import io
import math
import tempfile
import re
import unittest
import sys
from itertools import repeat
import os
from contextlib import contextmanager

import torch
import torch.cuda
import torch.cuda.comm as comm
from torch import multiprocessing as mp
from torch._six import inf, nan

from test_torch import TestTorch
from common import TestCase, get_gpu_type, to_gpu, freeze_rng_state, run_tests, \
    PY3, IS_WINDOWS, NO_MULTIPROCESSING_SPAWN

# We cannot import TEST_CUDA and TEST_MULTIGPU from common_cuda here,
# because if we do that, the TEST_CUDNN line from common_cuda will be executed
# multiple times as well during the execution of this test suite, and it will
# cause CUDA OOM error on Windows.
TEST_CUDA = torch.cuda.is_available()
TEST_MULTIGPU = TEST_CUDA and torch.cuda.device_count() >= 2

if not TEST_CUDA:
    print('CUDA not available, skipping tests')
    TestCase = object  # noqa: F811

TEST_MAGMA = TEST_CUDA
if TEST_CUDA:
    torch.ones(1).cuda()  # has_magma shows up after cuda is initialized
    TEST_MAGMA = torch.cuda.has_magma

floating_set = {torch.FloatTensor, torch.DoubleTensor, torch.cuda.FloatTensor,
                torch.cuda.DoubleTensor, torch.HalfTensor, torch.cuda.HalfTensor}


def is_floating(t):
    if not isinstance(t, type):
        raise TypeError('t should be an instance of type')
    assert t != torch.autograd.Variable
    return t in floating_set


def is_half(t):
    if isinstance(t, torch.Tensor):
        return t.dtype == torch.float16
    assert isinstance(t, type)
    assert t != torch.autograd.Variable
    return t in [torch.HalfTensor, torch.cuda.HalfTensor]


types = [
    torch.FloatTensor,
    torch.DoubleTensor,
    torch.LongTensor,
    torch.IntTensor,
    torch.ShortTensor,
    torch.CharTensor,
    torch.ByteTensor,
    torch.HalfTensor,
]

signed_types = [
    torch.FloatTensor,
    torch.DoubleTensor,
    torch.LongTensor,
    torch.IntTensor,
    torch.ShortTensor,
    torch.CharTensor,
]

unsigned_types = [
    torch.ByteTensor,
]

float_types = [
    torch.FloatTensor,
    torch.DoubleTensor,
    torch.HalfTensor,
]

float_types_no_half = [
    torch.FloatTensor,
    torch.DoubleTensor,
]


def number(floating, integer, t):
    return floating if is_floating(t) else integer


def cast_tensor(tensor, t):
    return t(tensor.size()).copy_(tensor)

S = 10
M = 50


def make_tensor(t, *sizes):
    if 'Half' in t.__name__:
        return t(*sizes).copy_(torch.randn(*sizes))
    else:
        tensor = t(*sizes)
        if tensor.is_floating_point():
            return tensor.normal_()
        else:
            return tensor.random_(0, 10)


def make_sparse_tensor(t, n, *sizes):
    assert t.is_sparse
    tensor = t()
    i = tensor._indices()
    i = i.new(len(sizes), n).copy_(
        torch.cat([torch.LongTensor(1, n).random_(s) for s in sizes], 0))
    v = tensor._values()
    v = v.new(n).copy_(torch.randn(n))
    return t(i, v, torch.Size(sizes))


def tensor_clamp(t, min, max):
    if is_half(t):
        return t.float().clamp(min, max).half()
    else:
        return t.clamp(min, max)


def tensor_mul(t, scale):
    if is_half(t):
        return t.float().mul(scale).half()
    else:
        return t.mul(scale)


def tensor_abs_(t):
    if is_half(t):
        return t.float().abs_().half()
    else:
        return t.abs_()


def constant_tensor_sub(a, b):
    # helper function to address const - torch.HalfTensor where it doesn't
    # have resize_as()
    if is_half(b):
        return (a - b.float()).half()
    else:
        return a - b


def constant_tensor_add(a, b):
    # helper function to address const + torch.HalfTensor where it doesn't
    # have add()
    if is_half(b):
        return (a + b.float()).half()
    else:
        return a + b


def small_2d(t):
    return make_tensor(t, S, S)


def small_2d_scaled(t, scale=10):
    return tensor_mul(make_tensor(t, S, S), scale)


def small_2d_oneish(t):
    if is_floating(t):
        return tensor_clamp(make_tensor(t, S, S), min=0.99, max=1.01)
    else:
        return t(S, S).fill_(1)


def small_3d(t):
    return make_tensor(t, S, S, S)


def medium_1d(t):
    return make_tensor(t, M)


def medium_2d(t):
    return make_tensor(t, M, M)


def medium_2d_expanded(t):
    return t(1).expand(M, M)


def medium_2d_scaled(t, scale=10):
    return tensor_mul(make_tensor(t, M, M), scale)


def small_3d_ones(t):
    return t(S, S, S).copy_(torch.ones(S, S, S))


def small_3d_positive(t):
    # In div_tensor(), half cannot achieve float precision
    min_val = 1e-3 if is_floating(t) and not is_half(t) else 2
    return tensor_clamp(make_tensor(t, S, S, S), min_val, 120)


def small_3d_unique(t):
    return t(S, S, S).copy_(torch.arange(1, S * S * S + 1).view(S, S, S))


def small_1d_lapack(t):
    return t(1, 3).copy_(torch.arange(1, 4).view(3))


def small_2d_lapack(t):
    return t(3, 3).copy_(torch.arange(1, 10).view(3, 3))


def small_2d_lapack_skinny(t):
    return t(3, 4).copy_(torch.arange(1, 13).view(3, 4))


def small_2d_lapack_fat(t):
    return t(4, 3).copy_(torch.arange(1, 13).view(4, 3))


def large_2d_lapack(t):
    return t(1000, 1000).normal_()


def long_type(t):
    return torch.cuda.LongTensor if 'cuda' in t.__module__ else torch.LongTensor


def new_t(*sizes):
    def tmp(t):
        return t(*sizes).copy_(torch.randn(*sizes))
    return tmp

# Content of each tuple:
# - function name
# - constructor for the tensor,    signature: fn(tensor_type) -> tensor
# - constructor for the arguments, signature: fn(tensor_type) -> list
# - postfix name for the test (must be unique for a given function) (default='')
# - tensor types to use (default=types)
# - disable inplace test, if set to True, no inplace test will be done (default=False)
# - decorator, e.g., unittest.skipIf (default is no decorator)
tests = [
    ('add', small_3d, lambda t: [number(3.14, 3, t)]),
    ('add', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
    ('add', small_3d, lambda t: [number(0.2, 2, t), small_3d_positive(t)], 'scalar_tensor'),
    ('sub', small_3d, lambda t: [number(3.14, 3, t)],),
    ('sub', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
    ('mul', small_3d, lambda t: [number(3.14, 3, t)],),
    ('mul', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
    ('div', small_3d, lambda t: [number(3.14, 3, t)],),
    ('div', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
    ('pow', small_3d, lambda t: [number(3.14, 3, t)], None, float_types),
    ('pow', small_3d, lambda t: [number(1., 1, t)], 'pow1', types),
    ('pow', small_3d, lambda t: [number(2., 2, t)], 'pow2', types),
    ('pow', small_3d, lambda t: [number(3., 3, t)], 'pow3', types),
    ('pow', small_3d, lambda t: [number(-1., -1, t)], 'pow-1', float_types),
    # HalfTensor gives bad result at pow-2 with data sampled from torch.randn
    ('pow', small_3d, lambda t: [number(-2., -2, t)], 'pow-2', float_types_no_half),
    ('pow', small_3d, lambda t: [tensor_abs_(small_3d(t))], 'tensor', float_types),
    ('addbmm', small_2d, lambda t: [small_3d(t), small_3d(t)], None, float_types),
    ('addbmm', small_2d, lambda t: [number(0.4, 2, t), small_3d(t), small_3d(t)], 'scalar'),
    ('addbmm', small_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), small_3d(t), small_3d(t)], 'two_scalars'),
    ('baddbmm', small_3d, lambda t: [small_3d(t), small_3d(t)],),
    ('baddbmm', small_3d, lambda t: [number(0.4, 2, t), small_3d(t), small_3d(t)], 'scalar'),
    ('baddbmm', small_3d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), small_3d(t), small_3d(t)], 'two_scalars'),
    ('addcdiv', small_2d_lapack, lambda t: [tensor_mul(small_2d_lapack(t), 2), small_2d_lapack(t)],),
    ('addcdiv', small_2d_lapack, lambda t: [number(2.8, 1, t),
                                            tensor_mul(small_2d_lapack(t), 2), small_2d_lapack(t)], 'scalar'),
    ('addcmul', small_3d, lambda t: [small_3d(t), small_3d(t)],),
    ('addcmul', small_3d, lambda t: [number(0.4, 2, t), small_3d(t), small_3d(t)], 'scalar'),
    ('addmm', medium_2d, lambda t: [medium_2d(t), medium_2d(t)],),
    ('addmm', medium_2d, lambda t: [number(0.4, 2, t), medium_2d(t), medium_2d(t)], 'scalar'),
    ('addmm', medium_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_2d(t), medium_2d(t)], 'two_scalars'),
    ('addmv', medium_1d, lambda t: [medium_2d(t), medium_1d(t)],),
    ('addmv', medium_1d, lambda t: [number(0.4, 2, t), medium_2d(t), medium_1d(t)], 'scalar'),
    ('addmv', medium_1d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_2d(t), medium_1d(t)], 'two_scalars'),
    ('addr', medium_2d, lambda t: [medium_1d(t), medium_1d(t)],),
    ('addr', medium_2d, lambda t: [number(0.4, 2, t), medium_1d(t), medium_1d(t)], 'scalar'),
    ('addr', medium_2d, lambda t: [number(0.5, 3, t), number(0.4, 2, t), medium_1d(t), medium_1d(t)], 'two_scalars'),
    ('atan2', medium_2d, lambda t: [medium_2d(t)], None, float_types + [torch.HalfTensor]),
    ('fmod', small_3d, lambda t: [3], 'value'),
    ('fmod', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
    ('chunk', medium_2d, lambda t: [4],),
    ('chunk', medium_2d, lambda t: [4, 1], 'dim'),
    ('chunk', medium_2d, lambda t: [4, -2], 'neg_dim'),
    ('clamp', medium_2d_scaled, lambda t: [-1, 5], None, signed_types),
    ('clamp', medium_2d_scaled, lambda t: [1, 5], None, unsigned_types),
    ('clone', medium_2d, lambda t: [],),
    ('contiguous', medium_2d, lambda t: [],),
    ('cross', new_t(M, 3, M), lambda t: [new_t(M, 3, M)(t)],),
    ('cumprod', small_3d, lambda t: [1],),
    ('cumprod', small_3d, lambda t: [-1], 'neg_dim'),
    ('cumsum', small_3d, lambda t: [1],),
    ('cumsum', small_3d, lambda t: [-1], 'neg_dim'),
    ('dim', small_3d, lambda t: [],),
    ('dist', small_2d, lambda t: [small_2d(t)],),
    ('dist', small_2d, lambda t: [small_2d(t), 3], '3_norm'),
    ('dist', small_2d, lambda t: [small_2d(t), 2.5], '2_5_norm'),
    ('dot', medium_1d, lambda t: [medium_1d(t)],),
    ('element_size', medium_1d, lambda t: [],),
    ('eq', small_3d_ones, lambda t: [small_3d(t)],),
    ('eq', small_3d_ones, lambda t: [small_3d_ones(t)], 'equal'),
    ('ne', small_3d_ones, lambda t: [small_3d(t)],),
    ('ne', small_3d_ones, lambda t: [small_3d_ones(t)], 'equal'),
    ('equal', small_3d_ones, lambda t: [small_3d_ones(t)], 'equal'),
    ('equal', small_3d_ones, lambda t: [small_3d(t)],),
    ('expand', new_t(M, 1, M), lambda t: [M, 4, M],),
    ('expand_as', new_t(M, 1, M), lambda t: [new_t(M, 4, M)(t)],),
    ('fill', medium_2d, lambda t: [number(3.14, 3, t)],),
    ('ge', medium_2d, lambda t: [medium_2d(t)],),
    ('le', medium_2d, lambda t: [medium_2d(t)],),
    ('gt', medium_2d, lambda t: [medium_2d(t)],),
    ('lt', medium_2d, lambda t: [medium_2d(t)],),
    ('is_contiguous', medium_2d, lambda t: [],),
    # TODO: can't check negative case - GPU copy will be contiguous
    ('is_same_size', medium_2d, lambda t: [small_3d(t)], 'negative'),
    ('is_same_size', medium_2d, lambda t: [medium_2d(t)], 'positive'),
    ('is_set_to', medium_2d, lambda t: [medium_2d(t)],),
    # TODO: positive case
    ('kthvalue', small_3d_unique, lambda t: [3],),
    ('kthvalue', small_3d_unique, lambda t: [3, 1], 'dim'),
    ('kthvalue', small_3d_unique, lambda t: [3, -1], 'neg_dim'),
    ('lerp', small_3d, lambda t: [small_3d(t), 0.3],),
    ('max', small_3d_unique, lambda t: [],),
    ('max', small_3d_unique, lambda t: [1], 'dim'),
    ('max', small_3d_unique, lambda t: [-1], 'neg_dim'),
    ('max', medium_2d, lambda t: [medium_2d(t)], 'elementwise'),
    ('min', small_3d_unique, lambda t: [],),
    ('min', small_3d_unique, lambda t: [1], 'dim'),
    ('min', small_3d_unique, lambda t: [-1], 'neg_dim'),
    ('min', medium_2d, lambda t: [medium_2d(t)], 'elementwise'),
    ('mean', small_3d, lambda t: [],),
    ('mean', small_3d, lambda t: [-1], 'neg_dim'),
    ('mean', small_3d, lambda t: [1], 'dim'),
    ('mode', small_3d, lambda t: [],),
    ('mode', small_3d, lambda t: [1], 'dim'),
    ('mode', small_3d, lambda t: [-1], 'neg_dim'),
    ('mvlgamma', lambda t: tensor_clamp(small_2d(t), 0.1, 10), lambda t: [1], '2d_p=1', float_types_no_half),
    ('mvlgamma', lambda t: tensor_clamp(small_2d(t), 0.6, 10), lambda t: [2], '2d_p=2', float_types_no_half),
    ('remainder', small_3d, lambda t: [3], 'value'),
    ('remainder', small_3d, lambda t: [-3], 'negative_value', signed_types),
    ('remainder', small_3d, lambda t: [small_3d_positive(t)], 'tensor'),
    ('remainder', small_3d, lambda t: [constant_tensor_sub(0, small_3d_positive(t))], 'negative_tensor', signed_types),
    ('std', small_3d, lambda t: [],),
    ('std', small_3d, lambda t: [1], 'dim'),
    ('std', small_3d, lambda t: [-1], 'neg_dim'),
    ('var', small_3d, lambda t: [],),
    ('var', small_3d, lambda t: [1], 'dim'),
    ('var', small_3d, lambda t: [-1], 'neg_dim'),
    ('ndimension', small_3d, lambda t: [],),
    ('nelement', small_3d, lambda t: [],),
    ('numel', small_3d, lambda t: [],),
    ('narrow', small_3d, lambda t: [1, 3, 2],),
    ('narrow', small_3d, lambda t: [-1, 3, 2], 'neg_dim'),
    ('nonzero', small_3d, lambda t: [],),
    ('norm', small_3d, lambda t: [],),
    ('norm', small_3d, lambda t: [3], '3_norm'),
    ('norm', small_3d, lambda t: [3, 0], '3_norm_dim'),
    ('norm', small_3d, lambda t: [3, -2], '3_norm_neg_dim'),
    ('ones', small_3d, lambda t: [1, 2, 3, 4, 5],),
    ('permute', new_t(1, 2, 3, 4), lambda t: [2, 1, 3, 0],),
    ('put_', new_t(2, 5, 3), lambda t: [long_type(t)([[0], [-2]]), t([[3], [4]])],),
    ('put_', new_t(2, 3), lambda t: [long_type(t)([]), t([])], 'empty'),
    ('put_', new_t(2, 2), lambda t: [long_type(t)([[1], [-3]]), t([[1], [2]]), True], 'accumulate'),
    ('prod', small_2d_oneish, lambda t: [],),
    ('prod', small_3d, lambda t: [1], 'dim'),
    ('prod', small_3d, lambda t: [-1], 'neg_dim'),
    ('sum', small_2d, lambda t: [],),
    ('sum', small_3d, lambda t: [1], 'dim'),
    ('sum', small_3d, lambda t: [-1], 'neg_dim'),
    ('renorm', small_3d, lambda t: [2, 1, 1], '2_norm'),
    ('renorm', small_3d, lambda t: [2, -1, 1], '2_norm_neg_dim'),
    ('renorm', small_3d, lambda t: [1.5, 1, 1], '1_5_norm'),
    ('repeat', small_2d, lambda t: [2, 2, 2],),
    ('size', new_t(1, 2, 3, 4), lambda t: [],),
    ('size', new_t(1, 2, 3, 4), lambda t: [1], 'dim'),
    ('size', new_t(1, 2, 3, 4), lambda t: [-2], 'neg_dim'),
    ('sort', small_3d_unique, lambda t: [],),
    ('sort', small_3d_unique, lambda t: [1], 'dim'),
    ('sort', small_3d_unique, lambda t: [-1], 'neg_dim'),
    ('sort', small_3d_unique, lambda t: [1, True], 'dim_descending'),
    ('sort', small_3d_unique, lambda t: [-1, True], 'neg_dim_descending'),
    ('split', small_3d, lambda t: [2],),
    ('split', small_3d, lambda t: [2, 1], 'dim'),
    ('split', small_3d, lambda t: [2, -3], 'neg_dim'),
    ('squeeze', new_t(1, 2, 1, 4), lambda t: [],),
    ('squeeze', new_t(1, 2, 1, 4), lambda t: [2], 'dim'),
    ('squeeze', new_t(1, 2, 1, 4), lambda t: [-2], 'neg_dim'),
    ('t', new_t(1, 2), lambda t: [],),
    ('take', new_t(3, 4), lambda t: [long_type(t)([[0], [-2]])],),
    ('transpose', new_t(1, 2, 3, 4), lambda t: [1, 2],),
    ('transpose', new_t(1, 2, 3, 4), lambda t: [-1, -2], 'neg_dim'),
    ('to_list', small_3d, lambda t: [],),
    ('topk', small_3d_unique, lambda t: [2, 1, False, True], 'dim_sort'),
    ('topk', small_3d_unique, lambda t: [2, -1, False, True], 'neg_dim_sort'),
    ('topk', small_3d_unique, lambda t: [2, 1, True, True], 'dim_desc_sort'),
    ('trace', medium_2d, lambda t: [],),
    ('tril', medium_2d, lambda t: [],),
    ('tril', medium_2d_expanded, lambda t: [], 'zero_stride', types, True),
    ('tril', medium_2d, lambda t: [2], 'positive'),
    ('tril', medium_2d, lambda t: [-2], 'negative'),
    ('triu', medium_2d, lambda t: [],),
    ('triu', medium_2d_expanded, lambda t: [], 'zero_stride', types, True),
    ('triu', medium_2d, lambda t: [2], 'positive'),
    ('triu', medium_2d, lambda t: [-2], 'negative'),
    ('unsqueeze', new_t(2, 3, 4), lambda t: [2],),
    ('unsqueeze', new_t(2, 3, 4), lambda t: [-2], 'neg_dim'),
    ('view', small_3d, lambda t: [100, 10], 'contiguous'),
    ('view_as', small_3d, lambda t: [make_tensor(t, 100, 10)],),
    ('zero', small_3d, lambda t: [],),
    ('zeros', small_3d, lambda t: [1, 2, 3, 4],),
    ('eye', small_2d, lambda t: [3, 4],),
    ('flip', small_3d, lambda t: [0], 'd0', types, True),
    ('flip', small_3d, lambda t: [0, 1, 2], 'd012', types, True),
    ('flip', small_3d, lambda t: [0, 2], 'd02', types, True),
    ('flip', small_3d, lambda t: [2, 0], 'd20', types, True),
    ('flip', small_3d, lambda t: [-1], 'neg_d', types, True),
    ('rot90', small_2d, lambda t: [1, [0, 1]], 'k1_d01', types, True),
    ('rot90', small_3d, lambda t: [1, [1, 2]], 'k1_d12', types, True),
    ('rot90', small_3d, lambda t: [1, [1, -1]], 'k1_neg_d', types, True),
    ('rot90', small_3d, lambda t: [], 'default', types, True),
    ('rsqrt', lambda t: constant_tensor_add(1, small_3d(t)), lambda t: [], None, float_types),
    ('sinh', lambda t: tensor_clamp(small_3d(t), -1, 1), lambda t: [], None, float_types),
    ('tan', lambda t: tensor_clamp(small_3d(t), -1, 1), lambda t: [], None, float_types),
    ('__lshift__', lambda t: torch.pow(2, cast_tensor(torch.arange(1, 5), t)),
        lambda t: [2], None, signed_types),
    ('__rshift__', lambda t: torch.pow(2, cast_tensor(torch.arange(3, 7), t)),
        lambda t: [2], None, signed_types),
    # lapack tests
    ('qr', small_2d_lapack, lambda t: [], 'square', float_types, False,
        unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
    ('qr', small_2d_lapack_skinny, lambda t: [], 'skinny', float_types, False,
        unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
    ('qr', small_2d_lapack_fat, lambda t: [], 'fat', float_types, False,
        unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
    ('qr', large_2d_lapack, lambda t: [], 'big', float_types, False,
        unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
    ('inverse', new_t(20, 20), lambda t: [], None, float_types, False),
    ('geqrf', new_t(20, 20), lambda t: [], None, float_types, False,
        unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
    ('svd', new_t(10, 10), lambda t: [], 'square', float_types_no_half, False,
        unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
    ('svd', lambda t: new_t(10, 10)(t).t(), lambda t: [True], 'square_col_maj',
        float_types_no_half, False,
        unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
    ('svd', new_t(20, 5), lambda t: [True], 'tall_some', float_types_no_half, False,
        unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
    ('svd', new_t(20, 5), lambda t: [False], 'tall_all', float_types_no_half, False,
        unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
    ('svd', lambda t: new_t(5, 20)(t).t(), lambda t: [True],
        'tall_some_col_maj', float_types_no_half, False,
        unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
    ('svd', lambda t: new_t(5, 20)(t).t(), lambda t: [False],
        'tall_all_col_maj', float_types_no_half, False,
        unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
    ('eig', new_t(10, 10), lambda t: [True], 'with_eigvec', float_types_no_half, False,
        unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")),
]

# TODO: random functions, cat, gather, scatter, index*, masked*,
#       resize, resizeAs, storage_offset, storage, stride, unfold

custom_precision = {
    'addbmm': 1e-4,
    'addmm': 1e-4,
    'addmv': 1e-4,
    'addr': 1e-4,
    'baddbmm': 1e-4,
    'rsqrt': 1e-4,
    'cumprod': 1e-4,
    'qr': 3e-4,
    'digamma': 1e0,  # large values lead to large absolute error but small relative error
}

custom_half_precision = {
    'add': 1e-2,
    'acos': 1e-3,
    'addbmm': 1e-1,
    'addcdiv': 1e-2,
    'addcmul': 1e-2,
    'addmm': 1e-1,
    'addmv': 1e-2,
    'addr': 1e-2,
    'asin': 1e-3,
    'atan2': 1e-3,
    'atan': 1e-3,
    'baddbmm': 1e-2,
    'cos': 1e-3,
    'cosh': 1e-2,
    'cross': 1e-2,
    'cumprod': 1e-2,
    'cumsum': 1e-2,
    'dist': 1e-2,
    'div': 1e-3,
    'dot': 1e-2,
    'erf': 1e-3,
    'erfc': 1e-3,
    'erfinv': 1e-3,
    'exp': 1e-2,
    'expm1': 1e-2,
    'fill': 1e-3,
    'lerp': 1e-2,
    'lgamma': 1e-2,
    'log': 1e-2,
    'log10': 1e-2,
    'log1p': 1e-3,
    'log2': 1e-2,
    'mean': 1e-3,
    'mul': 1e-2,
    'norm': 1e-1,
    'pow': 1e-1,
    'prod': 1e-3,
    'reciprocal': 1e-1,
    'remainder': 1e-3,
    'renorm': 1e-3,
    'rsqrt': 1e-2,
    'sigmoid': 1e-3,
    'sin': 1e-3,
    'sinh': 1e-3,
    'sqrt': 1e-3,
    'std': 1e-3,
    'sub': 1e-2,
    'sum': 1e-2,
    'tan': 1e-3,
    'tanh': 1e-3,
    'trace': 1e-3,
    'var': 1e-3,
    '__lshift__': 1e-3,
    '__rshift__': 1e-3,
}

simple_pointwise = [
    'abs',
    'sign',
]
for fn in simple_pointwise:
    tests.append((fn, small_3d, lambda t: []))

simple_pointwise_float = [
    'log',
    'log10',
    'log1p',
    'log2',
    'sigmoid',
    'sin',
    'sqrt',
    'tanh',
    'acos',
    'asin',
    'atan',
    'cos',
    'cosh',
    'erf',
    'erfc',
    'erfinv',
    'exp',
    'expm1',
    'reciprocal',
    'floor',
    'frac',
    'neg',
    'round',
    'trunc',
    'ceil',
    'lgamma',
    'digamma',
    'trigamma',
]

for fn in simple_pointwise_float:
    tests.append((fn, small_3d, lambda t: [], None, float_types))

_cycles_per_ms = None


def get_cycles_per_ms():
    """Approximate number of cycles per millisecond for torch.cuda._sleep"""
    global _cycles_per_ms
    if _cycles_per_ms is None:
        start = torch.cuda.Event(enable_timing=True)
        end = torch.cuda.Event(enable_timing=True)
        start.record()
        torch.cuda._sleep(1000000)
        end.record()
        end.synchronize()
        _cycles_per_ms = 1000000 / start.elapsed_time(end)
    return _cycles_per_ms


def compare_cpu_gpu(tensor_constructor, arg_constructor, fn, t, precision=1e-5):
    def tmp(self):
        cpu_tensor = tensor_constructor(t)
        gpu_tensor = to_gpu(cpu_tensor)
        cpu_args = arg_constructor(t)
        gpu_args = [to_gpu(arg) for arg in cpu_args]
        if is_half(t):
            cpu_tensor = cpu_tensor.float()
            cpu_args = [arg.float() if isinstance(arg, torch.Tensor) and is_half(arg) else arg for arg in cpu_args]
        cpu_result = getattr(cpu_tensor, fn)(*cpu_args)
        try:
            gpu_result = getattr(gpu_tensor, fn)(*gpu_args)
        except RuntimeError as e:
            reason = e.args[0]
            if 'only supports floating-point types' in reason or 'unimplemented data type' in reason:
                raise unittest.SkipTest('unimplemented data type')
            raise
        except AttributeError as e:
            reason = e.args[0]
            if 'object has no attribute' in reason:
                raise unittest.SkipTest('unimplemented data type')
            raise
        # If one changes, another should change as well
        self.assertEqual(cpu_tensor, gpu_tensor, precision)
        self.assertEqual(cpu_args, gpu_args, precision)
        # Compare results
        if fn == 'element_size' and t.__name__ == 'HalfTensor':
            # Workaround since cpu_result is float
            self.assertEqual(2, gpu_result)
        else:
            self.assertEqual(cpu_result, gpu_result, precision)
    return tmp


class TestCuda(TestCase):
    _do_cuda_memory_leak_check = True

    @staticmethod
    def _test_memory_stats_generator(self, device=None, N=35):
        if device is None:
            device = torch.cuda.current_device()

        m0 = torch.cuda.memory_allocated(device)
        last_m_arr = [torch.cuda.memory_allocated(device)]
        max_m_arr = [torch.cuda.max_memory_allocated(device)]
        last_c_arr = [torch.cuda.memory_cached(device)]
        max_c_arr = [torch.cuda.max_memory_cached(device)]

        def alloc(*size):
            with torch.cuda.device(device):
                # NOTE: do **not** use methods that can have additional
                #       memory overhead, e.g., inplace random sampling methods.
                #       they can leave some memory occupied even after being
                #       deallocated, e.g., initialized RNG state, causing some
                #       memory checks below to fail.
                return torch.cuda.FloatTensor(*size)

        def assert_change(comp=1, empty_cache=False):
            # comp > 0: increased
            # comp = 0: equal
            # comp < 0: decreased
            new_m = torch.cuda.memory_allocated(device)
            new_max_m = torch.cuda.max_memory_allocated(device)
            if comp > 0:
                self.assertGreater(new_m, last_m_arr[0])
            elif comp < 0:
                self.assertLess(new_m, last_m_arr[0])
            else:
                self.assertEqual(new_m, last_m_arr[0])
            self.assertLessEqual(new_m, new_max_m)
            self.assertGreaterEqual(new_max_m, max_m_arr[0])
            last_m_arr[0] = new_m
            max_m_arr[0] = new_max_m

            new_c = torch.cuda.memory_cached(device)
            new_max_c = torch.cuda.max_memory_cached(device)
            # emptying cache may happen (due to allocation or empty_cache), so
            # we can't assert new_c >= last_c
            self.assertLessEqual(new_c, new_max_c)
            self.assertGreaterEqual(new_max_c, max_c_arr[0])
            last_c_arr[0] = new_c
            max_c_arr[0] = new_max_c

            if empty_cache:
                torch.cuda.empty_cache()
                new_c = torch.cuda.memory_cached(device)
                new_max_c = torch.cuda.max_memory_cached(device)
                self.assertLessEqual(new_c, last_c_arr[0])
                self.assertLessEqual(new_c, new_max_c)
                self.assertEqual(new_max_c, max_c_arr[0])
                last_c_arr[0] = new_c

        assert_change(0)
        assert_change(0)
        yield

        tensors1 = [alloc(1), alloc(10, 20), alloc(200, 300, 2000)]
        m1 = torch.cuda.memory_allocated(device)
        assert_change(1)
        yield

        tensors2 = []

        for i in range(1, int(N / 2) + 1):
            # small ones
            tensors2.append(alloc(i, i * 4))
            assert_change(1)
            yield

        for i in range(5, int(N / 2) + 5):
            # large ones
            tensors2.append(alloc(i, i * 7, i * 9, i * 11))
            assert_change(1)
            yield

        tensors2.append(alloc(0, 0, 0))
        assert_change(0)
        yield

        permute = []
        for i in torch.randperm(len(tensors2)):
            permute.append(tensors2[i])
            assert_change(0)
            yield

        del tensors2
        assert_change(0)
        yield
        tensors2 = permute
        assert_change(0)
        yield
        del permute
        assert_change(0)
        yield

        for i in range(int(N / 2)):
            x = tensors2[i].numel()
            del tensors2[i]
            assert_change(-x)  # in case that tensors2[i] is empty
            yield

        for i in range(2, int(2 * N / 3) + 2):
            tensors2.append(alloc(i, i * 3, i * 8))
            assert_change(1)
            yield

        del tensors2
        assert_change(-1)
        assert_change(0)
        self.assertEqual(torch.cuda.memory_allocated(device), m1)
        yield True

        del tensors1
        assert_change(-1)
        self.assertEqual(torch.cuda.memory_allocated(device), m0)

        # test empty_cache
        assert_change(0, empty_cache=True)

    def test_memory_stats(self):
        torch.cuda.empty_cache()
        for _ in self._test_memory_stats_generator(self):
            pass

    @unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
    def test_memory_stats_multigpu(self):
        # advance a generator with a end flag
        def advance(gen, end):
            if not end:
                try:
                    next(gen)
                except StopIteration:
                    end = True
            return end

        # interlace
        torch.cuda.empty_cache()
        gen0 = self._test_memory_stats_generator(self, device=0, N=35)
        gen1 = self._test_memory_stats_generator(self, device=1, N=35)
        end0 = end1 = False
        while not (end0 and end1):
            end0 = advance(gen0, end0)
            end1 = advance(gen1, end1)

        # semi-random order
        torch.cuda.empty_cache()
        gen0 = self._test_memory_stats_generator(self, device=0, N=35)
        gen1 = self._test_memory_stats_generator(self, device=1, N=35)
        end0 = end1 = False

        while not (end0 and end1):
            end0 = advance(gen0, end0)
            if not end0:
                gen1_max_times = torch.LongTensor(1).random_(0, 3)[0]
            else:
                gen1_max_times = inf
            t = 0
            while t < gen1_max_times and not end1:
                end1 = advance(gen1, end1)
                t += 1

    @unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
    def test_autogpu(self):
        x = torch.randn(5, 5).cuda()
        y = torch.randn(5, 5).cuda()
        self.assertEqual(x.get_device(), 0)
        self.assertEqual(x.get_device(), 0)
        with torch.cuda.device(1):
            z = torch.randn(5, 5).cuda()
            self.assertEqual(z.get_device(), 1)
            q = x.add(y)
            self.assertEqual(q.get_device(), 0)
            w = torch.randn(5, 5).cuda()
            self.assertEqual(w.get_device(), 1)
            self.assertEqual(y.cuda().get_device(), 1)
        z = z.cuda()
        self.assertEqual(z.get_device(), 0)

    @unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
    def test_new(self):
        x = torch.randn(3, 3).cuda()
        self.assertEqual(x.new([0, 1, 2]).get_device(), 0)
        self.assertEqual(x.new([0, 1, 2], device=1).get_device(), 1)

        with torch.cuda.device(1):
            self.assertEqual(x.new([0, 1, 2]).get_device(), 0)
            self.assertEqual(x.new([0, 1, 2], device=1).get_device(), 1)

    @unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
    def test_copy_device(self):
        x = torch.randn(5, 5).cuda()
        with torch.cuda.device(1):
            y = x.cuda()
            self.assertEqual(y.get_device(), 1)
            self.assertIs(y.cuda(), y)
            z = y.cuda(0)
            self.assertEqual(z.get_device(), 0)
            self.assertIs(z.cuda(0), z)

        x = torch.randn(5, 5)
        with torch.cuda.device(1):
            y = x.cuda()
            self.assertEqual(y.get_device(), 1)
            self.assertIs(y.cuda(), y)
            z = y.cuda(0)
            self.assertEqual(z.get_device(), 0)
            self.assertIs(z.cuda(0), z)

    def test_serialization_array_with_storage(self):
        x = torch.randn(5, 5).cuda()
        y = torch.IntTensor(2, 5).fill_(0).cuda()
        q = [x, y, x, y.storage()]
        with tempfile.NamedTemporaryFile() as f:
            torch.save(q, f)
            f.seek(0)
            q_copy = torch.load(f)
        self.assertEqual(q_copy, q, 0)
        q_copy[0].fill_(5)
        self.assertEqual(q_copy[0], q_copy[2], 0)
        self.assertTrue(isinstance(q_copy[0], torch.cuda.DoubleTensor))
        self.assertTrue(isinstance(q_copy[1], torch.cuda.IntTensor))
        self.assertTrue(isinstance(q_copy[2], torch.cuda.DoubleTensor))
        self.assertTrue(isinstance(q_copy[3], torch.cuda.IntStorage))
        q_copy[1].fill_(10)
        self.assertTrue(q_copy[3], torch.cuda.IntStorage(10).fill_(10))

    def test_type_conversions(self):
        x = torch.randn(5, 5)
        self.assertIsInstance(x.float(), torch.FloatTensor)
        self.assertIsInstance(x.cuda(), torch.cuda.DoubleTensor)
        self.assertIsInstance(x.cuda().float(), torch.cuda.FloatTensor)
        self.assertIsInstance(x.cuda().float().cpu(), torch.FloatTensor)
        self.assertIsInstance(x.cuda().float().cpu().int(), torch.IntTensor)

        y = x.storage()
        self.assertIsInstance(y.float(), torch.FloatStorage)
        self.assertIsInstance(y.cuda(), torch.cuda.DoubleStorage)
        self.assertIsInstance(y.cuda().float(), torch.cuda.FloatStorage)
        self.assertIsInstance(y.cuda().float().cpu(), torch.FloatStorage)
        self.assertIsInstance(y.cuda().float().cpu().int(), torch.IntStorage)

    @unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
    def test_type_conversions_same_gpu(self):
        x = torch.randn(5, 5).cuda(1)
        self.assertEqual(x.int().get_device(), 1)
        self.assertEqual(x.type(torch.int).get_device(), 1)
        self.assertEqual(x.to(torch.int).get_device(), 1)

    def test_neg(self):
        TestTorch._test_neg(self, lambda t: t.cuda())

    def _test_broadcast(self, input):
        if not TEST_MULTIGPU:
            raise unittest.SkipTest("only one GPU detected")
        result = comm.broadcast(input, (0, 1))
        for i, t in enumerate(result):
            self.assertEqual(t.get_device(), i)
            self.assertEqual(t, input)
            if input.is_cuda and input.get_device() == i:
                self.assertEqual(t.data_ptr(), input.data_ptr())

    def test_broadcast_cpu(self):
        self._test_broadcast(torch.randn(5, 5))

    def test_broadcast_gpu(self):
        self._test_broadcast(torch.randn(5, 5).cuda())

    def test_min_max_nan(self):
        tests = [(lambda x: x.min(), 'min'),
                 (lambda x: x.max(), 'max'),
                 (lambda x: x.min(0)[0], 'min_dim'),
                 (lambda x: x.max(0)[0], 'max_dim')]
        for f, name in tests:
            a = torch.arange(25.0).view(5, 5)
            a[2, 2] = nan
            actual = f(a.cuda()).cpu()
            expected = f(a).cpu()
            self.assertEqual(torch.isnan(actual), torch.isnan(expected), 'nans for {}'.format(name))
            self.assertEqual(actual[~torch.isnan(actual)],
                             expected[~torch.isnan(expected)], 'nans for {}'.format(name))

    @staticmethod
    def _test_broadcast_coalesced(self, tensors, buffer_size):
        b_tensors = [comm.broadcast(t, (0, 1)) for t in tensors]
        for (_, bt), t in zip(b_tensors, tensors):
            self.assertEqual(bt.get_device(), 1)
            self.assertEqual(bt, t)
            self.assertIsInstance(bt, type(t))

        bc_tensors = comm.broadcast_coalesced(tensors, (0, 1), buffer_size=buffer_size)
        bc_tensors_t = list(zip(*bc_tensors))
        self.assertEqual(b_tensors, bc_tensors_t)
        for (_, bt), (_, bct) in zip(b_tensors, bc_tensors_t):
            self.assertEqual(bt.get_device(), bct.get_device())
            self.assertIsInstance(bct, type(bt))

    @unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
    def test_broadcast_coalesced(self):
        numel = 5
        num_bytes = numel * 8
        tensors = [
            make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 1, 2, 3),
            torch.randn(numel).long().cuda(),
            torch.randn(numel).cuda(),
            make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 10, 2, 3),
            make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 5, 2, 3),
            make_sparse_tensor(torch.cuda.sparse.LongTensor, 7, 3, 3),
            make_sparse_tensor(torch.cuda.sparse.FloatTensor, 2, 2, 3),
            torch.randn(numel).long().cuda(),
            torch.randn(numel).long().cuda(),
            make_sparse_tensor(torch.cuda.sparse.LongTensor, 3, 2, 7),
            torch.randn(numel * 2).int().cuda(),  # int is 2x shorter
            torch.randn(numel).cuda(),
        ]
        self._test_broadcast_coalesced(self, tensors, num_bytes * 5 // 2)

    @unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
    def test_broadcast_coalesced_dense_only(self):
        numel = 5
        num_bytes = numel * 8
        tensors = [
            torch.randn(numel).long().cuda(),
            torch.randn(numel).cuda(),
            torch.randn(numel).long().cuda(),
            torch.randn(numel).long().cuda(),
            torch.randn(numel * 2).int().cuda(),  # int is 2x shorter
            torch.randn(numel).cuda(),
        ]
        self._test_broadcast_coalesced(self, tensors, num_bytes * 5 // 2)

    @unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
    def test_reduce_add(self):
        x = torch.randn(5, 5)
        y = torch.randn(5, 5)
        x_cuda = x.cuda(0)
        y_cuda = y.cuda(1)
        result = comm.reduce_add((x_cuda, y_cuda))
        self.assertEqual(result.get_device(), 0)
        self.assertEqual(result.cpu(), x + y)

    @staticmethod
    def _test_reduce_add_coalesced(self, tensors, buffer_size):
        dup_tensors = [tensors, list(map(lambda t: t.cuda(1), tensors))]

        r_tensors = list(map(comm.reduce_add, zip(*dup_tensors)))
        for r, t in zip(r_tensors, tensors):
            self.assertEqual(r.get_device(), t.get_device())
            self.assertEqual(r, t * 2)
            self.assertEqual(r.type(), t.type())

        rc_tensors = comm.reduce_add_coalesced(dup_tensors, buffer_size=buffer_size)
        self.assertEqual(r_tensors, rc_tensors)
        for r, rc in zip(r_tensors, rc_tensors):
            self.assertEqual(rc.get_device(), r.get_device())
            self.assertEqual(rc.type(), r.type())

    @unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
    def test_reduce_add_coalesced(self):
        numel = 5
        num_bytes = numel * 8
        tensors = [
            make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 1, 2, 3),
            torch.randn(numel).long().cuda(),
            torch.randn(numel).cuda(),
            make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 10, 2, 3),
            make_sparse_tensor(torch.cuda.sparse.DoubleTensor, 5, 2, 3),
            make_sparse_tensor(torch.cuda.sparse.LongTensor, 7, 3, 3),
            make_sparse_tensor(torch.cuda.sparse.FloatTensor, 2, 2, 3),
            torch.randn(numel).long().cuda(),
            torch.randn(numel).long().cuda(),
            make_sparse_tensor(torch.cuda.sparse.LongTensor, 3, 2, 7),
            torch.randn(numel * 2).int().cuda(),  # int is 2x shorter
            torch.randn(numel).cuda(),
        ]
        self._test_reduce_add_coalesced(self, tensors, num_bytes * 5 // 2)

    @unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
    def test_reduce_add_coalesced_dense_only(self):
        numel = 5
        num_bytes = numel * 8
        tensors = [
            torch.randn(numel).long().cuda(),
            torch.randn(numel).cuda(),
            torch.randn(numel).long().cuda(),
            torch.randn(numel).long().cuda(),
            torch.randn(numel * 2).int().cuda(),  # int is 2x shorter
            torch.randn(numel).cuda(),
        ]
        self._test_reduce_add_coalesced(self, tensors, num_bytes * 5 // 2)

    def _test_scatter(self, input, chunk_sizes=None, dim=0):
        if not TEST_MULTIGPU:
            raise unittest.SkipTest("only one GPU detected")
        result = comm.scatter(input, (0, 1), chunk_sizes, dim)
        self.assertEqual(len(result), 2)
        if chunk_sizes is None:
            chunk_sizes = tuple(repeat(input.size(dim) // 2, 2))
        chunk_start = 0
        for i, r in enumerate(result):
            chunk_end = chunk_start + chunk_sizes[i]
            index = [slice(None, None), slice(None, None)]
            index[dim] = slice(chunk_start, chunk_end)
            self.assertEqual(r, input[tuple(index)], 0)
            chunk_start = chunk_end

    def test_scatter_cpu(self):
        self._test_scatter(torch.randn(4, 4), dim=0)

    def test_scatter_cpu_dim(self):
        self._test_scatter(torch.randn(4, 4), dim=1)

    def test_scatter_cpu_neg_dim(self):
        self._test_scatter(torch.randn(4, 4), dim=-2)

    def test_scatter_cpu_sizes(self):
        self._test_scatter(torch.randn(6, 4), chunk_sizes=(2, 4))

    def test_scatter_gpu(self):
        self._test_scatter(torch.randn(4, 4).cuda(), dim=0)

    def test_scatter_gpu_dim(self):
        self._test_scatter(torch.randn(4, 4).cuda(), dim=1)

    def test_scatter_gpu_neg_dim(self):
        self._test_scatter(torch.randn(4, 4).cuda(), dim=-2)

    def test_scatter_gpu_sizes(self):
        self._test_scatter(torch.randn(6, 4).cuda(), chunk_sizes=(2, 4))

    def _test_gather(self, dim):
        if not TEST_MULTIGPU:
            raise unittest.SkipTest("only one GPU detected")
        x = torch.randn(2, 5).cuda(0)
        y = torch.randn(2, 5).cuda(1)
        result = comm.gather((x, y), dim)

        expected_size = list(x.size())
        expected_size[dim] += y.size(dim)
        expected_size = torch.Size(expected_size)
        self.assertEqual(result.get_device(), 0)
        self.assertEqual(result.size(), expected_size)

        index = [slice(None, None), slice(None, None)]
        index[dim] = slice(0, x.size(dim))
        self.assertEqual(result[tuple(index)], x)
        index[dim] = slice(x.size(dim), x.size(dim) + y.size(dim))
        self.assertEqual(result[tuple(index)], y)

    def test_gather(self):
        self._test_gather(0)

    def test_gather_dim(self):
        self._test_gather(1)

    def test_from_sequence(self):
        seq = [list(range(i * 4, i * 4 + 4)) for i in range(5)]
        reference = torch.arange(0, 20).resize_(5, 4)
        for t in types:
            cuda_type = get_gpu_type(t)
            self.assertEqual(cuda_type(seq), reference)

    def test_torch_manual_seed_seeds_cuda_devices(self):
        with freeze_rng_state():
            x = torch.zeros(4, 4).float().cuda()
            torch.manual_seed(2)
            self.assertEqual(torch.cuda.initial_seed(), 2)
            x.uniform_()
            torch.manual_seed(2)
            y = x.clone().uniform_()
            self.assertEqual(x, y)
            self.assertEqual(torch.cuda.initial_seed(), 2)

    def test_manual_seed(self):
        with freeze_rng_state():
            x = torch.zeros(4, 4).float().cuda()
            torch.cuda.manual_seed(2)
            self.assertEqual(torch.cuda.initial_seed(), 2)
            x.uniform_()
            torch.cuda.manual_seed(2)
            y = x.clone().uniform_()
            self.assertEqual(x, y)
            self.assertEqual(torch.cuda.initial_seed(), 2)

    @unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
    def test_cat_autogpu(self):
        x = torch.randn(4, 4).cuda(1)
        y = torch.randn(4, 4).cuda(1)
        z = torch.cat([x, y], 0)
        self.assertEqual(z.get_device(), x.get_device())

    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).cuda()
            y = torch.rand(17, SIZE, SIZE).transpose(0, pos_dim).cuda()
            z = torch.rand(19, SIZE, SIZE).transpose(0, pos_dim).cuda()

            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).cuda()
        self.assertEqual(torch.cat(torch.split(x, 7)), x)
        self.assertEqual(torch.cat(torch.chunk(x, 7)), x)

        y = torch.randn(1, SIZE, SIZE).cuda()
        z = torch.cat([x, y])
        self.assertEqual(z.size(), (21, SIZE, SIZE))

    def test_cat_empty_legacy(self):
        TestTorch._test_cat_empty_legacy(self, use_cuda=True)

    def test_cat_empty(self):
        TestTorch._test_cat_empty(self, use_cuda=True)

    def test_bernoulli(self):
        x = torch.tensor([0, 1], dtype=torch.float32, device='cuda')
        self.assertEqual(x.bernoulli().tolist(), [0, 1])

    def test_cat_bad_input_sizes(self):
        x = torch.randn(2, 1).cuda()
        y = torch.randn(2, 1, 1).cuda()
        z = torch.randn(2, 1, 1).cuda()
        self.assertRaises(RuntimeError, lambda: torch.cat([x, y, z]))

        x = torch.randn(2, 1, 2).cuda()
        y = torch.randn(2, 1, 1).cuda()
        z = torch.randn(2, 2, 1).cuda()
        self.assertRaises(RuntimeError, lambda: torch.cat([x, y, z], dim=1))

    @unittest.skipIf(torch.cuda.device_count() >= 10, "Loading a cuda:9 tensor")
    @unittest.skipIf(not PY3, "Tensor was serialized with Python 3")
    def test_load_nonexistent_device(self):
        # Setup: create a serialized file object with a 'cuda:9' restore location
        tensor = torch.randn(2, device='cuda')
        buf = io.BytesIO()
        torch.save(tensor, buf)
        # NB: this might not work in the future if serialization changes
        buf = io.BytesIO(buf.getvalue().replace(b'cuda:0', b'cuda:9'))

        msg = r'Attempting to deserialize object on CUDA device 9'
        with self.assertRaisesRegex(RuntimeError, msg):
            _ = torch.load(buf)

    def test_serialization(self):
        x = torch.randn(4, 4).cuda()
        with tempfile.NamedTemporaryFile() as f:
            torch.save(x, f)
            f.seek(0)
            x_copy = torch.load(f)
        self.assertEqual(x_copy, x)
        self.assertIs(type(x_copy), type(x))
        self.assertEqual(x_copy.get_device(), x.get_device())

    def test_serialization_array_with_empty(self):
        x = [torch.randn(4, 4).cuda(), torch.cuda.FloatTensor()]
        with tempfile.NamedTemporaryFile() as f:
            torch.save(x, f)
            f.seek(0)
            x_copy = torch.load(f)
        for original, copy in zip(x, x_copy):
            self.assertEqual(copy, original)
            self.assertIs(type(copy), type(original))
            self.assertEqual(copy.get_device(), original.get_device())

    @unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
    def test_multigpu_serialization(self):
        x = [torch.randn(4, 4).cuda(0), torch.randn(4, 4).cuda(1)]
        with tempfile.NamedTemporaryFile() as f:
            torch.save(x, f)
            f.seek(0)
            x_copy = torch.load(f)
        for original, copy in zip(x, x_copy):
            self.assertEqual(copy, original)
            self.assertIs(type(copy), type(original))
            self.assertEqual(copy.get_device(), original.get_device())

    @unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
    def test_multigpu_serialization_remap(self):
        x = [torch.randn(4, 4).cuda(0), torch.randn(4, 4).cuda(1)]

        def gpu_remap(storage, location):
            if location == 'cuda:1':
                return storage.cuda(0)

        with tempfile.NamedTemporaryFile() as f:
            torch.save(x, f)
            f.seek(0)
            x_copy = torch.load(f, map_location=gpu_remap)

        for original, copy in zip(x, x_copy):
            self.assertEqual(copy, original)
            self.assertIs(type(copy), type(original))
            self.assertEqual(copy.get_device(), 0)

    @unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
    def test_multigpu_serialization_remap_dict(self):
        x = [torch.randn(4, 4).cuda(0), torch.randn(4, 4).cuda(1)]
        with tempfile.NamedTemporaryFile() as f:
            torch.save(x, f)
            f.seek(0)
            x_copy = torch.load(f, map_location={'cuda:1': 'cuda:0'})
        for original, copy in zip(x, x_copy):
            self.assertEqual(copy, original)
            self.assertIs(type(copy), type(original))
            self.assertEqual(copy.get_device(), 0)

    @unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
    def test_cuda_set_device(self):
        x = torch.randn(5, 5)
        with torch.cuda.device(1):
            self.assertEqual(x.cuda().get_device(), 1)
            torch.cuda.set_device(0)
            self.assertEqual(x.cuda().get_device(), 0)
            with torch.cuda.device(1):
                self.assertEqual(x.cuda().get_device(), 1)
            self.assertEqual(x.cuda().get_device(), 0)
            torch.cuda.set_device(1)
        self.assertEqual(x.cuda().get_device(), 0)

    def test_is_tensor(self):
        for t in types:
            tensor = get_gpu_type(t)()
            self.assertTrue(torch.is_tensor(tensor))
        self.assertTrue(torch.is_tensor(torch.cuda.HalfTensor()))

    def test_cuda_synchronize(self):
        torch.cuda.synchronize()

    def test_streams(self):
        default_stream = torch.cuda.current_stream()
        user_stream = torch.cuda.Stream()
        self.assertEqual(torch.cuda.current_stream(), default_stream)
        self.assertNotEqual(default_stream, user_stream)
        self.assertEqual(default_stream.cuda_stream, 0)
        self.assertNotEqual(user_stream.cuda_stream, 0)
        with torch.cuda.stream(user_stream):
            self.assertEqual(torch.cuda.current_stream(), user_stream)
        self.assertTrue(user_stream.query())
        # copy 10 MB tensor from CPU-GPU which should take some time
        tensor1 = torch.ByteTensor(10000000).pin_memory()
        tensor2 = tensor1.cuda(non_blocking=True)
        self.assertFalse(default_stream.query())
        default_stream.synchronize()
        self.assertTrue(default_stream.query())

    @unittest.skipIf(not TEST_MULTIGPU, "detected only one GPU")
    def test_streams_multi_gpu(self):
        default_stream = torch.cuda.current_stream()
        self.assertEqual(default_stream.device, 0)
        stream = torch.cuda.Stream(device=1)
        self.assertEqual(stream.device, 1)
        with torch.cuda.device(1):
            self.assertEqual(torch.cuda.current_stream().device, 1)
            self.assertNotEqual(torch.cuda.current_stream(), default_stream)

    @unittest.skipIf(not TEST_MULTIGPU, "multi-GPU not supported")
    def test_tensor_device(self):
        self.assertEqual(torch.cuda.FloatTensor(1).get_device(), 0)
        self.assertEqual(torch.cuda.FloatTensor(1, device=1).get_device(), 1)
        with torch.cuda.device(1):
            self.assertEqual(torch.cuda.FloatTensor(1).get_device(), 1)
            self.assertEqual(torch.cuda.FloatTensor(1, device=0).get_device(), 0)
            self.assertEqual(torch.cuda.FloatTensor(1, device=None).get_device(), 1)

    def test_events(self):
        stream = torch.cuda.current_stream()
        event = torch.cuda.Event(enable_timing=True)
        self.assertTrue(event.query())
        start_event = torch.cuda.Event(enable_timing=True)
        stream.record_event(start_event)
        torch.cuda._sleep(int(50 * get_cycles_per_ms()))
        stream.record_event(event)
        self.assertFalse(event.query())
        event.synchronize()
        self.assertTrue(event.query())
        self.assertGreater(start_event.elapsed_time(event), 0)

    def test_record_stream(self):
        cycles_per_ms = get_cycles_per_ms()

        t = torch.FloatTensor([1, 2, 3, 4]).pin_memory()
        result = torch.cuda.FloatTensor(t.size())
        stream = torch.cuda.Stream()
        ptr = [None]

        # Performs the CPU->GPU copy in a background stream
        def perform_copy():
            with torch.cuda.stream(stream):
                tmp = t.cuda(non_blocking=True)
                ptr[0] = tmp.data_ptr()
            torch.cuda.current_stream().wait_stream(stream)
            tmp.record_stream(torch.cuda.current_stream())
            torch.cuda._sleep(int(50 * cycles_per_ms))  # delay the copy
            result.copy_(tmp)

        perform_copy()
        with torch.cuda.stream(stream):
            tmp2 = torch.cuda.FloatTensor(t.size())
            tmp2.zero_()
            self.assertNotEqual(tmp2.data_ptr(), ptr[0], 'allocation re-used to soon')

        self.assertEqual(result.tolist(), [1, 2, 3, 4])

        # Check that the block will be re-used after the main stream finishes
        torch.cuda.current_stream().synchronize()
        with torch.cuda.stream(stream):
            tmp3 = torch.cuda.FloatTensor(t.size())
            self.assertEqual(tmp3.data_ptr(), ptr[0], 'allocation not re-used')

    def test_noncontiguous_pinned_memory(self):
        # See issue #3266
        x = torch.arange(0, 10).view((2, 5))
        self.assertEqual(x.t(), x.t().pin_memory())

    def test_caching_pinned_memory(self):
        cycles_per_ms = get_cycles_per_ms()

        # check that allocations are re-used after deletion
        t = torch.FloatTensor([1]).pin_memory()
        ptr = t.data_ptr()
        del t
        t = torch.FloatTensor([1]).pin_memory()
        self.assertEqual(t.data_ptr(), ptr, 'allocation not reused')

        # check that the allocation is not re-used if it's in-use by a copy
        gpu_tensor = torch.cuda.FloatTensor([0])
        torch.cuda._sleep(int(50 * cycles_per_ms))  # delay the copy
        gpu_tensor.copy_(t, non_blocking=True)
        del t
        t = torch.FloatTensor([1]).pin_memory()
        self.assertNotEqual(t.data_ptr(), ptr, 'allocation re-used too soon')
        self.assertEqual(list(gpu_tensor), [1])

    @unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
    def test_caching_pinned_memory_multi_gpu(self):
        # checks that the events preventing pinned memory from being re-used
        # too early are recorded on the correct GPU
        cycles_per_ms = get_cycles_per_ms()

        t = torch.FloatTensor([1]).pin_memory()
        ptr = t.data_ptr()
        gpu_tensor0 = torch.cuda.FloatTensor([0], device=0)
        gpu_tensor1 = torch.cuda.FloatTensor([0], device=1)

        with torch.cuda.device(1):
            torch.cuda._sleep(int(50 * cycles_per_ms))  # delay the copy
            gpu_tensor1.copy_(t, non_blocking=True)

        del t
        t = torch.FloatTensor([2]).pin_memory()
        self.assertNotEqual(t.data_ptr(), ptr, 'allocation re-used too soon')

        with torch.cuda.device(0):
            gpu_tensor0.copy_(t, non_blocking=True)

        self.assertEqual(gpu_tensor1[0], 1)
        self.assertEqual(gpu_tensor0[0], 2)

    @staticmethod
    def _select_broadcastable_dims(dims_full=None):
        return TestTorch._select_broadcastable_dims(dims_full)

    @unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
    def test_pinverse(self):
        TestTorch._test_pinverse(self, lambda t: t.cuda())

    @unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
    def test_det_logdet_slogdet(self):
        TestTorch._test_det_logdet_slogdet(self, lambda t: t.cuda())

    @unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
    def test_gesv_batched(self):
        TestTorch._test_gesv_batched(self, lambda t: t.cuda())

    @unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
    def test_gesv_batched_dims(self):
        TestTorch._test_gesv_batched_dims(self, lambda t: t.cuda())

    def test_view(self):
        TestTorch._test_view(self, lambda t: t.cuda())

    def test_flip(self):
        TestTorch._test_flip(self, use_cuda=True)

    def test_rot90(self):
        TestTorch._test_rot90(self, use_cuda=True)

    def test_signal_window_functions(self):
        TestTorch._test_signal_window_functions(self, device=torch.device('cuda'))

    def test_fft_ifft_rfft_irfft(self):
        TestTorch._test_fft_ifft_rfft_irfft(self, device=torch.device('cuda'))

        @contextmanager
        def plan_cache_max_size(n):
            original = torch.backends.cuda.cufft_plan_cache.max_size
            torch.backends.cuda.cufft_plan_cache.max_size = n
            yield
            torch.backends.cuda.cufft_plan_cache.max_size = original

        with plan_cache_max_size(max(1, torch.backends.cuda.cufft_plan_cache.size - 10)):
            TestTorch._test_fft_ifft_rfft_irfft(self, device=torch.device('cuda'))

        with plan_cache_max_size(0):
            TestTorch._test_fft_ifft_rfft_irfft(self, device=torch.device('cuda'))

        torch.backends.cuda.cufft_plan_cache.clear()

        # check that stll works after clearing cache
        with plan_cache_max_size(10):
            TestTorch._test_fft_ifft_rfft_irfft(self, device=torch.device('cuda'))

        with self.assertRaisesRegex(RuntimeError, r"must be non-negative"):
            torch.backends.cuda.cufft_plan_cache.max_size = -1

        with self.assertRaisesRegex(RuntimeError, r"read-only property"):
            torch.backends.cuda.cufft_plan_cache.size = -1

    def test_stft(self):
        TestTorch._test_stft(self, device=torch.device('cuda'))

    def test_multinomial(self):
        TestTorch._test_multinomial(self, torch.cuda.FloatTensor)

        # Test two corner cases from older PyTorch (Issue #4858)
        freqs = torch.cuda.FloatTensor([
            0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
            0.03178183361887932, 0.027680952101945877, 0.033176131546497345,
            0.046052902936935425, 0.07742464542388916, 0.11543981730937958,
            0.14148041605949402, 0.15784293413162231, 0.13180233538150787,
            0.08271478116512299, 0.049702685326337814, 0.027557924389839172,
            0.018125897273421288, 0.011851548217236996, 0.010252203792333603,
            0.007422595750540495, 0.005372154992073774, 0.0045109698548913,
            0.0036087757907807827, 0.0035267581697553396, 0.0018864056328311563,
            0.0024605290964245796, 0.0022964938543736935, 0.0018453967059031129,
            0.0010662291897460818, 0.0009842115687206388, 0.00045109697384759784,
            0.0007791675161570311, 0.00020504408166743815, 0.00020504408166743815,
            0.00020504408166743815, 0.00012302644609007984, 0.0,
            0.00012302644609007984, 4.100881778867915e-05, 0.0, 0.0, 0.0, 0.0,
            0.0, 0.0])

        torch.cuda.manual_seed(11042)
        sample = torch.multinomial(freqs, 1000, True)
        self.assertNotEqual(freqs[sample].min(), 0)

        p = torch.zeros(3421, 2, device="cuda", dtype=torch.float)
        p[:, 1] = 1
        torch.cuda.manual_seed(5214)
        r = torch.multinomial(p, 1)
        self.assertNotEqual(r.min().item(), 0)

    @staticmethod
    def mute():
        os.dup2(os.open(os.devnull, os.O_WRONLY), sys.stderr.fileno())

    def _spawn_method(self, method, arg):
        ctx = mp.get_context("spawn")
        with ctx.Pool(1, initializer=self.mute) as pool:
            errors = pool.map(method, [arg])
            for e in errors:
                if 'device-side assert triggered' not in str(e):
                    self.fail(e)

    @staticmethod
    def _test_multinomial_invalid_probs_cuda(probs):
        try:
            with torch.random.fork_rng(devices=[0]):
                torch.multinomial(probs.to('cuda'), 1)
                torch.cuda.synchronize()
            return False  # Should not be reached
        except RuntimeError as e:
            return e

    @unittest.skipIf(NO_MULTIPROCESSING_SPAWN, "Disabled for environments that \
                     don't support multiprocessing with spawn start method")
    @unittest.skipIf(IS_WINDOWS, 'FIXME: CUDA OOM error on Windows')
    @unittest.skipIf(not PY3,
                     "spawn start method is not supported in Python 2, \
                     but we need it for creating another process with CUDA")
    def test_multinomial_invalid_probs_cuda(self):
        test_method = TestCuda._test_multinomial_invalid_probs_cuda
        self._spawn_method(test_method, torch.Tensor([0, -1]))
        self._spawn_method(test_method, torch.Tensor([0, inf]))
        self._spawn_method(test_method, torch.Tensor([0, -inf]))
        self._spawn_method(test_method, torch.Tensor([0, nan]))

    def test_broadcast(self):
        TestTorch._test_broadcast(self, lambda t: t.cuda())

    def test_contiguous(self):
        TestTorch._test_contiguous(self, lambda t: t.cuda())

    def test_broadcast_fused_matmul(self):
        TestTorch._test_broadcast_fused_matmul(self, lambda t: t.cuda())

    def test_broadcast_batched_matmul(self):
        TestTorch._test_broadcast_batched_matmul(self, lambda t: t.cuda())

    def test_index(self):
        TestTorch._test_index(self, lambda t: t.cuda())

    def test_advancedindex(self):
        TestTorch._test_advancedindex(self, lambda t: t.cuda())

    def test_advancedindex_mixed_cpu_cuda(self):
        def test(x, ia, ib):
            # test getitem
            self.assertEqual(x[:, ia, None, ib, 0].cpu(),
                             x.cpu()[:, ia.cpu(), None, ib.cpu(), 0])
            self.assertEqual(x[ia], x.cpu()[ia.cpu()])
            # test setitem
            x_clone1 = x.clone()
            x_clone2 = x.clone()
            first_shape = x[:, ia, None, ib, 0].shape
            second_shape = x[ia].shape
            x_clone1[:, ia, None, ib, 0] = torch.randn(first_shape).to(x_clone1)
            x_clone2[ia] = torch.randn(second_shape).to(x_clone2)

        cpu = torch.device('cpu')
        for device in ['cuda:0', 'cuda:1'] if torch.cuda.device_count() > 1 else ['cuda']:
            # Index cpu tensor with cuda tensor
            x = torch.randn(3, 4, 4, 4, 3)
            ia = torch.tensor([0, 2, 1]).to(device)
            ib = torch.tensor([0, 2, 1]).to(device)
            test(x, ia, ib)

            # Index cuda tensor with cpu tensor
            x = x.to(device)
            ia = ia.to(cpu)
            ib = ib.to(cpu)
            test(x, ia, ib)

            # Index cpu tensor with mixed cpu, cuda tensors
            x = x.to(cpu)
            ia = ia.to(cpu)
            ib = ib.to(device)
            test(x, ia, ib)

            # Index cuda tensor with mixed cpu, cuda tensors
            x = x.to(device)
            ia = ia.to(cpu)
            ib = ib.to(device)
            test(x, ia, ib)

            if torch.cuda.device_count() > 1:
                other_device = 'cuda:0' if device != 'cuda:0' else 'cuda:1'
                # Index cuda tensor with mixed cpu, cuda tensors on different devices
                x = x.to(device)
                ia = ia.to(cpu)
                ib = ib.to(other_device)
                test(x, ia, ib)

    def test_advancedindex_big(self):
        TestTorch._test_advancedindex_big(self, lambda t: t.cuda())

    def test_btrifact(self):
        TestTorch._test_btrifact(self, lambda t: t.cuda())

    def test_btrisolve(self):
        TestTorch._test_btrisolve(self, lambda t: t.cuda())

    def test_dim_reduction(self):
        TestTorch._test_dim_reduction(self, lambda t: t.cuda())

    def test_tensor_gather(self):
        TestTorch._test_gather(self, lambda t: t.cuda(), False)

    def test_tensor_scatter(self):
        TestTorch._test_scatter_base(self, lambda t: t.cuda(), 'scatter_', test_bounds=False)

    def test_tensor_scatterAdd(self):
        TestTorch._test_scatter_base(self, lambda t: t.cuda(), 'scatter_add_', test_bounds=False)

    def test_tensor_scatterFill(self):
        TestTorch._test_scatter_base(self, lambda t: t.cuda(), 'scatter_', True, test_bounds=False)

    def test_min_max_inits(self):
        # Testing if THC_reduceAll received the correct index initialization.
        # This affects the result of THC_reduceAll operations at extreme values
        x = torch.cuda.ByteTensor([0])
        y = torch.cuda.ByteTensor([255])
        expected = torch.cuda.LongTensor([0])[0]

        _, v = x.max(dim=0)
        self.assertEqual(v, expected)

        _, v = y.min(dim=0)
        self.assertEqual(v, expected)

    def test_int_pow(self):
        TestTorch._test_int_pow(self, lambda x: x.cuda())

    def test_remainder_overflow(self):
        TestTorch._test_remainder_overflow(self, dtype=torch.int64, device='cuda')

    def test_var(self):
        cpu_tensor = torch.randn(2, 3, 3)
        gpu_tensor = cpu_tensor.cuda()
        self.assertEqual(gpu_tensor.var(), cpu_tensor.var())
        self.assertEqual(gpu_tensor.var(1), cpu_tensor.var(1))
        self.assertEqual(gpu_tensor.var(2), cpu_tensor.var(2))
        self.assertEqual(gpu_tensor.std(), cpu_tensor.std())
        self.assertEqual(gpu_tensor.std(1), cpu_tensor.std(1))
        self.assertEqual(gpu_tensor.var(2), cpu_tensor.var(2))

        cpu_tensor = torch.randn(100)
        gpu_tensor = cpu_tensor.cuda()
        self.assertEqual(gpu_tensor.var(), cpu_tensor.var())

    def test_var_unbiased(self):
        tensor = torch.randn(100).cuda()
        self.assertEqual(tensor.var(0), tensor.var(0, unbiased=True))
        self.assertEqual(tensor.var(), tensor.var(unbiased=True))
        self.assertEqual(tensor.var(unbiased=False), tensor.var(0, unbiased=False))

        tensor = torch.FloatTensor([1.0, 2.0]).cuda()
        self.assertEqual(tensor.var(unbiased=True), 0.5)
        self.assertEqual(tensor.var(unbiased=False), 0.25)

        tensor = torch.randn(100).cuda()
        self.assertEqual(tensor.std(0), tensor.std(0, unbiased=True))
        self.assertEqual(tensor.std(), tensor.std(unbiased=True))
        self.assertEqual(tensor.std(unbiased=False), tensor.std(0, unbiased=False))

    def test_var_large_input(self):
        # Large, not-nice input
        tensor_cpu = torch.randn(2 * 32 * 1024 + 1, 2, 67)
        tensor_cuda = tensor_cpu.cuda()

        self.assertEqual(tensor_cpu.var(2), tensor_cuda.var(2).cpu())

    def test_var_stability(self):
        tensor = torch.FloatTensor([2281.5, 2281.25]).cuda()

        # Stability for inner dim
        self.assertEqual(tensor.var(0), 0.03125)

        # General stability
        self.assertEqual(tensor.var(), 0.03125)

        # Stability for outer dimensions
        tensor = tensor.unsqueeze(1)
        self.assertEqual(tensor.var(0), 0.03125)

    def test_digamma(self):
        def test(use_double=False):
            cpu_tensor = torch.randn(10, 10, 10)
            gpu_tensor = cpu_tensor.cuda()
            zeros = torch.zeros(10, 10, 10)
            if (use_double):
                cpu_tensor = cpu_tensor.double()
                gpu_tensor = gpu_tensor.double()
                zeros = zeros.double()
            cpu_out = cpu_tensor.digamma()
            gpu_out = gpu_tensor.digamma()
            norm_errors = (gpu_out - cpu_out.cuda()) / gpu_out
            self.assertEqual(norm_errors, zeros)

        test(True)
        test(False)

        # Test float32 behavior near and at poles.
        cpu_tensor = torch.tensor([-0.999999994, -1.999999994, -2.0000000111,
                                  -100.99999994, -1931.99999994, 0.000000111,
                                  -0.000000111, 0, -1, -2, -931])
        expected_errors = torch.tensor([0, 0, 0, 0, 0, 0, 0, nan, nan, nan, nan])
        gpu_tensor = cpu_tensor.cuda()
        cpu_out = cpu_tensor.digamma()
        gpu_out = gpu_tensor.digamma()
        norm_errors = (gpu_out - cpu_out.cuda()) / gpu_out
        self.assertEqual(norm_errors, expected_errors)

    def test_polygamma(self):
        def test(use_double=False):
            cpu_tensor = torch.randn(10, 10, 10)
            gpu_tensor = cpu_tensor.cuda()
            zeros = torch.zeros(10, 10, 10)
            if (use_double):
                cpu_tensor = cpu_tensor.double()
                gpu_tensor = gpu_tensor.double()
                zeros = zeros.double()
            for n in [0, 1]:
                cpu_out = cpu_tensor.polygamma(n)
                gpu_out = gpu_tensor.polygamma(n)
                norm_errors = (gpu_out - cpu_out.cuda()) / gpu_out
                self.assertEqual(norm_errors, zeros)

        test(True)
        test(False)

    @unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
    def test_symeig(self):
        # Small case
        tensor = torch.randn(3, 3).cuda()
        tensor = torch.mm(tensor, tensor.t())
        eigval, eigvec = torch.symeig(tensor, eigenvectors=True)
        self.assertEqual(tensor, torch.mm(torch.mm(eigvec, eigval.diag()), eigvec.t()))

        # Large case
        tensor = torch.randn(257, 257).cuda()
        tensor = torch.mm(tensor, tensor.t())
        eigval, eigvec = torch.symeig(tensor, eigenvectors=True)
        self.assertEqual(tensor, torch.mm(torch.mm(eigvec, eigval.diag()), eigvec.t()))

    def test_arange(self):
        for t in ['IntTensor', 'LongTensor', 'FloatTensor', 'DoubleTensor']:
            a = torch.cuda.__dict__[t]()
            torch.arange(0, 10, out=a)
            b = torch.__dict__[t]()
            torch.arange(0, 10, out=b)
            self.assertEqual(a, b.cuda())

    def test_linspace(self):
        a = torch.linspace(0, 10, 10, device='cuda')
        b = torch.linspace(0, 10, 10)
        self.assertEqual(a, b.cuda())

    def test_logspace(self):
        a = torch.logspace(1, 10, 10, device='cuda')
        b = torch.logspace(1, 10, 10)
        self.assertEqual(a, b.cuda())

    def test_diagonal(self):
        TestTorch._test_diagonal(self, dtype=torch.float32, device='cuda')

    def test_diagflat(self):
        TestTorch._test_diagflat(self, dtype=torch.float32, device='cuda')

    @unittest.skipIf(not TEST_MAGMA, "no MAGMA library detected")
    def test_trtrs(self):
        TestTorch._test_trtrs(self, lambda t: t.cuda())

    @unittest.skipIf(not TEST_MULTIGPU, "only one GPU detected")
    def test_get_set_rng_state_all(self):
        states = torch.cuda.get_rng_state_all()
        before0 = torch.cuda.FloatTensor(100, device=0).normal_()
        before1 = torch.cuda.FloatTensor(100, device=1).normal_()
        torch.cuda.set_rng_state_all(states)
        after0 = torch.cuda.FloatTensor(100, device=0).normal_()
        after1 = torch.cuda.FloatTensor(100, device=1).normal_()
        self.assertEqual(before0, after0, 0)
        self.assertEqual(before1, after1, 0)

    def test_nvtx(self):
        # Just making sure we can see the symbols
        torch.cuda.nvtx.range_push("foo")
        torch.cuda.nvtx.mark("bar")
        torch.cuda.nvtx.range_pop()

    def test_randperm_cuda(self):
        cuda = torch.device('cuda:0')

        # For small inputs, randperm is offloaded to CPU instead
        with torch.random.fork_rng(devices=[0]):
            res1 = torch.randperm(100, device=cuda)
        res2 = torch.cuda.LongTensor()
        torch.randperm(100, out=res2, device=cuda)
        self.assertEqual(res1, res2, 0)

        with torch.random.fork_rng(devices=[0]):
            res1 = torch.randperm(100000, device=cuda)
        res2 = torch.cuda.LongTensor()
        torch.randperm(100000, out=res2, device=cuda)
        self.assertEqual(res1, res2, 0)

        with torch.random.fork_rng(devices=[0]):
            res1 = torch.randperm(100, dtype=torch.half, device=cuda)
        res2 = torch.cuda.HalfTensor()
        torch.randperm(100, out=res2, device=cuda)
        self.assertEqual(res1, res2, 0)

        with torch.random.fork_rng(devices=[0]):
            res1 = torch.randperm(50000, dtype=torch.half, device=cuda)
        res2 = torch.cuda.HalfTensor()
        torch.randperm(50000, out=res2, device=cuda)
        self.assertEqual(res1, res2, 0)

        # randperm of 0 elements is an empty tensor
        res1 = torch.randperm(0, device=cuda)
        res2 = torch.cuda.LongTensor(5)
        torch.randperm(0, out=res2, device=cuda)
        self.assertEqual(res1.numel(), 0)
        self.assertEqual(res2.numel(), 0)

    def test_random_neg_values(self):
        TestTorch._test_random_neg_values(self, use_cuda=True)

    def test_bincount_cuda(self):
        TestTorch._test_bincount(self, device='cuda')
        # ensure CUDA code coverage
        input_size = (5000,)
        w = torch.randn(input_size, device='cuda')
        w_cpu = w.cpu()
        # test shared memory impl
        t = torch.randint(50, input_size, dtype=torch.int8, device='cuda')
        self.assertEqual(t.cpu().bincount(), t.bincount())
        self.assertEqual(t.cpu().bincount(w_cpu), t.bincount(w))
        # test multi block memory impl
        # see `THRESH_NUMBER_BINS_FOR_MULTI_BLOCK_MEM` in SummaryOps.cu
        t = torch.randint(500, input_size, dtype=torch.int64, device='cuda')
        self.assertEqual(t.cpu().bincount(), t.bincount())
        self.assertEqual(t.cpu().bincount(w_cpu), t.bincount(w))
        # test global memory impl
        # see `THRESH_NUMBER_BINS_FOR_GLOBAL_MEM` in SummaryOps.cu
        t = torch.randint(2000, input_size, dtype=torch.int64, device='cuda')
        self.assertEqual(t.cpu().bincount(), t.bincount())
        self.assertEqual(t.cpu().bincount(w_cpu), t.bincount(w))

    def test_tiny_half_norm_(self):
        a = torch.arange(25).cuda().float()
        a /= 100000000
        b = a.half()
        self.assertGreater(b.norm().item(), 0)

    # Test that wrap_with_cuda_memory_check successfully detects leak
    def test_cuda_memory_leak_detection(self):
        l = []

        @self.wrap_with_cuda_memory_check
        def no_leak():
            pass

        @self.wrap_with_cuda_memory_check
        def leak_gpu0():
            l.append(torch.tensor(10, device=torch.device("cuda:0")))

        no_leak()

        with self.assertRaisesRegex(AssertionError, r"leaked \d+ bytes CUDA memory on device 0"):
            leak_gpu0()

        if TEST_MULTIGPU:
            @self.wrap_with_cuda_memory_check
            def leak_gpu1():
                l.append(torch.tensor(10, device=torch.device("cuda:1")))

            with self.assertRaisesRegex(AssertionError, r"leaked \d+ bytes CUDA memory on device 1"):
                leak_gpu1()


def load_ignore_file():
    from os.path import join, dirname
    global ignores
    path = join(dirname(__file__), 'data', 'test_cuda_ignores.txt')
    with open(path, 'r') as f:
        ignores = {l for l in f.read().splitlines() if not l.startswith('#')}


def generate_tests():
    for decl in tests:
        for t in types:
            tensor = t()

            # Default values
            desc = ''
            type_subset = types
            no_inplace = False
            decorator = None
            if len(decl) == 3:
                name, constr, arg_constr = decl
            elif len(decl) == 4:
                name, constr, arg_constr, desc = decl
            elif len(decl) == 5:
                name, constr, arg_constr, desc, type_subset = decl
            elif len(decl) == 6:
                name, constr, arg_constr, desc, type_subset, no_inplace = decl
            elif len(decl) == 7:
                name, constr, arg_constr, desc, type_subset, no_inplace, decorator = decl

            if t not in type_subset:
                continue

            precision = custom_precision.get(name, TestCuda.precision)
            if is_half(t):
                precision = custom_half_precision.get(name, precision)

            for inplace in (True, False):
                if inplace and no_inplace:
                    continue
                if inplace:
                    name_inner = name + '_'
                else:
                    name_inner = name

                if t != torch.HalfTensor and not hasattr(tensor, name_inner):
                    # torch.HalfTensor doesn't support most operations,
                    # but we use torch.FloatTensor as cpu baseline
                    continue
                full_name = '{}.{}'.format(tensor.type(), name_inner)
                if full_name in ignores:
                    continue

                test_name = 'test_' + t.__name__ + '_' + name_inner
                if desc:
                    test_name += '_' + desc

                assert not hasattr(TestCuda, test_name), "Duplicated test name: " + test_name

                test_fn = compare_cpu_gpu(constr, arg_constr, name_inner, t, precision)

                if decorator is not None:
                    test_fn = decorator(test_fn)

                setattr(TestCuda, test_name, test_fn)


if __name__ == '__main__':
    if TEST_CUDA:
        load_ignore_file()
        generate_tests()

    # skip TestTorch tests
    # hide in __name__ == '__main__' because we don't want this to be run when
    # someone imports test_cuda
    def load_tests(loader, tests, pattern):
        test_suite = unittest.TestSuite()
        for test_group in tests:
            for test in test_group:
                if test.__class__.__name__ == 'TestTorch':
                    continue
                test_suite.addTest(test)
        return test_suite

    run_tests()