.assertEqual(x[idx], x[int(idx)])
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_numpy_array_interface(self):
types = [
torch.DoubleTensor,
torch.FloatTensor,
torch.HalfTensor,
torch.LongTensor,
torch.IntTensor,
torch.ShortTensor,
torch.ByteTensor,
]
dtypes = [
np.float64,
np.float32,
np.float16,
np.int64,
np.int32,
np.int16,
np.uint8,
]
for tp, dtype in zip(types, dtypes):
if np.dtype(dtype).kind == 'u':
x = torch.Tensor([1, 2, 3, 4]).type(tp)
array = np.array([1, 2, 3, 4], dtype=dtype)
else:
x = torch.Tensor([1, -2, 3, -4]).type(tp)
array = np.array([1, -2, 3, -4], dtype=dtype)
# Test __array__ w/o dtype argument
asarray = np.asarray(x)
self.assertIsInstance(asarray, np.ndarray)
self.assertEqual(asarray.dtype, dtype)
for i in range(len(x)):
self.assertEqual(asarray[i], x[i])
# Test __array_wrap__, same dtype
abs_x = np.abs(x)
abs_array = np.abs(array)
self.assertIsInstance(abs_x, tp)
for i in range(len(x)):
self.assertEqual(abs_x[i], abs_array[i])
# Test __array__ with dtype argument
for dtype in dtypes:
x = torch.IntTensor([1, -2, 3, -4])
asarray = np.asarray(x, dtype=dtype)
self.assertEqual(asarray.dtype, dtype)
if np.dtype(dtype).kind == 'u':
wrapped_x = np.array([1, -2, 3, -4], dtype=dtype)
for i in range(len(x)):
self.assertEqual(asarray[i], wrapped_x[i])
else:
for i in range(len(x)):
self.assertEqual(asarray[i], x[i])
# Test some math functions with float types
float_types = [torch.DoubleTensor, torch.FloatTensor]
float_dtypes = [np.float64, np.float32]
for tp, dtype in zip(float_types, float_dtypes):
x = torch.Tensor([1, 2, 3, 4]).type(tp)
array = np.array([1, 2, 3, 4], dtype=dtype)
for func in ['sin', 'sqrt', 'ceil']:
ufunc = getattr(np, func)
res_x = ufunc(x)
res_array = ufunc(array)
self.assertIsInstance(res_x, tp)
for i in range(len(x)):
self.assertEqual(res_x[i], res_array[i])
# Test functions with boolean return value
for tp, dtype in zip(types, dtypes):
x = torch.Tensor([1, 2, 3, 4]).type(tp)
array = np.array([1, 2, 3, 4], dtype=dtype)
geq2_x = np.greater_equal(x, 2)
geq2_array = np.greater_equal(array, 2).astype('uint8')
self.assertIsInstance(geq2_x, torch.ByteTensor)
for i in range(len(x)):
self.assertEqual(geq2_x[i], geq2_array[i])
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_multiplication_numpy_scalar(self):
for np_dtype in [np.float32, np.float64, np.int32, np.int64, np.int16, np.uint8]:
for t_dtype in [torch.float, torch.double]:
np_sc = np_dtype(2.0)
t = torch.ones(2, requires_grad=True, dtype=t_dtype)
r1 = t * np_sc
self.assertIsInstance(r1, torch.Tensor)
self.assertTrue(r1.dtype == t_dtype)
self.assertTrue(r1.requires_grad)
r2 = np_sc * t
self.assertIsInstance(r2, torch.Tensor)
self.assertTrue(r2.dtype == t_dtype)
self.assertTrue(r2.requires_grad)
def test_error_msg_type_translation(self):
with self.assertRaisesRegex(
RuntimeError,
# message includes both Double and Long
'(?=.*Double)(?=.*Long)'):
# Calls model with a DoubleTensor input but LongTensor weights
input = torch.autograd.Variable(torch.randn(1, 1, 1, 6).double())
weight = torch.zeros(1, 1, 1, 3).long()
model = torch.nn.Conv2d(1, 1, (1, 3), stride=1, padding=0, bias=False)
model.weight.data = weight
out = model(input)
def test_tensor_from_sequence(self):
class MockSequence(object):
def __init__(self, lst):
self.lst = lst
def __len__(self):
return len(self.lst)
def __getitem__(self, item):
raise TypeError
class GoodMockSequence(MockSequence):
def __getitem__(self, item):
return self.lst[item]
bad_mock_seq = MockSequence([1.0, 2.0, 3.0])
good_mock_seq = GoodMockSequence([1.0, 2.0, 3.0])
with self.assertRaisesRegex(ValueError, 'could not determine the shape'):
torch.Tensor(bad_mock_seq)
self.assertEqual(torch.Tensor([1.0, 2.0, 3.0]), torch.Tensor(good_mock_seq))
def test_comparison_ops(self):
x = torch.randn(5, 5)
y = torch.randn(5, 5)
eq = x == y
for idx in iter_indices(x):
self.assertEqual(x[idx] == y[idx], eq[idx] == 1)
ne = x != y
for idx in iter_indices(x):
self.assertEqual(x[idx] != y[idx], ne[idx] == 1)
lt = x < y
for idx in iter_indices(x):
self.assertEqual(x[idx] < y[idx], lt[idx] == 1)
le = x <= y
for idx in iter_indices(x):
self.assertEqual(x[idx] <= y[idx], le[idx] == 1)
gt = x > y
for idx in iter_indices(x):
self.assertEqual(x[idx] > y[idx], gt[idx] == 1)
ge = x >= y
for idx in iter_indices(x):
self.assertEqual(x[idx] >= y[idx], ge[idx] == 1)
def test_bitwise_ops(self):
x = torch.randn(5, 5).gt(0)
y = torch.randn(5, 5).gt(0)
and_result = x & y
for idx in iter_indices(x):
if and_result[idx]:
self.assertTrue(x[idx] and y[idx])
else:
self.assertFalse(x[idx] and y[idx])
or_result = x | y
for idx in iter_indices(x):
if or_result[idx]:
self.assertTrue(x[idx] or y[idx])
else:
self.assertFalse(x[idx] or y[idx])
xor_result = x ^ y
for idx in iter_indices(x):
if xor_result[idx]:
self.assertTrue(x[idx] ^ y[idx])
else:
self.assertFalse(x[idx] ^ y[idx])
invert_result = ~x
for idx in iter_indices(x):
self.assertEqual(1 - x[idx], invert_result[idx])
x_clone = x.clone()
x_clone &= y
self.assertEqual(x_clone, and_result)
x_clone = x.clone()
x_clone |= y
self.assertEqual(x_clone, or_result)
x_clone = x.clone()
x_clone ^= y
self.assertEqual(x_clone, xor_result)
def test_invert(self):
x = torch.ByteTensor([0, 1, 1])
self.assertEqual((~x).tolist(), [1, 0, 0])
def test_apply(self):
x = torch.arange(1, 6)
res = x.clone().apply_(lambda k: k + k)
self.assertEqual(res, x * 2)
self.assertRaises(TypeError, lambda: x.apply_(lambda k: "str"))
def test_map(self):
x = torch.autograd.Variable(torch.randn(3, 3))
y = torch.autograd.Variable(torch.randn(3))
res = x.clone()
res.map_(y, lambda a, b: a + b)
self.assertEqual(res, x + y)
self.assertRaisesRegex(TypeError, "not callable", lambda: res.map_(y, "str"))
def test_map2(self):
x = torch.autograd.Variable(torch.randn(3, 3))
y = torch.autograd.Variable(torch.randn(3))
z = torch.autograd.Variable(torch.randn(1, 3))
res = x.clone()
res.map2_(y, z, lambda a, b, c: a + b * c)
self.assertEqual(res, x + y * z)
z.requires_grad = True
self.assertRaisesRegex(
RuntimeError, "requires grad",
lambda: res.map2_(y, z, lambda a, b, c: a + b * c))
def test_Size(self):
x = torch.Size([1, 2, 3])
self.assertIsInstance(x, tuple)
self.assertEqual(x[0], 1)
self.assertEqual(x[1], 2)
self.assertEqual(x[2], 3)
self.assertEqual(len(x), 3)
self.assertRaises(TypeError, lambda: torch.Size(torch.ones(3)))
self.assertIsInstance(x * 2, torch.Size)
self.assertIsInstance(x[:-1], torch.Size)
self.assertIsInstance(x + x, torch.Size)
def test_Size_scalar(self):
three = torch.tensor(3)
two = torch.tensor(2)
x = torch.Size([0, 1, two, three, 4])
for i in range(1, 5):
self.assertEqual(x[i], i)
def test_Size_iter(self):
for sizes in [iter([1, 2, 3, 4, 5]), range(1, 6)]:
x = torch.Size(sizes)
for i in range(0, 5):
self.assertEqual(x[i], i + 1)
def test_t_not_2d_error(self):
self.assertRaises(RuntimeError, lambda: torch.randn(2, 3, 4).t())
self.assertRaises(RuntimeError, lambda: torch.randn(2, 3, 4).t_())
# unit test for special case transposed copy (see ATen/native/Copy.cpp for details)
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_big_transpose(self):
t = torch.rand(456, 789)
t1 = t.t().contiguous()
t2 = torch.from_numpy(t.numpy().transpose())
self.assertEqual(t1, t2)
def test_inplace_division(self):
t = torch.rand(5, 5)
id_before = id(t)
t /= 2
id_after = id(t)
self.assertEqual(id_before, id_after)
def test_simple_scalar_cast(self):
ok = [torch.Tensor([1.5]), torch.zeros(1, 1, 1, 1)]
ok_values = [1.5, 0]
not_ok = map(torch.Tensor, [[], [1, 2], [[1, 2], [3, 4]]])
for tensor, value in zip(ok, ok_values):
self.assertEqual(int(tensor), int(value))
self.assertEqual(float(tensor), float(value))
if sys.version_info[0] < 3:
self.assertEqual(long(tensor), long(value))
for tensor in not_ok:
self.assertRaises(ValueError, lambda: int(tensor))
self.assertRaises(ValueError, lambda: float(tensor))
if sys.version_info[0] < 3:
self.assertRaises(ValueError, lambda: long(tensor))
def test_offset_scalar_cast(self):
x = torch.Tensor([1, 2, 3])
y = x[2:]
self.assertEqual(int(y), 3)
# skip this test for now as it affects all tests
@unittest.skipIf(True, "flush_denormal not supported")
def test_set_flush_denormal(self):
tiny_float = 1e-42
tiny_double = 1e-320
float_tensor = torch.FloatTensor([1.0, tiny_float])
double_tensor = torch.DoubleTensor([1.0, tiny_float, tiny_double])
self.assertEqual(float_tensor[0], 1.0, prec=0.0)
self.assertEqual(float_tensor[1], tiny_float, prec=tiny_float / 16)
self.assertEqual(double_tensor[0], 1.0, prec=0.0)
self.assertEqual(double_tensor[1], tiny_float, prec=0.0)
self.assertEqual(double_tensor[2], tiny_double, prec=0.0)
torch.set_flush_denormal(True)
self.assertEqual(float_tensor[0], 1.0, prec=0.0)
self.assertEqual(float_tensor[1], 0.0, prec=0.0) # tiny_float to zero
self.assertEqual(double_tensor[0], 1.0, prec=0.0)
# tiny_float is not converted to zero in double type
self.assertEqual(double_tensor[1], tiny_float, prec=0.0)
self.assertEqual(double_tensor[2], 0.0, prec=0.0) # tiny_double to zero
torch.set_flush_denormal(False)
def test_unique(self):
x = torch.LongTensor([1, 2, 3, 2, 8, 5, 2, 3])
expected_unique = torch.LongTensor([1, 2, 3, 5, 8])
expected_inverse = torch.LongTensor([0, 1, 2, 1, 4, 3, 1, 2])
x_unique = torch.unique(x)
self.assertEqual(
expected_unique.tolist(), sorted(x_unique.tolist()))
x_unique, x_inverse = x.unique(return_inverse=True)
self.assertEqual(
expected_unique.tolist(), sorted(x_unique.tolist()))
self.assertEqual(expected_inverse.numel(), x_inverse.numel())
x_unique = x.unique(sorted=True)
self.assertEqual(expected_unique, x_unique)
x_unique, x_inverse = torch.unique(
x, sorted=True, return_inverse=True)
self.assertEqual(expected_unique, x_unique)
self.assertEqual(expected_inverse, x_inverse)
# Tests per-element unique on a higher rank tensor.
y = x.view(2, 2, 2)
y_unique, y_inverse = y.unique(sorted=True, return_inverse=True)
self.assertEqual(expected_unique, y_unique)
self.assertEqual(expected_inverse.view(y.size()), y_inverse)
# Tests unique on other types.
int_unique, int_inverse = torch.unique(
torch.IntTensor([2, 1, 2]), sorted=True, return_inverse=True)
self.assertEqual(torch.IntTensor([1, 2]), int_unique)
self.assertEqual(torch.LongTensor([1, 0, 1]), int_inverse)
double_unique, double_inverse = torch.unique(
torch.DoubleTensor([2., 1.5, 2.1, 2.]),
sorted=True,
return_inverse=True,
)
self.assertEqual(torch.DoubleTensor([1.5, 2., 2.1]), double_unique)
self.assertEqual(torch.LongTensor([1, 0, 2, 1]), double_inverse)
byte_unique, byte_inverse = torch.unique(
torch.ByteTensor([133, 7, 7, 7, 42, 128]),
sorted=True,
return_inverse=True,
)
self.assertEqual(torch.ByteTensor([7, 42, 128, 133]), byte_unique)
self.assertEqual(torch.LongTensor([3, 0, 0, 0, 1, 2]), byte_inverse)
def test_unique_dim(self):
def run_test(dtype=torch.float):
x = torch.tensor([[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]],
[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]]], dtype=dtype)
expected_unique_dim0 = torch.tensor([[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]]], dtype=dtype)
expected_inverse_dim0 = torch.tensor([0, 0])
expected_unique_dim1 = torch.tensor([[[0., 1.],
[1., 1.],
[2., 1.]],
[[0., 1.],
[1., 1.],
[2., 1.]]], dtype=dtype)
expected_inverse_dim1 = torch.tensor([1, 0, 2, 0])
expected_unique_dim2 = torch.tensor([[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]],
[[1., 1.],
[0., 1.],
[2., 1.],
[0., 1.]]], dtype=dtype)
expected_inverse_dim2 = torch.tensor([0, 1])
# dim0
x_unique = torch.unique(x, dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
x_unique, x_inverse = torch.unique(x, return_inverse=True, dim=0)
self.assertEqual(expected_unique_dim0, x_unique)
self.assertEqual(expected_inverse_dim0, x_inverse)
# dim1
x_unique = torch.unique(x, dim=1)
self.assertEqual(expected_unique_dim1, x_unique)
x_unique, x_inverse = torch.unique(x, return_inverse=True, dim=1)
self.assertEqual(expected_unique_dim1, x_unique)
self.assertEqual(expected_inverse_dim1, x_inverse)
# dim2
x_unique = torch.unique(x, dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
x_unique, x_inverse = torch.unique(x, return_inverse=True, dim=2)
self.assertEqual(expected_unique_dim2, x_unique)
self.assertEqual(expected_inverse_dim2, x_inverse)
run_test(torch.float)
run_test(torch.double)
run_test(torch.long)
run_test(torch.uint8)
@staticmethod
def _test_bincount(self, device):
# negative input throws
with self.assertRaisesRegex(RuntimeError, '1-d non-negative integral'):
torch.bincount(torch.tensor([1, -1], device=device))
# n-d input, with n > 1 throws
with self.assertRaisesRegex(RuntimeError, '1-d non-negative integral'):
torch.bincount(torch.tensor([[1, 2], [3, 4]], device=device))
# floating input type throws
with self.assertRaisesRegex(RuntimeError, 'not implemented'):
torch.bincount(torch.tensor([1., 0.3], device=device))
# minlength < 0 throws
with self.assertRaisesRegex(RuntimeError, 'minlength should be >= 0'):
torch.bincount(torch.tensor([1, 3], device=device),
torch.tensor([.2, .2], device=device),
minlength=-1)
# input and weights dim mismatch
with self.assertRaisesRegex(RuntimeError, 'same length'):
torch.bincount(torch.tensor([1, 0], device=device),
torch.tensor([1., 0.3, 0.5], device=device))
# 1-d input with no elements and default minlength
self.assertEqual(torch.bincount(torch.tensor([], device=device, dtype=torch.long)),
torch.zeros(0, dtype=torch.long, device=device))
# 1-d input with no elements and specified minlength
self.assertEqual(torch.bincount(torch.tensor([], device=device, dtype=torch.long), minlength=10),
torch.zeros(10, dtype=torch.long, device=device))
# test tensor method without weights
long_counts = torch.tensor(
[0, 3, 2, 1, 3], dtype=torch.uint8, device=device).bincount()
self.assertEqual(
torch.tensor([1, 1, 1, 2], dtype=torch.int64, device=device),
long_counts)
# test minlength functionality
int_counts = torch.bincount(
torch.tensor([1, 1, 1, 1], device=device), minlength=5)
self.assertEqual(
torch.tensor([0, 4, 0, 0, 0], dtype=torch.int64, device=device),
int_counts)
# test weights
byte_counts = torch.bincount(
torch.tensor([0, 1, 1, 1, 4], device=device),
torch.tensor([.1, .2, .3, .4, .5], device=device))
self.assertEqual(
torch.tensor([0.1, 0.9, 0, 0, 0.5], device=device), byte_counts)
byte_counts = torch.bincount(
torch.tensor([0, 1, 1, 1, 4], device=device),
torch.tensor([1, 2, 3, 4, 5], dtype=torch.int8, device=device))
self.assertEqual(
torch.tensor([1, 9, 0, 0, 5], device=device), byte_counts)
# test non-contiguous inputs and weights
inputs = torch.tensor([[0, 0], [3, 1], [2, 1], [1, 1], [3, 4]], device=device)
weights = torch.tensor([[.1, 1], [.2, 2], [.3, 3], [.4, 4], [.5, 5]], device=device)
print(inputs[:, 1])
print(weights[:, 1])
for i in [0, 1]:
assert not inputs[:, i].is_contiguous(), "Inputs are supposed to be non-contiguous"
assert not weights[:, i].is_contiguous(), "Weights are supposed to be non-contiguous"
# inputs are non-contiguous but weights are contiguous
self.assertEqual(inputs[:, 0].bincount(), torch.tensor([1, 1, 1, 2]))
# inputs and weights are non-contiguous
print(inputs[:, 1].bincount(weights[:, 1]))
self.assertEqual(inputs[:, 1].bincount(weights[:, 1]), torch.tensor([1, 9, 0, 0, 5]))
# weights are non-contiguous but inputs are contiguous
self.assertEqual(inputs[:, 1].contiguous().bincount(weights[:, 1]),
torch.tensor([1, 9, 0, 0, 5]))
# test large number of bins - global memory use
big_exp = torch.zeros(10000000, device=device)
big_exp[-1] = 50.0
big_w = torch.tensor([.5] * 100, device=device)
big_out = torch.tensor([9999999] * 100, device=device).bincount(big_w)
self.assertEqual(big_exp, big_out)
# test large input size
big_exp = torch.zeros(2, device=device)
big_exp[1] = 1000000
big_out = torch.ones(1000000, dtype=torch.int8, device=device).bincount()
self.assertEqual(big_exp, big_out)
def test_bincount_cpu(self):
self._test_bincount(self, device='cpu')
def test_is_nonzero(self):
self.assertExpectedRaises(RuntimeError, lambda: torch.tensor([]).is_nonzero(), subname="empty")
self.assertExpectedRaises(RuntimeError, lambda: torch.tensor([0, 0]).is_nonzero(), subname="multiple")
self.assertFalse(torch.tensor(0).is_nonzero())
self.assertTrue(torch.tensor(1).is_nonzero())
self.assertFalse(torch.tensor([0]).is_nonzero())
self.assertTrue(torch.tensor([1]).is_nonzero())
self.assertFalse(torch.tensor([[0]]).is_nonzero())
self.assertTrue(torch.tensor([[1]]).is_nonzero())
def test_meshgrid(self):
a = torch.tensor(1)
b = torch.tensor([1, 2, 3])
c = torch.tensor([1, 2])
grid_a, grid_b, grid_c = torch.meshgrid([a, b, c])
self.assertEqual(grid_a.shape, torch.Size([1, 3, 2]))
self.assertEqual(grid_b.shape, torch.Size([1, 3, 2]))
self.assertEqual(grid_c.shape, torch.Size([1, 3, 2]))
grid_a2, grid_b2, grid_c2 = torch.meshgrid(a, b, c)
self.assertEqual(grid_a2.shape, torch.Size([1, 3, 2]))
self.assertEqual(grid_b2.shape, torch.Size([1, 3, 2]))
self.assertEqual(grid_c2.shape, torch.Size([1, 3, 2]))
expected_grid_a = torch.ones(1, 3, 2, dtype=torch.int64)
expected_grid_b = torch.tensor([[[1, 1],
[2, 2],
[3, 3]]])
expected_grid_c = torch.tensor([[[1, 2],
[1, 2],
[1, 2]]])
self.assertTrue(grid_a.equal(expected_grid_a))
self.assertTrue(grid_b.equal(expected_grid_b))
self.assertTrue(grid_c.equal(expected_grid_c))
self.assertTrue(grid_a2.equal(expected_grid_a))
self.assertTrue(grid_b2.equal(expected_grid_b))
self.assertTrue(grid_c2.equal(expected_grid_c))
@unittest.skipIf(torch.cuda.is_available() or IS_SANDCASTLE, "CUDA is available, can't test CUDA not built error")
def test_cuda_not_built(self):
msg = "Torch not compiled with CUDA enabled"
self.assertRaisesRegex(AssertionError, msg, lambda: torch.cuda.current_device())
self.assertRaisesRegex(AssertionError, msg, lambda: torch.tensor([1], device="cuda"))
self.assertRaisesRegex(AssertionError, msg, lambda: torch.tensor([1]).cuda())
self.assertRaisesRegex(AssertionError, msg, lambda: torch.cuda.FloatTensor())
self.assertRaisesRegex(AssertionError, msg, lambda: torch.tensor([1]).to(device="cuda"))
def test_cast_binary_op(self):
# Scalar
a = torch.tensor(2)
b = torch.tensor(3)
a_copy = a.clone()
b_copy = b.clone()
self.assertEqual(torch.tensor(6), a.float() * b)
self.assertEqual(a.type(), a_copy.type())
self.assertEqual(a.data.type(), a_copy.data.type())
self.assertEqual(b.type(), b_copy.type())
self.assertEqual(b.data.type(), b_copy.type())
@unittest.skipIf(torch.cuda.device_count() < 2, 'only one GPU detected')
def test_reverse_binary_ops_multiple_device(self):
self.assertEqual(2 + torch.tensor(3), 2 + torch.tensor(3).to("cuda:1")) # __radd__
self.assertEqual(2 - torch.tensor(3), 2 - torch.tensor(3).to("cuda:1")) # __rsub__
self.assertEqual(2 * torch.tensor(3), 2 * torch.tensor(3).to("cuda:1")) # __rmul__
self.assertEqual(2 / torch.tensor(3), 2 / torch.tensor(3).to("cuda:1")) # __rtruediv__
self.assertEqual(2 // torch.tensor(3), 2 // torch.tensor(3).to("cuda:1")) # __rfloordiv__
with self.assertRaisesRegex(RuntimeError, "expected both inputs to be on same device"):
torch.tensor(2).to("cuda:1") + torch.tensor(3).to("cuda:0")
with self.assertRaisesRegex(RuntimeError, "expected both inputs to be on same device"):
torch.tensor(2).to("cuda:1") - torch.tensor(3).to("cuda:0")
with self.assertRaisesRegex(RuntimeError, "expected both inputs to be on same device"):
torch.tensor(2).to("cuda:1") * torch.tensor(3).to("cuda:0")
with self.assertRaisesRegex(RuntimeError, "expected both inputs to be on same device"):
torch.tensor(2).to("cuda:1") / torch.tensor(3).to("cuda:0")
with self.assertRaisesRegex(RuntimeError, "expected both inputs to be on same device"):
torch.tensor(2).to("cuda:1") // torch.tensor(3).to("cuda:0")
# Functions to test negative dimension wrapping
METHOD = 1
INPLACE_METHOD = 2
FUNCTIONAL = 4
DIM_ARG = None
def make_neg_dim_test(name, tensor_arg, arg_constr, types, extra_dim=0):
def neg_dim_test(self):
if isinstance(tensor_arg, list):
assert METHOD not in types and INPLACE_METHOD not in types
x = [torch.randn(arg) for arg in tensor_arg]
ndim = len(tensor_arg[-1])
else:
x = torch.randn(*tensor_arg)
ndim = len(tensor_arg)
ndim += extra_dim
n_dim_to_test = sum(map(lambda e: e is DIM_ARG, arg_constr()))
for dims_val in combinations(range(ndim), n_dim_to_test):
arg = arg_constr()
arg_neg = copy.deepcopy(arg)
idx = 0
for i, v in enumerate(arg):
if v is DIM_ARG:
arg[i] = dims_val[idx]
arg_neg[i] = dims_val[idx] - ndim
idx += 1
if METHOD in types:
a = getattr(x, name)(*arg)
b = getattr(x, name)(*arg_neg)
self.assertEqual(a, b)
if INPLACE_METHOD in types:
a = x.clone()
getattr(a, name + '_')(*arg)
b = x.clone()
getattr(b, name + '_')(*arg_neg)
self.assertEqual(a, b)
if FUNCTIONAL in types:
a = getattr(torch, name)(x, *arg)
b = getattr(torch, name)(x, *arg_neg)
self.assertEqual(a, b)
return neg_dim_test
def idx_tensor(size, max_val):
return torch.LongTensor(*size).random_(0, max_val - 1)
def add_neg_dim_tests():
neg_dim_tests = [
('narrow', (10, 20, 30), lambda: [DIM_ARG, 0, 5], [METHOD]),
('transpose', (10, 20, 30), lambda: [DIM_ARG, DIM_ARG], [METHOD, INPLACE_METHOD, FUNCTIONAL]),
('size', (10, 20, 30), lambda: [DIM_ARG], [METHOD]),
('cat', [(2, 3, 4), (2, 3, 4)], lambda: [DIM_ARG], [FUNCTIONAL]),
('chunk', (10, 20, 30), lambda: [5, DIM_ARG], [METHOD, FUNCTIONAL]),
('gather', (10, 20), lambda: [DIM_ARG, idx_tensor((10, 20), 10)], [METHOD, FUNCTIONAL]),
('index_select', (10, 10), lambda: [DIM_ARG, idx_tensor((10,), 10)], [METHOD, FUNCTIONAL]),
('split', (10, 20), lambda: [5, DIM_ARG], [METHOD, FUNCTIONAL]),
('squeeze', (10, 1, 20, 1), lambda: [DIM_ARG], [METHOD, INPLACE_METHOD, FUNCTIONAL]),
('unbind', (2, 3, 4), lambda: [DIM_ARG], [FUNCTIONAL]),
('unsqueeze', (10, 20), lambda: [DIM_ARG], [METHOD, INPLACE_METHOD, FUNCTIONAL], 1),
('cumprod', (10, 20), lambda: [DIM_ARG], [METHOD, FUNCTIONAL]),
('cumsum', (10, 20), lambda: [DIM_ARG], [METHOD, FUNCTIONAL]),
('mean', (10, 20), lambda: [DIM_ARG], [METHOD, FUNCTIONAL]),
('median', (10, 20), lambda: [DIM_ARG], [METHOD, FUNCTIONAL]),
('mode', (10, 20), lambda: [DIM_ARG], [METHOD, FUNCTIONAL]),
('norm', (10, 20), lambda: [2, DIM_ARG], [METHOD, FUNCTIONAL]),
('prod', (10, 20), lambda: [DIM_ARG], [METHOD, FUNCTIONAL]),
('std', (10, 20), lambda: [DIM_ARG], [METHOD, FUNCTIONAL]),
('sum', (10, 20), lambda: [DIM_ARG], [METHOD, FUNCTIONAL]),
('var', (10, 20), lambda: [DIM_ARG], [METHOD, FUNCTIONAL]),
('kthvalue', (10, 20), lambda: [3, DIM_ARG], [METHOD, FUNCTIONAL]),
('max', (10, 20), lambda: [DIM_ARG], [METHOD, FUNCTIONAL]),
('min', (10, 20), lambda: [DIM_ARG], [METHOD, FUNCTIONAL]),
('sort', (10, 20), lambda: [DIM_ARG], [METHOD, FUNCTIONAL]),
('topk', (10, 20), lambda: [5, DIM_ARG], [METHOD, FUNCTIONAL]),
('renorm', (10, 20), lambda: [2, DIM_ARG, 1], [METHOD, INPLACE_METHOD, FUNCTIONAL]),
('index_add', (10, 10), lambda: [DIM_ARG, idx_tensor((10,), 10), torch.randn(10, 10)], [INPLACE_METHOD]),
('index_copy', (10, 10), lambda: [DIM_ARG, idx_tensor((10,), 10), torch.randn(10, 10)], [INPLACE_METHOD]),
('index_fill', (10, 10), lambda: [DIM_ARG, idx_tensor((10,), 10), 12], [INPLACE_METHOD]),
('scatter', (10, 10), lambda: [DIM_ARG, idx_tensor((10, 10), 10), torch.randn(10, 10)], [INPLACE_METHOD]),
('select', (10, 20), lambda: [DIM_ARG, 3], [METHOD]),
('unfold', (10, 20), lambda: [DIM_ARG, 5, 2], [METHOD]),
]
for decl in neg_dim_tests:
if len(decl) == 4:
name, tensor_arg, arg_constr, types = decl
extra_dim = 0
elif len(decl) == 5:
name, tensor_arg, arg_constr, types, extra_dim = decl
test_name = 'test_' + name + '_neg_dim'
assert not hasattr(_TestTorchMixin, test_name), "Duplicated test name: " + test_name
setattr(_TestTorchMixin, test_name, make_neg_dim_test(name, tensor_arg, arg_constr, types, extra_dim))
add_neg_dim_tests()
class TestTorch(TestCase, _TestTorchMixin):
pass
if __name__ == '__main__':
run_tests()