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android / platform / external / pytorch / 7cdd018db4 / . / test / test_torch.py
blob: 4a1fd6c09376d20ed0564b6dc299be9800effc54 [file] [log] [blame]
import sys
import os
import math
import random
import copy
import torch
import torch.cuda
import tempfile
import unittest
import warnings
from itertools import product, combinations
from common import TestCase, iter_indices, TEST_NUMPY, run_tests, download_file, skipIfNoLapack, \
suppress_warnings
if TEST_NUMPY:
import numpy as np
SIZE = 100
class TestTorch(TestCase):
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)
def _testMath(self, torchfn, mathfn):
size = (10, 5)
# contiguous
m1 = torch.randn(*size)
res1 = torchfn(m1[4])
res2 = res1.clone().zero_()
for i, v in enumerate(m1[4]):
res2[i] = mathfn(v)
self.assertEqual(res1, res2)
# non-contiguous
m1 = torch.randn(*size)
res1 = torchfn(m1[:, 4])
res2 = res1.clone().zero_()
for i, v in enumerate(m1[:, 4]):
res2[i] = mathfn(v)
self.assertEqual(res1, res2)
def _testMathByName(self, function_name):
torchfn = getattr(torch, function_name)
mathfn = getattr(math, function_name)
self._testMath(torchfn, mathfn)
def test_sin(self):
self._testMathByName('sin')
def test_sinh(self):
self._testMathByName('sinh')
def test_lgamma(self):
self._testMathByName('lgamma')
def test_asin(self):
self._testMath(torch.asin, lambda x: math.asin(x) if abs(x) <= 1 else float('nan'))
def test_cos(self):
self._testMathByName('cos')
def test_cosh(self):
self._testMathByName('cosh')
def test_acos(self):
self._testMath(torch.acos, lambda x: math.acos(x) if abs(x) <= 1 else float('nan'))
def test_tan(self):
self._testMathByName('tan')
def test_tanh(self):
self._testMathByName('tanh')
def test_atan(self):
self._testMathByName('atan')
def test_log(self):
self._testMath(torch.log, lambda x: math.log(x) if x > 0 else float('nan'))
def test_sqrt(self):
self._testMath(torch.sqrt, lambda x: math.sqrt(x) if x > 0 else float('nan'))
def test_exp(self):
self._testMathByName('exp')
def test_floor(self):
self._testMathByName('floor')
def test_ceil(self):
self._testMathByName('ceil')
def test_rsqrt(self):
self._testMath(torch.rsqrt, lambda x: 1 / math.sqrt(x) if x > 0 else float('nan'))
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._testMath(torch.frac, lambda x: math.fmod(x, 1))
def test_trunc(self):
self._testMath(torch.trunc, lambda x: x - math.fmod(x, 1))
def test_round(self):
self._testMath(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())
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_has_storage_numpy(self):
arr = np.array([], dtype=np.float32)
self.assertIsNotNone(torch.Tensor(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] = float('nan')
res1val, res1ind = torch.max(m1, 0)
self.assertNotEqual(res1val[0], res1val[0])
self.assertEqual(res1ind[0], index)
res1val = torchfn(m1)
self.assertNotEqual(res1val, res1val)
def test_max(self):
self._testSelection(torch.max, max)
def test_min(self):
self._testSelection(torch.min, min)
def test_dim_reduction(self):
dim_red_fns = [
"mean", "median", "mode", "norm", "prod",
"std", "sum", "var", "max", "min"]
def normfn_attr(t, dim, keepdim=False):
attr = getattr(torch, "norm")
return attr(t, 2, dim, keepdim)
for fn_name in dim_red_fns:
x = torch.randn(3, 4, 5)
fn_attr = getattr(torch, fn_name) if fn_name != "norm" else normfn_attr
def fn(t, dim, keepdim=False):
ans = fn_attr(x, dim, keepdim)
return ans if not isinstance(ans, tuple) else ans[0]
dim = random.randint(0, 2)
self.assertEqual(fn(x, dim).unsqueeze(dim), fn(x, dim, True))
self.assertEqual(x.ndimension() - 1, fn(x, dim).ndimension())
self.assertEqual(x.ndimension(), fn(x, dim, True).ndimension())
# check 1-d behavior
x = torch.randn(1)
dim = 0
self.assertEqual(fn(x, dim), fn(x, dim, True))
self.assertEqual(x.ndimension(), fn(x, dim).ndimension())
self.assertEqual(x.ndimension(), fn(x, dim, True).ndimension())
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_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)
# [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)
def test_neg(self):
a = torch.randn(100, 90)
zeros = torch.Tensor().resize_as_(a).zero_()
res_add = torch.add(zeros, -1, a)
res_neg = a.clone()
res_neg.neg_()
self.assertEqual(res_neg, res_add)
def test_reciprocal(self):
a = torch.randn(100, 89)
zeros = torch.Tensor().resize_as_(a).zero_()
res_pow = torch.pow(a, -1)
res_reciprocal = a.clone()
res_reciprocal.reciprocal_()
self.assertEqual(res_reciprocal, res_pow)
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_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
m1 = torch.Tensor(10, 10).uniform_(-10., 10.)
res1 = m1.clone()
res2 = m1.clone()
qs = torch.range(-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)
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
long_m1 = torch.LongTensor(10, 10).random_(-10, 10)
long_res1 = long_m1.clone()
long_res2 = long_m1.clone()
long_qs = torch.range(-5, 4).long()
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)
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))
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)
info = cast(torch.IntTensor())
a_LU = a.btrifact(info=info)
self.assertEqual(info.abs().sum(), 0)
P, a_L, a_U = torch.btriunpack(*a_LU)
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)
info = cast(torch.IntTensor())
LU_data, pivots = a.btrifact(info=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])
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)
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)
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)
def test_pow(self):
# [res] torch.pow([res,] x)
# base - tensor, exponent - number
# contiguous
m1 = torch.randn(100, 100)
res1 = torch.pow(m1[4], 3)
res2 = res1.clone().zero_()
for i in range(res2.size(0)):
res2[i] = math.pow(m1[4][i], 3)
self.assertEqual(res1, res2)
# non-contiguous
m1 = torch.randn(100, 100)
res1 = torch.pow(m1[:, 4], 3)
res2 = res1.clone().zero_()
for i in range(res2.size(0)):
res2[i] = math.pow(m1[i, 4], 3)
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_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: float('nan') if x < 0 else math.pow(x, y))
# TODO: these tests only check if it's possible to pass a return value
# it'd be good to expand them
def test_sum(self):
x = torch.rand(100, 100)
res1 = torch.sum(x, 1)
res2 = torch.Tensor()
torch.sum(x, 1, out=res2)
self.assertEqual(res1, res2)
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_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_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_diag(self):
x = torch.rand(100, 100)
res1 = torch.diag(x)
res2 = torch.Tensor()
torch.diag(x, out=res2)
self.assertEqual(res1, res2)
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))
def test_multinomial(self):
# with replacement
n_row = 3
for n_col in range(4, 5 + 1):
prob_dist = torch.rand(n_row, n_col)
prob_dist.select(1, n_col - 1).fill_(0) # index n_col shouldn't be sampled
n_sample = n_col
sample_indices = torch.multinomial(prob_dist, n_sample, True)
self.assertEqual(prob_dist.dim(), 2)
self.assertEqual(sample_indices.size(1), n_sample)
for index in product(range(n_row), range(n_sample)):
self.assertNotEqual(sample_indices[index], n_col, "sampled an index with zero probability")
# without replacement
n_row = 3
for n_col in range(4, 5 + 1):
prob_dist = torch.rand(n_row, n_col)
prob_dist.select(1, n_col - 1).fill_(0) # index n_col shouldn't be sampled
n_sample = 3
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 = {}
for j in range(n_sample):
sample_idx = sample_indices[i, j]
self.assertNotEqual(sample_idx, n_col - 1,
"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 = torch.rand(n_col)
n_sample = n_col
sample_indices = torch.multinomial(prob_dist, n_sample, True)
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")
@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_arange(self):
res1 = torch.arange(0, 1)
res2 = torch.Tensor()
torch.arange(0, 1, out=res2)
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.size(0), 3)
res1 = torch.arange(1, 10, 0.3, out=torch.FloatTensor())
self.assertEqual(res1.size(0), 31)
# DoubleTensor
res1 = torch.arange(0.6, 0.89, 0.1, out=torch.DoubleTensor())
self.assertEqual(res1.size(0), 3)
res1 = torch.arange(1, 10, 0.3, out=torch.DoubleTensor())
self.assertEqual(res1.size(0), 31)
# 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)
@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 hasattr(large_expanded, fn):
# 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 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)
@staticmethod
def _test_broadcast_fallback(self, cast):
# functions that should fallback to pointwise behavior
fns_fallback = {"add", "sub", "div", "mul", "pow", "fmod", "remainder",
"eq", "ge", "gt", "le", "lt", "max", "min", "ne",
"addcdiv", "addcmul", "masked_scatter", "masked_select", "masked_fill",
"map", "map2", "copy", "dist", "atan2", "lerp"}
# functions with three tensor arguments
fns_3_args = {"addcdiv", "addcmul", "map2"}
# functions that don't broadcast result size_ -- don't check result shape but
# still run functions to verify that broadcastable arguments don't error out
fns_no_result_broadcast = {"masked_select"}
for fn in fns_fallback:
# case 1: both broadcastable and nElems equal -- verify that we broadcast
t0 = cast(torch.randn(1, 4).float())
t1 = cast(torch.randn(4, 1).float())
t2 = cast(torch.randn(4).float())
broadcast_size = torch.Size([4, 4])
if not hasattr(t0, fn):
continue
t0_fn = getattr(t0, fn)
t1_fn = getattr(t1, fn)
def tensorfn(myfn, t1, t2):
if fn == "lerp":
return myfn(t1, 0.5)
elif fn == "masked_scatter":
return myfn(t1 < 0.5, cast(torch.randn(4 * 4).float()))
elif fn == "masked_fill":
return myfn(t1 < 0.5, 1.0)
elif fn == "masked_select":
return myfn(t1 < 0.5)
elif fn == "map":
return myfn(t1, lambda x, y: x + y)
elif fn == "map2":
return myfn(t1, t2, lambda x, y, z: x + y + z)
elif fn in fns_3_args:
return myfn(1.0, t1, t2)
else:
return myfn(t1)
r0 = tensorfn(t0_fn, t1, t2)
r1 = tensorfn(t1_fn, t0, t2)
if torch.is_tensor(r0) and fn not in fns_no_result_broadcast:
self.assertEqual(broadcast_size, r0.size())
self.assertEqual(broadcast_size, r1.size())
# case 2: broadcastable and not nElemes equal -- tested by test_fallback
# case 3: not broadcastable nElems equal -- verify we fallback
for inplace in False, True:
t0 = cast(torch.randn(1, 6).float())
t1 = cast(torch.randn(2, 3).float())
t2 = cast(torch.randn(3, 2).float())
if not hasattr(t0, fn if not inplace else fn + "_"):
continue
t0_fn = getattr(t0, fn if not inplace else fn + "_")
t1_fn = getattr(t1, fn if not inplace else fn + "_")
t2_fn = getattr(t2, fn if not inplace else fn + "_")
def verify_fallback_warnings(w):
self.assertEqual(len(w), 1)
self.assertTrue(issubclass(w[0].category, UserWarning))
self.assertTrue("Falling back" in str(w[0].message))
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always', UserWarning)
r0 = tensorfn(t0_fn, t1, t2)
verify_fallback_warnings(w)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always', UserWarning)
r1 = tensorfn(t1_fn, t0, t2)
verify_fallback_warnings(w)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always', UserWarning)
r2 = tensorfn(t2_fn, t0, t1)
verify_fallback_warnings(w)
if torch.is_tensor(r0) and fn not in fns_no_result_broadcast:
self.assertEqual(t0.size(), r0.size())
self.assertEqual(t1.size(), r1.size())
self.assertEqual(t2.size(), r2.size())
# case 4: not broadcastable and not nEleme equal -- tested by test_fallback
def test_broadcast_fallback(self):
self._test_broadcast_fallback(self, lambda t: t)
@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)
# 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_broadcast_copy_fn(self):
torch.zeros(5, 6).copy_(torch.zeros(6))
def verify_fallback_warnings(w):
self.assertEqual(len(w), 1)
self.assertTrue(issubclass(w[0].category, UserWarning))
self.assertTrue("Falling back" in str(w[0].message))
# suppress broadcastable warning
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always', UserWarning)
torch.zeros(5, 6).copy_(torch.zeros(30), broadcast=True)
verify_fallback_warnings(w)
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)
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)
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)
# 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)
# 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')
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))
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, 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, 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, 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], torch.Tensor((3,)), 0)
self.assertEqual(torch.kthvalue(y, 2)[0], torch.Tensor((1,)), 0)
def test_median(self):
for size in (155, 156):
x = torch.rand(size, size)
x0 = x.clone()
res1val, res1ind = torch.median(x, 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, 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 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([]))
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)
expected_size = x.size()[:dim] + (3,) + x.size()[dim:]
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_unbind(self):
x = torch.rand(2, 3, 4, 5)
for dim in range(4):
res = torch.unbind(x, dim)
self.assertEqual(x.size(dim), len(res))
for i in range(dim):
self.assertEqual(x.select(dim, i), res[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, 0, 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, 0, 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_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)
@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)
@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)
@skipIfNoLapack
def test_trtrs(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()
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 = torch.Tensor()
tb = 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_gels(self):
def _test(a, b, expectedNorm):
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)
# 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(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(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(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')
@skipIfNoLapack
def test_symeig(self):
xval = torch.rand(100, 3)
cov = torch.mm(xval.t(), xval)
rese = torch.zeros(3)
resv = 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 non-contiguous
X = torch.rand(5, 5)
X = X.t() * X
e = torch.zeros(4, 2).select(1, 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.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_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')
@skipIfNoLapack
def test_inverse(self):
M = torch.randn(5, 5)
MI = torch.inverse(M)
E = torch.eye(5)
self.assertFalse(MI.is_contiguous(), 'MI is contiguous')
self.assertEqual(E, torch.mm(M, MI), 1e-8, 'inverse value')
self.assertEqual(E, torch.mm(MI, M), 1e-8, 'inverse value')
MII = torch.Tensor(5, 5)
torch.inverse(M, out=MII)
self.assertFalse(MII.is_contiguous(), 'MII is contiguous')
self.assertEqual(MII, MI, 0, 'inverse value in-place')
# second call, now that MII is transposed
torch.inverse(M, out=MII)
self.assertFalse(MII.is_contiguous(), 'MII is contiguous')
self.assertEqual(MII, MI, 0, 'inverse value in-place')
@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
xx = x.clone()
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.sum(), xne.sum(), 0)
self.assertEqual(x.nelement(), all.sum())
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(gen, 1000)
self.assertEqual(target_value, forked_value, 0, "RNG has not forked correctly.")
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)
@skipIfNoLapack
def test_cholesky(self):
x = torch.rand(10, 10) + 1e-1
A = torch.mm(x, x.t())
# default Case
C = torch.potrf(A)
B = torch.mm(C.t(), C)
self.assertEqual(A, B, 1e-14)
# test Upper Triangular
U = torch.potrf(A, True)
B = torch.mm(U.t(), U)
self.assertEqual(A, B, 1e-14, 'potrf (upper) did not allow rebuilding the original matrix')
# test Lower Triangular
L = torch.potrf(A, False)
B = torch.mm(L, L.t())
self.assertEqual(A, B, 1e-14, 'potrf (lower) did not allow rebuilding the original matrix')
@skipIfNoLapack
def test_potrs(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()
# make sure 'a' is symmetric PSD
a = torch.mm(a, a.t())
# upper Triangular Test
U = torch.potrf(a)
x = torch.potrs(b, U)
self.assertLessEqual(b.dist(torch.mm(a, x)), 1e-12)
# lower Triangular Test
L = torch.potrf(a, False)
x = torch.potrs(b, L, False)
self.assertLessEqual(b.dist(torch.mm(a, x)), 1e-12)
@skipIfNoLapack
def tset_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 = a * a.t()
# compute inverse directly
inv0 = torch.inverse(a)
# default case
chol = torch.potrf(a)
inv1 = torch.potri(chol)
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# upper Triangular Test
chol = torch.potrf(a, 'U')
inv1 = torch.potri(chol, 'U')
self.assertLessEqual(inv0.dist(inv1), 1e-12)
# lower Triangular Test
chol = torch.potrf(a, 'L')
inv1 = torch.potri(chol, 'L')
self.assertLessEqual(inv0.dist(inv1), 1e-12)
@skipIfNoLapack
def test_pstrf(self):
def checkPsdCholesky(a, uplo, inplace):
if inplace:
u = torch.Tensor(a.size())
piv = torch.IntTensor(a.size(0))
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)[0])).cumsum(0)
sequence.add_(start - 1)
return sequence.resize_(*size)
def test_index(self):
reference = self._consecutive((3, 3, 3))
self.assertEqual(reference[0], self._consecutive((3, 3)), 0)
self.assertEqual(reference[1], self._consecutive((3, 3), 10), 0)
self.assertEqual(reference[2], self._consecutive((3, 3), 19), 0)
self.assertEqual(reference[0, 1], self._consecutive((3,), 4), 0)
self.assertEqual(reference[0:2], self._consecutive((2, 3, 3)), 0)
self.assertEqual(reference[2, 2, 2], 27, 0)
self.assertEqual(reference[:], self._consecutive((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 = self._consecutive((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 = self._consecutive((5, 5, 5))
idx = 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 with step
reference = self._consecutive((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 = 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(TypeError, lambda: reference[0.0])
self.assertRaises(TypeError, lambda: reference[0.0:2.0])
self.assertRaises(TypeError, lambda: reference[0.0, 0.0:2.0])
self.assertRaises(TypeError, lambda: reference[0.0, :, 0.0:2.0])
self.assertRaises(TypeError, lambda: reference[0.0, ..., 0.0:2.0])
self.assertRaises(TypeError, lambda: reference[0.0, :, 0.0])
@staticmethod
def _test_advancedindex(self, conv_fn):
# Tests for Integer Array Indexing, Part I - Purely integer array
# indexing
def consec(size, start=1):
sequence = torch.ones(int(torch.Tensor(size).prod(0)[0])).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 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[ri([0]), ], consec((1,)))
self.assertEqual(reference[ri([3]), ], consec((1,), 4))
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[ri([0],), ] = -1
self.assertEqual(reference[ri([0]), ], torch.Tensor([-1]))
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[ri([0]), ], torch.Tensor([1]))
self.assertEqual(strided[ri([3]), ], torch.Tensor([7]))
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[ri([0]), ], torch.Tensor([5]))
self.assertEqual(strided[ri([1]), ], torch.Tensor([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 Tranposed (i.e. non-contiguous) Tensors
reference = conv_fn(torch.Tensor([[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11]])).t_()
# Tranposed: [[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]]))
# TODO: error raising tests
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)
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(TypeError):
reference[0.0] = 1
with self.assertRaises(TypeError):
reference[0.0:2.0] = 1
with self.assertRaises(TypeError):
reference[0.0, 0.0:2.0] = 1
with self.assertRaises(TypeError):
reference[0.0, :, 0.0:2.0] = 1
with self.assertRaises(TypeError):
reference[0.0, ..., 0.0:2.0] = 1
with self.assertRaises(TypeError):
reference[0.0, :, 0.0] = 1
# LongTensor assignments are not fully supported yet
with self.assertRaises(TypeError):
reference[0, torch.LongTensor([2, 4])] = 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]].copy_(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]].add_(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)
# 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]
@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)
TestTorch._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))
TestTorch._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)
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)
def test_abs(self):
size = 1000
max_val = 1000
original = torch.rand(size).mul(max_val)
# Tensor filled with values from {-1, 1}
switch = torch.rand(size).mul(2).floor().mul(2).add(-1)
types = ['torch.DoubleTensor', 'torch.FloatTensor', 'torch.LongTensor', 'torch.IntTensor']
for t in types:
data = original.type(t)
switch = switch.type(t)
res = torch.mul(data, switch)
# abs is used in assertEqual so we use the slow version instead
self.assertTensorsSlowEqual(res.abs(), data, 1e-16)
# Checking that the right abs function is called for LongTensor
bignumber = 2 ^ 31 + 1
res = torch.LongTensor((-bignumber,))
self.assertGreater(res.abs()[0], 0)
def test_view(self):
tensor = torch.rand(15)
template = torch.rand(3, 5)
empty = 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))
# suppress broadcastable warning
with warnings.catch_warnings(record=True):
self.assertEqual((tensor_view - tensor).abs().max(), 0)
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))
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]
assert not 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())
def test_repeat(self):
result = torch.Tensor()
tensor = torch.rand(8, 4)
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).abs().max(), 0, 'Error in repeat (not equal)')
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)
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]
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]
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 = list(torch.randperm(7))
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)
def test_storageview(self):
s1 = torch.LongStorage((3, 4, 5))
s2 = torch.LongStorage(s1, 1)
self.assertEqual(s2.size(), 2)
self.assertEqual(s2[0], s1[1])
self.assertEqual(s2[1], s1[2])
s2[1] = 13
self.assertEqual(13, s1[2])
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]], 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]], 0)
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_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_bernoulli(self):
t = torch.ByteTensor(10, 10)
def isBinary(t):
return torch.ne(t, 0).mul_(torch.ne(t, 1)).sum() == 0
p = 0.5
t.bernoulli_(p)
self.assertTrue(isBinary(t))
p = torch.rand(SIZE)
t.bernoulli_(p)
self.assertTrue(isBinary(t))
q = torch.rand(5, 5)
self.assertTrue(isBinary(q.bernoulli()))
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)
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_serialization(self):
a = [torch.randn(5, 5).float() for i in range(2)]
b = [a[i % 2] for i in range(4)]
b += [a[0].storage()]
b += [a[0].storage()[1:4]]
b += [torch.arange(1, 11).int()]
t1 = torch.FloatTensor().set_(a[0].storage()[1:4], 0, (3,), (1,))
t2 = torch.FloatTensor().set_(a[0].storage()[1:4], 0, (3,), (1,))
b += [(t1.storage(), t1.storage(), t2.storage())]
b += [a[0].storage()[0:2]]
for use_name in (False, True):
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.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)
self.assertEqual(c[4][1:4], c[5], 0)
# 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_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, xh2)
@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().cpu(), 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, xc2)
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
def test_serialization_cuda(self):
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 tempfile.NamedTemporaryFile() 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)
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].storage()[1:4]]
DATA_URL = 'https://download.pytorch.org/test_data/legacy_serialized.pt'
data_dir = os.path.join(os.path.dirname(__file__), 'data')
test_file_path = os.path.join(data_dir, 'legacy_serialized.pt')
succ = download_file(DATA_URL, test_file_path)
if not succ:
warnings.warn(("Couldn't download the test file for backwards compatibility! "
"Tests will be incomplete!"), RuntimeWarning)
return
c = torch.load(test_file_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)
self.assertEqual(c[4][1:4], c[5], 0)
def test_serialization_container(self):
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
import os
with tempfile.NamedTemporaryFile() as checkpoint:
fname = os.path.join(os.path.dirname(__file__), 'data/network1.py')
module = import_module('tmpmodule', 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))
self.assertEquals(len(w), 0)
# Replace the module with different source
fname = os.path.join(os.path.dirname(__file__), 'data/network2.py')
module = import_module('tmpmodule', fname)
checkpoint.seek(0)
with warnings.catch_warnings(record=True) as w:
loaded = torch.load(checkpoint)
self.assertTrue(isinstance(loaded, module.Net))
self.assertEquals(len(w), 1)
self.assertTrue(w[0].category, 'SourceChangeWarning')
def test_serialization_map_location(self):
DATA_URL = 'https://download.pytorch.org/test_data/gpu_tensors.pt'
data_dir = os.path.join(os.path.dirname(__file__), 'data')
test_file_path = os.path.join(data_dir, 'gpu_tensors.pt')
succ = download_file(DATA_URL, test_file_path)
if not succ:
warnings.warn(
"Couldn't download the test file for map_location! "
"Tests will be incomplete!", RuntimeWarning)
return
def map_location(storage, loc):
return storage
tensor = torch.load(test_file_path, map_location=map_location)
self.assertEqual(type(tensor), torch.FloatTensor)
self.assertEqual(tensor, torch.FloatTensor([[1.0, 2.0], [3.0, 4.0]]))
tensor = torch.load(test_file_path, map_location={'cuda:0': 'cpu'})
self.assertEqual(type(tensor), torch.FloatTensor)
self.assertEqual(tensor, torch.FloatTensor([[1.0, 2.0], [3.0, 4.0]]))
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)
def test_from_file(self):
size = 10000
filename = 'testPytorchStorageFromFile'
try:
s1 = torch.FloatStorage.from_file(filename, True, size)
t1 = torch.FloatTensor(s1).copy_(torch.randn(size))
# check mapping
s2 = torch.FloatStorage.from_file(filename, 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)
finally:
if os.path.exists(filename):
os.remove(filename)
def test_print(self):
for t in torch._tensor_classes:
if t in torch.sparse._sparse_tensor_classes:
continue
if t.is_cuda and not torch.cuda.is_available():
continue
obj = t(100, 100).fill_(1)
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)
x = torch.Tensor([4, float('inf'), 1.5, float('-inf'), 0, float('nan'), 1])
x.__repr__()
str(x)
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))
self.assertRaises(RuntimeError, lambda: torch.Tensor().unsqueeze(0))
def test_iter(self):
x = torch.randn(5, 5)
for i, sub in enumerate(x):
self.assertEqual(sub, x[i])
def test_accreal_type(self):
x = torch.randn(2, 3, 4) * 10
self.assertIsInstance(x.double().sum(), float)
self.assertIsInstance(x.float().sum(), float)
self.assertIsInstance(x.long().sum(), int)
self.assertIsInstance(x.int().sum(), int)
self.assertIsInstance(x.short().sum(), int)
self.assertIsInstance(x.char().sum(), int)
self.assertIsInstance(x.byte().sum(), int)
@unittest.skipIf(not torch.cuda.is_available(), 'no CUDA')
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.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)
# 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)
# non-contiguous 2D
x = torch.randn(sz2, sz1).t().mul(255).type(tp)
y = x.numpy()
check2d(x, y)
# 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)
# 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)
@unittest.skipIf(not TEST_NUMPY, "Numpy not found")
def test_from_numpy(self):
dtypes = [
np.double,
np.float,
np.int64,
np.int32,
np.int16,
np.uint8
]
for dtype in dtypes:
array = np.array([1, 2, 3, 4], dtype=dtype)
self.assertEqual(torch.from_numpy(array), torch.Tensor([1, 2, 3, 4]))
# 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)
@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)])
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.assertIs(x[idx] == y[idx], eq[idx] == 1)
ne = x != y
for idx in iter_indices(x):
self.assertIs(x[idx] != y[idx], ne[idx] == 1)
lt = x < y
for idx in iter_indices(x):
self.assertIs(x[idx] < y[idx], lt[idx] == 1)
le = x <= y
for idx in iter_indices(x):
self.assertIs(x[idx] <= y[idx], le[idx] == 1)
gt = x > y
for idx in iter_indices(x):
self.assertIs(x[idx] > y[idx], gt[idx] == 1)
ge = x >= y
for idx in iter_indices(x):
self.assertIs(x[idx] >= y[idx], ge[idx] == 1)
def test_logical_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_apply(self):
x = torch.arange(1, 6)
res = x.clone().apply_(lambda k: k + k)
self.assertEqual(res, x * 2)
self.assertRaises(RuntimeError, lambda: x.apply_(lambda k: "str"))
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)
# unit test for THTensor_(copyTranspose)
@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)
# 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)
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]),
('stack', [(2, 3, 4), (2, 3, 4)], lambda: [DIM_ARG], [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(TestTorch, test_name), "Duplicated test name: " + test_name
setattr(TestTorch, test_name, make_neg_dim_test(name, tensor_arg, arg_constr, types, extra_dim))
if __name__ == '__main__':
run_tests()